optimal pricing strategy case study

Mastering the Pricing Case Study: A Comprehensive Guide

• Last Updated November, 2022

Setting the optimal pricing for products or services is important for a company as it directly impacts profitability. So every management consulting firm helps its clients with pricing strategies. The primary goal of a pricing case is to recommend a price that maximizes profit, taking costs for product/service and market considerations into account.

A typical pricing case interview would start something like this –

A manufacturer of kitchen knives sells a range of products, from low-end to professional, to customers at different price points. They’ve developed a new line of knives in collaboration with a celebrity chef and would like help setting the prices for these products.

Pricing cases might not seem straightforward initially, but with the right frameworks and practice cases, we will help you prepare for it.

In this article, we’ll discuss:

• Examples of pricing cases.
• The alternative pricing methods.
• How to approach a pricing case interview.
• An end-to-end pricing case example.

Let’s get started!

Pricing Case Examples

How to approach a pricing case interview, what are the most common pricing strategies, an end-to-end pricing case example, the relevant pricing strategy for our pricing case examples, 6 tips for solving a pricing case interview.

As companies mature, pricing becomes more complex because:

• Companies develop multiple products with different cost structures.
• Clients have different product/service needs and price sensitivities.

Pricing can also be a source for driving revenue growth if you can identify opportunities to price based on value to the customer or in a way that optimizes the tradeoff between revenue and costs. Let’s explore a few situations where consultants can help with pricing:

New Art Museum

A new modern art museum is scheduled to open next year in a major European city. The project lead has requested your help with the pricing of the admission tickets. He has two questions: How would you approach selecting a pricing method for the museum? What price would you recommend and why?

Nail the case & fit interview with strategies from former MBB Interviewers that have helped 89.6% of our clients pass the case interview.

Animal Healthcare

Our client provides healthcare services for animals and develops veterinary drugs. The client has recently developed a product that enables cows to increase milk production by 20%. They have turned to you to figure out how to price this new product in order to maximize profits.

California Municipality

Your client is a local municipality in California. The town recently built a complex of six parking lots, encircling a nearby community center and outdoor mall, which features shopping, restaurants, and some light attractions. In total there are 20,000 parking spots in these lots. Our client wants to maximize the profit it generates from the parking lots with a focus on revenue generation. How would you think about different types of pricing structures and revenue models for the parking lots?

In most pricing case questions, you’ll have to work through one or a combination of the following pricing strategies:

Most companies use a combination of these alternative pricing strategies to maximize profitability. For example, a manufacturer of diet pills that costs $10 to produce may be able to charge $100 per bottle if the target customers have low-price sensitivity and high perceived value (a savings of many hours working out in the gym and/or eliminating the negative health effects of being overweight).

Note that for these examples, multiple correct solutions are possible. The important thing in pricing case interviews is to back up your answer well with analysis and logic.

Relevant pricing strategies + sample approach:

• Since the costs of running the museum are mostly fixed (e.g., staff, maintenance, and utilities), a cost-based pricing strategy will not provide much insight. 
• The new museum should therefore use a combination of demand-pull and market-based pricing.
• Since the museum is new, they should set their pricing below the average market price in order to draw in early customers to check out the museum and spread awareness to their friends.
• Do a check that the proposed price point will cover a good portion of the museum’s costs with expected attendance numbers.
• Note that for a museum, ticket sales are probably not expected to fully cover costs. Exhibit sponsors, grants, and donations will be additional sources of funding.

Our client provides healthcare services for animals and develops veterinary drugs . The client has recently developed a product that enables cows to increase milk production by 20%. They have turned to you to figure out how to price this new product in order to maximize profits.

• The cost of making a dose is $30.
• The competition charges $300 per dose.
• Clients are willing to pay $300 per dose because the 20% increase in their revenues will more than offset the product’s price.
• Therefore, the client should set its price based on a combination of market-based pricing and demand-pull.
• The recommendation of whether to price above or below the competitor’s $300 price depends upon how our product compares to theirs. If the client’s product is superior in any way, we may be able to command a higher price. If we are entering the market late and with a comparable product, we’ll need to set our price lower in order to provide an incentive for customers to try our product.
• At $300, this will provide a very attractive gross margin of close to 90%.

Your client is a local municipality in California. The town recently built a complex of six parking lots, encircling a nearby community center and outdoor mall, which features shopping, restaurants, and some light attractions. In total there are 20,000 parking spots in these lots. Our client wants to maximize the profit it generates from the parking lots with a focus on generating additional revenue. How would you think about different types of pricing structures and revenue models for the parking lots?

• Costs are mostly fixed (staff salaries and bond payments for garage construction), so a cost-based strategy doesn’t provide much insight. 
• Competitive garages are priced similarly, but less convenient for shoppers.
• Excess capacity provides the opportunity to identify additional revenue sources by driving higher utilization of the parking spaces and offering customers additional services while parked in the garage. 
• Brainstorming options to increase revenue identifies a variety of options (offer monthly passes, charge stores monthly fees to validate customer parking, provide valet parking and car washing, use excess capacity for concerts, fairs, or other large events that require parking spaces.)
• Do a check that the proposed price point, including value-added services, will cover salary and bond payments.

Like any other case interview, you want to spend the first few moments thinking through all the elements of the problem. Also, there is no one right way to approach a pricing case study but it should include the following:

• Cost-based: What is the cost of making the product or delivering the service?
• Market-based: What is the pricing of a comparable product or service? Can we price above what the competition is charging?
• Value-based: What is the customer’s willingness to pay? Will we lose customers if we charge higher prices? Are there incremental services we could provide that customers would value?  
• What is the volume impact of the alternative pricing model? What is the incremental revenue expected?
• What significant costs will be incurred if the pricing model changes?
• What revenues and costs will be realized if value-added services are launched?
• It can be hard to raise prices once customers are used to a low price. Price anchoring (establishing a higher price but discounting it) may be needed for some time to transition customer expectations.
• What is the expected response from the competition?
• What is the impact on the brand if we reduce prices?
• What is the impact on volume if we increase prices?

Let’s go through the pricing case for the California municipality with 6 parking lots. Remember that the instructions said to focus on incremental revenue. As you develop your structure for the case, remember the key components of our pricing issue tree approach:

• Pricing strategies including offering value-added services
• Financial Impact

Tailor Your Pricing Case Approach for this Client

The first thing you will need to do in a pricing case study, as well as any other consulting case, is to ensure you understand the problem you need to solve by repeating it back to your interviewer. If you need a refresher on the 4 Steps to Solving a Consulting Case Interview , check out our guide.

Second, you’ll structure your approach to the case. Stop reading for a moment and consider how you’d structure your analysis of this case. We gave you some hints in our sample cases section. After you’ve outlined your approach, read on and see what issues you addressed, and which you missed. Remember that you want your structure to be MECE and to have a couple of levels in your Issue Tree . 

• What is the municipality’s cost structure? 
• How much revenue is required to cover costs?
• How much of a profit expectation does the municipality have? Do they want to generate as much revenue as possible or cover their costs and provide a service to their community at an attractive price?
• What are the municipality’s current pricing structure and prices?
• How do the municipality’s current prices compare to alternative parking options?
• What alternative pricing structures could the municipality use (hourly rates, daily rates, monthly rates, store validation, etc.)
• What non-price considerations are there? (Proximity to popular destinations, roof vs. no roof, lighting/safety, cleanliness)
• What services could the parking lots provide in addition to the parking spot?
• How much space would providing additional services require?
• How much revenue would they generate?
• What are the expected revenues of alternative pricing models?
• What are the expected revenues of value-based services?
• Would additional costs be incurred?
• How might customers react to alternative pricing models?
• To value-based services?
• How might competitors react?

Pricing Case Brainstorming Exercise

After you structure your approach, the interviewer asks you to brainstorm some revenue growth opportunities for the California municipality. Again, stop reading for a moment to do this exercise yourself because you’ll learn more if you do. When you’re done, note the ideas you didn’t consider. Few candidates hit every possibility, but to move on to the next round of interviews, you’ll definitely want to go beyond the straightforward responses. 

• Charge store owners for parking spots to offer free parking for visitors.
• Charge higher pricing for spots closest to the stores.
• Offer annual/monthly parking passes.
• Valet parking
• Car washing
• Quick car servicing (e.g., oil change)
• Locate public transportation/bus stops adjacent to the lots and provide parking to commuters at a monthly rate.
• Rent space to event attendees (e.g., sporting events, concerts, fairs).

When you ask about the municipality’s current pricing and parking space utilization rates, your interviewer provides you with the following exhibit and asks you to calculate the daily revenue. Note that the parking lot has two sources of revenue:

• Tourists/shoppers buying parking tickets for an hourly rate.
• Store owners buying monthly parking permits for their staff.

Calculation of current daily revenue:

• 3 hour parking: Revenue = (20,000 parking spots) * (30% of total lot occupancy for tourists/shoppers) * (75% of tourists/shoppers occupancy for 3hr parking) * ($2/hr) * (*3hrs) = $27,000
• 5 hour parking: Revenue = (20,000 parking spots) * (30% of total lot occupancy for tourists/shoppers) * (25% of tourists/shoppers occupancy for 5hr parking) * ($10 flat fee) * = $15,000
• Total tourist/shopper revenue – $42,000
• Store owners: Revenue = (20,000 parking spots) (5% lot occupancy for owners) *($240/month) (1/30 to convert to daily revenue) = $8,000
• Total tourist/shopper + store owner daily revenues= $50,000

Alternative Pricing Model: Store Validation

If we move to a store validation model, in which a store validates the ticket of any customer who buys something, the spots taken would increase to 10,000, or 50% of available capacity. This is because the cost of parking is currently a deterrent to customers shopping at this mall. More shoppers at the mall would be a significant benefit to store owners.

The cost per validation would be $5 to the store. Assume every person parking a car purchases something. The number of store owner permits would drop to 750 since store owners will likely decide to save money on permits to pay for visitor parking spots.

What would be the impact on daily revenue?

• Increase in the tourist/shopper revenues = $8,000 = (10,000 spots) * ($5 per spot) – $42,000
• Store owner revenues would decrease by $2,000= (750 permits) * ($240 per permit/30 days per month) – $8,000 
• Change in total daily revenue = + $6,000
• A good candidate will recognize that the increase of 12% in daily revenues is a positive move forward.

Risks to the Change to a Validation Pricing Model

Do you see any risks to a validation pricing model? Do you think you’re likely to run into any resistance? From which types of stores and why?

• Stores with low price per transaction (such as ice cream shops) will likely lose money if they pay for the $5 validation fee, therefore 100% of stores will not be willing to participate.
• An alternative validation model would be to charge stores based on a percentage of transactions or profits. This would get less pushback.
• Under the percentage model, there would need to be a cap on the price charged to stores. A 5% charge on an ice cream may be reasonable but a 5% charge on a $1000 handbag would not be.
• You could note that while the focus of this case is on revenue generation, the costs to run a validation model might be slightly higher because the municipality will need to process the validation numbers and bill the stores.

Recommendation

Lastly, provide your recommendation for the client. Try coming up with your own before reading our sample.

The California Municipality should proceed with the transition to the validation pricing model because it provides an incremental $6000 per day or an increase in revenue of 12%. While doing this, it should study the risk of pushback from store owners with low transaction value and the possibility of charging based on a percentage of the transaction. Additionally, the municipality should roll out revenue growth opportunities such as renting out excess capacity and offering value-added services (e.g., valet parking) over time.

Determine the relevant pricing strategy to apply (e.g., cost-based vs. market-based or demand pull).

Brainstorm all possible changes in pricing methodologies that might bring in additional revenue (e.g., hourly, daily, or monthly pricing, a store-validation model for our parking lot case), don’t forget that charging for value-added services could be part of a broader pricing & revenue generation strategy (e.g., oil changes, car wash for our parking lot case)., calculate the incremental revenues from suggested changes in pricing., always have an answer to whether to proceed or not., detail the risks associated with the pricing changes..

– – – – –

In this article, we’ve covered:

• Examples of pricing cases
• Approaches for solving a pricing case
• Different pricing strategies 
• Tips for solving a pricing case

Still have questions?

If you have more questions about pricing case study interviews, leave them in the comments below. One of My Consulting Offer’s case coaches will answer them.

Other people prepping for pricing case studies found the following pages helpful:

• Our Ultimate Guide to Case Interview Prep .
• Types of Case Interviews .
• Consulting Case Interview Examples .
• M&A Case Study Interview.
• Market Sizing Case Questions .

Help with Case Study Interview Prep

Thanks for turning to My Consulting Offer for advice on pricing case study interviews. My Consulting Offer has helped almost 85% of the people we’ve worked with to get a job in management consulting. We want you to be successful in your consulting interviews too. For example, here is how David  was able to get his offer from  Deloitte.

2 thoughts on “Mastering the Pricing Case Interview: A Comprehensive Guide”

In the Alternative Pricing Model: Store Validation

The original tourist/shopper revenue is $42,000 Under the alternative pricing model, (10,000 spots) * ($5 per spot) = $50,000, an $8,000 increase

The original store owner revenue is $8,000 Under the alternative pricing model, (750 permits) * ($240 per permit/30 days per month) = $6,000, a $2000 decrease.

The new total daily revenue = $56,000 Original daily revenue = $50,000

Shouldn’t the change in total daily revenue be = $56,000 – $50,000 = $6,000, a 12% increase?

I’m confused about the change in total daily revenue $2,000 and 4% numbers.

Yes, good catch! We’ll make the change. Sorry for the confusion.

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Case Study | Developing Pricing Strategy for Consumer Products

Consumer Products Company

Consumer Products

CONSULTANT ROLE

Pricing Strategy Consultant

DACH Region

The Challenge

Pricing Complexity in a Competitive Market

The client, a B2C Consumer Products company, faced two key challenges within the fiercely competitive market. The company’s current pricing set-up was unable to effectively attract and retain customers , and emerging market trends indicated that consumers were becoming more sensitive to price fluctuations .

“Our pricing approach needed a fresh perspective for us to stay competitive and maintain our growth trajectory.” — Head of Pricing of Consumer Products Company

The client recognized the need for a seasoned pricing consultant with in-depth understanding of advanced pricing methodologies and tools, consumer behavior and market trends.

Role of Consultport

Consultport proposed 3 strong candidates within 72 hours. The client interviewed 2 candidates and selected a former senior consultant at Oliver Wyman. The consultant started working with the client team 1 week after the initial request.

Comprehensive Pricing Analysis

The consultant conducted in-depth analysis of the current pricing set-up. Competitive benchmarking, conjoint analysis, and value-based pricing frameworks were utilized to help identify optimal price points, understand customer preferences, and determine the perceived value of the company’s products.

“A thorough examination of current pricing practices is essential before introducing novel strategies.” — Pricing Strategy Consultant

Evaluate & Prioritize Pricing Initiatives

After the analysis phase, the consultant identified and evaluated potential pricing initiatives, including promotional bundles, loyalty pricing and competitive price matching. 

After thorough discussions with the client, the consultant proceeded with a pricing strategy focused on customer loyalty and retention to maximize returns on pricing investments. The initiative was selected due to its potential to increase customer retention and alignment with the company’s customer-centric values.

Clear Roadmap for Implementation

In collaboration with the client, the consultant devised a detailed roadmap with actionable steps to execute the loyalty pricing initiative, from data infrastructure upgrades to customer segmentation and pilot program launch . 

