Clustering stores

Stores can be clustered on the basis of various store performance metrics and differential treatment strategies can be adopted. This could be with respect to the

1) Store format to be adopted for each store cluster

2) Mix of merchandize to be stocked for each store cluster

3) Pricing strategy for each store cluster

4) Promotion mix strategy for each store cluster. Coupons vs Temporary price reduction vs Gift off

5) Instore experience strategy . Lighting vs staff per square feet vs Displays vs sampling

6) Shelf placement strategy

Stores can be clustered on a variety of attributes like

1) Price sensitivity to strategic products

2) Footfall in store

3) Spend dispersion observed in basket across categories like grocery, hair care, electronics, juices etc

4) % revenue accrued from loyalty card holders vs anonymous buyers

How can statistics be used to optimize store operations ? 12 scenarios

How can one use sophisticated mathematics / statistical techniques to get competitive differentiation while running store optimally. Here are 12 areas where statistics has been found to add dispropotionate value to the store related decision making process and thereby bringing game changing opportunities for the organisation
1. Promotion uplift modeling using regression

2 Sales forecasting using multivariate analysis, holt winters model,ARIMA, exponential smoothing etc

3. Store segmentation using K means clustering

4. Life time value modeling for loyalty card holders using Survival analysis, regression etc

5. Store experience sentiment analysis using unstructured text data mining

6. Survey analysis using discrete choice modeling, factor analysis etc

7. Pricing analysis using constraint based optimisation techniques

8. Understanding drivers of store performance using structural equations modeling

9. Shelf visibility analysis using A/B testing, design of experiments and multivariate analysis, chi square hypothesis testing

10.Cross sell recommendation engines using collaborative filtering and MB analysis

11. Shopper behavior based segmentation using K means clustering

12. New product launch analysis using engagement segmentor

Each of the above techniques will be ellaborated one by one in a separate blog