Store segmentation using SAS clustering Baofu Ma Merchandising AUTOZONE ANALYST,MERCH RESEARCH.
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Store segmentation using SAS clustering Baofu Ma Merchandising AUTOZONE ANALYST,MERCH RESEARCH
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Transcript of Store segmentation using SAS clustering Baofu Ma Merchandising AUTOZONE ANALYST,MERCH RESEARCH.
Store segmentation using SAS clustering
Baofu MaMerchandisingAUTOZONEANALYST,MERCH RESEARCH
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Introduction Motivation:
Need to create similar business model for stores with either similar product sales or customer GBB brand preference.
And explain these clusters in terms of demographic variables.
Challenges: Business rule requires that the store cluster size has
to be greater than certain number. Enforce a minimum cluster size with proc cluster.
Explore the relationship between the clusters and demographic variables.
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Overview of hierarchical clustering
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Each observation begins in a cluster by itself. The two closest clusters are merged to form a new cluster.
Using Proc cluster to get the tree.
Using Proc tree to get the desired cluster.
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Solution
Get the history of the clustering process using ODS.
ods output ClusterHistory=history;
proc cluster data=indatatemp METHOD=ward outtree=Tree;
Search clusters which satisfy the minimum size criteria from top to bottom.
Number FreqOfOf New
Obs Clusters Idj1 Idj2 Cluster
1 1 CL7 CL2 40022 2 CL6 CL3 30843 3 CL5 CL4 26084 4 CL11 CL9 8525 5 CL16 CL8 17566 6 CL14 CL12 4767 7 CL10 CL13 9188 8 CL19 CL18 10979 9 CL15 CL21 475
10 10 CL20 CL38 42111 11 CL17 CL36 37712 12 CL28 CL27 15913 13 CL32 CL25 49714 14 CL23 CL33 31715 15 CL34 CL35 28016 16 CL31 CL26 65917 17 CL41 CL30 33718 18 CL39 CL40 50219 19 CL29 CL46 59520 20 CL22 CL50 243. . . . .. . . . .. . . . .
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Example
CLUSTER 1 2 3 4 5 6 7 8
pct _Hi gh_Mi l eage
- 4
- 3
- 2
- 1
0
1
2
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pct _Bl ends
- 3 - 2 - 1 0 1 2 3 4 5 6
CLUSTER str_ctstore count1 6592 3173 10974 3775 4976 1597 4218 475
Classify autozone stores based on market share of 2 oil brands, high mileage and blends.
Business rule requires minimum cluster size is 300.
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Example
node2 1 2 3 4 5 6 7 8
pct _Hi gh_Mi l eage
- 4
- 3
- 2
- 1
0
1
2
3
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pct _Bl ends
- 3 - 2 - 1 0 1 2 3 4 5 6
node2 1 2 3 4 5 6 7 8
pct _Hi gh_Mi l eage
- 4
- 3
- 2
- 1
0
1
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pct _Bl ends
- 3 - 2 - 1 0 1 2 3 4 5 6
Even borders. 1. Find centers of each cluster.
2. Calculate distance between store and each cluster center.
3. Reassign store to the closest cluster .
CLUSTER str_ctstore count1 3902 4273 3744 3645 5246 5467 6788 699
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ExampleRelationship between clusters and demographic variables.Blue-positive Orange- negative
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Thank you!