Additionally, the consultant outlined best practices for the client to differentiate between products in the same category and reacting to the cost price of their products. 

After full implementation of the pricing initiative, the consultant expected a 12% increase in customer retention and a 10% growth in overall revenue .

Optimized Pricing Initiative

Clear Implementation Roadmap

The consultant’s insights and strategies really drove our pricing strategy forward. We’re happy with the collaboration.

Head of Pricing of Consumer Products Company

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Developing an Optimal Pricing Strategy for New Product Launches

Case study: pharmaceuticals pricing strategies.

in Estimated Sales

The Challenge

The company did not have a strong understanding of new product pricing, and the drug would be the first indicated treatment for a rare condition. The pharma company had concerns it would not capture optimal revenue. It lacked a robust pricing strategy. Further complicating matters, drug payers had been exposed to very high launch prices but had mechanisms to limit prescribing. It also was not uncommon to face media backlash in the US market due to pricing.

Pharmaceuticals and Biotech Industries

Without a strong understanding of current market conditions and key value drivers, pharmaceutical companies struggle to set optimal launch prices. However, sales expectations are higher than ever for new drugs. Poor launch performance signals long-term challenges that can prove extremely difficult to overcome.

So while in the process of preparing for the release of a new orphan drug in the US market, one pharmaceutical company appointed Pricing Solutions to help them execute a confident go to-market strategy. The pharma company, among the world’s 50 largest, estimated sales for the drug would peak at approximately $500 million though it believed its orphan drug brought much more value to the market.

Building a Solution that Fits

Pricing Solutions would need to conduct a full 360 0  launch price evaluation of the major orphan product. That meant starting with a thorough research study. Multiple rounds of interviews would be essential:

• First, internal interviews with senior managers from the pharmaceutical company’s Sales, Marketing and Commercial teams were scheduled
• In-depth interviews were done with key payer groups from commercial, Medicare and Medicaid plans
• A quantitative study with specialists was needed
• Finally, in-depth interviews with key opinion leaders – those with extensive experience and knowledge of the rare disease – would be held

These gave Pricing Solutions an understanding of the importance of clinical and commercial factors in the payers’ decision-making process around coverage. The acceptability of different price points and price structures were tested in order to establish the key psychological thresholds. Insight from Specialists were captured from a larger sample to understand the prescribing price sensitivity on a practice level. This data was used to estimate expected volume usage levels, taking into account dosing, compliance and persistence. The  research was able to confidently forecast prescribing of the drug , allowing for restrictions imposed by payers (see Chart A).

By testing a number of price points, it was possible to evaluate how physician prescribing and payer restrictions changed as price increased. This served as the basis for Pricing Solutions’ calculation of a price volume chart. (see Chart B).

The final step in Pricing Solutions’ 360 0 assessment would combine the existing wisdom of the client’s project team and senior stakeholders with Pricing Solutions data analysis. Pricing Solutions met with the client to present and discuss strategic options. Pricing Solutions presented each in terms of revenue, risk volume, company reputation and logistics impact so the most comprehensive go-to-market strategy could be employed.

The Result?

The pharmaceutical company’s product team was now in a position to make strategic and tactical pricing decisions with confidence.  Pricing Solutions prioritized six recommendations to implement during launch, and identified the price that returned optimal revenue  in the current market (see Chart B). Today, the product has successfully launched under the prices recommended by Pricing Solutions.

The Pricing Solutions Difference

Pricing Solutions has in-depth knowledge of healthcare launch pricing in major global markets. Our experience across 700+ pricing research and modelling projects instills client confidence in our recommendations. We leverage our full healthcare expertise, and model of Pricing Research through the 5 Critical Stages of the Product Lifecycle, to deliver solutions for both established and alternative business models.

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How to Build a Better Pricing Strategy

Hint: It’s all about good market research.

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With rapidly changing markets and emerging technologies, setting the right price is harder than ever. But pricing strategy consultant Rafi Mohammed tells HBR IdeaCast host Sarah Green Carmichael that it’s possible to make better decisions about pricing if you understand how pricing and demand interact in your business and you have good market research to guide you.

“The front line really has a lot of intuition on what customers are willing to pay. [They have] a lot of market research that they can share with the people who set prices to help set the right price,” he explains.

Key topics include: pricing strategy, dynamic pricing, market research, supply and demand, innovation, media, entertainment, professional sports, and the travel and tourism industry.

HBR On Strategy curates the best case studies and conversations with the world’s top business and management experts, to help you unlock new ways of doing business. New episodes every week.

Listen to the original HBR IdeaCast episode: Pricing Secrets of Ticket Scalpers (July 2011)

Find more episodes of HBR IdeaCast

Discover 100 years of Harvard Business Review articles, case studies, podcasts, and more: HBR.org

ANNOUNCER: HBR On Strategy.

HANNAH BATES: Welcome to HBR On Strategy , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock new ways of doing business. With rapidly changing markets and emerging technologies, how can you make sure you’re setting your prices right? Today, we bring you a conversation with pricing strategy consultant Rafi Mohammed.  In this episode, you’ll learn how pricing and demand interact, and how dynamic pricing differs across a range of industries – from music and sports to airlines, hotels, and even online shopping. You’ll also learn why your frontline workers can be a valuable source of market research to help you set the right price.  This episode originally aired on HBR IdeaCast in July 2011. And just a note — we recorded this by phone. While the audio quality isn’t great, the conversation is. I think you’ll enjoy it.  Here it is.

SARAH GREEN: Welcome to the HBR IdeaCast from Harvard Business Review. I’m Sarah Green. Today we’re talking about something that affects every business, pricing. But we’re looking to the fringes of ticket scalping for some advice. I’m talking with Rafi Mohammed, who is a pricing strategy consultant and author of The 1% Windfall: How Successful Companies Use Price to Profit and Grow . He also blogs for HBR.org, and, so I hear, gets a lot of great tickets on the secondary market. Rafi, thanks so much for joining us today.

RAFI MOHAMMED: Sarah, thank you. It’s always enjoyable to discuss pricing.

SARAH GREEN: Yes, especially when it’s something as much fun, I think, as either summer concerts or playoff tickets. But before we get into the ins and outs of that, I have to ask, why is there a scalping market at all? Shouldn’t sports teams, musicians, shouldn’t these people be charging more in the first place?

RAFI MOHAMMED: It’s a great question. Why should the people who should be getting the revenue, why aren’t they capturing it? And there’s a couple of key reasons.

And the first is, there’s just a great deal of uncertainty when a ticket price is set, whether it’s for a baseball game that the Red Sox are doing well or not, or even a rock concert. The Rolling Stones can be very hot in some cities and not so hot in other cities. And so, one of the key reasons is due to this uncertainty, many sports teams and musicians tend to be conservative, and set a low price. The second key reason is there’s generally a hesitancy to set prices too high, because there’s a brand or goodwill associated with these entities, and they don’t want to set prices too high to damage that.

And the third sort of interesting thing is that demand comes in waves. So, when tickets go on sale, there’s a lot of demand initially, but there’s also more demand over time. So, for instance, in the music market, the sort of rule of thumb is whatever you sell in the first five days, you double that, and that’s going to be your total attendance. So, there’s this disconnect between selling and when the demand arrives. So, a lot of times people just speculate and buy tickets, and they buy it up when tickets go on sale and later sell them to people who want tickets at a later date.

And finally, it’s important to remember that the scalping market, while generally people think of it as a way to capture higher prices, the scalping market also does set lower prices when demand is low, and that’s a good way for ticket prices to be lowered. And generally speaking, teams and musicians are somewhat wary of lowering prices once they’ve set it. So, for those four key reasons, that’s why there’s an existence of a scalping market today.

SARAH GREEN: So that’s interesting. And it’s a good point at the prices can also be lower from a scalper sometimes. But isn’t there a way for teams to learn from the scalping market, and to implement that kind of flexible strategy on their own?

RAFI MOHAMMED: Exactly. And this is the new wave of ticket pricing in the future. And so, for instance, now the scalping market is about $3 billion a year. So, if there’s some way the teams and musicians can capture that, that’s great extra revenue to make. And so. the new way of thinking about pricing for these events is dynamic pricing. So, much like an airline or a hotel, you fluctuate price based on how demand is going. And so, it makes intuitive sense, but I think what most people miss on this is demand is very different. So. for instance, demand for a flight from Boston to LA, there’s actually nine non-stops a day. And in fact, I looked for Thursday. The prices for these non-stops range from $369 to $2,278. So, what happens is if I’m an airline and I have low capacity, I can just lower my price, and steal customers from other flights. And so that’s how dynamic pricing typically works for hotels and airlines. So, you have to remember, for rock concerts or sports teams, you’re not stealing demand from other places. So, generally there’s a fixed demand, and what you try and do is get the right price given the current demand structure. You can’t steal customers from– it’s rare to steal it from other events. So, that’s a key difference.

SARAH GREEN: That does seem like a key point. Like, for instance, if I’m a Red Sox fan, I wouldn’t necessarily go to see the Yankees just because they were cheaper.

RAFI MOHAMMED: Exactly. Exactly. But if you were going to LA, and there was a price difference between $369 to $2,278, if you’re keeping your eye on your budget, you might, instead of going on your favorite flight, go on an earlier light and save a lot of money.

SARAH GREEN: So how should people in the entertainment industry, who are in this event-driven business, how should they try to implement some of this knowledge on dynamic pricing?

RAFI MOHAMMED: It’s a great question. So, first of all, obviously when demand is a little higher than what you expected, that’s the best case scenario. So, all of a sudden the Rolling Stones come to town and demand is much higher. Well, you can constantly, over time, play with prices to capture the highest amount of revenue. So, in that case, that’s fine. But getting to your Yankees game analogy, when demand is low, and it’s lower than expected, what do you do? And there’s two key things. The first is that you’re getting people coming to your site, the existing demand coming to your site. And if demand is low, intuitively people might think to make all prices cheaper. But I think if people are coming to the site to buy a ticket, they’re interested, and I would focus on trying to upsell into higher-priced seats. So they’re interested. They wouldn’t normally sit in the best seats, but if you have an attractive price, you might be able to get more money out of people who have an interest, who have a demand. And the second thing that you have to do, and I haven’t seen anyone discuss this, is that for low demand events, you have to have a way to let consumers know that, gee, we’ve lowered our prices. So, if I’m interested in going to a rock concert, I’m not going to go to the website 10 or 12 times to see what the prices is. But if I know, much like on Broadway, that the day of, that they lower prices for some Broadway shows, there has to be an event that consumers will know, oh, maybe I should go back and check and see what the price is. So, those are the two ways to think about dynamic pricing when demand is high or demand is lower than expected.

SARAH GREEN: That’s interesting. So, I want to, if we can, get into an example here of maybe a team or a musical act trying to implement this and see how well it’s working. Is there anyone on your radar screen who’s either doing something that’s working, or maybe doing something that you’d want to avoid?

RAFI MOHAMMED: Right. Sure. Well, the classic example is that the San Francisco Giants did a test market for dynamic pricing a couple of years ago. And what they did is, in certain sections, they would lower and increase price. And what they found is that, in these sections, the revenue increased by 20%. So that sounds like a really great figure, doesn’t it? But here’s where I think that they missed the boat on, is this notion of cannibalization. And it’s not just for tickets. It can be for any product. People tend to say, oh, well, we lowered the price, and we got more people to buy. But what you have to take into account is the fact that some people would have bought at the higher price. So, let’s go back to the San Francisco Giants. If they have an experimental section and they drop the price, why would I buy a ticket in the next section over that’s at a much higher price? So, if I were going to buy that ticket, I would say, well, gee, I can save $10 by going to the experimental section. Why not? So, my hunch is that there was a lot of cannibalization going on, and that 20% figure really didn’t represent new revenue, getting people price sensitive, in the door. My hunch is that the majority of this increased 20% came from people who would have actually paid a higher price. That’s a negative of dynamic pricing that I don’t think has been satisfactorily accounted for.

SARAH GREEN: So, we’ve been talking about dynamic pricing across a range of industries, sports, music. You mentioned hotels. You mentioned airlines. And I think it has seeped even further, even more than we know, into other industries, especially since online shopping makes it pretty easy for online retailers to figure out what kind of shopper you are, and what you might be willing to pay. Is there any industry that you think if safe from dynamic pricing, or are we just going to be all getting different prices all the time in the future?

RAFI MOHAMMED: Well, Amazon. In my experience with Amazon, they do change prices. So, by the day, for instance, I see that my book price goes from $18 to $20. And so, they definitely change prices. Several years ago, they did get caught up in a pricing scandal, where they were offering different prices to different consumers at the same time. So, people are like, gee, I just bought this DVD, and I paid this. And someone else would say, I bought it at the same time. I paid a very different price.

And so after that, there was a lot of discussion about this. Amazon came out and said that, we aren’t going to offer different prices to different customers at the same time. So, what they didn’t say is that, we are not going to vary prices over time. They just said they were going to stop that practice. So, what you are seeing on the web is that, since it’s a great experimental venue and you could see how people react, you are going to see on the web more price experimentation by all types of retailers, to try and figure out what is exactly the right price for products.

SARAH GREEN: So as companies like that start experimenting, I think part of the reason it’s useful, for instance, to talk about ticket scalping is that it becomes obvious when you’re leaving money on the table, because, well, either people are willing to pay more or they’re not, or, as you mentioned at the beginning, they’ll pay less. So, it’s sort of easy to see how close you are to the mark by how close you are to that secondary market. But if you are in a business where your product or service doesn’t get quote, unquote “scalped,” how do you know if you’ve got it right?

RAFI MOHAMMED: I always ask people on the front line, because they deal with customers. And oftentimes people on the front line can tell you a lot of people would have paid a lot more, or we’re getting a lot of people who are very interested, they take the product off the shelf. They’re interested, but once they see the price, they put the product back. So, there’s the two ways of doing it, one, a market research type, which we discuss on the Amazon by varying prices. Or second, I feel that the front line really has a lot of intuition on what customers are willing to pay. And that front line has a lot of market research that they can share with the people who set prices to help set the right price.

SARAH GREEN: That’s interesting. It’s always interesting to know how much of this always comes back to those people on the front line. So, I can’t let you go without going back to ticket scalping, and just asking the question that I know is on everyone’s minds. How do I get the best deal on tickets that I want?

RAFI MOHAMMED: Well, you know, we can’t tell all the secrets, but I’m happy to share some of the key secrets. And it really comes down to uncertainty, and how you deal with uncertainty. And it’s been my experience that the closer you get to an event, whether it’s a rock concert or a sporting event, you see prices go down. And so obviously, if you’re taking a significant other, or celebrating a very important event, or going out with clients, you really don’t want to be sweating it out until the last second and hoping that prices are going to go down. So, that goes back to the notion of value. So, I value the certainty of having great tickets to the Rolling Stones or the Red Sox versus the Yankees. So, I’m willing to pay a premium just to get that certainty. But much like what you see in life, and in pricing in general, if you’re willing to wait it out and deal with the uncertainty, you can get the best tickets at face value, if not lower, if you wait until the very last minute.

SARAH GREEN: So, just a little negotiating ploy there.

