US Multicultural

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

Five main sources of research are used in the compilation of Mintel multicultural reports:

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

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

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 of African Americans and Hispanics. 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 which are fielded via specialty research firms. Mintel uses best in class consumer research strategies to ensure data is of the highest quality. 



Founded in 1996, Lightspeed’s double opt-in U.S. online consumer panel contains approximately 1.27 million U.S. consumers. Lightspeed recruits its panelists through many different sources including web advertising, permission-based databases and partner-recruited panels.

Note: Lightspeed GMI was re-branded as Lightspeed in September 2016

Online surveys

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.

Mintel uses set quotas to ensure an accurate reflection of African-Americans or Hispanics based on that segment’s internet population. 

Specific quotas for a sample of 1,000 African-American and 1,000 Hispanic adults aged 18+ are shown below. 

Please note: these quotas are only representative of the above stated samples. Sample size, targets, and quotas may vary per report. Please see the Report Appendix for further details. 

African American studies’ quotas

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 7.51% 74 75
Male, 25-34 9.59% 95 96
Male, 35-44 7.90% 78 79
Male, 45-54 7.76% 77 78
Male, 55-64 7.35% 72 73
Male, 65+ 5.26% 52 53
: : : :
: : : :
  % N



Northeast  17.96% 179
Midwest 14.37% 144
South  55.97% 560
West 11.71% 117
: : :


 Household income % N
Less than $25,000 21.98% 220
$25,000 - $49,999 21.87% 219
$50,000 - $74,999 14.92% 149
$75,000 - $99,999 12.42% 124
$100,000 and over 28.80% 288
: : :


Hispanic studies’ quotas

The surveys for Mintel's Hispanic studies may be taken online by respondents in either English or Spanish. 

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 9.16% 91 92
Male, 25-34 14.38% 143 144
Male, 35-44 11.34% 112 113
Male, 45-54 7.06% 70 71
Male, 55+ 8.34% 82 83
Female, 18-24 8.75% 87 88
: : : :
: : : :
Preferred language % N
Only or mostly English 58.11% 581
Only or mostly Spanish 41.89% 419
Total 100 1,000


Region % N
Northeast  14.18% 142
Midwest 9.05% 91
South  38.49% 384
West 38.28% 383
Total 100 1,000


Household income % N
Less than $25,000 12.82% 129
$25,000 - $49,999 21.73% 217
$50,000 - $74,999 19.60% 196
$75,000 and over 45.85% 458
: : :


Hispanic origin % N
Puerto Rico/Cuba (maximum)  18.0% 180
Other Spanish/Hispanic/Latino heritage  - -
Total 100 1,000

Multicultural Young Adults studies’ quotas

For these studies, Mintel samples 400 each of young adult respondents (aged 18-34) who self-identify as White (non-Hispanic), Black or African American, Asian or Pacific Islander, and Hispanic (any race except for White). In addition, Mintel surveys 200 respondents who self-identify as other race or mixed race. Within each of the groups, Mintel selects respondents by age and gender to be representative of the internet population and also includes soft quotas on region and household income. Young adults aged 18-34 who are not online and/or do not speak English are not included in Mintel’s survey results.

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.

White, Non-Hispanic quotas

  N=400   %
Age by gender Min Max  
Male, 18-24 76 77 20.00%
Male, 25-29 56 57 14.00%
Male, 30-34 59 60 15.00%
Female, 18-24 76 77 19.00%
Female, 25-29 64 65 16.00%
Female, 30-34 63 64 16.00%
: : : :

Black quotas

  N=400 %
Age by gender Min Max  
Male, 18-24 87 88 22.00%
Male, 25-29 67 68 17.00%
Male, 30-34 47 48 12.00%
Female, 18-24 83 84 21.00%
Female, 25-29 55 56 14.00%
Female, 30-34 55 56 14.00%
: : : :
: : : :

