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Market Research Analyst job description

A Market Research Analyst collects and analyzes data on consumers, competitors, and the marketplace. They provide insights and recommendations to support decision-making, identify market trends, and improve competitiveness. Strong analytical skills, knowledge of statistical packages, and excellent communication are essential.

Eleni Kourmentza

This Market Research Analyst job description template is optimized for posting on online job boards or careers pages and easy to customize for your company.

What is a Market Research Analyst?

A Market Research Analyst is a professional who collects and analyzes data on consumers, competitors, and the marketplace. They interpret the findings to provide valuable insights and recommendations to businesses for informed decision-making and to identify market trends and opportunities.

What does a Market Research Analyst do?

A Market Research Analyst conducts research and gathers data using various methods such as surveys, interviews, and data analysis tools. They analyze the collected data, interpret trends, and provide reports and presentations to clients or internal stakeholders. They play a crucial role in helping businesses understand consumer preferences, market dynamics, and competitive landscape to drive strategic decision-making and improve overall business performance.

Market Research Analyst responsibilities include:

  • Collecting data on consumers, competitors and market place and consolidating information into actionable items, reports and presentations
  • Understanding business objectives and designing surveys to discover prospective customers’ preferences
  • Compiling and analyzing statistical data using modern and traditional methods to collect it

market research analyst job description

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We are seeking a detail-oriented Market Research Analyst to conduct surveys and analyze customer preferences and statistical data.

Your role will involve providing valuable insights to support customers in their decision-making processes related to product designs, pricing, and promotions.

As a successful Market Research Analyst, you will have the ability to independently analyze qualitative data, identify trends, assess strategies, and evaluate competition, with the ultimate goal of enhancing competitiveness.

Your responsibilities will include gathering and interpreting market research data, generating reports, and presenting findings to stakeholders.

We are looking for a self-motivated professional with strong analytical skills and a deep understanding of market dynamics.

Join our team and contribute to our company’s success by helping us make data-driven decisions to optimize our products and strategies.


  • Collect data on consumers, competitors and market place and consolidate information into actionable items, reports and presentations
  • Understand business objectives and design surveys to discover prospective customers’ preferences
  • Compile and analyze statistical data using modern and traditional methods to collect it
  • Perform valid and reliable market research SWOT analysis
  • Interpret data, formulate reports and make recommendations
  • Use online market research and catalogue findings to databases
  • Provide competitive analysis on various companies’ market offerings, identify market trends, pricing/business models, sales and methods of operation
  • Evaluate program methodology and key data to ensure that data on the releases are accurate and the angle of the release is correct
  • Remain fully informed on market trends, other parties researches and implement best practices

Requirements and skills

  • Proven Market Research Analysis experience
  • Ability to interpret large amounts of data and to multi-task
  • Strong communication and presentation skills
  • Excellent knowledge of statistical packages (SPSS, SAS or similar), databases and MS Office
  • Search engines, web analytics and business research tools acumen
  • Familiarity with CRM programs
  • Adequate knowledge of data collection methods (polls, focus groups, surveys etc)
  • Working knowledge of data warehousing, modelling and mining
  • Strong analytical and critical thinking
  • BS degree in Statistics, Marketing or related field

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3 entry-level remote jobs that pay $100,000+ in 2024.

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One of the best ways to eliminate student debt faster is to start off your career with a lucrative ... [+] salary, so that from there, it only gets higher

The class of 2024 is waking up from graduation to realize they are in a financial black hole.

"The cost of college has steadily increased over the last 30 years," notes recent Forbes research into student loan debt statistics. "In that timeframe, tuition costs at public four-year colleges grew from $4,160 to $10,740 and from $19,360 to $38,070 at private non-profit institutions (adjusted for inflation). As costs have risen, so has the need for student loans and other forms of financial aid."

Unfortunately, the older you are, the thicker the shackles of college debt entwine. Noticeably, the Forbes research pointed out that those who are in their senior years are most likely to be encumbered with up to $100,000 or more of student debt. And the sad reality is that this ongoing vicious cycle is only getting worse. "Today, more than half of students leave school with debt," the report said.

What if that cycle could be reversed? What if it was possible to graduate from college and start your career on the right foot?