RAFI MOHAMMED: It’s not really negotiating, but it’s sort of as events get nearer, I have this theory that people often buy tickets for their friends. And I think the older that you get, the more of life’s obstacles that you face, and at the end, oftentimes friends can’t make it. And so I often see, when I’m going to a show or a sporting event, people are like, oh, my friends were supposed to come, but now we have two extras. And since there’s so many people in that situation, the market has set a lower price. So that’s really the key to getting the best tickets at the lowest price. And what’s always surprising to me, when I go to these events or I’m looking for tickets at the last minute, is how good of a seat comes up. It’s shocking that, generally speaking, the day of, or two days before, you’ll see on craigslist or eBay, tickets in the first 10 rows that you can get at face value, and if you bargain a bit, even lower.

SARAH GREEN: Well, it’s excellent, excellent advice. Rafi, thanks so much for talking with us today.

RAFI MOHAMMED: Thanks so much, Sarah. I appreciate it.

HANNAH BATES: That was pricing strategist Rafi Mohammed – in conversation with Sarah Green Carmichael, former host of the HBR IdeaCast . If you liked this episode, check out HBR Ideacast on Apple Podcasts, Spotify, or wherever you get your podcasts. They release new episodes every week. HBR On Strategy will be back next Wednesday with another hand-picked conversation about business strategy from the Harvard Business Review. And in the meantime, we have another curated feed that you should check out: HBR On Leadership . And visit us any time at HBR.org, where you can subscribe to Harvard Business Review and explore articles, videos, case studies, books, and of course, podcasts, that will help you manage yourself, your teams, and your career. This episode of HBR On Strategy was produced by Anne Saini [“Sanny”] and me, Hannah Bates. The show was created by Anne Saini, Ian Fox, and me. Special thanks to Maureen Hoch, Adi Ignatius, Karen Player, Anne Bartholomew, and you – our listener. See you next week.

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Optimal Pricing Model: Case of Study for Convenience Stores

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• First Online: 02 August 2017
• Cite this conference paper

• Laura Hervert-Escobar 15 , 16 ,
• Jesus Fabian López-Pérez 16 &
• Oscar Alejandro Esquivel-Flores 15  

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10062))

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• Mexican International Conference on Artificial Intelligence

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Pricing is one of the most vital and highly demanded component in the mix of marketing along with the Product, Place and Promotion. An organization can adopt a number of pricing strategies, which usually will be based on corporate objectives. The purpose of this paper is to propose a methodology to define an optimal pricing strategy for convenience stores. The solution approach involves a multiple linear regression as well as a linear programming optimization model. To prove the value of the proposed methodology a pilot was performed for selected stores. Results show the value of the solution methodology. This model provides an innovative solution that allows the decision maker include business rules of their particular environment in order to define a price strategy that meet the objective business goals.

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Acknowledgments

The authors are grateful to Sintec for financial and technical support during the development of this research. Sintec is the leading business consulting firm for Supply Chain, Customer and Operations Strategies with a consultative model in Developing Organizational Skills that enable their customers to generate unique capabilities based on processes, organization and IT. Also, we appreciate the financial support of CONACYT-SNI program in order to promote quality research.

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Laura Hervert-Escobar & Oscar Alejandro Esquivel-Flores

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Laura Hervert-Escobar & Jesus Fabian López-Pérez

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Hervert-Escobar, L., López-Pérez, J.F., Esquivel-Flores, O.A. (2017). Optimal Pricing Model: Case of Study for Convenience Stores. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_28

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Data science & multidimensional analysis: the recipe to create optimal pricing strategies

A case study on better retail pricing based on location-based competitors’ price data through Machine Learning

Retailers are relying more and more on technology to optimize their pricing strategies to maintain or improve their sales and bottom line. Deploying those strategies has become increasingly profitable – and necessary to stay competitive – with the widespread availability of a variety of data, such as competitor prices.

In this article, we will see how a retailer can group their shops into sets based on the geographical distribution of competitors and apply different price strategies based on the characteristics of each cluster. Machine learning helps us identify the clusters of shops and we use Atoti to analyze the characteristics of each cluster and optimize pricing.

This will provide a good example of using Atoti to exploit the results of machine learning algorithms, 

Quick links to sections:

Implementation

Interpreting machine learning results with atoti, price optimization with atoti, our imaginary use case.

The competitor outlets are shown as colored dots and blue pins on the map. 

Every shop performs differently. For the purpose of optimization, it is necessary to identify a pattern in their environment and competitive strengths to explain this difference in performance.

What data do we have?

We have geographical information for each of our shops and for our competitors’. Using haversine formula on the latitude and longitude of the shops, we determined the distance between each retail shop and all its competitors.

We also have data on the products and their sales figures for each shop as well as the local competitors’ selling price of the same products.

To identify the clusters of shops, we need to prepare the feature inputs of each shop for machine learning:

• The number of competitors by distance buckets of e.g. 1km, 5km, 10km, etc.
• Price index – our price position against competitors

Once we have our clusters output from machine learning, we will optimize prices using a pricing engine and analyze the impact using Atoti.

Number of competitors per distance bucket

Every one of our shops has a different number of competitors within its region. With Atoti visualization , we see the number of competitors within different distance buckets from each shop.

From the clustered column chart above, we see that shops in Montpellier, Nantes, Nice, Saint-Etienne, etc. have very little competition within a 20km distance radius. If we look at the shops around Paris, for instance, MyShop Paris 11 , 12, 15 , we see that while there are few competitors within 1km, a lot of competitors are located within a 5km radius.

Price Index

The price index is a measurement of where a retailer is positioned compared to one or several competitors.

When we look at the price index, a value of 1.00 indicates that the selling price of the product in our shop is the same as the average selling price of our competitors. A value greater than 1 shows that we are selling at a higher price. Similarly, a value lower than 1 shows that we are selling at a lower price than most of our competitors. 

We could adjust the selling price of each product, or clusters of products, as we have demonstrated in a different article – Data dive: How to improve pricing strategies with a set of checkout receipts . However, in this article, we adjust the selling price for our shops such that if there is high competition, we lower the price index. If there is low competition, we can afford to have similar or higher selling prices.

We will skip the implementation of the Atoti datacube as we focus more on the machine learning and data analytics aspect here.  

Data Schema

We have the resulting snowflake schema :

Measure computation

Computing the number of competitors per distance bucket.

While there are many ways to compute the number of distance buckets, we wanted to showcase how we can do so with Atoti’s scenario feature. If we set the distance radius from the shop as a threshold, the scenario feature allows us to run simulations with different values for the distance radius, hence achieving the distance bucket. 

For instance, we start by looking at 1km distance radius from each shop:

m[“Distance Threshold”] = 1

We count the number of competitors who are located within the distance threshold:

Did you notice here how we use the average distance between the competitors and the shop for the distance comparison? This is because the join between the CompetitorPrices and DistanceMatrix store may result in the value Competitor Distance KM to be multiplied by the number of products.

Now that we have computed the number of competitors within 1km, we set this original data as the base scenario, named as “01 km”.

We can now easily create as much new distance buckets as we needed, simply by replacing the distance threshold value in each scenario:

We use the cube.visualize feature to present the number of competitors in each scenario as follows:

Price Index computation

We have the following weighted price index formula:

Firstly, note that we are only interested in the relevant CompetitorPrice of competitors within a given distance threshold . We can achieve this by using the “where” condition to return the CompetitorPrice if the competitor lies within the distance threshold .

Instead of using Pandas to do pre-aggregation, we perform the margin computation with Atoti so that we can see the change in its value after we optimise the selling price later on.

Finally, let’s see the equivalent of the above formula in Atoti.

For each shop, we can see the difference in price index as the number of competitors changes with the distance threshold.

Data output for machine learning

We can output the price index and number of competitors per distance bucket for each shop into a pandas DataFrame simply by querying the cube:

We did 2 separate queries and merged them using pandas. In the event we have to add more distance buckets using the scenario, we simply call this function to regenerate the output for machine learning.

Machine learning

K-means clustering partitions n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers), serving as a prototype of the cluster. 

K-means clustering is the perfect machine learning algorithm that we needed to find groups in the data. 

Let’s assume 5 clusters for retail shops and feed the below features to the algorithm:

We see that the price index for ShopId 20 and 21 is 1.0. This is because the competitors of these shops are more than 20km away from them. Hence there is literally no competition nearby.

Using the distance bucket of 1km, let’s try to understand the output from the model:

In the above plot, each color represents a cluster. We can see that clusters seem to be strongly based on the number of competitors rather than on the price index.

Let’s use seaborn to visualize the clustering results for every pair of features:

Let’s focus on the last row that depicts the relationship between the price index and the distance buckets.

The shops in cluster 1 (blue box) have a much higher number of competitors (>50) in a 10km radius, compared to those of cluster 0 (red box) having less than 20 competitors in the same radius. While cluster 1 has more competitors, its price index is generally higher than cluster 0 and greater than 1.

Continuing this analysis tells us that:

• Cluster 0 is a big cluster with few competitors around and its price index is generally around 1.
• Cluster 1 has a high number of competitors even within a 5km distance radius. However, its price index is slightly skewed towards a higher price index even with the high competition.
• Cluster 2 is a small cluster and the number of competitors increases tremendously as the distance radius increases. Generally, it has a lower price index than its competitors.
• Cluster 3 is a small cluster and the number of competitors remains about the same across all buckets. Its price index remains consistent at around 1 across the distance bucket, although one of its shops started having a higher price index and the rest fall below 1 as we consider competitors in the 15-20km radius.
• Cluster 4 is a small cluster that has a higher price index against the nearest competitors. This is reasonable considering the number of competitors nearby is not high. The price index becomes much lower as the number of competitors increases from 15km onward.

Thankfully Atoti allows us to easily create another store and join to the existing cube on the fly. Let’s load the clustering information back to the cube:

With Atoti’s visualization, let’s see the spread of the clusters around France.

Interestingly, cluster 0 (blue) is distributed all over France except Paris, and mostly they are the only shops in their neighbourhood that belong to our retailer. Cluster 3 (green) is a small cluster around Lille, the capital of the Hauts-de-France region in northern France. The rest of the clusters (red, yellow and purple) have shops belonging to our retailer in close proximity, and most of them are spread around Paris.

The size of the points on the map reflects the number of competitors within 5km – we can see the competition around the city is the highest, specifically for cluster 1 (red).

In the case of cluster 0, most of the shops are the only ones belonging to the retailer in the neighbourhood. The number of competitors is low, hence the price index is less affected by competition. Rather, other factors such as the variety of products, branding, market demands, etc. could weigh more heavily on the price index – these are to be considered when applying a pricing strategy for this cluster. Generally, the price index could be higher.

For the rest of the clusters, there are a few considerations. Within the same proximity, the shops face the same competitors. Not only that, consumers can easily detect the price differences of products between the shops of the same retailer if they are close to one another. Hence it makes more sense to align their price index and it should be slightly lower to push up their competitiveness.

Pricing Simulations around clusters

Now that we have obtained the clusters of shops and understood the level of competition around them, we will use a pricing engine to optimize the price index accordingly. The pricing method we use in this article tries to reduce the prices if the competitiveness is strong, and increase them if there are few competitors.  

Let’s visualize the impact of the new pricing strategy against the existing price index by loading the price-optimized DataFrame as a new scenario into the Atoti cube:

Thanks to Atoti’s built-in simulations capabilities, we can easily create a new pricing scenario by loading the price-optimized DataFrame directly.

All the previously defined KPIs, like the price index, will be re-computed on the fly, enabling us to compare the scenarios and their benefits immediately.

We see an increase in margin for all clusters except for cluster 1. Although the overall margin has decreased, we should have an increase in sales if the strategy works well and subsequently an increase in the overall margin.

We see the price index is lowered for cluster 1 while it is increased for clusters 0, 3 and 4. 

The pricing method decreased the price index of shops in clusters that have high competition in order for them to attract more customers. On the other hand, it increased the prices in shops belonging to low competition clusters in order to maximize margin. The price index of Cluster 2 is reasonable considering the amount of competition it has within 10km radius, therefore it is not adjusted.

Selling price simulation by clusters and shops

Zooming in on cluster 1, we see that MyShop Paris 6 has one of the highest competition within the cluster and also the highest price index within its cluster. 

Likewise, MyShop Paris 9 also has a price index that is close to 1, despite the number of competitions nearby.

Let’s scale down the price index of the shop.

With Atoti’s measure simulation, we are able to scale the Selling Price either across clusters or by the specific shop.

The price indices after applying price optimization and shop-specific adjustment for MyShopParis 6 and MyShopParis 9 look more aligned with the rest now.

Using a very simple machine learning example, we saw how it could help identify clusters based on the intensity of local competition.

With this information, we applied different strategies to each cluster using simulations from Atoti to understand the impact on our KPIs. We also looked inside a cluster to adjust the parameters of a specific unit so that it’s more consistent with the parameters of the other units in the cluster.

The result was that even with limited data, we could already optimize our strategy with Atoti.

If we integrated more data such as sales figures, we could additionally see the difference in margin for each pricing strategy, with the possibility to drill down to other valuable attributes.

From there we could decide what the best prices would be based on the combination of simulations. With Atoti, we can easily introduce more factors into the simulations, such as holidays, promotions, seasons etc.

If you want to go further on the topic, check out how you can optimize the price index depending on the product class in the pricing-simulations-around-product-classes notebook and its corresponding article . Maybe you can even try combining the two strategies to get your own ideal price index!

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Using big data to make better pricing decisions

It’s hard to overstate the importance of getting pricing right. On average, a 1 percent price increase translates into an 8.7 percent increase in operating profits (assuming no loss of volume, of course). Yet we estimate that up to 30 percent of the thousands of pricing decisions companies make every year fail to deliver the best price. That’s a lot of lost revenue. And it’s particularly troubling considering that the flood of data now available provides companies with an opportunity to make significantly better pricing decisions. For those able to bring order to big data’s complexity, the value is substantial.

We’re not suggesting it’s easy: the number of customer touchpoints keeps exploding as digitization fuels growing multichannel complexity. Yet price points need to keep pace. Without uncovering and acting on the opportunities big data presents, many companies are leaving millions of dollars of profit on the table. The secret to increasing profit margins is to harness big data to find the best price at the product—not category—level, rather than drown in the numbers flood.

Too big to succeed

For every product, companies should be able to find the optimal price that a customer is willing to pay. Ideally, they’d factor in highly specific insights that would influence the price—the cost of the next-best competitive product versus the value of the product to the customer, for example—and then arrive at the best price. Indeed, for a company with a handful of products, this kind of pricing approach is straightforward.

It’s more problematic when product numbers balloon. About 75 percent of a typical company’s revenue comes from its standard products, which often number in the thousands. Time-consuming, manual practices for setting prices make it virtually impossible to see the pricing patterns that can unlock value. It’s simply too overwhelming for large companies to get granular and manage the complexity of these pricing variables, which change constantly, for thousands of products. At its core, this is a big data issue (exhibit).

Patterns in the analysis highlight opportunities for differentiated pricing at a customer-product level, based on willingness to pay.

Many marketers end up simply burying their heads in the sand. They develop prices based on simplistic factors such as the cost to produce the product, standard margins, prices for similar products, volume discounts, and so on. They fall back on old practices to manage the products as they always have or cite “market prices” as an excuse for not attacking the issues. Perhaps worst of all, they rely on “tried and tested” historical methods, such as a universal 10 percent price hike on everything.

