Mintel Methodologies

Qualitative Research

Revelation by FocusVisionFocusVision provides Mintel with qualitative bulletin board 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 AnalysisMintel 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 AnalysisThis 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 AnalysisThis 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 AnalysisThis 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 analysisCHAID (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 AnalysisKey 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 AnalysisTURF (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

InformalMintel 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.

FormalInternally, 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 ResearchMintel 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 modellingFor the majority of reports, Mintel produces five-year forecasts based on an advanced statistical technique known as ‘multivariate time series auto-regression’ using the statistical software package SPSS.

The model is based on historical market size data taken from Mintel’s own market size database and supplemented by published macroeconomic and demographic data from various private and public sources including the Federal Reserve Board, the U.S. Commerce Department, the Census Bureau, the Council of Economic Advisers, and the Congressional Budget Office.

The model searches for relationships between actual market sizes and a selection of relevant and significant macroeconomic and demographic factors (independent variables) 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 insightAt 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 past and future events that may impact the market play a crucial role in our post statistical modeling evaluation process.

As a result, the Mintel forecast allows for additional factors or market conditions outside of the capacity of the data analysis to impact the market forecast model; using a rigorous statistical process complemented by in-depth market knowledge and expertise.

The Mintel fan chartForecasts of future economic outcomes are always subject to uncertainty. In order to raise awareness amongst our clients and to illustrate this uncertainty, Mintel has introduced a new way of displaying market size forecasts in the form of a fan-chart.

Next to historical market sizes and a current year estimate, the fan chart illustrates the probability of various outcomes for the market value/volume over the next five years.

At a 95% confidence interval, we are saying that 95 out of 100 times the forecast will fall within these outer limits, which we call the best and worst case forecasts. These, based on the statistically driven forecast, are the highest (best case) and lowest (worst case) market sizes the market is expected to achieve.

Over the next five years, the widening bands successively show the developments that occur within 95%, 90%, 70% and 50% probability intervals. Statistical processes predict the central forecast to fall within the darker shaded area which illustrates 50% probability, i.e. a 5 in 10 chance.

In general, 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 assume that in 95% of the time the actual market size will fall within the purple shaded fan. In 5% of all cases this model might not be correct due to random errors and the actual market size will fall out of these boundaries.

Weather analogyTo 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.

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

• Consumer research
• Brand & social media research
• Desk research
• Trade research
• Statistical forecasting
• Mintel’s exclusive archive of over 40 years of analysis and expertise.

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 almost all Mintel reports. In addition, qualitative research is also undertaken for a large proportion of reports in the form of online discussion groups. Mintel invests a considerable sum each year in consumer research, and the purchaser of a Mintel report benefits, as the price of an individual report is less than the cost of the original research alone. 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 either adults or internet users and is generally carried out by Lightspeed GMI (online), Ipsos Mori (face to face), while other suppliers are used on an ad hoc basis as required. The results are only available in Mintel reports.

Sampling and weighting

Face to Face Surveys

Ipsos Mori

Ipsos MORI Capibus uses a two-stage random location sample design which generates a very high quality sample representative of the Great Britain adult population. Interviews are conducted via c170-180 sampling points, randomly selected every week, and CACI ACORN is employed to set interlocking quota controls specific to each interviewer location. This ensures consistent accurate representation of the locations interviewed every week. By using this proven sample design, all sub-sectors of the population are represented – at a national and regional level.

All information collected on Capibus is then weighted to reflect the known profile of the adult population in Great Britain. Capibus uses a rim weighting system which weights to mid-2010 census and NRS defined profiles for age, social grade, region, ethnicity and working status – within gender. Additional profiles used include tenure and car in household, for example.

Because the sampling process is repeated every week, the Capibus sample is matched wave on wave, making it ideal for taking successive measurements on the same issue.

