Mintel is an independent market analysis company that prides itself on supplying objective information on a whole range of markets and marketing issues.

There are five main sources of research that are used in the compilation of Mintel reports:

  • Consumer research
  • Social media research
  • Desk research
  • Trade research
  • Statistical forecasting

Reports are written and managed by analysts with experience in the relevant markets.

Mintel analyzes and interprets data from a variety of sources. Sources are identified below each Figure, table and graph. Data sourced as 'Mintel' are derived from multiple sources, then interpreted and expanded by Mintel analysts. When referenced as 'estimated' the information is either not finalized in the original source or has been extrapolated by Mintel analysts.

Consumer research

In-depth consumer research examines how social, economic, cultural and psychological influences affect attitudes and purchasing decisions. Mintel combines exclusive primary research with syndicated data to provide an accurate and unique analysis.

Primary Data Analysis

For each report Mintel develops custom primary research questions and uses specialty research firms for data collection. Mintel uses best in class consumer research strategies to ensure data is of the highest quality.


Online surveys

Mintel uses set quotas based on gender, age, household income, region, race, ethnicity, and parental status to ensure that survey samples are proportionally representative of the entire U.S. adult internet population. 

Starting in July 2017, Mintel’s consumer research has been conducted using a device agnostic platform for online surveys (ie respondents can now take surveys from a smartphone in addition to a computer or tablet). This methodology change may result in data differences from previous years; any trending should be done with caution.

Specific quotas for a sample of 2,000 adults aged 18+ are shown below.

Please note: these quotas are only representative of a standard General Population survey sample of 2,000 internet users aged 18+. Sample size, targets, and quotas may vary per report. Please see the Report Appendix for further details. 

Mintel uses soft interlocked quotas on age and gender to be inclusive of non-binary respondents while still ensuring the sample remains representative of the overall population.

Age by gender  %               N
    Min Max
Male, 18-24 5.89% 116 118
Male, 25-34 9.08% 180 182
Male, 35-44 8.45% 167 169
Male, 45-54 8.25% 163 165
Male, 55-64 8.07% 159 161
Male, 65-74 5.47% 107 109
: : : :
: : : :
Region % N
Northeast  17.44% 349
Midwest 20.73% 415
South  38.01% 760
West 23.81% 476
Total 100 2,000


Household income % N
Less than $25,000 10.68% 214
$25,000 - $49,999 16.11% 322
$50,000 - $74,999 15.34% 307
$75,000 - $99,999 12.88% 257
$100,000 and over 44.99% 900
: : :


Children in the household % N
Household with children aged 5 and under 11.90% 238
Household with children aged 6-11 11.90% 238
Household with children aged 12-17 11.90% 238
Household with no children 64.30% 1,286
Total 100 2,000

To ensure an adequate representation of these groups in our survey results and to allow for more realistic interpretation of our reported findings,  Hispanic and African American respondents are over-sampled relative to the overall population.

Ethnicity % N
Hispanic 20.00% 400
Not Hispanic 80.00% 1,600
Total 100 2,000

Race  % N
White 69.89% 1,398
Black 15.00% 300
Asian 6.10% 121
Other race 9.10% 181
Total 100 2,000


Kantar Profiles

Kantar Profiles is the industry's largest single source of permission-based panels with the largest number of profile attributes globally, with a respondent reach of over 21 million 100% permission-based respondents in the US. 

Kantar Profiles only works with permission-based panelists and ensures data collection is done in respect of data protection regulations around the world, whether it’s double opt-in (DOI) panelists or programmatic supply. Recruitment methodologies for their DOI respondent panels are done through traditional advertising, as well as both internal and external affiliate networks. Kantar measures recruitment sources on multiple metrics to track both activity and engagement by demographic group, which contributes to the quality of data from their panelists. Kantar Profiles also uses unique quality check tools to add an extra layer of data verification and quality control from recruitment through project execution. 

Note: Lightspeed has been re-branded as Kantar Profiles and is referred to as such in Mintel publications from September 2021 onwards. 

Secondary Data Analysis

In addition to exclusively commissioned surveys, Mintel gathers syndicated data from other respected consumer research firms. This allows Mintel analysts to form objective and cohesive analyses of consumer attitudes and behaviors.

MRI-Simmons USA

Mintel reports frequently draw on the MRI-Simmons USA national and teen studies conducted by MRI-Simmons. MRI-Simmons USA is a comprehensive survey of American consumers aged 18 and older. It provides a nationally representative measurement of major media, products, services, and in-depth consumer demographic and lifestyle/psychographic characteristics.

