Customer Attrition Modeling (Analytics)

Churn or Attrition Modeling

Customer is the key for any business. As Don Peppers once said - the only value that a company adds during the lifetime is the Customer.

Losing customer is losing business. But, how to avoid them moving away?

How to measure Customer Attrition

How to know if the customer is leaving . Probably if he/she is not shopping / visiting / interacting / subscribing like the past, it is an indication that he/she might leave.

Good datamining should measure the customer attrition easily. Predicitve models can be built on data for Customer attrition.

The key data points to be considered for Attrition modeling are the Recency, Latency, Average Spend per Visit, Average Time spent on Website, Duration of Visit (Website) etc

The predictive model then attaches a probabily score to each customer. The least churner to the most ..

How to retain the customer?

Starbucks has its own Loyalty program -My Starbucks Rewards™ . One in four customers use the Loyalty card, which provides a wealth of data for Starbucks analyitcs team. The customers are then analyzed and segmented. The customers who are more Loyal are ignored! Yes, the ones who are about to Churn are Rewarded with appropriate offers. The offers are based on the past purchase history!

How to Deliver Next Best Action (NBAs) in Real-Time

How to create Location-based Real-Time Next Best Offers (NBOs)


NBAs are supposed to understand the customer's need and fulfil them even before he/she wants to act on it.

The main purpose of the NBA/NBO is to happen real-time. With the advent of smartphones delivering the Action in appropriate location and time is now easy.

Any Long distance passenger have to wait for few hours for the connecting flight; The Airline knows the customer’s demographic info, location, time (both are very much accurate) - a good amount of info for Analytics

The data of all passengers are fed to Analytical system and their past travel history, lounge purchases, social habits and syndicate data is crunched to figure out the need for each passenger. The needs are then matched with the Offerings of the Airline's partners (Hotels, Boutiques, Spas) in that particular airport and appropriate recommendations are created

Real-Time Next Best Offer (NBO) Flow

Timely message / offer is sent to passenger’s mobile phone (e.g., offer on antique jewelry in Airport boutique ) based on the time/location and her Inflight purchase/interaction behaviour. The last part - knowing his/her immediate purchase/interaction is more important. This is fed into the Analytical system to validate against the recommendaion that are waiting to be delivered - the recommendations are refreshed and delivered. This is what makes the 'Offer' - Real-time (Right Product/Service)

The above example uses Mobile Phone as the Delivery Channel. Please refer What are the key channels to be considered for Promotions for other Channels.


The omni-channel world requires product information to be managed consistently. The main focus areas are
• Provide consistent data across all channels and update the information real-time
• Offer product information at the right time and place;
• Compile a complete data set and deliver rich, unique data;
• Optimum utilization of social media information