Calculation of socio-economic grade in Ipsos MORI Capibus surveys

Socio-economic grade is classified according to the occupation of the chief income earner in the household. A number of questions are asked by the interviewer in order to assign social grade accurately. The interviewer probes the respondent for information about the occupation of the chief income earner, the type of organisation he or she works for, job actually done, job title/rank/grade, and whether the chief income earner is self-employed. Additionally, questions are asked about the number of people working at the place of employment and whether the chief income earner is responsible for anyone, together with confirmation of qualifications. Once the interviewer is satisfied that sufficient information has been gathered in order to determine social grade, their estimate is recorded and this is later double checked for accuracy by experts in social grading coding at Ipsos MORI when it can be amended if necessary.

Online Surveys

Lightspeed GMI

Founded in 1999, GMI’s double opt-in online consumer panel has reach to approximately 350,000 consumers in Great Britain.  Re-branded as Lightspeed GMI in September 2014 (after its acquisition by Kantar in 2011), it delivers uniquely identified online respondents via extensive use of fraud detection and location-verification technology at multiple points in the research cycle, from initial registration through survey fielding and incentive redemption. Lightspeed GMI panellists are profiled on a wide variety of attri­butes to deliver the specific hard-to-reach demographics.

To ensure our surveys are nationally representative of internet users, Mintel sets quotas for each age group, split by gender. Specific quotas for a sample of 2,000 adults aged 16+ are shown below:

Calculation of socio-economic grade in Lightspeed GMI surveys 

To calculate panellist’s socio-economic grade Lightspeed GMI ask a set of screening questions at registration to their panel. These questions include employment status and profession (both panellists own and the household’s chief income earner). Once the panellist has completed these initial questions they are invited to take an additional set of follow-up questions that allow Lightspeed GMI to further fine tune their socio-economic grade. Panellists are required to retake the socio-economic screening questions once a year to ensure that any changes in circumstance are accounted for.

Qualitative Research

FocusVision Revelation

FocusVision provides Mintel with qualitative bulletin board software ‘Revelation’. 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 we can get 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, we are able to identify which factors or attributes 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.

Brand & Social Media Research

Mintel’s brand research includes more in-depth knowledge about brands covered in relevant markets.  Brands are evaluated on a number of areas including usage, commitment, dynamism, differentiation, satisfaction, image, recommendation and attitudes. When evaluating attitudes, up to ten brand attitude statements are selected and tested, depending on the actual consumer market.

To complement its exclusive consumer research, Mintel tracks social media data for inclusion in selected reports. Using Infegy’s Atlas software, Mintel analyses online conversations across a range of social platforms including 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 to 2007.

Trade research

Informal

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.

Formal

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 British 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 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 the British Library or the Department of Trade and Industry. Other information is also gathered from store and exhibition visits across Europe, 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, going back some 20 years.

The database is used in conjunction with an SPSS forecasting program which uses weighted historical correlations of market dynamics, with independent variables, to produce future spending scenarios.

Statistical forecasting

Statistical modelling

For the majority of reports, Mintel produces five-year forecasts based on an advanced statistical technique known as ‘multivariate time series auto-regression’ using the statistical software package SPSS.

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 organisations such as the Office for National Statistics, HM Treasury, the Bank of England and the Economist Intelligence Unit.

Within the forecasting process, the model searches for, and analyses 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 modeling 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.

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

Next to historical market sizes and a current year estimate, the fan chart illustrates the probability of various outcomes for the market value/volume over the next five years.

At a 95% confidence interval, we are saying that 95 out of 100 times the forecast will fall within these outer limits, which we call the best and worst case forecasts. These, based on the statistically driven forecast, are the highest (best case) and lowest (worst case) market sizes the market is expected to achieve.

Over the next five years, the widening bands successively show the developments that occur within 95%, 90%, 70% and 50% probability intervals. Statistical processes predict the central forecast to fall within the darker shaded area which illustrates 50% probability, i.e. a 5 in 10 chance.

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 assume that in 95% of the time the actual market size will fall within the purple shaded fan. In 5% of all cases this model might not be correct due to random errors and the actual market size will fall out of these boundaries.

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.

Now, how accurate is this forecast and 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 respectively 50% certain it will be between about 14.5°C and 15.5°C. Again, only in 5% of all cases this model might not be correct due to random errors and the actual temperature on Saturday will fall out of these boundaries and thus will be below 13°C or above 17°C.

