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Cluster analysis
- 2. NOW FOCUS ON BUSINESS KEY PARAMETERS Price paid Discount Sales Unit sold ANALYSIS SOFTWARE SAS
- 3. DESCRIBTION OF THE PROJECT: The project aims to construct a cluster analysis model on the basis of a given set of variables. Cluster Analysis means a techniques used to classify cases into groups that areRelatively Homogeneous within themselves & Heterogeneous between each other. Heterogeneity & Homogeneity are measured on the basis of given set of variables.
- 4. DESCRIPTION OF VARIABLES: Variable Label Cust_id med_Actual_price_12 AVG_Actual_price_12 TOTAL_DAILY_ORDER_NET_AMT_12 AVG_DAILY_ORDER_NET_AMT_12 TOTAL_LINE_ITEM_QTY_12 AVG_LINE_ITEM_QTY_12 TOTAL_DOLLAR_DISCOUNT_12 AVG_DOLLAR_DISCOUNT_12 SIZE_NET_SALES SIZE_QTY SIZE_DOLLAR_DISC RATIO_DISC_SALES med_DAILY_ORDER_NET_AMT_12 med_LINE_ITEM_QTY_12 med_DOLLAR_DISCOUNT_12 MONTH_SINCE_LAST_TRANSACTION sum_wtd_disc_12 avg_wtd_disc_per count sum_value_12 Group Customers DAILY_ORDER_NET_AMT DAILY_ORDER_NET_AMT LINE_ITEM_QTY LINE_ITEM_QTY DAILY_ORDER_NET_AMT LINE_ITEM_QTY
- 5. CLUSTER DEVELOPMENT: Outlier treatment. Missing value treatment. Multicollinearity treatment using Factor Analysis. Variables standardization. Cluster solution development & validation.
- 6. By Factor Analysis we consider those factors which explain 95% of total variation with data. Med_Actual_price_12 AVG_Actual_price_12 TOTAL_LINE_ITEM_QTY_12 AVG_LINE_ITEM_QTY_12 AVG_DOLLAR_DISCOUNT_12 SIZE_QTY SIZE_DOLLAR_DISC RATIO_DISC_SALES Med_LINE_ITEM_QTY_12 Med_DOLLAR_DISCOUNT_12 MONTH_SINCE_LAST_TRANSACTION Count Sum_value_12
- 7. For these selected factors we Standardized the variables to make them unit free since the units of measurement of different variables are different.
- 8. Next step is cluster building using “K-means clustering” which satisfies the following criterion: Individual R-Squared > 0.25 Overall R-Squared > 0.5 Approximate Expected Overall R-Squared > 0.3 (This is R-Squared if there was no Multicollineraity) The difference Between Overall R-Squared and Approximate Expected Overall R-Squared should not be greater than 0.2 RMS Standard Deviation < 1.4 Distance Between Cluster Centroids > 1.4
- 9. Cluster Summary Cluster Frequency RMS Std Deviation Maximum Distance from Seed Radius Nearest Cluster Distance Between Cluster Centroids Exceeded to Observation 1 76 0.4902 2.0023 4 1.6051 2 83 0.7721 3.1463 3 2.2462 3 78 0.574 1.7854 4 2.0749 4 116 0.3785 1.2812 1 1.6051 5 245 0.4705 2.1401 4 2.0135 6 109 0.5133 2.2405 1 1.7234
- 10. Statistics for Variables Variable Total STD Within STD R-Square RSQ/(1-RSQ) med_Actual_price_12 1 0.55456 0.694646 2.274882 AVG_DOLLAR_DISCO UNT_12 1 0.4812 0.770086 3.349459 med_DOLLAR_DISCO UNT_12 1 0.45842 0.791343 3.792553 MONTH_SINCE_LAST _TRANSACTION 1 0.58483 0.660395 1.944595 OVER-ALL 1 0.52232 0.729117 2.691636 Approximate Expected Over-All RSquared = 0.59727
- 13. CLUSTER VALIDATION NO OF CLUSTER 1 2 CLUSTER NAME EXTREMELY VALUE SEEKER EXTREMELY DISCOUNT MINDED DISCOUNT ORIENTED BUT ALSO VALUE SEEKER 3 FREQUENCY 76 83 78 4 VALUE SEEKER BUT DON'T LET DISCOUNT 116 5 MIXED ORIENTATION 245 6 VALUE SEEKER 109
- 14. SEGMENTATION EXTREMELY VALUE SEEKER 11% EXTREMELY DISCOUNT MINDED 12% VALUE SEEKER 15% MIXED ORIANTATION 35% VALUE SEEKER BUT DON'T LET DISCOUNT 16% DISCOUNT ORIENTED BUT ALSO VALUE SEEKER 11%
- 18. VALUE SEEKER BUT DON'T LET DISCOUNT 1% 17% 15% med_Actual_price_12 med_DOLLAR_DISCOUNT_12 67% AVG_DOLLAR_DISCOUNT_12 MONTH_SINCE_LAST_TRANSACTION
- 19. DISCOUNT ORIENTED BUT ALSO VALUE SEEKER 0% 36% 36% med_Actual_price_12 med_DOLLAR_DISCOUNT_12 AVG_DOLLAR_DISCOUNT_12 MONTH_SINCE_LAST_TRANSACTION 28%
- 21. Based on our analysis we can conclude that , we get six different cluster ‘s of customer orientation. In first cluster customer extremely oriented towards value for money. In second cluster we saw just extreme opposite pattern of first cluster , here customer preferred discount more. In this cluster customer’s are drawn towards discount but they also look for value.
- 22. In the forth cluster customer are value seeker but they also look for discount , here we also found out the significance of another variable(Month since last transaction), which was insignificant in other five cluster’s. Although this variable account for only one percent in this cluster . We can’t ignore single customer. In this cluster customer preference ix mixed between three variable. Last but not the least this cluster full with purely value seeker customer.
- 23. Based on so far cluster profiling this software company can make different strategies for different customer group in different cluster. Thank You