We've decided to start this week off by sharing a case study. We're pretty proud of this one--RRW helped a leading telecommunications client retain more customers and sell those customers more services.
Hopefully, this success story will spark some good direct marketing ideas for you. And, it might give you a feeling for what RRW is all about :)
Our client, one of the Regional Bell Operating Companies (RBOCs) was faced with the following two marketing challenges:
- They needed to increase cross-sell and up-sell revenues from their large customer portfolio. Their strategy was to develop a “best product path” approach. They knew that if they could predict what was the next logical (and the most profitable) product to offer to each customer, they could improve cross-sell and up-sell results. Just a small increase in performance meant millions of dollars to this communications giant.
- The Wireless division was faced with increased churn, especially among their most profitable customer segments. Competition was fierce as wireless carriers fought to grow their market share. They knew that they needed to improve retention efforts and – quickly – implement customer “save” programs.
RRW Consulting recommended a data-centric, analytical approach to help solve both of these challenges. The idea was to effectively mine existing customer data assets, and augment these efforts through the use of external demographic and behavioral data. Accurate and relevant data, combined with effective predictive modeling and customer profiling allowed the communications company to understand:
- What were the common traits of existing customers, considered “gold” customers—those who had at least 3 products (i.e.: caller ID, call waiting and voice mail)?
- What were the migration habits of customers who had become “gold”? How did they migrate from basic local service to become a gold customer? Which products did they buy first, second and third, or did they sign up for all services when they were acquired?
Answers to the above helped us to understand the product migration and allowed us to build the “best product path” approach.
Similarly for the Wireless Division, we examined attributes of profitable customers who had recently defected. We searched for reasons why an individual customer churned and developed a series of predictive models to predict who among active customers was likely to defect, and for what reason.
We then periodically scored the active customer portfolio with these churn models and fed the communications firm files of customers likely to defect. Using this data, the communications firm was able to devise and implement appropriate save strategies that allowed them to communicate with their customers with the right message and offer.
Following is a sampling of the data we analyzed to solve these challenges:
Data included: Products held, Price Plan, Payment behavior, Activation date, Calls to Customer Service and Handset type.
Data indluded: Individual-level data such as age and gender, Telecommunications-specific segmentation tools, Business vs. Personal use of service, Likely switching behavior and Neighborhood-level data (credit/census)
- Retention of wireless customers was improved by 20%.
- Cross-sell and up-sell efforts improved dramatically (actual $ results are confidential).