(To learn more about uncertainty in weather forecasts visit: http://research.metoffice.gov.uk/research/nwp/ensemble/uncertainty.html)

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 four main sources of research that are used in the compilation of Mintel’s business-to-business (B2B) reports:

• Desk Research
• Trade Research
• Statistcal forecasting
• Mintel’s exclusive archive of over 40 years analysis and expertise
• Consumer research (selected reports only)

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

Trade research

Informal

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.

Formal

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 on topical issues.

Desk research

Mintel has an internal team of desk researchers 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 British 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.

In addition to in-house sources, researchers also occasionally use outside libraries such as the British Library or the Department of Trade and Industry.

Consumer research

Exclusive and original quantitative consumer research is commissioned for almost all Mintel reports. In addition, qualitative research is also undertaken for a large proportion of reports in the form of online discussion groups. Mintel invests a considerable sum each year in consumer research, and the purchaser of a Mintel report benefits, as the price of an individual report is less than the cost of the original research alone. 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 either adults or internet users and is generally carried out by Lightspeed GMI (online), Ipsos Mori (face to face), while other suppliers are used on an ad hoc basis as required. The results are only available in Mintel reports.

Sampling and weighting

Face to Face Surveys

Ipsos Mori

Ipsos MORI Capibus uses a two-stage random location sample design which generates a very high quality sample representative of the Great Britain adult population. Interviews are conducted via c170 sampling points, randomly selected every week, and MOSAIC is employed to set interlocking quota controls specific to each interviewer location. This ensures consistent accurate representation of the locations interviewed every week. By using this proven sample design, all sub-sectors of the population are represented – at a national and regional level.

All information collected on Capibus is then weighted to reflect the known profile of the adult population in Great Britain. Capibus uses a rim weighting system which weights to mid-2010 census and NRS defined profiles for age, social grade, region, ethnicity and working status – within gender. Additional profiles used include tenure and car in household, for example.

Because the sampling process is repeated every week, the Capibus sample is matched wave on wave, making it ideal for taking successive measurements on the same issue.

Online Surveys

Lightspeed GMI

Founded in 1999, GMI’s double opt-in online consumer panel has reach to approximately 350,000 consumers in Great Britain.  Re-branded as Lightspeed GMI in September 2014 (after its acquisition by Kantar in 2011), it delivers uniquely identified online respondents via extensive use of fraud detection and location-verification technology at multiple points in the research cycle, from initial registration through survey fielding and incentive redemption. Lightspeed GMI panellists are profiled on a wide variety of attri­butes to deliver the specific hard-to-reach demographics.

To ensure our surveys are nationally representative of internet users, Mintel sets quotas for each age group, split by gender. Specific quotas for a sample of 2,000 adults aged 16+ are shown below:

Definitions

Socio-economic group

Socio-economic groups are based on the head of household or chief income earner and are defined as follows:

Retired persons who have a company pension or private pension, or who have private means are graded on their previous occupation.

Students in higher education living at home are graded on the occupation of the head of the household. Students living away from home are graded C1 (no account is taken of casual or vacation jobs).

Qualitative Research

Toluna/ 2020 Research

Toluna in partnership with 20/20 Research provide Mintel with qualitative bulletin board 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 we can get 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, we are able to identify which factors or attributes 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.

Statistical forecasting

Statistical modelling

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 organisations such as the Office for National Statistics, HM Treasury and the Bank of England. Forecasts are modelled in the unique Mintel database, based on trade insight and expectations, combined with historic performance, taking into account a combination of economic factors, end use market activity, the regulatory environment where applicable, industry expectations and technological developments.

Qualitative insight

Crucially the vast majority of reports also draw on the Mintel quantitative and qualitative understanding and analysis of the relevant end use markets. 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.

For Europe, Mintel surveys five major European markets – France, Germany, Spain, Italy and Poland – with samples of either 1,000 or 2,000 adults aged 16+. The surveys are conducted online, via Lightspeed GMI, with ten waves of research a year.