“What happened in practice then was that every year we had price increases based on scale and volume, but not based on science,” says the head of sales operations at a multinational energy company. “Our people just didn’t think it was possible to do it any other way. And, quite frankly, our people were not well prepared to convince our customers of the need to increase prices.”

Four steps to turn data into profits

The key to better pricing is understanding fully the data now at a company’s disposal. It requires not zooming out but zooming in. As Tom O’Brien, group vice president and general manager for marketing and sales at Sasol, said of this approach, “The [sales] teams knew their pricing, they may have known their volumes, but this was something more: extremely granular data, literally from each and every invoice, by product, by customer, by packaging.”

In fact, some of the most exciting examples of using big data in a B2B context actually transcend pricing and touch on other aspects of a company’s commercial engine. For example, “dynamic deal scoring” provides price guidance at the level of individual deals, decision-escalation points, incentives, performance scoring, and more, based on a set of similar win/loss deals. Using smaller, relevant deal samples is essential, as the factors tied to any one deal will vary, rendering an overarching set of deals useless as a benchmark. We’ve seen this applied in the technology sector with great success—yielding increases of four to eight percentage points in return on sales (versus same-company control groups).

To get sufficiently granular, companies need to do four things.

Listen to the data. Setting the best prices is not a data challenge (companies generally already sit on a treasure trove of data); it’s an analysis challenge. The best B2C companies know how to interpret and act on the wealth of data they have, but B2B companies tend to manage data rather than use it to drive decisions. Good analytics can help companies identify how factors that are often overlooked—such as the broader economic situation, product preferences, and sales-representative negotiations—reveal what drives prices for each customer segment and product.

Automate. It’s too expensive and time-consuming to analyze thousands of products manually. Automated systems can identify narrow segments, determine what drives value for each one, and match that with historical transactional data. This allows companies to set prices for clusters of products and segments based on data. Automation also makes it much easier to replicate and tweak analyses so it’s not necessary to start from scratch every time.

Build skills and confidence. Implementing new prices is as much a communications challenge as an operational one. Successful companies overinvest in thoughtful change programs to help their sales forces understand and embrace new pricing approaches. Companies need to work closely with sales reps to explain the reasons for the price recommendations and how the system works so that they trust the prices enough to sell them to their customers. Equally important is developing a clear set of communications to provide a rationale for the prices in order to highlight value, and then tailoring those arguments to the customer. Intensive negotiation training is also critical for giving sales reps the confidence and tools to make convincing arguments when speaking with clients. The best leaders accompany sales reps to the most difficult clients and focus on getting quick wins so that sales reps develop the confidence to adopt the new pricing approach. “It was critical to show that leadership was behind this new approach,” says the managing director of a multinational energy company. “And we did this by joining visits to difficult customers. We were able to not only help our sales reps but also show how the argumentation worked.”

Actively manage performance. To improve performance management, companies need to support the sales force with useful targets. The greatest impact comes from ensuring that the front line has a transparent view of profitability by customer and that the sales and marketing organization has the right analytical skills to recognize and take advantage of the opportunity. The sales force also needs to be empowered to adjust prices itself rather than relying on a centralized team. This requires a degree of creativity in devising a customer-specific price strategy, as well as an entrepreneurial mind-set. Incentives may also need to be changed alongside pricing policies and performance measurements.

We’ve seen companies in industries as diverse as software, chemicals, construction materials, and telecommunications achieve impressive results by using big data to inform better pricing decisions. All had enormous numbers of SKUs and transactions, as well as a fragmented portfolio of customers; all saw a profit-margin lift of between 3 and 8 percent from setting prices at much more granular product levels. In one case, a European building-materials company set prices that increased margins by up to 20 percent for selected products. To get the price right, companies should take advantage of big data and invest enough resources in supporting their sales reps—or they may find themselves paying the high price of lost profits.

For more from McKinsey on the topic of marketing and sales, visit the McKinsey on Marketing & Sales website.

Walter Baker is a principal in McKinsey’s Atlanta office, Dieter Kiewell is a director in the London office, and Georg Winkler is a principal in the Berlin office.

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6 Optimal Pricing Strategies to Master Market Dynamics

Spotlighting Key Points

In the competitive realm of e-commerce, understanding market dynamics is crucial for setting prices that attract customers and maximize profits. Market dynamics encompass pricing forces, including supply and demand fluctuations, competitor actions, and consumer behavior. By grasping these factors, businesses can adjust their strategies to remain competitive and achieve optimal pricing.

This article will discuss the essentials of market dynamics, explore the concept of optimal pricing, and outline actionable steps to adapt these insights for your business success.

What Are Market Dynamics?

Market dynamics refer to the forces that influence pricing and behavior within an economy. These forces can be complex and multifaceted, encompassing changes in supply and demand, competitor actions, and consumer preferences.

For example, during the COVID-19 pandemic, shifts in consumer behavior and supply chain disruptions significantly impacted pricing strategies across various industries. Similarly, a sudden increase in raw material costs can drive up prices, while new technological advancements might lower production costs and affect market prices.

Competitors’ pricing strategies also play a crucial role; for instance, a significant retailer offering deep discounts can pressure others to adjust their prices to stay competitive.

Understanding these dynamics allows businesses to predict trends and adjust their strategies to maintain the optimal pricing for profitability and competitiveness.

Critical aspects of market dynamics include:

Consumer Reactions

Consumer reactions to price changes can significantly impact a business’s sales and profitability. For example, if a company raises prices, customers might reduce their purchases or switch to cheaper alternatives.

Conversely, lowering prices can increase demand but may also reduce profit margins.

Understanding how consumers respond to different pricing strategies helps businesses set prices that maximize sales volume and profit.

Supply and Demand Shifts

Supply and demand shifts directly influence pricing and market behavior. For instance, increased demand for certain products during the holiday season can lead to higher prices.

Conversely, an oversupply of goods might necessitate price reductions to clear inventory. Recognizing these patterns allows businesses to adjust prices proactively, ensuring they remain competitive and profitable even as market conditions fluctuate.

Supplier Responses

Supplier responses to changes in demand are crucial for maintaining supply chain efficiency and cost management. When demand increases, suppliers might raise prices due to higher production costs or scarcity of materials.

Conversely, decreased demand might lead suppliers to lower prices to stimulate sales. Businesses that understand supplier dynamics can negotiate better terms and adjust their pricing strategies to reflect changes in supply costs, maintaining their margins and competitive edge.

Grasping these dynamics allows businesses to anticipate market trends and make informed pricing decisions, ultimately leading to better financial performance and market positioning.

What is the Optimal Pricing?

Optimal pricing is the price point where total profits are maximized. This involves balancing what customers are willing to pay and the business’s profitability. For example, a luxury brand might set higher prices because their customers perceive a higher value in their products, whereas a discount retailer would need to keep prices low to attract bargain hunters. 

Optimal pricing can fluctuate due to rapid changes in e-commerce environments, such as new market entrants, technological advancements, or shifts in consumer behavior. Regularly revisiting and adjusting pricing strategies ensures businesses stay competitive and profitable.

Factors influencing optimal pricing include:

Perceived Value

Understanding how customers perceive the value of your product is crucial for setting optimal prices.

For instance, Apple products often command higher prices because consumers perceive them as premium due to their design, quality, and brand reputation. Marketing, customer reviews, and brand loyalty can influence perceived value.

By accurately gauging how customers value their products, businesses can set prices that maximize willingness to pay, enhancing sales and profitability.

Competitor Pricing

Continuously tracking and analyzing competitors’ prices is essential for informed pricing strategies. For example, if a competitor lowers their prices, it might necessitate a strategic response to maintain market share.

Conversely, if competitors increase prices, you may be able to adjust your pricing for higher margins. Utilizing competitor price monitoring tools helps businesses stay aware of market trends and adapt their pricing strategies accordingly, ensuring they remain competitive without sacrificing profitability.

By incorporating these elements, businesses can set prices that attract customers while maximizing profits. Understanding perceived value and competitor pricing allows for a balanced approach that adapts to market conditions and customer expectations.

Fundamental Market Dynamics to Monitor

Understanding and responding to market dynamics is essential for any business aiming to optimize its pricing strategies. Companies can make informed decisions that enhance profitability and competitiveness by monitoring key aspects such as customer behavior, competitor actions, and economic conditions. Below, we delve into the critical market dynamics every business should watch.

1. Understand Customer Shopping Habits

Customer behavior significantly impacts market dynamics. To optimize pricing effectively, businesses must:

• Identify customer segments and their shopping patterns. For instance, a fashion retailer might find that younger customers prefer shopping late at night, whereas older customers shop during the day.
• Analyze when and what customers purchase. Seasonal trends, such as increased toy sales during the holidays, provide valuable insights into customer habits.
• Adapt strategies to enhance customer experiences and meet changing preferences. Implementing user-friendly website features or personalized marketing can improve customer satisfaction and drive sales. For example, using data analytics to offer product recommendations based on previous purchases can increase conversion rates.

By understanding these habits, businesses can tailor their pricing and marketing strategies to meet customer needs and optimize sales.

2. Implement a Personalized Pricing Strategy

Tailoring prices to individual customer needs can significantly boost sales. To implement an effective personalized pricing strategy:

• Gather data on customer behavior, preferences, and purchase history. For example, e-commerce platforms can use cookies and purchase history to segment customers and understand their preferences.
• Offer personalized deals to loyal customers and new visitors based on their profile. A subscription-based service might offer a discounted rate for first-time users or exclusive deals for long-term subscribers to enhance loyalty and attract new customers.

Personalized pricing strategies increase sales and foster customer loyalty by making customers feel valued and understood. This approach can lead to higher customer retention and lifetime value.

3. Track Competitor Actions

Staying ahead in the market requires continuous monitoring of competitors. To effectively track competitor actions:

• Conduct regular market research to understand competitor strategies. For instance, using competitive analysis tools to monitor competitors’ promotions, product launches, and market positioning can provide valuable insights.
• Use competitor price tracking tools for up-to-date pricing data. These tools can alert businesses to price changes in real-time, allowing them to adjust their prices accordingly.

This information helps you make strategic pricing decisions that keep you competitive. For example, if a competitor significantly lowers their prices, a business can decide whether to match the prices or highlight the superior value of their products.

4. Ensure High-Quality Products and Services

Quality plays a vital role in pricing and customer perception. To ensure high quality:

Offer high-quality products to build a strong brand image. A company known for its durable and reliable products, like Apple, can command higher prices and maintain customer loyalty.

Address customer needs and pain points effectively. Providing excellent customer service and standing behind product warranties can enhance perceived value and justify premium pricing.

High-quality offerings justify higher prices and build customer trust. This trust can lead to repeat business and positive word-of-mouth referrals, further solidifying the brand’s market position.

5. Adapt Pricing Strategies During Economic Uncertainty

Economic conditions greatly influence market dynamics. To adapt pricing strategies during periods of economic uncertainty:

Monitor economic trends and adjust prices accordingly. For instance, during an economic downturn, businesses might lower prices or offer more promotions to maintain sales volume.

Communicate transparently with customers about price changes. Explaining the reasons behind price adjustments, such as increased costs or maintaining product quality, can help keep customer trust.

By adapting optimal pricing strategies to economic conditions, businesses can remain resilient and continue to attract customers even in challenging times. Transparent communication ensures customers understand and accept necessary price changes, maintaining loyalty and trust.

6. Use Pricefy.io for Optimal Pricing

Pricefy offers advanced tools to help businesses navigate market dynamics and set optimal prices. With real-time competitor price monitoring , you can quickly respond to market changes and stay ahead.

Advanced data analytics provide insights into customer behavior and market trends, helping you tailor your pricing strategies. 

Personalized pricing features allow customized offers based on customer profiles, boosting sales and loyalty.

Optimal pricing strategies ensure your prices remain competitive and profitable. Comprehensive reporting tools offer detailed insights into pricing performance, enabling data-driven decisions for continuous improvement.

Understanding and adapting to market dynamics is essential for setting optimal pricing.

By monitoring customer behavior, competitor actions, and economic conditions, businesses can make informed pricing decisions that enhance profitability and competitiveness.

Implementing personalized pricing strategies and ensuring product quality further strengthens your market position.

• Pricing Strategies

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Hacking the Case Interview

Pricing consulting cases are one of the most common types of case interviews. You will most likely see at least one pricing strategy case in your upcoming interviews.

A pricing case interview may look something like the following:

Apple is about to launch their latest version of the iPhone. What is the optimal price that they should set for this new product?

Fortunately, pricing cases follow a predictable pattern. Once you have practiced a few pricing cases, you should be able to solve any pricing case interview.

In this article, we’ll cover:  

• A comprehensive pricing case interview framework
• The 7 steps to solve any pricing case interview
• A full-length pricing case interview example
• Recommended pricing case interview resources

If you’re looking for a step-by-step shortcut to learn case interviews quickly, enroll in our case interview course . These insider strategies from a former Bain interviewer helped 30,000+ land consulting offers while saving hundreds of hours of prep time.

Pricing Case Interview Framework

The best pricing case interview framework looks like the following:

The framework starts by looking more closely at the company and product. Understanding the company and product better will help you get a sense for how you should be pricing the product.

Afterwards, the framework covers the three different ways to price a product or service. We’ll cover each of these pricing strategies in detail so you can fully understand how to use this framework. You’ll likely use a combination of all three of these strategies to solve a pricing consulting case.

• Pricing based on costs

The simplest way to price a product is to look at the costs to produce a product and set a higher price. If the company has a specific profit margin figure in mind, they can set a price to reach their profit margin goals.

Example: If it costs Apple $200 to produce their iPhone and they are aiming for at least a 20% profit margin, they would need to price their iPhone for at least $240.

Pricing based on costs ensures that the company will be profitable from selling the product. It does not make sense to price a product lower than its costs because the company would be losing money on each sale.

Pricing based on costs sets the lower end of the pricing range you should consider.

• Pricing based on value provided

Pricing based on value provided is the most complex way of pricing. To price a product using this strategy, you need to identify all of the benefits that the product provides and quantify how much value these benefits provide to customers.

This will equal the customer’s maximum willingness to pay. For example, if a product provides $800 of value to the customer, they will not be willing to pay more than $800 for it. It does not make sense to price the product for more than this because no customers would buy it.

Example: The iPhone provides various benefits such as entertainment, productivity, communication, and status. If customers get $300 of value from entertainment, $200 of value from productivity, $400 value from communication, and $100 value from status, customers would be willing to pay a maximum of $1,000.

Pricing based on costs helps set the upper end of the pricing range you should consider.

• Pricing based on competition

Pricing based on competition will help you determine where in between your lower and upper range of prices you should price your product for. To price based on competition, you will need to identify competitor products that are substitutes for your product.

Pricing based on competition is based on two factors, the price that competitors set for their product and the customer’s maximum willingness to pay for their products.

The difference between these two numbers is the amount of value the customer captures from purchasing the competitor’s product. In economics terms, this is known as consumer surplus.

In order for customers to purchase your product, you will need to give customers more value than they get from purchasing a competitor’s product.

Example: Apple’s main competitor, Samsung, has a competing smartphone that they are selling for $400. This product provides customers a value of $600 from the benefits it provides. Therefore, customers get $200 in value from purchasing this product.

