Thailand

Consumer Research Methodology

Mintel uses an online research approach to interview consumers covering age groups from 18-45+. Respondents are interviewed in regions and/or metro cities to represent the population distribution across each market for reporting

Mintel applies a quota-sampling approach with quotas on age, gender and broad region or metro city. Our sample data is not nationally representative. Our online, quota sampling approach provides comparable, statistically robust data and allows analysis of key demographic and geographic groups by market.   

Our research partners

Dynata

Mintel partners with Dynata (formerly Research Now SSI) to complete online research in India, Australia, Indonesia, the Republic of Korea, Thailand and New Zealand.  As a leading provider of first party data, Dynata serves nearly 6,000 market research agencies, media and advertising agencies, consulting and investment firms, and healthcare and corporate customers in North America, South America, Europe, and Asia-Pacific.

Sample sizes by demographics and geographies

Mintel applies a quota-sampling approach with broad quotas on age, gender and region in all markets at minimum. Below outlines the broad quotas employed per market.                                                                                            

Quotas:                                                                                               

The quotas on age and gender are selected in a consistent way per market to allow ease of comparison and analysis across a variety of key target groups.                  

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 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 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.