  • Over 40,000 Adults 18+, including over 4,000 English and Spanish speaking Hispanics
  • Measurement of over 60,000 consumer elements, including over 1,800 psychographic and lifestyle characteristics and consumption of 6,500 products and services in 600+ categories
  • Robust multicultural variables covering acculturation, identity, and media questions
  • Employees address-based probabilistic sampling, measuring real people randomly chosen to represent the US population for an accurate view of the American consumer 

The samples for the MRI-Simmons Teens Study are taken from the same households participating in the adult study. The Teens Study delivers a complete picture of the demographics, media usage, product consumption, and lifestyle choices of America's teenagers aged 12 to 19 years old. 

Qualitative Research and Further Analysis


Recollective provides Mintel with online qualitative research software. This allows the creation of Internet-based, 'virtual' venues where participants recruited from Mintel's online surveys gather and engage in interactive, text-based discussions led by Mintel moderators.


Further Analysis


Mintel employs numerous quantitative data analysis techniques to enhance the value of our consumer research. The techniques used vary form one report to another.  Below describes some of the more commonly used techniques. 


Repertoire Analysis

This is used to create consumer groups based on reported behaviour or attitudes. Consumer responses of the same value (or list of values) across a list of survey items are tallied into a single variable. The repertoire variable summarizes the number of occurrences in which the value or values appear among a list of survey items.  For example, a repertoire of brand purchasing might produce groups of those that purchase 1-2 brands, 3-4 brands and 5 or more brands. Each subgroup should be large enough (ie N=75+) to analyze. 


Cluster Analysis

This technique assigns a set of individual people in to groups called clusters on the basis of one or more question responses, so that respondents within the same cluster are in some sense closer or more similar to one another than to respondents that were grouped into a different cluster.


Correspondence Analysis 

This is a statistical visualization method for picturing the associations between rows (image, attitudes) and columns (brands, products, segments, etc) of a two-way contingency table. It allows us to display brand images (and/or consumer attitudes towards brands) related to each brand covered in this survey in a joint space that is easy to understand. The significance of the relationship between a brand and its associated image is measured using the Chi-square test. If two brands have similar response patterns regarding their perceived images, they are assigned similar scores on underlying dimensions and will then be displayed close to each other in the perceptual map.


CHAID analysis

CHAID (Chi-squared Automatic Interaction Detection), a type of decision tree analysis, is used to highlight key target groups in a sample by identifying which sub-groups are more likely to show a particular characteristic. This analysis subdivides the sample into a series of subgroups that share similar characteristics towards a specific response variable and allows us to identify which combinations have the highest response rates for the target variable. It is commonly used to understand and visualize the relationship between a variable of interest such as “interest in trying a new product” and other characteristics of the sample, such as demographic composition.


Key Driver Analysis

Key driver analysis can be a useful tool in helping to prioritize focus between different factors which may impact key performance indicators (eg satisfaction, likelihood to switch providers, likelihood to recommend a brand, etc). Using correlations analysis or regression analysis provides an understanding of which factors or attributes of a market have the strongest association or “link” with a positive performance on key performance indicators (KPIs). Hence, factors or attributes are identified which are relatively more critical in a market category compared to others and ensures that often limited resources can be allocated to focusing on the main market drivers.


TURF Analysis 

TURF (Total Unduplicated Reach & Frequency) analysis identifies the mix of features, attributes, or messages that will attract the largest number of unique respondents.  It is typically used when the number of features or attributes must be or should be limited, but the goal is still to reach the widest possible audience. By identifying the Total Unduplicated Reach, it is possible to maximize the number of people who find one or more of their preferred features or attributes in the product line. The resulting output from TURF is additive, with each additional feature increasing total reach. The chart is read from left to right, with each arrow indicating the incremental change in total reach when adding a new feature. The final bar represents the maximum reach of the total population when all shown features are offered. 

Social Media Research

To complement our exclusive consumer research, Mintel tracks and analyses social media data for inclusion in Mintel reports. Using Infegy’s Atlas software, Mintel 'listens in' on online conversations across a range of social platforms including Facebook, Twitter, consumer forums and the wider web.


Atlas provides rich consumer insight via the analysis of commentary posted publicly on the internet. The system performs comprehensive and broad collection of data from millions of internet sources, working to ensure a faithful and extensive sampling of feedback from the widest range of individuals. The dataset contains commentary posted in real time, as well as a substantial archive dating back through 2007.

Trade research


Mintel conducts informal trade research for all reports. This involves contacting key players in the trade not only to gain information concerning their own operations, but also to obtain explanations and views of the strategic issues pertinent to the market being researched in order to address current brand and marketing issues. To ensure accuracy, Mintel sends draft copies of reports to key industry representatives for review, taking their feedback into consideration before publishing the report. Comments, where appropriate, are incorporated into the report.



