Using T test to understand shelf placement decisions

Lets consider a simple scenario.

Say ABC retail is setting up a new store in Frazer town area

Pre store launch catchment area/competitive store analysis helped in deciding store format and product mix to display

The store in Frazer town, the following categories are relevant for the consumer ( Mangloreans, Muslims,Goans )
Non veg food items
Hair care
Basmati Rice
Atta
Kerala Parottas

The store manager of Frazer town branch wants to optimize the 12000 sq ft of shelf space he has

He wants to know which “Hot” shelves and he has a few hypothesis based on experience which he wants to test

A/B Multivariate testing can come handy to discern which shelf combination maximises revenue and minimize inventory carrying cost Or if the product is moving slow ( it becomes a slow moving item and a promotion /advertising may be required to stimulate sales )

HOW DOES A STORE MANAGER DECIDE SHELF PLACEMENTS TO MAXIMIZE STORE REVENUE AND MINIMIZE SHELF SPACE INVENTORY ?

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