If Apple’s customers get a value of $1,000 from purchasing an iPhone, Apple will need to give customers at least $200 of value to make customers purchase an iPhone instead of a Samsung smartphone. Therefore, Apple can charge a maximum of $800 for their iPhone.

The 7 Steps to Solve any Pricing Case Interview

1. Understand the goal or objective of the company

The first step to solving any pricing strategy case interview is to determine the goal or objective of the company.

Most of the time, the company is looking to price a product to maximize profits. However, there are times when a company may be looking to maximize revenues, market share, or number of customers.

Your pricing strategy will differ tremendously based on the company’s specific goals. Therefore, it is important that you understand what the company’s exact goals are.

2. Develop a framework

Next, develop a framework to help you structure your approach to solving this pricing case.

Depending on how much context of the company or product that you have, you may also need to understand the company or product better. These could be the first one or two areas of your framework.

The rest of your framework should include the three different pricing strategies we have covered:

Make sure to walk the interviewer through your framework to see if they agree with your approach. They may provide feedback or offer a few suggestions.

3. Determine the minimum price point

Using the pricing based on costs strategy, determine what the minimum price point of your product should be. Remember, price needs to be greater than costs in order for the company to achieve a profit.

4. Determine the maximum price point

Next, use the pricing based on value provided strategy to determine what the maximum price point of your product should be.

Identify all of the benefits that the product provides customers. Then, quantify these benefits into a dollar value. The sum of the value of the benefits represents the customer’s maximum willingness to pay.

5. Determine the optimal price point

Afterwards, use the pricing based on competition strategy to determine which price point between your lower and upper price points is optimal.

Identify competitor products that are substitutes for your product. Quantify the benefits that competitor products provide customers and compare that to the prices they are set at.

The difference between these two values is the minimum amount of value that you need to give to customers in order for them to want to purchase your product.

6. Consider additional pricing factors

Now that you have an idea for the optimal price, you can consider additional pricing factors:

• Are there additional products that the company can cross-sell or up-sell?
• Can different versions of the product be sold at different price points?
•  How do you think competitors will respond to your pricing decisions?

All of these considerations may change the price point that you set.

• If you can cross-sell or up-sell additional products, you may be willing to lower the price of your product further if it means generating more overall profits
• If there are multiple customer segments with different needs and willingness to pay, it may make sense to have multiple versions of the product to sell at different price points
• If you set a price that undercuts competitors, they may also cut prices to compete with you. Therefore, you should anticipate the consequences of the price point that you set

For simple pricing cases, you may not need to look at any of these considerations. However, more complex pricing cases may require you to think more creatively or thoroughly on what other factors may dictate the price point the company should set.

7. Deliver a recommendation

At the end of the pricing case interview, you’ll synthesize all of the work you have done to deliver a clear, concise recommendation.

You should try to structure your recommendation in the following way:

• Recommend the optimal price point or price range
• Provide the two to three reasons that support this
• Propose next steps

Next steps can include areas of your framework that you have not covered yet, additional pricing considerations, or any open questions that you don’t have answers to.

If you can’t think of next steps, ask yourself what you would need to know to make you more confident in your recommendation. This is a helpful way to generate ideas for next steps.

Pricing Case Interview Example

Watch the video below for a comprehensive example of a pricing case interview. This example came from BCG’s interactive case interview library.

For more practice, check out our article on 23 MBA consulting casebooks with 700+ free practice cases .

In addition to pricing case interviews, we also have additional step-by-step guides to: profitability case interviews , market entry case interviews , growth strategy case interviews , M&A case interviews , operations case interviews , marketing case interviews , and private equity case interviews .

Recommended Consulting Interview Resources

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• Hacking the Case Interview Book   (available on Amazon): Perfect for beginners that are short on time. Transform yourself from a stressed-out case interview newbie to a confident intermediate in under a week. Some readers finish this book in a day and can already tackle tough cases.
• The Ultimate Case Interview Workbook (available on Amazon): Perfect for intermediates struggling with frameworks, case math, or generating business insights. No need to find a case partner – these drills, practice problems, and full-length cases can all be done by yourself.

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Pricing case interviews

An overview of the different types of pricing case interview questions with a full wallkthrough of a pricing case question from an ex-mckinsey & company business analyst..

