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

10 behavorial variables to segment Retail shopper

Here are some possible variables to segment customers using their behavorial profile

1) Customer Purchase Recency
The number of days which have elapsed since the customer last purchased from a store
Example : less than 30 days, less than 3 months, less than 6 months etc

2)Tenure
The number of months the customer has been a member of the loyalty card program
Some targeted campaigns could have increased loyalty subscriptions during certain periods

3)Average basket value
Average amount spent by the shopper during each visit to the store
Example : less than $ 50, 50à125 $, greater than 125 $

4)Average basket size
The number of items the shopper purchases during each visit

5)Spend dispersion
The % of spend dispersed across various categories of products like music, books, stationery items, perfumes etc
Example : 12 % on stationery, 35 % on books, 43 % on perfumes

6)Customer Purchase frequency
The number of times the customer purchases from the store in a year
Example : 10 times in a year

7)Overall spend dispersion profile
The % of deviation between spend of this customer and an average store shopper to benchmark the intensity of shopping on various categories relative to an average buyer
Example: An average Joe would spend lets say 20 % on stationery, 50 % on books and 30 % on perfumes. If David’s spend dispersion profile is 60 % on stationery, 35 % on books and 5 % on perfumes, his spend bias helps us understand his profile better relative to an average Joe.

8)Demographic spend dispersion
The % of deviation between spend of this customer and the demographic segment to which the customer belongs to

9)Range of products purchased
Out of the overall number of categories present in the store, what % of the categories has the customer purchased

10)Range of channels used
A product can be sold thru multiple channels – company owned store, franchisee store, web , phone/contact center

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