Saturday, 30 March 2013

Case Study: Customer Analytics for Telecom Operator to Cross-Sell and Up-Sell

How to find products/services to Cross-Sell / Up-Sell using Customer analytics / NBO

The Telecom operator has a good customer base and wants to drive more sales using existing customers. The operator decided to craft a NBO strategy for increasing sales from existing customers (this is not just retaining the customer - by reducing Churn)

As with any NBO - the first step is to determine the Objective

Step 1: Define The Objective

The objective here is - Increase sales from existing customer through cross-sell or up-sell

A customer with two or more services is four times less likely to Churn than a customer with a singl service - this means that cross-sell not only increases the incremental sales but also increases Loyalty. The strategic objectives like these should be driving the NBO objective

With the goal being defined very clearly, the next step is to collect required data for the NBO

Step 2: Collect Data

The Telecom operator has data on the services/products that are used by each customer, the plan, the usage history, subscription data,  the CDRs , Customer Demographics data and Internet usage data (for the subscribers) . This data holds wealth of information and needs to be mined.

The entry channel (the channel through which the customer was acquired) would be a vital information.

The Location data also plays an important role in understanding the customer, Location history would explain if the customer is a frequent traveller, when and where he/she makes uses the service.

If the internal data collection is done, one can look for Syndicate data or External Data (Payment Card data)

Step 3: Analyze, Model  and Execute

Once the Data is available, it needs to be cleaned and normalized. Then the customers are segmented based on their purchase behavior, subscription plans etc. There are also industry specific segmentation standards like Prizm. This should be a good input for rolling up data

Understanding the customer is a first stage in NBO. The customer's behavioral attributes are
analyzed using statistical analysis (clustering, link analysis, decision trees), predictive modelling etc.

It's just not about understanding the customer, the Telecom company needs to analyze the product/service that can be offered to a particular segment. Understanding the Service/Product Offering is the next stage .

For example,
  • Wireless devices
  • WiFi Services
  • VOIP Call services
  • Local call services
  • Broadband Direct connection
  • loud hosting

For example, a propensity model can be created for knowing the probability of the customer for purchasing/trying a particular product / service. For example, a Broadband plan for a customer who is not using the services from the operator (and using the one from a Competitor) with competitive pricing / value add would entice the customer to switch-over.

The propensity of a customer who is currently using the service is set to Zero. This will avoid irittating the customer with the product/service she already uses.

The propensity of each service is ranked along with the best suited time of  sending the offer. The Offer is then sent to the customer through his prefered Channel and at the appropriate context.

Context is always the key - if someone has a 4G-enabled phone, he has a fair chance of upgrading to a 4G network. Knowing the user's device information is helpful here.

Tools like SAS and SPSS can help in achieving this faster. There are Open Source Software - like R, which is catching up fast

Step 4: Measure and Recalibrate

Every offer that is sent is an Test Offer. An offer can be accepted/ignored/rejected. The best NBO strategy is to measure the response of the offer and take the learning back to Analysis stage.

The offer response data is fed back to the database. If there is any need for further data points, it needs to be collected. The model is then refreshed based on the new analysis and deployed.

Next Best Offer Solution Framework

This is a continous cycle and the role of Statisticans, Business Leaders play an important role


  1. Customer Analytics in Telecom is a dynamic one. Thanks for sharing a good case study

  2. Association rules used in Cross-Sell can also be used for Fraud Detection, Promotion Affinity etc


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