Perhaps it would be better to consider making as much money as possible in the earlier years of your career, to get your student loan repayments off to a flying headstart, than to pay ludicrous fees in dribs and drabs for a lifetime.

If you're graduating and setting your sights on landing your first job so you can launch a successful career, here are three entry-level jobs you might want to consider concentrating on so you can get out of debt faster (salaries extracted from and Glassdoor):

1. Remote Software Developer/Engineer

By now, it's probably a well-known fact that software development and software engineering are by far the most highly compensated careers globally, even when starting at the entry-level. Even from graduation, you can make up to $87,000, and then as you gain more technical skills and experience, go on to earning six figures annually.

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024.

Typically, software engineers and developers need degrees in computer science and even mathematics, but these days, hopeful developers are able to get their foot in the door through attending bootcamps and online training courses, and joining groups.

Average salary (after graduation): $85,000 to $128,000

Average salary range (with experience): $114,013 to $135,979

2. Remote Technical Product Manager

Careers within the project management profession (product, project, and program management) are very similar by nature and in some of their foundational principles, but there are some striking differences . Product managers tend to make the most money in the tech sector, or within finance/fintech, and healthcare, and they take the lead in paving the way for some of the world's best innovations within these sectors, managing a team of developers and others involved with the creation and testing of the product.

Common degrees that are suitable for this career include business management and marketing (although you do not need a degree to be a product manager as you can start your career with a product management certification).

Average salary (after graduation): $91,204 to $116,958

Average salary range: $110,125 to $138,220

3. Remote Digital Marketing Manager/Marketing Analyst

Digital marketing is a highly lucrative industry to jumpstart your career in, and it can be rewarding and thrilling to see the results and positive impact of your marketing campaigns.

If you graduated with a degree in fields such as business management, marketing, advertizing, or even communications or psychology, you would be able to get your foot in the door in this career, as all these subjects are very much connected to successful digital marketing. You can work your way up in this field from junior level, or even decide to start as a digital marketing analyst and climb the ladder to more senior titles as you gain experience over a few years.

Average salary (after graduation): $100,498 to $132,448

Average salary range: $108,389 to $141,892

You can land a high-paying entry-level job without even needing a degree

Whether you decide to get a remote entry-level job as a software developer, technical product manager, or work in digital marketing, deciding on an entry-level job is one of the most important decisions of your professional (and financial) life. Even if you don't have a relevant degree, you can still launch these careers through undertaking relevant training and certifications, many of which are free or reasonably priced compared to a four-year degree. Start your career right, and you won't regret it.

Rachel Wells

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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Savvino-storozhevsky monastery and museum.

Savvino-Storozhevsky Monastery and Museum

Zvenigorod's most famous sight is the Savvino-Storozhevsky Monastery, which was founded in 1398 by the monk Savva from the Troitse-Sergieva Lavra, at the invitation and with the support of Prince Yury Dmitrievich of Zvenigorod. Savva was later canonised as St Sabbas (Savva) of Storozhev. The monastery late flourished under the reign of Tsar Alexis, who chose the monastery as his family church and often went on pilgrimage there and made lots of donations to it. Most of the monastery’s buildings date from this time. The monastery is heavily fortified with thick walls and six towers, the most impressive of which is the Krasny Tower which also serves as the eastern entrance. The monastery was closed in 1918 and only reopened in 1995. In 1998 Patriarch Alexius II took part in a service to return the relics of St Sabbas to the monastery. Today the monastery has the status of a stauropegic monastery, which is second in status to a lavra. In addition to being a working monastery, it also holds the Zvenigorod Historical, Architectural and Art Museum.

Belfry and Neighbouring Churches

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Located near the main entrance is the monastery's belfry which is perhaps the calling card of the monastery due to its uniqueness. It was built in the 1650s and the St Sergius of Radonezh’s Church was opened on the middle tier in the mid-17th century, although it was originally dedicated to the Trinity. The belfry's 35-tonne Great Bladgovestny Bell fell in 1941 and was only restored and returned in 2003. Attached to the belfry is a large refectory and the Transfiguration Church, both of which were built on the orders of Tsar Alexis in the 1650s.  