Internally, Mintel's analysts undertake extensive trade interviews with selected key experts in the field for the majority of reports. The purpose of these interviews is to assess key issues in the market place in order to ensure that any research undertaken takes these into account.


In addition, using experienced external researchers, trade research is undertaken for some reports. This takes the form of full trade interview questionnaires and direct quotes are included in the report and analysed by experts in the field. This gives a valuable insight into a range of trade views of topical issues. Direct quotations are included in the reports, giving valuable insight into a range of trade views on topical issues.


Desk Research

Mintel has an internal team of market analysts who monitor: government statistics, consumer and trade association statistics, manufacturer sponsored reports, annual company reports and accounts, directories and press articles from around the world and online databases. The latter are extracted from hundreds of publications and websites, both U.S. and overseas. All information is cross-referenced for immediate access. Data from other published sources are the latest available at the time of writing the report. This information is supplemented by an extensive library of Mintel's reports produced since 1972 globally and added to each year by the 500+ reports which are produced annually.


In addition to in-house sources, researchers also occasionally use outside libraries or data from Trade Associations. Other information is also gathered from store and exhibition visits across the U.S., as well as using other databases within the Mintel Group, such as the Global New Product Database (GNPD), which monitors FMCG sales promotions.


Intelligence gathered through desk research is used to guide research and enrich data findings.

Statistical Forecasting

Statistical modelling

For the majority of reports, Mintel produces five-year central forecasts based on ‘regression with ARIMA errors’ which is a combination of two simple yet powerful statistical modelling techniques: regression and ARIMA (Auto Regressive Integrated Moving Average). Regression allows us to model, thus predict, market sizes using exogenous information (eg GDP, unemployment). ARIMA allows us to model market sizes using endogenous information (lagged values). To estimate this type of model, Mintel uses the software R.

Historical market size data feeding into each forecast are collated in Mintel’s own market size database and supplemented by macro- and socio-economic data sourced from organizations such as the Economist Intelligence Unit and the US Census Bureau. 

Within the forecasting process, we analyze relationships between actual market sizes and a selection of key economic and demographic determinants (independent variables) in order to identify those predictors having the most influence on the market.

Factors used in a forecast are stated in the relevant report section alongside an interpretation of their role in explaining the development in demand for the product or market in question.


Qualitative insight

At Mintel we understand that historic data is limited in its capacity to act as the only force behind the future state of markets. Thus, rich qualitative insights from industry experts regarding future events that might impact upon various markets play an invaluable role in our post statistical modelling evaluation process.

As a result, the Mintel forecast complements a rigorous statistical process with in-depth market knowledge and expertise to allow for additional factors or market conditions outside of the capacity of the statistical forecast. 

[graphic: image 1]


The Mintel fan chart

 Forecasts of future economic outcomes are always subject to uncertainty. In order to raise awareness amongst our clients and to illustrate this uncertainty, Mintel displays market size forecasts in the form of a fan chart.

The fan chart shows the actual market size for the past 5 or 6 years, in some cases a current year estimate, a 5-year or 6-year horizon central forecast (resulting from statistical modelling and qualitative insight), and the forecast’s prediction intervals (resulting from statistical modelling).

The prediction intervals represent the range of values which the actual future market size will fall in with a specific probability.

A general conclusion: based on our current knowledge of given historic market size data as well as projections for key macro- and socio-economic measures that were used to create the forecast, we can say that  the future actual market size will fall within the shaded fan with a probability of 95%. There is a small probability of 5% that the future actual market size will fall out of these boundaries.

Since 95% is in most applications the threshold that defines whether we can accept or refuse a statistical result, the outer limits of the 95% prediction interval can be seen as the forecast’s best and worst cases.

Weather analogy

To illustrate uncertainty in forecasting in an everyday example, let us assume the following weather forecast was produced based on the meteorologists’ current knowledge of the previous weather condition during the last few days, atmospheric observations, incoming weather fronts etc.

[graphic: image 2]

Now, how certain can we be that the temperature on Saturday will indeed be 15°C?

To state that the temperature in central London on Saturday will rise to exactly 15°C is possible but one can’t be 100% certain about that fact.

To say the temperature on Saturday will be between 13°C and 17°C is a broader statement and much more probable.

In general, we can say that based on the existing statistical model, one can be 95% certain that the temperature on Saturday will be between 13°C and 17°C, and only 50% certain it will be between about 14.5°C and 15.5°C. Finally, there is a small probability of 5% that the actual temperature on Saturday will fall out of these boundaries and thus will be below 13°C or above 17°C.