Cluster Analysis
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The document explores sales data across different store clusters, identifying fresh foods as the highest sales category and health and beauty as the lowest. Using the k-means clustering technique, four initial clusters were formed, with Cluster 3 identified as an outlier due to significantly higher average sales. Recommendations include leveraging outdoor spaces for selling fresh food items based on unique store characteristics and performance.
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Cluster Analysis
- 2. Data Exploration ? The percentage sales is the highest for the category 1 ,Fresh Foods section(38%) ? The percentage sales is the lowest for the category 3 , Health and Beauty section at 13% ? Average Sale is around 210 Rs/sqft.
- 3. Clustering Methodology used ? K – Means technique used to cluster the different stores. ? Worked to understand the techniques with different number of clusters : 4 , 5 and 6 at the initial clustering level. ? The 4 initial cluster works the best with the different clusters having a z score with a good difference from that of the population. The cluster strengths are greater too .So this looks to be the optimal number of cluster. ? One of the clusters is then again processed to get 3 more clusters .
- 4. Cluster Profiling ? Cluster 1 ? No of stores in the cluster : 91 ? Cluster Strength : 4.71 ? The significant difference between the cluster and the population is the avg sale amount.It is around 291 Rs/sqft compared to 210Rs/sqft for the population. ? Frozen food higher than in the population.(28% > 25%) ? Tobacco sales are less compared to the population. (20% < 23%)
- 5. Cluster Profiling ? Cluster 2 ? No of stores in the cluster : 151 , Cluster Strength : 3.10 ? The biggest differentiator here is the Tobacco and Alcohol sections.(27.5% > 23% in the population) ? Average sale is significantly lower than that of the population(163 Rs/sqft << 210 Rs/sqft) ? The fresh food sections sales are also very low compared to the overall population.(32% << 38%) ? Health and Beauty sales is slightly better than the population(16.2% > 13.7%)
- 6. Cluster Profiling ? Cluster 3 ? No of stores in the cluster : 3 , Cluster Strength : 8.33 ? Very high difference between the average sales in these stores(471Rs/sqft > 210Rs/sqft) ? Tobacco and Alcohol forms considerably low percentage if compared to the population.(18% < 23%) ? Fresh Foods among these stores has a higher share of the sales compared to that in the overall population. ? Frozen food share also is a little greater than in the population.(26.4% > 24.9%) ? Looks to be an outlier group with very less no of stores and significantly different characteristics from the population.
- 7. Cluster Profiling ? Cluster 4 ? No of stores in the cluster : 270 .A much higher than expected no of stores(100-140) in the cluster . ? Cluster Strength : 3.12 ? Not very significant changes from the characteristics of the whole population. ? The highest differentiator being the Fresh Food Section ( 41.4% > 38.4%) ? The Tobacco and Alcohol section sales accounts a little less than the average population(21.4% < 22.8%) ? Another iteration on the stores of this clusters is done to get more details.
- 8. Cluster Profiling ? Cluster 4_1 ? No of stores in the cluster : 101 , Cluster Strength : 3.26 ? Significant difference in the Fresh Food sales in this cluster compared to the population.(45% > 38%) ? Average sales is around 184 Rs/sqft compared to the 210 Rs/sqft for the population. ? Tobacco & Alcohol , Health & Beauty sections sales are a tad lower to the overall population.
- 9. Cluster Profiling ? Cluster 4_2 ? No of stores in the cluster : 85 , Cluster Strength : 3.42 ? It has a very similar characteristics for all the store sections and average sales when compared to the overall population.
- 10. Cluster Profiling ? Cluster 4_3 ? No of stores in the cluster : 84 , Cluster Strength : 3.55 ? Frozen Food section sales are a little lower than the overall population(20% < 22%) ? Sales/sqft a little higher than the population for the stores in this cluster (233 Rs/sqft > 210 Rs/sqft)
- 11. Recommendations ? We find that the cluster 3 with the 3 stores is an outlier .On further check(as mentioned in the class videos) , its found to be the open area outside the store to sell the fresh foods that is not accounted for. ? Hence , the stores can maybe try to sell the fresh food items in the extra open area outside the stores. ? We would need more data to analyze why certain stores and sections have greater sales in different clusters.