Asian quotas

  N=400 %
Age by gender Min Max  
Male, 18-24 82 83 21.00%
Male, 25-29 82 83 21.00%
Male, 30-34 47 48 12.00%
Female, 18-24 70 71 17.00%
Female, 25-29 46 47 12.00%
Female, 30-34 67 68 17.00%
: : : :
: : : :

Hispanic quotas

  N=400 %
Age by gender Min Max  
Male, 18-24 83 84 21.00%
Male, 25-29 67 68 17.00%
Male, 30-34 55 56 14.00%
Female, 18-24 75 76 19.00%
Female, 25-29 55 56 14.00%
Female, 30-34 59 60 15.00%
: : : :
: : : :

Other/Mixed race quotas

  N=200 %
Age by gender Min Max  
Male, 18-24 33 34 17.00%
Male, 25-29 33 34 17.00%
Male, 30-34 35 36 18.00%
Female, 18-24 37 38 19.00%
Female, 25-29 29 30 15.00%
Female, 30-34 27 28 14.00%
: : : :
: : : :

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 behavior.

Simmons National Consumer Studies

Mintel reports frequently draw on the Simmons National Consumer surveys conducted by Simmons Research. The Simmons National Consumer Study (NCS) is a comprehensive survey of American consumers aged 18 and older. It provides single-source measurement of major media, products, services, and in-depth consumer demographic and lifestyle/psychographic characteristics.

  • Over 20,000 Adults 18+
  • Two-phase data collection
    • Phase 1: : Mail-based recruitment questionnaire
    • Phase 2: Mail-out, mail-back self-administered survey 
  • Dual upfront and back-end cash incentive structure
  • All qualified household members aged 18 or over participate by completing their own personal booklets
  • Principal shopper completes one Household Survey
  • Released twice a year—Spring and Fall data releases

The Simmons National Hispanic Study (NHCS) is the only national, multi-media syndicated research instrument targeting the Hispanic market, and is particularly valuable in identifying media habits, product and service usage and attitudes and opinions among this segment.

  • Over 5,000 Hispanic adults 18+
  • Two-phase data collection
    • Phase 1: Mail-based recruitment questionnaire
    • Phase 2: Mail-out, mail-back self-administered survey booklets
  • Survey offered in Spanish or English - respondent’s choice
  • Dual upfront and back-end cash incentive structure
  • Released twice a year—Spring and Fall data releases

The samples for the Teens Study are taken from the same households participating in the adult study. The Teens Study provides in-depth insights into this consumer segment to understand teens' effect on the marketplace, and how and where to reach them.

In some instances Mintel uses Experian’s Mosaic segmentation system to further analyze Simmons NHCS data. Mosaic is a household-based segmentation system, which classifies all U.S. households and neighborhoods into 71 unique Mosaic segments and 19 groupings that share similar demographic and socioeconomic characteristics. Descriptive content is sourced from Simmons NCS/NHCS data.

  • 2,000 Teens 12-17 Gathered from within NCS participating households
  • Dual upfront and back-end cash incentive structure
  • All teens in household participate by completing their own personal booklets
  • Released twice a year—Spring and Fall data releases

Simmons Connect Plus measures consumer behaviors in the US across 11 traditional, digital, and mobile media platforms. Often referred to as behavioral data, it is based on actual usage of select websites and mobile apps and uses passive measurement applications to capture data on consumers’ digital behaviors on their smartphones, tablets, and/or computers. Enhanced fusion techniques bring together Simmons NCS and NCHS data and digital consumption Connect Plus data on over 15,000 devices.

  • Over 8,000 unique respondents
  • 15,000 devices (computers, smartphones, and tablets)
  • Over 4,000 websites
  • And over 900 apps
  • Released twice a year—Spring and Fall data releases


Qualitative Research 


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 Social Radar software, Mintel 'listens in' on online conversations across a range of social platforms including Facebook, Twitter, consumer forums and the wider web.

Social Radar 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.

To perform the analysis, related commentary is read by machine, with specialized proprietary software able to break down and understand human language and complex grammatical structures. This process can then extract topical information, sentiment and tonality, emotional expressiveness and more with accuracy and speed.

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.