, ex-McKinsey & Company, Founder at Forkright Published: August 25, 2021 | Last updated: March 28, 2024 Example pricing cases | Pricing frameworks | Full case example Amongst the many archetypes of case interview questions – such as profitability case interviews and market sizing case interviews, discussed in other Rocket Blocks blog posts -- pricing case interviews are one style that come up time and time again. The high-level goal of a pricing question is to use a variety of data to triangulate the optimal price for a client’s offering, or to explain why a given price may be suboptimal. There are three common formats for pricing drills, one of which you are reasonably likely to come across as you march your way through the dozens of interviews often required to land an offer: Set a price for a new product, service, or market: These questions take the form of a go-to-market strategy, sometimes involving an extant business unit that is looking to expand its offerings and other times involving the launch of a brand-new business unit or subsidiary. Less commonly, you may be asked to determine pricing for an existing product or service as the client expands into a market where it doesn’t have a presence today – though questions in this format are more likely to touch on pricing as one piece of a broader market entry strategy, with less time and energy spent on pricing strategy alone. Evaluate a proposed change to the price of an existing product or service: These questions are usually anchored to the goal of either growing revenues or expanding margins. Sometimes an interviewer will be more interested in your analysis of price elasticity (i.e., how sensitive demand is to a change in unit price) to determine whether a price increase (or decrease) will successfully grow overall revenues. Other times, the interviewer will be more focused on how you think about price in conjunction with changes in cost – including potential variable cost increases to support a price change (e.g., through increased marketing expense to justify a pricing increase to customers) and fixed cost defrayment as price interplays with volume. Explain a change in prices observed in the market: These question formats are focused on assessing your understanding of how price, volume, and profit interact. Expect charts and data to analyze. This question format is differentiated from the first two formats in that it’s more focused on helping a client understand market behavior than it is on setting the client’s own prices. That said, don’t forget to tie it back to the client in the end, and provide a specific recommendation on how the client should respond to the market behavior – be it by increasing prices, decreasing prices, or holding them steady. Pricing case interview questions are popular because they allow an interviewer to evaluate a candidate’s fluency with one of the key levers of profitability, while also allowing the candidate to demonstrate their understanding of how price interplays with demand to shape market forces. Example pricing cases (Top) Read through the following examples of pricing interview questions to make sure you can identify them in all their forms: “Your client is launching a new fast casual restaurant banner, offering better-for-you salads and grain bowls. Their thesis is that they can differentiate the brand based on a social responsibility message, emphasizing environmental stewardship and employee pay & working conditions. This model obviously carries higher input costs than their competitors. How much should they charge customers for each meal they sell?” Note that this question is asking you to set a price for a new product that is differentiated from the market and thus requires fresh pricing analysis. This is the case that we will solve in the full example case below. “JetSmart airlines, like all major commercial carriers in the US, saw customer traffic decline precipitously during the height of the COVID-19 pandemic in 2020. Management is desperate to grow revenues, and brings you in to evaluate whether a price drop of up to 50% will improve volume and flatten or reverse the revenue decline.” Note that this question is asking you to evaluate a proposed change to the price of an existing service . You should expect to come up with a go / no-go decision on a price reduction, as well as a specific percentage reduction to pre-pandemic fares. “In Q2 2021, commodity lumber futures in the United States spiked to $1,671 per board foot, up from $701 at the start of the year, due to a tightening of lumber supply on the market. As furniture manufacturers see profits get squeezed, how are they likely to respond, and how much of the lumber cost increase will be passed on to consumers in finished furniture prices?” Note that this question is asking you to explain a change in prices observed in the lumber market, and hypothesize about how those shifts are likely to impact equilibrium pricing in the finished furniture market. 💡 Shameless plug: Our consulting interview prep can help build your skills Pricing frameworks (Top) As you likely know, in case interviews there’s no such thing as a one-size-fits all framework for any given type of question – and the same, unfortunately, holds true for pricing problems. That said, applying a combination of the following three strategies – and often all three – can help set you on the right path. Don’t forget to customize this framework to the specifics of the question at hand. Three key strategies for price setting: Competitive benchmarking: This is the gold standard in pricing. Knowing where similar products and services are priced is the foundation for pricing your own. This effectively allows you to piggy-back on years of real-world price discovery by the competitors that came before you. Often the close competitors are obvious – for example, if you were asked to price a new ridesharing service, you could probably easily think of Uber and Lyft to benchmark – but more distant alternatives can also be indicative, especially when close comps don’t exist – for example, by benchmarking the prices of public transit or the amortized cost of car ownership. Cost analysis: Think of cost analysis as uncovering the minimum price you can afford to charge. By adding up the components costs of delivering your offering, you can determine the breakeven price you would have to charge to avoid losing money. From there, you can add the client’s target contribution margin on top of the costs to determine the minimum acceptable price. Margins vary widely by industry, but many industries see contribution margins in the 10-30% range. It’s worth noting that while price generally must exceed costs, this isn’t always true. You may want to consider whether engaging a loss leader is a viable strategy for driving profitability in other areas. For example, the under-market $4.99 rotisserie chicken at Costco is sold at a loss as part of a strategy for keeping foot traffic high. Willingness to pay: To the extent that cost analysis dictates the minimum price, determining customer willingness to pay can set the ceiling for maximum price. To do this, consider customer price elasticity – for example, you might survey customers to ask how much they would pay for theoretical products or try rolling out different prices for the same product in different markets, to assess the impact on demand. Analyzing willingness to pay is a thorny problem which can become hugely complex – think airline seat-level dynamic pricing models, for example – so unless you’re going into the interview with expertise on the subject, stick to the basics. Full case example (Top) Now, let’s work through the case we introduced earlier step-by-step, using the three-pronged framework above. As always, your first step is to structure, structure, structure. You should jot down the key information as you are given it, and then you might list out the three approaches you are going to engage on your page. Benchmarking: It sounds like the client is looking to launch an offering unlike (and higher cost than) those already on the local market. Given this, we should understand that we’re benchmarking an alternative product, rather than a direct competitor. You might ask who the closest existing competitors are, and where their meals are priced. You’re likely to be read out some numbers and tasked with some mental math (ugh) or given some printed data which may involve more complex arithmetic. Let’s say you run the math, and determine that these competitors are priced at $10.50 per meal on average. Remembering that these competitors are working with a lower cost structure, you might ask what their contribution margin is, for additional triangulation, and learn that they’re working with a 20% margin Cost Analysis: Shifting tacks, you work on identifying the major variable costs (you might list out food cost and labor) and fixed costs (you might list out occupancy and store-level SG&A). When you probe on competitors’ base costs for each cost bucket, your interviewer might give you a full P&L for a sample competitor. There you discover aggregate per-unit costs of $8.50, of which $3.15 comes from labor and $2.60 comes from food cost. Absent more information, you decide to estimate the increase to these two cost buckets under the client’s new business model. Let’s say you use +20% for each, giving (rounded!) increases of $0.65 and $0.50, respectively. Adding these increases to your base cost of $8.50, you find your new unit cost to be $9.65. You note that holding flat at the market price of $10.50 would leave you with a contribution margin of less than 10%. Willingness to pay: You might observe that the client’s thesis on the value proposition is unproven, and suggest the client conduct some market research to determine whether customers in fact care about social responsibility, and if so, how much more they would be willing to pay for a meal that meets these values (e.g., via a survey). In response, you are told that customers reported being willing to pay up to 20% more for an environmentally-responsible lunch and up to 15% more to a restaurant that treats its staff fairly. Running the math, you note that a 15-20% hike from the market price of $10.50 is approximately $12.00-12.50, which would give a 20% or higher contribution margin on your new costs of $9.65. Don’t forget that your final task is always to provide a specific, actionable recommendation. Synthesizing your three approaches together, you are likely to conclude that an above-market price for your differentiated product is necessary – after all, your costs are higher and customers seem willing to pay a premium for your offering. Given that any price premium is likely to hit volume, you might suggest the client only take an ~15% price increase to $12.00 per meal. This still leaves you with a competitive 20% margin – better to maximize profitable traffic up front, you reason, than turn off potential customers with sticker shock by trying to be too greedy up front. This case demonstrates the usefulness of having this pricing framework in your pocket – giving you a simple heuristic to structure your work as you triangulate optimal pricing. All that said, don’t expect every case to fit into this framework exactly – every interview question is unique, and the only way to gain fluency with this approach is to practice until you can tailor the framework effortlessly. Read this next: Full cases from RocketBlocks 29 full cases from consulting firms Profitability case interviews Market entry case interviews M&A case interviews Weird and unusual case interviews Market sizing case interviews PE due diligence interviews Supply chain case interviews Digital transformation consulting cases See all RocketBlocks posts . Get interview insights in your inbox: New mock interviews, mini-lessons, and career tactics. 1x per week. Written by the Experts of RocketBlocks. P.S. Are you preparing for consulting interviews? Real interview drills. Sample answers from ex-McKinsey, BCG and Bain consultants. Plus technique overviews and premium 1-on-1 Expert coaching. Launch your career. For schools Expert program Testimonials Free resources Behavioral guide Consulting guide Product management guide Product marketing guide Strategy & BizOps guide Consulting case book Interview prep Product management Product marketing Strategy & Biz Ops Resume advice Part I: Master resume Part II: Customization Focus: PM resumes Focus: Consulting resumes Focus: BizOps resumes A Pricing Case Study Can Either Stand Alone or Be Part of a Broader Case Like ‘Entering a New Market’ Pricing plays a crucial role in a company's profitability as it directly contributes to it. For this reason, establishing optimal prices for products or services is of great importance. Business consultants therefore assist their clients in developing pricing strategies.   A case study on pricing is an analysis focusing on the pricing of a product or service. It can stand alone or be part of a broader case, such as entering a new market . In a case interview , you can approach this case type in three steps: 1. Investigate The Company At the outset of your case, you should gain a solid understanding of your client's business model . What products does the company sell and where does the company stand in the market? For instance, is the company a market leader? In terms of volume or quality or both? What is the company’s key objective? Profits? Market share? Growth ? Brand positioning? Make sure to clarify the objective before starting the analysis. 2. Investigate The Product After familiarizing yourself with the company's business model, it's time to learn more about the product . When examining the product, it's important to pay attention to the following aspects: Product differentiation: Analyze how the client's product differs from those of competitors. Explore not only the product's features but also its production processes and methods. Unique Selling Proposition (USP): Identify the unique selling point of the product. What makes it unique and attractive to potential customers? Alternatives and substitutes: Consider alternative or substitute products in the market as well. How do they compare to the client's product? Product lifecycle: Determine the stage of the product lifecycle . This can influence the pricing and marketing strategy. Predictability of supply and demand: Examine whether supply and demand for the product are predictable. This can help assess risks in pricing and take appropriate measures. Once you've thoroughly assessed these aspects, you'll have a clearer understanding of the product and its positioning in the market, which will inform your pricing strategy recommendations. 3. Choose a Pricing Strategy  The choice of strategy depends on the information gathered in the first two steps. There are three important pricing strategies: (1) Competitor-based pricing ( Benchmarking ): With this strategy, the price is determined based on the prices set by our competitors. So, you want to find out: Are there comparable products/services? If yes, how do they compare to the client's product? What are their prices? Important: Keep in mind that competitors are likely to adjust their prices once the client introduces their product. (2) Cost-based pricing: With cost-based pricing, the price of a product or service is set based on the accumulated item costs ( break-even ) plus a reasonable profit margin. This strategy varies by industry due to different cost structures and margins. Therefore, it's important to understand the specific customer costs before setting a price (taking into account fixed and variable costs ).  Although cost-based pricing offers a simple and transparent method, it does not consider the perceived value of the product or service to customers and may be less effective in certain markets. To determine customer willingness to pay, it's important to consider this and possibly break down the price into different components, such as a separate price for the product and delivery costs. (3) Value-based pricing: Value-based pricing is a strategic approach based on assessing the customer's perception of the product or the amount customers are willing to pay. Different customer segments may have different willingness to pay . This means that companies can set different prices for different customer segments by adjusting the perceived value to justify price changes. A good example of this is the iPhone, a highly differentiated product for which customers are often willing to pay significantly more than the pure costs plus a "typical" margin. This illustrates how customers are inclined to accept a higher price for products they perceive as particularly valuable or differentiated . Key Takeaways From what we've learned previously, we can now extract the following insights as key takeaways: There are three key pricing strategies: Competitor-based pricing, cost-based pricing, and value-based pricing . Cost-based pricing alone is sometimes considered insufficient. Understand the primary objective of the company (profit, market share, growth, brand positioning) as the basis for the pricing strategy. Know the business model, products/services, and market position of the company and consider it in your strategic approach.  Understand the customers' willingness to pay and needs, and adjust the pricing strategy to customer preferences and market conditions. You Are Looking for More Pricing Cases to Practice with?  Check out our recommended resources or browse the Case Library for all cases on this topic.  👉 Company Case by INVERTO: VacuLuxe Innovations' Supply Chain Makeover 👉 Expert Case by Hagen : Retail Banking Profitability 👉 PrepLounge Case: Deep Water Rescue Related Cases Simon-Kucher Case: GST Cruise Company Bank envelope, shaving & co. MBB Final Round Case – Non-Profit Museum Revenue Increase Select category General Feedback Case Interview Preparation Technical Problems Press ENTER to search or ESC to exit Hotel Revenue Management: Strategies and Benefits Learn how hotel revenue management strategies can maximize profitability in a growing industry. Discover dynamic pricing, forecasting and tools for success. As the hotel industry overcomes past downfall and enters a major growth stage, hotel revenue management strategies are essential to maximize profitability in a growing industry. Revenue management combines data analytics and the implementation of dynamic pricing strategies to enable hotel chains to optimize both occupancy and revenue. What Is Hotel Revenue Management? Hotel revenue management is the process of predicting market demand to optimize a hotel's availability and room rates, maximizing revenue. This discipline relies on analyzing historical data, demand patterns, local events, and other external factors influencing occupancy and rates. Essentially, it involves selling the right room to the right customer, at the right time, at the right price, through the right channel, and with optimal efficiency. This multifaceted approach requires a deep understanding of consumer behavior and the ability to respond quickly to market fluctuations. Don't miss: Hotel Challenges 2022 - 2025 Discover the challenges the hotel industry will face in the coming years and how to overcome them. Revenue Management Strategies Rate optimization and dynamic pricing. Rate optimization and dynamic pricing are fundamental to revenue management. This strategy involves adjusting room rates in real time based on demand. During high-demand periods, prices increase to maximize revenue, while in low-demand periods, prices decrease to attract more bookings. Market segmentation and price differentiation according to customer type are also key. For example, business travelers may pay more for high-speed internet or meeting rooms, while tourists might seek packages including breakfast or sightseeing. Hotel Forecasting Hotel forecasting , or demand forecasting , is essential for revenue management. By using historical data and current trends, hotels can predict future demand and adjust pricing strategies accordingly. Predictive analytics allows hoteliers to anticipate demand fluctuations and adjust pricing and promotions to maximize occupancy and revenue. Advanced algorithms in predictive models consider factors such as local events, vacation seasons, and economic trends to forecast occupancy. Capacity Management Capacity management involves optimizing room availability and other hotel services to maximize revenue. This includes decisions on when to close sales of certain rates to avoid overbooking and ensure rooms are sold at the best price . A common practice in capacity management is c ontrolled overbooking , which accepts bookings exceeding the hotel's capacity based on forecasted cancellations and no-shows. Although risky, controlled overbooking can significantly increase revenue if managed correctly. Revenue Management Technologies The use of specialized revenue management software is crucial to implement effective strategies. Tools such as BeOnPrice and IDeaS G3 RMS use artificial intelligence to analyze data and optimize prices in real time. Revenue Management Software Tools like BeOnPrice and IDeaS G3 RMS use artificial intelligence to analyze data and optimize prices in real time. BeOnPrice connects with leading property management systems ( PMS ), central reservation systems (CRS), and channel managers, providing algorithm-based analysis to determine optimal pricing. IDeaS G3 RMS employs AI for price optimization and offers advanced reporting and forecasting tools, enabling informed and strategic decisions. Hotel Management Dashboards Hotel Management Dashboards is a comprehensive technology solution that combines multiple data sources to provide a 360º view of hotel performance , including revenue management . This hotel management software integrates an intelligent revenue management system that automates occupancy forecasting and dynamic pricing strategies. It allows for quick and efficient forecasting analysis and “what if” scenario simulations, facilitating informed decision-making. By centralizing information, Hotel Management Dashboards supports decision-making based on accurate and up-to-date data, essential for successful revenue management.  