Saturday, 22 March 2014

How to Identify Right Shopper for Promotions / How to Categorize Shoppers based on Loyalty

Identify Profitable Shoppers using Statistical Modeling

Not all shoppers are profitable - the statement sounds bit confusing. But a careful analysis shows that there are some who buy only during Mark Downs, select low margin products in discounts etc. In the previous posts What are the steps in NBO (Next Best Offer) to give Personalized Promotion and Find Right Channel we get an idea of how to select a right product for promotion and what would be the appropriate time for sending the promotion/offer

Well before attaching an offer to the customer, one needs to select if the customer is profitable for the loyalty program

There are many methods to check if the customer is profitable. This is is based on the past transaction history and applying methods / models on them

CLV and NPV are some techniques to use in the current scenario. Segmentation of customers based on the above would provide a good insights

This is a challenge for many US Retailers whose customers enroll in multiple loyalty programs (Average US household enrolls to 23 loyalty programs) and hip hop to other retailer based on Sale/Discount. Price plays an important role in this

According to a study by +McKinsey on Marketing & Sales targeting High Value customers is one of the way to improve the Basket Size (both items and Value). One of every retailer's goal should be to Increase the Basket Size by appropriate promotions

Saturday, 25 January 2014

Sports Analytics - Use of Big Data Analytics in Sports

Sports Analytics - The next use case for Big Data Analytics

Sportsmen and Sportswoman are not data mines, their actions are supposed to be based on fitness, confidence and skills and are supposed to be subjective.

Not anymore. Though their height, weight and the fitness were measured earlier .. the new Big Data analytics stores every

SaberMetrics - the Bellwether 

Billy Beane - Oakland Athletics general manager and baseball guru - used metrics to choose baseball players. He used an evidence-based analytics approach called sabermetrics to pick certain “diamond in the rough” players for his team. These players were evaluated by situational and predictive metrics, rather than common stats like home runs and batting average, representing a significant shift in the way the baseball industry established their teams.

THere are many sports and games wHere analytiCs an Help, for example, Tennis

Understanding tHe player's potential would Help the manager fine tune the player
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