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To the left of the belfry is another, smaller, refectory which is attached to the Trinity Gate-Church, which was also constructed in the 1650s on the orders of Tsar Alexis who made it his own family church. The church is elaborately decorated with colourful trims and underneath the archway is a beautiful 19th century fresco.

Nativity of Virgin Mary Cathedral

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The Nativity of Virgin Mary Cathedral is the oldest building in the monastery and among the oldest buildings in the Moscow Region. It was built between 1404 and 1405 during the lifetime of St Sabbas and using the funds of Prince Yury of Zvenigorod. The white-stone cathedral is a standard four-pillar design with a single golden dome. After the death of St Sabbas he was interred in the cathedral and a new altar dedicated to him was added.

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Under the reign of Tsar Alexis the cathedral was decorated with frescoes by Stepan Ryazanets, some of which remain today. Tsar Alexis also presented the cathedral with a five-tier iconostasis, the top row of icons have been preserved.

Tsaritsa's Chambers

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The Nativity of Virgin Mary Cathedral is located between the Tsaritsa's Chambers of the left and the Palace of Tsar Alexis on the right. The Tsaritsa's Chambers were built in the mid-17th century for the wife of Tsar Alexey - Tsaritsa Maria Ilinichna Miloskavskaya. The design of the building is influenced by the ancient Russian architectural style. Is prettier than the Tsar's chambers opposite, being red in colour with elaborately decorated window frames and entrance.

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At present the Tsaritsa's Chambers houses the Zvenigorod Historical, Architectural and Art Museum. Among its displays is an accurate recreation of the interior of a noble lady's chambers including furniture, decorations and a decorated tiled oven, and an exhibition on the history of Zvenigorod and the monastery.

Palace of Tsar Alexis

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The Palace of Tsar Alexis was built in the 1650s and is now one of the best surviving examples of non-religious architecture of that era. It was built especially for Tsar Alexis who often visited the monastery on religious pilgrimages. Its most striking feature is its pretty row of nine chimney spouts which resemble towers.

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Location approximately 2km west of the city centre
Website Monastery - Museum -

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Suburbanization Problems in the USSR : the Case of Moscow


  • Référence bibliographique

Gornostayeva Galina A. Suburbanization Problems in the USSR : the Case of Moscow . In: Espace, populations, sociétés , 1991-2. Les franges périurbaines Peri-urban fringes. pp. 349-357.


  • RIS (ProCite, Endnote, ...)



Moscow University

Suburbanization Problems

in the USSR :

the Case of Moscow

Suburbanization processes typical to cities in Western Europe, the USA and other countries are not observed in the USSR or they are distorted to such an extent that they may not be compared with existing standards. This states the question how Soviet cities-succeeded in escaping this stage of urban development. In order to answer this question, we should first summarize the main aspects of Western suburbanization.

Firstly, it is well known that the urbanization processes are linked to structural changes in the economy. Thus the transition from the stage of concentration to this of suburbanization is associated with industrialization, and the transition to the third stage - déconcentration - is related with the rapid growth of employment in the non-industrial sphere. Secondly, a suburbanization of economic activities can be distinguished. It applies in the first place to the building and iron- working industry, transports, engineering and chemical works. These are polluting and requiring extensive areas. This suburbanization of industry is caused by the following factors: rising demand for land from firms ; worsening of transport

tions in the inner cities ; demand for lower land costs and taxation levels in suburbs ; rapid growth of road transports; state policies regulating the growth of large cities ; migration of the labour force to the suburban zones. Scientific and educational activities are also transferred from the centre to the suburbs.

The third important aspect of suburbanization applies to the population. In the suburbs two opposite flows of population meet ; one is centripetal, coming from non- metropolitan regions, the other is centrifugal, coming from the central city. The reasons for the migration to the suburbs are as follows : declining living standards in large cities (overcrowding, slow housing renewal, environmental problems, etc.); growth of motorization of the population, development of communications (telephone, telex, fax, computer) ; intensifying decentralization of working places ; lower land prices in the suburbs ; state support for the intensification of real estate development in the suburbs. The above-mentioned factors and reasons for suburbanization are altered in the Soviet cities. Let us explore them, by taking for example the largest one - Moscow.

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