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 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
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.
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| Next Best Offer Solution Framework |
This is a continous cycle and the role of Statisticans, Business Leaders play an important role
Customer Analytics in Telecom is a dynamic one. Thanks for sharing a good case study
ReplyDeleteAssociation rules used in Cross-Sell can also be used for Fraud Detection, Promotion Affinity etc
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