Japan

Consumer Research Methodology

For its Japan Report series, Mintel uses an online research approach to interview consumers covering all ages from 18+.  Respondents are interviewed fromin eight regions which is then grouped into six broad regional groups for reporting.

  • Mintel applies a quota-sampling approach with quotas on age, sex and broad region.
  • Our sample data can only be considered representative of Japan in terms of age and gender only. 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.  

 

Our research partners – Rakuten Insight

Mintel partners with Rakuten Insight to complete online research in Japan.  Established in 1997, it’s since grown into a pioneer of Asian online sample providers, recruiting respondents from their member database and networks, serving both domestic Japanese and international clients. 

 

Sample sizes by demographics and geographies

Mintel runs consumer research in the 6 regions of Japan.  Specific region quotas and used to represent a sample of approximately 2,000 respondents are shown below:

 

%

N

Male

18-24

5%

100

 

25-29

5%

100

 

30-39

10%

200

 

40-49

10%

200

 

50-59

10%

200

 

60-64

5%

100

 

65+

5%

100

: : : :
: : : :

Region

%

N

District Included

1

11.0

220

Hokkaido, Hokkaido, Tohoku, Tohoku

2

34.4

688

Kanto, Kanto, 

3

18.2

364

Chubu, Hokuriku and Tokai, 

4

16.3

326

Kinki (Kansai), Kinki, 

5

8.8

176

Chugoku, Chugoku, Shikoku, Shikoku

6

11.3

226

Kyushu, Kyushu, Ryukyu, Okinawa

Total

100

2000

 

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.