• Our consumer research is based on a random sample of consumers from each of the five markets.
• The research is representative of the online population in each market. Mintel applies a quota-sampling approach with quotas based on age, gender and region (see more details below).
• For category-focused reports (eg chocolate confectionery), consumers will typically be asked about usage, frequency and location of purchase, consumption occasion, brand usage and a series of attitudinal statements about the category.

Sample sizes by demographic and geographies

Mintel’s consumer research is representative of the online population of each market by region. Specific quotas for a sample of 2,000 respondents are shown below:

France

Germany

Confidence levels

Statistical confidence levels of +/- 2% or 3% can be applied to the data, depending on sample size and percentage of respondents. For example, if 20% of a total sample of 1,000 adults say that they do something, you can be 95% certain that the figure for the population lies between 17% and 23%. For a sample of 3,000 adults, you can be 95% certain that the figure lies between 18% and 22%.

Consumer research is stored in a database supervised by Mintel’s statisticians. Additional analysis of information too abundant to be included in published reports can be made available.

Sample sizes by city

When the overall population of a city is large enough (> 20,000), sample size is not determined by the size of population. It is only when the population becomes quite small (eg less than 10,000) that the sample size calculation is affected.

As a result a sample size of 300 per city across all 10 cities in our survey was set. Statistically, this enables us to apply a confidence level at 95% with a margin of error of 5.66%.

Sampling methodology and sampling structure

According to government figures, there are 645 cities in China. These cities are very different in terms of size, economic development, culture, history and lifestyle. To meet Mintel’s client interests (by region, by tier), ten cities are selected in each wave of research based on their geographic coverage and level of economic development (GDP and per capita income).

The table below shows an example of cities covered:

Note 1: Tier 2, Tier 3 or lower cities in the table above are shown as examples only

Note 2: Mintel defines the tier levels of cities in China as follows:

• Tier 1: Major economic hubs
• Tier 2: Provincial capital cities and some developed non-provincial capital cities
• Tier 3 or lower: Other cities beside Tier 1 and Tier 2 cities

Within each city, our sampling structure is presented below:

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• Monthly personal income

Brazil Consumer Research

For its Brazilian Report series, Mintel uses either online research or face-to-face research to interview consumers representative of the five regions of Brazil, covering all socio-economic groups and ages from 16 to 55+.

• Our consumer research is based on a random sample of Brazilian consumers from all 5 regions (see details below).
• Mintel applies a quota-sampling approach with quotas on age, sex, region, income and education in these regions.
• Our sample data can only be considered as indicative of urban consumers rather than representative of Brazil as a whole. While it does give a proxy of the nation’s attitudes it has been designed to provide comparable, statistically robust results of the most relevant demographic and geographic segments.
• For category-focused reports (eg chocolate confectionery), consumers will typically be asked about usage, frequency and location of purchase, consumption occasion, brand usage and a series of attitudinal statements about the category.
• Lifestyle Reports cover a broader range of attitudinal and behavioural topics.

Our research partners – Lightspeed GMI

Mintel partners with Lightspeed GMI to complete online research.  Founded in 1999, and re-branded as Lightspeed GMI in September 2014 (after its acquisition by Kantar in 2011), it delivers online respondents via extensive use of fraud detection and location-verification technology at multiple points in the research cycle, from initial registration through survey fielding and incentive redemption. Lightspeed GMI panellists are profiled on a wide variety of attri­butes to deliver the specific hard-to-reach demographics.  To ensure our surveys are indicative of urban internet users, Mintel sets quotas for each age group, split by gender and weights this to represent Brazil’s major’s regions.

Our research partners – Ipsos Observer Brazil

Subsidiary of the French research company IPSOS S.A., Ipsos Observer Brazil is a leading consumer research company in Brazil. The operation in Brazil is the largest among the “survey based” companies in Brazil, has more than 850 employees, counts with ISO certification and runs more than 2 million interviews per year.

For Mintel Ipsos research, a CAPI (Computer assisted personal interview) methodology is carried out using a Palm Top device, which allows reduced fieldwork time and continuous review of the survey process.

