A case study: Market grouping on food consumption patterns 10/19/2004 Xiangming.
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Transcript of A case study: Market grouping on food consumption patterns 10/19/2004 Xiangming.
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A case study: Market grouping on food consumption patterns
10/19/2004
Xiangming
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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
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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
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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)
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Step Two: Select Variables
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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
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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
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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
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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
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Results
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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
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