Brazil (English)

Brazil Consumer Research

 For its Brazilian Report series, Mintel uses online research research to interview consumers aged 16+.

Our research partner – Lightspeed

Mintel partners with Lightspeed to complete online research in Brazil.  Founded in 1996, 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 panelists are profiled on a wide variety of attri­butes to deliver on specific hard-to-reach demographics.

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



Mintel applies a quota-sampling approach with quotas on age, sex, region, and social class to be representative of the Brazil internet population aged 16+.

Specific quotas for a sample of 1,500 adults aged 16+ are show below. 

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 groups by gender                N=1,500            %
          Min        Max  
Male, 16-24 172 174 11.60%
Male, 25-34 174 176 11.73%
Male, 35-44 158 160 10.68%
Male, 45-54 109 111 7.37%
Male, 55+ 102 104 6.93%
Female, 16-24 150 152 10.16%
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Further Analysis

Mintel employs numerous quantitative data analysis techniques to enhance the value of our consumer research. The techniques used vary from 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 behavior 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 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 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 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. 

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.

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 such as the IBGE (Brazilian Institute of Geography and Statistics) and the EIU (Economist Intelligence Unit).

The model searches for relationships between actual market sizes and a selection of relevant and significant macroeconomic and demographic determinants (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 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.

[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 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 ie 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.

[graphic: image 2]

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.