Implementation of a Revenue Management System Data integration. Data integration is paramount to revenue management . Modern systems must process large volumes of data from various sources, including PMS , CRM, social media, and weather data. Also, artificial intelligence and machine learning play crucial roles in analyzing this data, allowing for demand forecasting, price adjustments, and the identification of hidden patterns, providing actionable insights in a competitive market. Usability and Accessibility For a revenue management system to be effective, it must be easy to use and accessible to all hotel staff. Intuitive hotel performance dashboards are essential for employees to make informed decisions quickly without extensive technical training. Multi-language technical support and personalized assistance ensure any integration or usage issues are resolved efficiently, minimizing impact on hotel operations. Benefits of Revenue Management for Hotels Revenue maximization. The main benefit of revenue management is revenue maximization . By adjusting prices and room availability based on demand, hotels can significantly increase their revenues. Forecasting techniques enable anticipation of demand changes and adaptation of marketing and sales strategies to capitalize on every opportunity. Improving Profitability High occupancy does not always equate to high profitability . Revenue management allows hotels to focus on profitability per available room (RevPAR), optimizing not only occupancy but also revenue per room. Implementing dynamic pricing and market segmentation helps attract various customer types, maximizing profitability for each segment. Informed Decision Making Con el análisis de datos en tiempo real, los hoteleros pueden tomar decisiones estratégicas basadas en información precisa y actualizada. Es decir, data-driven decisions . Real-time data analysis empowers hoteliers to make strategic, data-driven decisions . Advanced hotel management dashboards provide a clear visualization of hotel performance, facilitating the identification of areas for improvement and growth opportunities. Revenue management is indispensable for hotels aiming to maximize profitability and remain competitive in a global market. Implementing advanced pricing, forecasting, and capacity management strategies, supported by technologies like Hotel Management Dashboards , allows hoteliers to optimize operations and significantly improve financial results. The key to successful revenue management lies in data analysis and the ability to adapt quickly to demand changes. With efficient management and advanced technological tools, hotels can ensure sustainable profitability and continuous growth in an increasingly competitive environment. Keep up-to-date with the world of data! Recent posts, data quality management: data quality testing to data observability, bismart and databricks: partners in data transformation, customer 360: transform your business with a 360 customer view, the hotel industry 2024 - 2025 in 10 insights, explore more posts. How to Improve Your Hotel Occupancy With Machine Learning and Predictive... Predictive analytics and machine learning have great potential in helping companies optimise their operations, strategies and resources. When it... Hotel Industry and Technology: Needs and Areas for Investment What technologies do hotel chains need to invest in by 2023? We discuss the current role of data analytics, artificial intelligence and data... 6 Ways to Use Artificial Intelligence in Your Hotel Artificial intelligence (AI) is revolutionising the business world. However, not all sectors are taking advantage of its capabilities equally. The... Information Author Services Initiatives You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader. All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. 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JSmol Viewer Retinitis pigmentosa and therapeutic approaches: a systematic review. 1. Introduction 2. materials and methods, 3.1. study characteristics, 3.2. mesenchymal stem cells, 3.3. gene therapy, 3.4. docoexhanoic acid, 3.5. oral valproic acid, 3.6. oral qlt091001, 3.7. retina implant alpha ims, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest. MEDLINE (Ovid) 855 Embase (Ovid) 1339 Cochrane Systematic Reviews, Cochrane Register of Controlled Trials 85 Number of records before deduplication 2279 Number of records after deduplication 1612 retinitis pigmentosa/or alstrom syndrome/or bardet-biedl syndrome/or (retinitis pigmentosa or alstrom* or (bardet adj1 biedl) or biedl syndrome* or ((retina* pigment* or tapetoretina* or tapeto retina*) adj3 (dystroph* or degenerat*))).tw,kf. genetic therapy/or rnai therapeutics/or targeted gene repair/or ((gene* adj3 (therap* or treatment*)) or (gene* adj3 repair*) or ((antisense or anti-sense) adj3 (therap* or treatment*)) or ((germ line or germline) adj3 (therap* or treatment*)) or ((oligonucleotide* or oligo-nucleotide*) adj3 (therap* or treatment*)) or (ribozyme* adj3 (therap* or treatment*))).tw,kf. ((gene* or DNA or RNA) adj2 (immuni?ation* or vaccination*)).tw,kf. ((gene* replacement* or mitochondrial replacement* or RNA interference* or RNAi or viral based or virus based) and (therap* or treatment*)).tw,kf. Stem Cell Transplantation/or Induced Pluripotent Stem Cells/or stem cell*.tw,kf. stem cells/or pluripotent stem cells/or embryonic stem cells/or human embryonic stem cells/or induced pluripotent stem cells/or (IPSC or IPS cell*).tw,kf. Optogenetics/or (optogenetic* or opto-genetic*).tw,kf. exp models, animal/or ((Animal Experimentation/or exp Animals/) not Humans/) or (veterinar* or animal or animals or swine or rabbit or rabbits or rodent or rodents or rat or rats or mouse or mice or hamster or hamsters or pig or pigs or piglet or piglets or porcine or pigeon* or horse* or equine or cow or cows or bovine or goat or goats or sheep or lamb or lambs or monkey or monkeys or murine or ovine or dog or dogs or canine or cat or cats or feline or dolphin* or fish or zebrafish).ti. limit 11 to english language retina pigment degeneration/or retinitis pigmentosa/or Alstrom syndrome/or Bardet Biedl syndrome/or (retinitis pigmentosa or alstrom* or (bardet adj1 biedl) or biedl syndrome* or ((retina* pigment* or tapetoretina* or tapeto retina*) adj3 (dystroph* or degenerat*))).tw,kf. gene therapy/or antisense therapy/or cell based gene therapy/or exp gene replacement therapy/or exp genetic immunization/or germ line gene therapy/or nonviral gene therapy/or oligonucleotide therapy/or ribozyme therapy/or rnai therapeutics/or somatic gene therapy/or stem cell gene therapy/or viral gene therapy/ ((gene* adj3 (therap* or treatment*)) or (gene* adj3 repair*) or ((antisense or anti-sense) adj3 (therap* or treatment*)) or ((germ line or germline) adj3 (therap* or treatment*)) or ((oligonucleotide* or oligo-nucleotide*) adj3 (therap* or treatment*)) or (ribozyme* adj3 (therap* or treatment*))).tw,kf. stem cell transplantation/or induced pluripotent stem cell/or pluripotent stem cell/or embryonic stem cell/or human embryonic stem cell/or (stem cell* or IPSC or IPS cell*).tw,kf. optogenetics/or (optogenetic* or opto-genetic*).tw,kf. exp animal model/or ((exp animal/or nonhuman/) not exp human/) or (veterinar* or animal or animals or swine or rabbit or rabbits or rodent or rodents or rat or rats or mouse or mice or hamster or hamsters or pig or pigs or piglet or piglets or porcine or pigeon* or horse* or equine or cow or cows or bovine or goat or goats or sheep or lamb or lambs or monkey or monkeys or murine or ovine or dog or dogs or canine or cat or cats or feline or dolphin* or zebrafish* or fish).ti. limit 11 to (conference abstracts or “preprints (unpublished, non-peer reviewed)”) limit 13 to english language ° of Eyes) 1 Clinical trial of intravitreal injection of autologous bone marrow stem cells in patients with retinitis pigmentosa. ClinicalTrials.gov, 0(0). Retrieved from (2014), Access on 1 January 2020. Excluded Unpublished article 2 Zhao, T., Lie, H., Wang, F., Liu, Y., Meng, X., Yin, Z., & Li, S. (2021). Comparative study of a modified sub-Tenon’s capsule injection of triamcinolone acetonide and the intravenous infusion of umbilical cord mesenchymal stem cells in retinitis pigmentosa combined with macular edema. Frontiers in Pharmacology, 12. [ ] [ ] 40 eyes (20 patients) 3 Hoffman, D. R., Hughbanks-Wheaton, D. K., Spencer, R., Fish, G. E., Pearson, N. S., Wang, Y. Z., Klein, M., Takacs, A., Locke, K. G., & Birch, D. G. (2015). Docosahexaenoic acid slows visual field progression in X-linked retinitis pigmentosa: Ancillary outcomes of the DHAX trial. Investigative Ophthalmology & Visual Science, 56(11), 6646–6653. [ ] [ ] 102 eyes (51 patients) 4 Birch, D. G., Bernstein, P. S., Iannacone, A., Pennesi, M. E., Lam, B. L., Heckenlively, J., Csaky, K., Hartnett, M. E., Winthrop, K. L., Jayasundera, T., et al. (2018). Effect of oral valproic acid vs placebo for vision loss in patients with autosomal dominant retinitis pigmentosa: A randomized phase 2 multicenter placebo-controlled clinical trial. JAMA Ophthalmology, 136(8), 849–856. [ ] [ ] 180 eyes (90 patiens) 5 Russell, S., Bennett, J., Wellman, J. A., Chung, D. C., Yu, Z. F., Tillman, A., Wittes, J., Pappas, J., Elci, O., McCague, S., et al. (2017). Efficacy and safety of voretigene neparvovec (AAV2-hRPE65v2) in patients with RPE65-mediated inherited retinal dystrophy: A randomised, controlled, open-label, phase 3 trial. The Lancet, 390(10097), 849–860. Excluded Preliminary results of an included article/aim out of scope 6 Maguire, A. M., Russell, S., Wellman, J. A., Chung, D. C., Yu, Z. F., Tillman, A., Wittes, J., Pappas, J., Elci, O., Marshall, K. A., et al. (2019). Efficacy, safety, and durability of voretigene neparvovec-rzyl in RPE65 mutation-associated inherited retinal dystrophy: Results of phase 1 and 3 trials. Ophthalmology, 126(9), 1273–1285. Excluded Preliminary results of an included article/aim out of scope 7 Euctr, F. R. (2014). Study of SAR421869 in Patients With Retinitis Pigmentosa associated with Usher Syndrome Type 1B. [Journal Article]. Access on 20 February 2024 Excluded Unpublished article 8 Maguire, A. M., Russell, S., Chung, D. C., Yu, Z. F., Tillman, A., Drack, A. V., Simonelli, F., Leroy, B. P., Reape, K. Z., High, K. A., et al. (2021). Durability of voretigene neparvovec for biallelic RPE65-mediated inherited retinal disease: Phase 3 results at 3 and 4 years. Ophthalmology, 128(10), 1460–1468. [ ] [ ] 58 eyes (29 patients) 9 Cehajic-Kapetanovic, J., Xue, K., Martinez-Fernandez de la Camara, C., Nanda, A., Davies, A., Wood, L. J., Salvetti, A. P., Fischer, M. D., Aylward, J. W., Barnard, A. R., Jolly, J. K., Luo, E., Lujan, B. J., Ong, T., Girach, A., Black, G. C. M., Gregori, N. Z., Davis, J. L., Rosa, P. R., Lotery, A. J., Lam, B. L., Stanga, P. E., & MacLaren, R. E. (2020). Initial results from a first-in-human gene therapy trial on X-linked retinitis pigmentosa caused by mutations in RPGR. Nature Medicine, 26(3), 354–359. [ ] [ ] (18 patients) 10 Zhao, T., Liang, Q., Meng, X., Duan, P., Wang, F., Li, S., Liu, Y., & Yin, Z. Q. (2020). Intravenous Infusion of Umbilical Cord Mesenchymal Stem Cells Maintains and Partially Improves Visual Function in Patients with Advanced Retinitis Pigmentosa. Stem Cells & Development, 29(16), 1029–1037. [ ] [ ] 64 eyes (32 patients) 11 Tuekprakhon, A., Sangkitporn, S., Trinavarat, A., Pawestri, A. R., Vamvanij, V., Ruangchainikom, M., Luksanapruksa, P., Pongpaksupasin, P., Khorchai, A., Dambua, A., Boonchu, P., Yodtup, C., Uiprasertkul, M., Sangkitporn, S., & Atchaneeyasakul, L. O. (2021). Intravitreal autologous mesenchymal stem cell transplantation: a non-randomized phase I clinical trial in patients with retinitis pigmentosa. Stem Cell Research & Therapy, 12(1), 52. [ ] [ ] 14 eyes (14 patients) 12 Liao, D., Boyer, D. S., Kaiser, P., Kuppermann, B. D., Heier, J., Mehta, M., Joseph, A., Kammer, R., Mills, B., Yang, J., et al. (2021). Intravitreal injection of allogeneic human retinal progenitor cells (hRPC) for treatment of retinitis pigmentosa: a prospective randomized controlled phase 2b trial. Investigative Ophthalmology & Visual Science, 62(8). Excluded Meeting abstract/unpublished article 13 Ozmert, E., & Arslan, U. (2023). Management of retinitis pigmentosa via Wharton’s jelly-derived mesenchymal stem cells or combination with Magnovision: 3-year prospective results. Stem Cells Translational Medicine, 12(10), 631–650. [ ] [ ] 130 eyes (80 patients) 14 Limoli, P. G., Limoli, C. S. S., Morales, M. U., & Vingolo, E. M. (2020). Mesenchymal stem cell surgery, rescue and regeneration in retinitis pigmentosa: Clinical and rehabilitative prognostic aspects. Restorative Neurology & Neuroscience, 38(3), 223–237. [ ] [ ] 34 eyes (25 patients) 15 Hu, Y., Du, Y., Jin, Y., Feng, K., Chen, H., Han, L., Qu, H., & Ma, Z. (2023). A Novel Surgical Approach for Big Sheet Allogenic Retinal Pigment Epithelium-Bruch Membrane Complex Transplantation Into the Subretinal Space. Retina, 43(10), 1816–1819. Excluded Small case series 16 Hoffman, D. R., Locke, K. G., Wheaton, D. H., Fish, G. E., Spencer, R., & Birch, D. G. (2004). A randomized, placebo-controlled clinical trial of docosahexaenoic acid supplementation for X-linked retinitis pigmentosa. American Journal of Ophthalmology, 137(4), 704–718. [ ] [ ] 88 eyes (44 patients) 17 Scholl, H. P., Moore, A. T., Koenekoop, R. K., Wen, Y., Fishman, G. A., van den Born, L. I., … & Bowles, K. (2015). Safety and proof-of-concept study of oral QLT091001 in retinitis pigmentosa due to inherited deficiencies of retinal pigment epithelial 65 protein (RPE65) or lecithin: retinol acyltransferase (LRAT). PloS one, 10(12), e0143846. [ ] [ ] 36 eyes (18 patients) 18 Sobaci, G., Sevinc, K., Ovali, E., Ozmert, E., Ozdek, S., Yilmaz, G., … & Akkoyun, I. (2015). Submacular allogeneic ectomesenchymal stem cell transplantation in retinitis pigmentosa: one-year results. Investigative ophthalmology & visual science, 56(7), 2276. Excluded Small case series 19 Stingl, K., Bartz-Schmidt, K. U., Besch, D., Chee, C. K., Gekeler, F., Groppe, M., Jackson, T. L., MacLaren, R. E., Koitschev, A., & Kusnyerik, A. (2015). Subretinal Visual Implant Alpha IMS–Clinical trial interim report. Vision research, 111, 149–160. [ ] [ ] 29 eyes (29 patients) 20 Kahraman, N. S., & Oner, A. (2020). Umbilical cord derived mesenchymal stem cell implantation in retinitis pigmentosa: a 6-month follow-up results of a phase 3 trial. International Journal of Ophthalmology, 13(9), 1423–1429. [ ] [ ] 124 eyes (82 patients) 21 Russell, S., Bennett, J., Maguire, A. M., & High, K. A. (2018). Voretigene neparvovec-rzyl for the treatment of biallelic RPE65 mutation-associated retinal dystrophy. Expert Opinion on Orphan Drugs, 6(8), 457–464. Excluded Expert opinion 22 Euctr, G. B. (2016). A Clinical Trial of Retinal Gene Therapy for X-linked Retinitis Pigmentosa using AAV8. [Protocol]. Retrieved from Accesed on 20 February 2024 Excluded Unpublished article 23 A clinical trial to evaluate the effect of bone marrow-derived stem cells in diseases like dry age-related macular degeneration and retinitis pigmentosa. [Protocol]. Retrieved from (2010) Accesed on 20 February 2024 Excluded Unpublished article 24 Mohanty, S., Batabyal, S., Kim, S., Carlson, M., Ayyagari, A., Rittimann, J., Tchedre, K., & Chavala, S. H. (2022). Double-masked, Randomized, sham-controlled, Multicenter Phase 2b study of Multi-Characteristic Opsin enabled vision restoration in patients with advanced retinitis pigmentosa: design and Development of novel endpoints. Investigative ophthalmology & visual science, 63(7), 1722-F0040. Excluded Meeting abstract/unpublished article 25 Boyer, D. S., Bergstrom, L., Emanuelli, A., Gonzalez, V. H., Wykoff, C. C., Gupta, S., Liao, D. S., Zak, V., Chavala, S. H., Mohanty, S., et al. (2023). Efficacy and safety of MCO-010 optogenetic therapy for vision restoration in patients with severe vision loss due to retinitis pigmentosa: a phase 2b randomized, sham-controlled, multi-center, multi-dose, double-masked clinical trial (RESTORE). Investigative ophthalmology & visual science, 64(8), 5443. Excluded Meeting abstract/unpublished article 26 Nct. (2021). Efficacy and Safety of MCO-010 Optogenetic Therapy in Adults With Retinitis Pigmentosa. Journal of Clinical Trials, 0(0). Retrieved from Accesed on 20 February 2024 Excluded Unpublished article 27 Euctr, D. K. (2021). Gene Therapy Trial for Patients with Retinitis Pigmentosa Due to a Gene Defect on Chromosome X. Journal of Clinical Trials, 0(0). Retrieved from Accesed on 20 February 2024 Excluded Unpublished article 28 Yusupov, A. Y. (1956). Implantation of catgut in the treatment of some eye diseases. Sborn, 0(0), 303-307. Retrieved from Accesed on 20 February 2024 Excluded 29 Satarian, L., Nourinia, R., Safi, S., Kanavi, M. R., Jarughi, N., Daftarian, N., Arab, L., Aghdami, N., Ahmadieh, H., & Baharvand, H. (2017). Intravitreal Injection of Bone Marrow Mesenchymal Stem Cells in Patients with Advanced Retinitis Pigmentosa; a Safety Study. Journal of Ophthalmic & Vision Research, 12(1), 58–64. Excluded Aim out of scope 30 Liao, D., Gonzalez, V., Emanuelli, A., Gupta, S., Wykoff, C., Zak, V., Ayyagari, A., Bataybal, S., Chavala, S., Koester, J., et al. (2023). Optogenetic Therapy with MCO-010 for Vision Restoration in Patients with Severe Sight Loss Due to Retinitis Pigmentosa: the Phase 2b RESTORE Study. Molecular Therapy, 31(4), 399. Excluded Abstract/unpublished article 31 Euctr, N. O. (2021). A Phase 2/3 study to evaluate efficacy, safety, and tolerability of QR-421a in subjects with advanced vision loss. Accesed on 20 February 2024 Excluded Unpublished article 32 Euctr, N. O. (2021). A Phase 2/3 study to evaluate efficacy, safety, and tolerability of QR-421a in subjects with advanced vision loss. [Journal Article]. Accesed on 20 February 2024 Excluded Unpublished article 33 Nct. (2023). Role of UC-MSC and CM to Inhibit Vision Loss in Retinitis Pigmentosa Phase I/II. [Journal Article]. Accesed on 20 February 2024 Excluded Unpublished article 34 Euctr, D. K. (2016). A study in subjects with rare inherited eye conditions caused by gene mutations to see if treatment with QLT091001 is safe and works to improve subjects’ vision. Excluded Unpublished article 35 Euctr, F. R. (2018). Study to evaluate QR-421a in subjects with retinitis pigmentosa (RP) due to mutations in exon 13 of the USH2A Gene Excluded Unpublished article 36 Euctr, G. B. (2008). 