Sampling and Quality control

In order to achieve the required quotas for age, gender and socio economic group, the Mintel-Ipsos survey combines both fixed-point interviews with door-to-door interviews across all the ten selected cities. The proportion of fixed-point and door-to-door interviews is about equal, although it may change from wave to wave in order to secure the quotas required for each survey. The average proportion of the questioners run is:

To guarantee accuracy and consistency of the data, a four stage quality control process is applied that involves the following stages:

a.       On the ground supervision of the surveys run to avoid adulteration when applied (Ipsos)

b.      Contact call of a proportion of the people sampled to confirm that the survey has actually been run (Ipsos)

c.       In-depth data analysis and socio-economics crossings of the complete database to check inconsistencies or possible typos by the London consumer research team (Mintel)

d.      Analyst check and comparison with secondary sources and international sources to ensure data is coherent for the category analysed (Mintel).

Sample sizes by demographics and geographies

Mintel runs consumer research in the 5 regions of Brazil.  Rather than mirror exactly Brazil population distribution, region sample sizes have been selected to provide comparable figures between regions.  Specific region quotas and used and weighted to represent a sample of approximately 1,500 respondents are shown below:

Mintel’s consumer research is also planned to cover a representative number of the socio-economic groups (SEGs) ABC1 and C2DE with the purpose of gathering comparable data among the different SEGs in Brazil. The classification of the socio economic groups is done by the use of the Criterio Brasil (Socio-economic group segmentation defined by ABEP, the Brazilian Association of Research Companies) based on a combination of educational as well as ownership of properties and consumer goods. The number of interviews used by different socio economic group was defined with the intention to get a representative number of responses across all SEGs:

To also receive representative and comparable age data, Mintel has defined five age segments.

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 Lightspeed GMI. The results are only available in Mintel reports. Note that Mintel’s exclusive research is conducted online in both English and French. Sampling and weighting   Online Surveys   Lightspeed GMI   Founded in 1996, Lightspeed GMI’s double opt-in online consumer panel contains approximately 187,000 Canadian consumers. Lightspeed GMI recruits its panelists through many different sources including web advertising, permission-based databases and partner-recruited panels.
 

 
Mintel also sets quotas on region and household income. Specific quotas for a sample of 2,000 adults aged 18+ are shown below:

Our Research Partners – Ipsos Observer India

Ipsos Observer is part of Ipsos India and is specialized in providing research support services across India and globally. Ipsos India has one of the best infrastructure and operations support with more than 650 employees locally.  Ipsos India runs all projects in accordance with the standards laid out in ISO 20252:2012, ensuring a consistent quality of work to the highest standards in the industry. Ipsos’ processes are annually audited by external accredited quality assessors.

Sampling and Quality control

A CAPI (Computer assisted personal interview) methodology is carried out using internet enabled Andriod Tablets. The scripting of the survey is done on Ipsos India’s iF-ield platform.  In order to achieve the required quotas for age, gender and socio economic group, interviews were conducted door-to-door across 16 selected cities in 6 local languages.

Ipsos India applies the following 4 layers of quality checks:

Sample size

Mintel runs consumer research in cities spanning metros and 3 tiers and 4 broad regions of India.  Rather than mirror exactly India’s population distribution, sample sizes have been selected to provide comparable figures between regions, tiers and age groups.  Specific quotas for the 3,000 sample are listed below:

Region	Male (N)	Female (N)	Total (N
North
18-24	75	75	150
25-34	75	75	150
35-44	75	75	150
45-54	75	75	150
55-64	75	75	150
Total	375	375	750
East
18-24	75	75	150
25-34	75	75	150
35-44	75	75	150
45-54	75	75	150
55-64	75	75	150
Total	375	375	750
South
18-24	75	75	150
25-34	75	75	150
35-44	75	75	150
45-54	75	75	150
55-64	75	75	150
Total	375	375	750
West
18-24	75	75	150
25-34	75	75	150
35-44	75	75	150
45-54	75	75	150
55-64	75	75	150
Total	375	375	750

Further Analysis