1. A case study: Market grouping on food consumption patterns 10/19/2004 Xiangming

2. Regional Purchase Patterns Variations in customer characteristics and preferences across the geographies Initial efforts to cluster consumer market areas –Based on lifestyles, leisure activities, media usage, and product purchases Food consumption patterns –Researchers provide updated patterns with new data –Mangers can use the patterns to test marketing programs, to identify areas with growth opportunities, and to track changes Tailor their products to regional tastes Provided customized promotional programs –Police makers also concerns the change of patterns

3. Seven Steps of Cluster Analysis Select objects Select variables Standardize variables Select similarity measure Select clustering method Select stopping rules Interpret, test, and replicate the results

4. Step One: Select objects for analysis Purchase scanner-based data from the ACNielsen Company –Dollar sales per capita indices for a 52-weeks (ended on June 16,2001)

5. Step Two: Select Variables

6. 62 food categories were used based on ACNielsen classification Identification of outliers –Identify three unique categories based on pattern analysis: Frozen Juices and Drinks Fresh Meat Ice---store cashiers my record them miscellaneous rather than scan the bags

7. Step Three: Standardize variables Filter out the effect of “noisy” variables –i.e, Population, average age Standardization can impact a cluster analysis with percentage variables The best approach---divided each variable by its range

8. Step Four and Five: Select similarity measure and clustering methods Similarity measure –Euclidean distance is used to measure similarity Clustering methods –A combination of Ward’s, Beta-Flexible hierarchical, and a Kmeans partitioning algorithms –Two-stage process Using hierarchical algorithms to develop starting points Kmeans algorithm will then be employed

9. Step Six and Seven: selecting stop rules and test results Stopping rules –The principle– how many clusters to use in the final solution –Pseudo-F and Pseudo-T2 statistic methods Interpretation, testing, and replicating the results

10. Results

12. Level 11 could be a good stopping point Figure 4 –Southwest cluster –Northeast cluster Figure 5 –Texas and Florida markets were slplit into three different clusters –Miami consumption patterns were similar to those in New York and Philadelphia