CRISA is an Asian market research agency that specializes in tracking consumer purchase behavior in consumer goods (both durable and nondurable).
In one major research project, CRISA tracks numerous consumer product categories (e.g., “detergents”) and, within each category, perhaps dozens of brands.
To track purchase behavior, CRISA constituted household panels in over 100 cities and towns in India, covering most of the Indian urban market. The households were carefully selected using stratified sampling to ensure a representative sample; a subset of 600 records is analyzed here. The strata were defined on the basis of socioeconomic status and the market (a collection of cities).
CRISA has both transaction data (each row is a transaction) and household data (each row is a household), and for the household data, it maintains the following information:
• Demographics of the households (updated annually)
• Possession of durable goods (car, washing machine, etc., updated annually;
an “affluence index” is computed from this information)
• Purchase data of product categories and brands (updated monthly)
CRISA has two categories of clients: (1) advertising agencies that subscribe to the database services, obtain updated data every month, and use the data to advise their clients on advertising and promotion strategies and (2) consumer goods manufacturers, which monitor their market share using the CRISA database.
Key Problems CRISA has traditionally segmented markets on the basis of purchaser demographics. They would now like to segment the market based on two key sets of attributes more directly related to the purchase process and to brand loyalty:
1. Purchase behavior (volume, frequency, susceptibility to discounts, and brand loyalty)
2. Basis of purchase (promotion, price)
Doing so would allow CRISA to gain information about what demographic attributes are associated with different purchase behaviors and degrees of brand loyalty and thus deploy promotion budgets more effectively. More effective market segmentation would enable CRISA’s clients (in this case, a firm called IMRB) to design more cost-effective promotions targeted at appropriate segments. Thus, multiple promotions could be launched, each targeted at different market segments at different times of the year. This would result in a more cost-effective allocation of the promotion budget to different market segments. It would also enable IMRB to design more effective customer reward systems and thereby increase brand loyalty. 

1. Use k-means clustering to identify clusters of households based on:

a. The attributes that describe purchase behavior (including brand loyalty)

b. The attributes that describe the basis for purchase

c. The attributes that describe both purchase behavior and basis of purchase Note 1: How should k be chosen? Think about how the clusters would be used. It is likely that the marketing efforts would support two to five different promotional approaches.
Note 2: How should the percentages of total purchases accounted for by various brands be treated? Isn’t a customer who buys all brand A just as loyal as a customer who buys all brand B? What will be the effect on any distance measure of using the brand share attributes as is? Consider using a single derived attribute.
2. Select what you think is the best segmentation, and comment on the characteristics (demographic, brand loyalty, and basis for purchase) of these clusters. (This information would be used to guide the development of advertising and promotional campaigns.)
3. Develop a model that classifies the data into these segments. Since this information would most likely be used in targeting direct-mail promotions, it would be useful to selecta market segment that would be defined as a success in the classification model.