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

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

Consumer research

Exclusive and original quantitative consumer research is commissioned for Mintel reports. In addition, qualitative research is also undertaken for reports in the form of online discussion groups. The research brings an up-to-date and unique insight into topical issues of importance.

Consumer research is conducted among a nationally representative sample of internet users in Canada and is carried out by Kantar Profiles. The results are only available in Mintel reports. Note that Mintel’s exclusive research is conducted online in both English and French.

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.


Online Surveys

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 2 million 100% permission-based respondents in Canada. 

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. 

Mintel sets quotas on age and gender, region, and household income. Specific quotas for samples of 2,000 and 1,500 adults aged 18+ are shown below. 

Please note: these quotas are only representative of a standard General Population survey sample of 2,000 and 1,500 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=2,000 N=1,500 Canada pop %
  Min Max Min Max  
Male, 18-24 136 138 101 103 6.90%
Male, 25-34 150 152 112 114 7.60%
Male, 35-44 190 192 142 144 9.60%
Male, 45-54 127 129 95 97 6.50%
Male, 55-64 161 163 120 122 8.10%
Male, 65+ 203 205 152 154 10.30%
: : : : : :
: : : : : :
Region N=2,000 N=1,500 Canada pop %
Ontario  738 554 36.90%
Quebec 491 368 24.60%
British Columbia  271 203 13.60%
Alberta 242 182 12.10%
Saskatchewan 64 48 3.20%
Manitoba 63 47 3.20%
Atlantic Provinces  (New Brunswick, Newfoundland/ Labrador, Nova Scotia, Prince Edward Island) 131 98 6.50%
: : : :

*Mintel does not include rural regions such as the Yukon or the Northwest Territories (including Nunavut) in its research. Thus the consumer research data does not reflect opinions and behaviours of the population living in those areas.

Household income N=2,000 N=1,500 Canada pop %
Less than $25,000 281 210 14.10%
$25,000 - $49,999 416 312 20.80%
$50,000 - $69,999 300 225 15.00%
$70,000 - $99,999 356 267 17.80%
$100,000 and over 647 486 32.40%
Total (all Canadian internet users aged 18+)  2,000 1,500 100%

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 behaviour.

Qualitative Research


Recollective provides Mintel with online 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 summarises 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 analyse.

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 visualisation 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 visualise 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 prioritise 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 its exclusive consumer research, Mintel tracks and analyses social media data for inclusion in selected 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


Trade research is undertaken for all reports. This involves contacting relevant 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. Such is Mintel’s concern with accuracy that draft copies of reports are sent to industry representatives, to get their feedback and avoid any misrepresentation of the market. These comments are incorporated into reports prior to final publication.


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.

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, press articles from around the world and online databases. The latter are extracted from hundreds of publications and websites, both Canada 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 such as Statistics Canada and the Canadian Grocer. Other information is also gathered from store and exhibition visits across Canada, as well as using other databases within the Mintel Group, such as the Global New Product Database (GNPD), which monitors FMCG sales promotions.

All analysts have access to Mintel’s Market Size and Macroeconomic Databases – a database containing many areas of consumer spending and retail sales as well as macroeconomic and demographic factors which impinge on consumer spending patterns.

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 Statistics Canada.

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.