Co-relating shopper sentiments to footfall and basket size

Statistics collected by Media agencies suggest that Teenagers are spending more time on the Web than watching TV. This is a huge inflection point as web has replaced TV as a more engaging channel. And within Online channel , Blogging and Online videos ( youtube etc ) seem to be most engaging activity. What that means is that it is important for retailers to track if shoppers express sentiment about the instore experience or product attributes online ? There are 2 kinds of scenarios which can be envisioned here.

Scenario-1 : When shoppers are expressing about their instore experience on http://www.yelp.com/ or http://www.mouthshut.com/ or http://www.eopinions.com/. But the sentiment volume has not reached a threshold where it has started influencing footfall, basket size and revenue per shopper.

Scenario-2 : The volume of sentiment expressed on online platform has reached a critical stage where more shoppers are coming to the store or the number of shoppers / basket size has decreased.

What this means is to that the retailer needs to have a framework which can keep track of the buzz velocity online and track in real time the effect of buzz velocity on instore footfall and basket size.

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