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[ Google Scholar ] [ CrossRef ] [ PubMed ] Click here to enlarge figure N° Title Author (Year) Study Design Study Sample Investigated Treatment Methods Outcomes Main Findings GRADE 1 Intravenous Infusion of Umbilical Cord Mesenchymal Stem Cells Maintains and Partially Improves Visual Function in Patients with Advanced Retinitis Pigmentosa Zhao T. et al. (2020) [ ] Prospective, open label, single-arm, phase I/II clinical trial 32 adult patients (64 eyes), male and female Intravenous infusion of human umbilical cord mesenchymal stem cells (UCMSCs) Single infusion of 10 UCMSCs (250 mL); 12-month follow-up; evaluated safety, CMT, visual field sensitivity, BCVA, FVEP, and NEI-VFQ-25 scores at set intervals No adverse effects Stable CMT Non-significant BCVA increase No significant change in visual field sensitivity and FVEP Improved NEI-VFQ-25 at 3 months UCMSC infusion well-tolerated; short-term improvement in BCVA and quality of life; significant NEI-VFQ-25 improvement at 3 months; no significant long-term effects on visual field sensitivity or FVEP. Short-term benefits likely due to diminishing functional properties over time Moderate 2 Comparative Study of a Modified Sub-Tenon’s Capsule Injection of Triamcinolone Acetonide and the Intravenous Infusion of Umbilical Cord Mesenchymal Stem Cells in Retinitis Pigmentosa Combined With Macular Edema Zhao T. et al. (2021) [ ] Prospective, open label, randomized, phase I/II clinical trial 20 adult patients (40 eyes), male and female Comparison of sub-Tenon’s injection of triamcinolone acetonide (TA) and intravenous infusion of umbilical cord mesenchymal stem cells (UCMSCs) in RP patients with macular edema TA: 20 mg injection; UCMSCs: 3 × 10 (250 mL) infusion; 6-month follow-up; evaluated safety, CMT, visual field sensitivity, BCVA, and FVEP No severe adverse effects in both groups TA reduced CMT significantly at 1 week, 1 month, and 2 months; UCMSCs at 1 month and had greater reduction at 6 months TA increased FVEP P2 at 2 months; UCMSCs at 6 months No significant visual acuity or field differences Both treatments were safe. TA reduced macular edema quickly; UCMSCs had longer-lasting effects and better visual function improvement over time Moderate 3 Umbilical cord derived mesenchymal stem cell implantation in retinitis pigmentosa: a 6-month follow-up results of a phase 3 trial Neslihan Sinim Kahraman (2020) [ ] Prospective, single-center, phase III clinical study 82 patients (124 eyes) 5 million UCMSCs injected into the suprachoroidal area via surgery Injection by experienced surgeon; evaluations at presurgery, 1 day, 1 week, 1-, 3-, and 6-months postsurgery No serious systemic or ocular complications Significant improvements in BCVA and VF (p < 0.05) Significant improvements in mfERG P1 wave amplitudes and implicit times in central areas Safe procedure with significant improvements in visual acuity, visual field, and retinal function Moderate 4 Management of Retinitis Pigmentosa Via Wharton’s Jelly-Derived Mesenchymal Stem Cells or Combination With Magnovision: 3-Year Prospective Results Ozmert (2023) [ ] Prospective, sequential, open-label clinical study 80 patients (130 eyes) Comparison of sub-Tenon WJ-MSC-only, Magnovision-only, combined WJ-MSC and Magnovision, and control groups in RP patients Group 1: Sub-Tenon WJ-MSC (34 eyes)—Group 2: Magnovision (32 eyes)—Group 3: Combined WJ-MSC and Magnovision (32 eyes)—Group 4: Control (no treatment, 32 eyes) Primary: Fundus autofluorescence surface area (FAF-field) Secondary: ETDRS visual acuity (BCVA), ellipsoid zone widths (EZWs), fundus perimetry deviation index (FPDI), full-field multiluminance ERG FAF-field changes: Group 1: 0.39 mm , Group 2: 1.50 mm , Group 3: 0.07 mm , Group 4: 12.04 mm EZW, BCVA, FPDI changes: Group 4 > Groups 1, 2 > Group 3—ERG-m changes: Group 3 > Groups 1, 2, 4 High 5 Intravitreal autologous mesenchymal stem cell transplantation: a non-randomized phase I clinical trial in patients with retinitis pigmentosa Tuekprakhon et al. (2021) [ ] Prospective, open-label, non-randomized phase I clinical trial 14 adult patients (14 eyes treated, fellow eye control), male and female Intravitreal injection of autologous BM-MSCs from posterior iliac crest Single injection in right eye; left eye as control; 3 groups based on MSC quantity (1 × 10 cells, 5 × 10 cells, and 1 × 10 cells) Primary: Safety assessed through various measures Secondary: BCVA, visual fields, central subfield thickness, ERG No serious adverse events Stable IOP Transient increase in anterior chamber cells and flare Slight BCVA improvement Subjective quality of life improvements reported Low 6 Mesenchymal stem cell surgery, rescue and regeneration in retinitis pigmentosa: clinical and rehabilitative prognostic aspects Limoli et al. (2020) [ ] Retrospective clinical study 25 patients, 11 women and 14 men, with an average age of 45.9 ± 18.36 years (34 eyes) Autograft of mesenchymal cells of fat cells and PRP using LRRT (between the choroid and sclera) All eyes were divided into two groups based on central retinal thickness (FT recorded by SD-OCT): Group A (≤190 μm) and Group B (>190 μm) Mean BCVA, mean close-up visus, average threshold sensitivity, average threshold of close-up visus with magnifying system, percentage of change BCVA changes: Group A varied from 0.89 to 0.85 logMAR (+4.16%, p = 0.9701); Group B varied from 0.45 to 0.37 logMAR (+16.31%, p = 0.9083) Moderate 7 Initial results from a first-in-human gene therapy trial on X-linked retinitis pigmentosa caused by mutations in RPGR Kapetanovic (2020) [ ] Gene therapy trial 18 patients Increasing concentrations of codon-optimized AAV2 serotype 8 vector (AAV8.coRPGR) Vector delivery into subretinal space via two-step injection Primary safety endpoint: Incidence of dose-limiting toxicities and treatment-emergent adverse events over 24 months. Secondary endpoints: Changes in retinal sensitivity, BCVA, SD-OCT, and autofluorescence over 24 months Dose–response effects observed, with mid-dose patients showing gains in retinal sensitivity and visual field improvement. Visual acuity returned to baseline levels by 3 months postsurgery. Subjective improvement in visual clarity and field of vision reported by all patients at 1-month follow-up. Functional assessment showed similar visual acuity to baseline Moderate 8 Durability of Voretigene Neparvovec for Biallelic RPE65-Mediated Inherited Retinal Disease Phase 3 Results at 3 and 4 Years Albert M Maguire et al. (2021) [ ] Open-label, randomized, controlled phase III trial 29 male and female patients with RPE65-mutated IRD Gene augmentation therapy with recombinant AAV vector voretigene neparvovec-rzyl (VN) Randomization between original intervention (n = 19) and delayed intervention control (n = 10). Treatment: Intervention group received 1.5 × 10 vg of VN in each eye. Controls switched to intervention after 1 year Long-term efficacy and safety assessment over 5 years: multiluminance mobility test, full-field light sensitivity threshold, visual field, and visual acuity Safety: No product-related serious adverse events Both groups showed consistent but not significant improvements in ambulatory navigation, light sensitivity, and visual field over 3 to 4 years compared to baseline One delayed intervention group patient experienced foveal loss attributed to the administration procedure High 9 Docosahexaenoic Acid Slows Visual Field Progression in X-Linked Retinitis Pigmentosa: Ancillary Outcomes of the DHAX Trial Dennis R. Hoffman et al. (2015) [ ] Single-site, placebo-controlled, randomized clinical trial 51 patients (29 treated and 22 placebo) Oral DHA supplementation XLRP patients (age: 7–31) received 30 mg/kg/d or placebo for 4 years. Follow-up: Visual outcomes annually; RBC-DHA every 6 months RBC-DHA levels increased 4-fold over placebo (p < 0.0001) No significant differences in visual acuity, shape discrimination, or fundus appearance Reduced progression in dark-adapted thresholds and visual field sensitivity with DHA supplementation (p < 0.05) No significant changes in ERG function between groups DHA supplementation showed less progression in dark-adapted thresholds compared to placebo over 4 years Significant reductions in disease progression rates for various visual field measures with DHA supplementation High 10 A Randomized, Placebo-controlled Clinical Trial of Docosahexaenoic Acid Supplementation for X-linked Retinitis Pigmentosa Dennis R. Hoffman et al. (2003) [ ] 4-year prospective randomized clinical trial 44 male patients with XLRP (mean age = 16 years; range = 4–38 years) received DHA (400 mg/d; n = 23; +DHA group) or placebo (n = 21) Oral supplementation of docosahexaenoic acid Male patients with XLRP (mean age = 16 years; range = 4–38 years) received DHA (400 mg/d; n = 23; +DHA group) or placebo (n = 21). Follow-up: RBC-DHA concentrations assessed every 6 months. Full-field cone ERGs, visual acuity, dark adaptation, visual fields, rod ERGs, and fundus photos recorded annually RBC-DHA increased 2.5-fold in +DHA group No significant difference in cone ERG function between groups Preservation of cone ERG function correlated with RBC-DHA Less change in fundus appearance in +DHA group Subset analysis showed DHA supplementation reduced rod ERG loss in patients aged < 12 years and preserved cone ERG function in patients ≥ 12 years Although DHA-supplemented patients had significantly higher RBC-DHA levels, cone ERG functional loss rate was not significantly different between groups High 11 Effect of Oral Valproic Acid vs. Placebo for Vision Loss in Patients With Autosomal Dominant Retinitis Pigmentosa A Randomized Phase 2 Multicenter Placebo-Controlled Clinical Trial David G. Birch et al. (2018) [ ] Multicenter, phase II, prospective, interventional, placebo-controlled, double-masked randomized clinical trial 90 male and female patients with autosomal dominant RP Oral VPA 500–1000 mg daily Participants randomized to receive VPA (n= 46) or placebo (n = 44) for 12 months. Dose selection based on proof-of-concept studies. Follow-up visits at 8, 26, 39, 52, and 65 weeks Primary outcome: Change in kinetic perimetry (KP) visual field area (VFA) assessed by the III4e isopter between baseline and week 52. Secondary outcomes: Visual function measures The study did not meet its primary endpoint at 12 months, showing no change in visual field area between groups No significant improvement in any secondary outcomes observed between the two groups The study does not support the use of valproic acid to enhance visual function in AD-RP patients Very high 12 Safety and Proof-of-Concept Study of Oral QLT091001 in Retinitis Pigmentosa Due to Inherited Deficiencies of Retinal Pigment Epithelial 65 Protein (RPE65) or Lecithin: Retinol Acyltransferase (LRAT) Hendrik P. n. Scholl et al. (2015) [ ] International, multicenter, open-label, proof-of-concept study 18 patients with RPE65- or LRAT-related retinitis pigmentosa with autosomal recessive RP due to biallelic mutations in either the RPE65 or LRAT gene confirmed in an accredited molecular genetic laboratory and between 5 and 65 years of age Oral QLT091001 Patients received 40 mg/m /day QLT091001 for 7 days Within 2 months, 44% showed a 20% increase in retinal area 67% showed a 5-letter ETDRS score increase Baseline outer segment OS layer value was significantly lower in non-responders QLT091001 improved visual field and/or acuity in RP patients High 13 Subretinal Visual Implant Alpha IMS—Clinical trial interim report Katarina Stingl et al. (2015) [ ] International multicenter, single-arm, clinical trial 29 male and female patients with hereditary retinal degeneration (retinitis pigmentosa n = 25; cone–rod dystrophy n = 4). Patients had either light perception without projection (20 participants) or no light perception Retina Implant Alpha IMS Surgical implantation of microchip subretinally in one eye. Participants compared vision with implant’s power on or off. Follow-up for 1 year with visual function tests and monitoring Primary: Improvements in daily activities and mobility. Secondary: Enhanced visual acuity and object recognition 72% showed better daily living and mobility 86% demonstrated improved visual acuity and recognition - Better detection and recognition of shapes and objects with the implant on - Improved gray level perception and light localization 86% experienced improved light perception and localization with the implant Few SAEs reported, mostly resolved successfully High The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Share and Cite Confalonieri, F.; La Rosa, A.; Ottonelli, G.; Barone, G.; Ferraro, V.; Di Maria, A.; Romano, M.; Randazzo, A.; Vallejo-Garcia, J.L.; Vinciguerra, P.; et al. Retinitis Pigmentosa and Therapeutic Approaches: A Systematic Review. J. Clin. Med. 2024 , 13 , 4680. https://doi.org/10.3390/jcm13164680 Confalonieri F, La Rosa A, Ottonelli G, Barone G, Ferraro V, Di Maria A, Romano M, Randazzo A, Vallejo-Garcia JL, Vinciguerra P, et al. Retinitis Pigmentosa and Therapeutic Approaches: A Systematic Review. Journal of Clinical Medicine . 2024; 13(16):4680. https://doi.org/10.3390/jcm13164680 Confalonieri, Filippo, Antonio La Rosa, Giovanni Ottonelli, Gianmaria Barone, Vanessa Ferraro, Alessandra Di Maria, Mary Romano, Alessandro Randazzo, Josè Luis Vallejo-Garcia, Paolo Vinciguerra, and et al. 2024. "Retinitis Pigmentosa and Therapeutic Approaches: A Systematic Review" Journal of Clinical Medicine 13, no. 16: 4680. https://doi.org/10.3390/jcm13164680 Article Metrics Article access statistics, further information, mdpi initiatives, follow mdpi. Subscribe to receive issue release notifications and newsletters from MDPI journals 113 IMAGES Pricing Case Interview: Step-by-Step Guide 9 Pricing Strategies Case Study Pricing Strategy Ppt Powerpoint Presentation Pictures Format Pharma Pricing Consultants Use Case For Product Pricing Strategy Product Development Plan Optimal Pricing Strategy, 7 Steps to Find COMMENTS Mastering the Pricing Case Study: A Comprehensive Guide Setting the optimal pricing for products or services is important for a company as it directly impacts profitability. So every management consulting firm helps its clients with pricing strategies. The primary goal of a pricing case is to recommend a price that maximizes profit, taking costs for product/service and market considerations into account. The Good-Better-Best Approach to Pricing The author, a consultant who has helped many organizations adopt G-B-B pricing, presents a step-by-step guide to devising, testing, and launching the strategy. PDF Pricing Case Study: Analyzing the Impact of Dynamic Pricing Vs Mather has seen success with dynamic Market Based Pricing (MBP), which incorporates individual subscriber characteristics, such as tenure, autopay status and demographics to determine optimal pricing strategies. Optimal pricing strategy: How to sell to strategic consumers? Technological advances are preparing consumers to plan their purchases strategically. Selling to strategic consumers at a fixed price forgoes the profit from salvaging inventory, whereas high-low pricing, as a ubiquitous pricing strategy, is costly due to the offered markdown discount. This research explores the overall impact of consumer's ... Case Study: Pricing Strategy for Consumer Products Competitive benchmarking, conjoint analysis, and value-based pricing frameworks were utilized to help identify optimal price points, understand customer preferences, and determine the perceived value of the company's products. "A thorough examination of current pricing practices is essential before introducing novel strategies.". Pricing Case Studies and Whitepapers Pricing case studies, success stories and whitepapers from multiple industries, including pharmaceutical, medical, CPG, food & beverage and more... Pharmaceutical Pricing Case Study: Pricing Strategy for New Product Launch Pharmaceutical pricing case study: developing an optimal pricing strategy for new product launches. Our pricing strategy identified $500MM in estimated sales. Optimal pricing model based on reduction dimension: A case of study for This paper proposes a methodology to define an optimal pricing strategy for convenience stores based on dimension reduction methods and uncertainty of… How to Build a Better Pricing Strategy HBR On Strategy curates the best case studies and conversations with the world's top business and management experts, to help you unlock new ways of doing business. New episodes every week. Optimal pricing strategy: How to sell to strategic consumers? Selling to strategic consumers at a fixed price forgoes the profit from salvaging inventory, whereas high-low pricing, as a ubiquitous pricing strategy, is costly due to the offered markdown discount. Understanding your options: Proven pricing strategies and how they work In an analysis of hundreds of companies and pricing approaches, we found four pricing strategies that deliver sustainable results (see Exhibit 1). Not every strategy will be relevant or even feasible for every company - much depends on the market context, the business strategy, and your own capabilities. Still, we've found that periodically reviewing strategic options is helpful in ... Optimal Pricing Model: Case of Study for Convenience Stores The purpose of this paper is to propose a methodology to define an optimal pricing strategy for convenience stores. The solution approach involves a multiple linear regression as well as a linear programming optimization model. To prove the value of the proposed methodology a pilot was performed for selected stores. Pricing strategies with data science & multidimensional analysis Create optimal pricing strategies thanks to data science and multidimensional analysis:better retail pricing based on location-based competitors price. Using big data to make better pricing decisions Too big to succeed For every product, companies should be able to find the optimal price that a customer is willing to pay. Ideally, they'd factor in highly specific insights that would influence the price—the cost of the next-best competitive product versus the value of the product to the customer, for example—and then arrive at the best price. Indeed, for a company with a handful of ... 6 Optimal Pricing Strategies to Master Market Dynamics Optimize e-commerce success with insights on market dynamics, competitor strategies, and optimal pricing techniques. Learn to stay competitive and boost profitability. On the Importance of Pricing Strategy in Marketing Strategy: A Case By employing a literature review and case study methodology, we analyze the challenges faced by businesses when implementing pricing strategies, along with the potential future trends in this domain. Pricing Optimization Case study Pricing Optimization Strategy 1. Problem Statement 2. Exploratory Data Analysis 2. Price Segments 2. Profit Segments 3. Incremental CVR Segments 4. Incremental Sales Segments 5. POC for Profit ... Pricing Case Interview: Step-by-Step Guide Learn the seven steps to solve any pricing case interview by using a comprehensive pricing framework that covers three distinct pricing strategies. Optimal pricing strategy in the closed-loop supply chain using game Accordingly, this study proposes a set of optimal pricing strategies and efficient policies for paying government subsidies to maximize profitability in the closed-loop SC related to the car battery recycling industry. Pricing case interviews An overview of the different types of pricing case interview questions with a full wallkthrough of a pricing case question from an ex-McKinsey & Company Business Analyst. How to solve Pricing Case Studies in Consulting Interviews For this reason, establishing optimal prices for products or services is of great importance. Business consultants therefore assist their clients in developing pricing strategies. A case study on pricing is an analysis focusing on the pricing of a product or service. It can stand alone or be part of a broader case, such as entering a new market. Hotel Revenue Management: Strategies, Technologies and Case Studies Learn how hotel revenue management strategies can maximize profitability in a growing industry. Discover dynamic pricing, forecasting and tools for success. Pricing strategies and models Strategies, such as market segmentation, discount, revenue management, price skimming, are introduced. A particular attention is paid to the relationship among margin, price and selling level. Then, the impact of prices on selling volume is analyzed, and the notion of selling curve is introduced. Related pricing methods are discussed such as ... Case Study (Optimal Pricing Strategy) (1) Business Case Study (Optimal Pricing Strategy) When identifying the best fee structure for a wealth management firm, it's crucial to consider the firm's strategic objectives, the characteristics of its clientele, and its position within the market. The following steps ought to be followed: Step 1: Understanding the firm's strategic goals and client base is a pivotal first step in optimizing ... Particle Swarm Optimisation Algorithm-Based Renewable Energy ... Motivated by South Africa's need for the transition to a net-zero economy, this study investigates the integration of renewable energy sources (RESs) into oil refineries, considering the unique challenges and opportunities therein. The research focuses on optimising RES allocation using particle swarm optimisation (PSO), a data-driven approach that adapts to real-time operational conditions ... Retinitis Pigmentosa and Therapeutic Approaches: A Systematic Review However, substantial gaps in evidence and heterogeneity in study methodologies underscore the need for further research to elucidate optimal treatment strategies, refine patient selection criteria, and enhance long-term outcomes. essay homework paper phd project resume review speech study thesis