A case study: Market grouping on food consumption patterns

10/19/2004

Xiangming

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

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

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)

Step Two: Select Variables

Step Two: Select Variables

• 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

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

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

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

Results

Results

• 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