Decision Science in Action

It is desirable for a retailer to have information of the life cycle stage of  a customer. This helps greatly to a retailer in offer optimizations. Targeting right customer with rights offers is greatest challenge for a retailer. They run hundreds of promotions and optimizing it saves huge cost and helps in retaining customers.  In the era of super markets, large retailers issue loyalty cards and maintain customer purchase data. But most of the demographic data is not available with them as customers normally do not fill these fields and leave them blank. Although customer demographic data are not available using purchase data certain life stages of customer can be predicted using rule base techniques or predictive modeling. Life stages bring changes in purchasing pattern. 1.  A single male will mostly buy his needs and daily eatables.  Eating habits gets changed if he is married etc.  A man can occasionally buy woman's product but it will be regular if ...

The objective of this article is to propose a design of a personalized recommendations application for restaurants owners which can be used by the customers of the given credit/debit card issuer. The developed recommendation application can be installed as mobile application in the mobile phone of the customers. The recommendation engine will use machine learning algorithms and predictive analytics techniques to derive information from  the credit transaction data, mobile location data, social media data (Facebook, Twitter), public data (Yelp.com/Bundle.com etc.), mobile browser data and data of the restaurants*. The recommendation engine will have two modules. First module will use the clustering algorithm to derive actionable information from credit card data and restaurants data. It will also use the data mining techniques to integrate the useful information available in the web space with restaurant data (explained later). This module will execute in server of the bank...

Social Network Analysis is a well-researched area of computer Science and Mathematics. Any structure which can be represented in the form of edges and nodes is a network. Nodes are connected to each other by edges (sometimes called links). When we study any network, our objective is to evaluate the importance of each node and link. Network Analysis can be used in the areas of genetic engineering, social engineering, marketing, fraud detection, crime detection and economic research etc. Network analysis has been widely used in genetic engineering. Recently the application of network analysis has gained momentum in the field of business marketing and analytical sciences.  In this blog, I will briefly discuss the verticals of businesses in which network analysis can be used to gain insights of customers.  I will exclude the discussion on the application of network analysis in any of the biological fields as I do not have concrete knowledge of this area.    1. Tel...