Real-Time Customer Decisioning Reduces Churn for A Wireless Communications Provider
In an increasingly competitive market with a growing churn problem, a leading US wireless communications provider needed to provide richer “save” offers to retain customers while controlling overall retention spend. Several challenges hampered the success of the client’s retention program, and a common thread was that save offers were lacking in consistency and compliance.
- Judgment calls: Customer service reps in call centers were asked to reference a short list of available save offers and manually evaluate the documented business rules to decide which offer to make to the customer. Often, the reps would present a save offer even if the customer did not meet the business rules approved by the Finance department. Other times, they would pre-judge the customer’s response and not make any offer at all, hastily disconnecting the line of service without so much as an attempt to save the customer.
- Resistable offers: Part of the problem was that the save offers were not very attractive. But Finance would not approve richer save offers because of the lack of compliance control. If there was a rich save offer, the reps could over-subscribe to it, applying it to low-value customers. This could deplete the approved retention budget too quickly, causing the offer to become unavailable for its intended purpose – retaining high-value customers.
- Disconnected implementation: The limited save offers that did exist were not made available to sales reps in the company’s retail stores – again due to the lack of compliance control. So a customer who was shopping in the mall could be attracted by a competitor’s offer to switch, then walk into the current provider’s store and not be presented an offer to remain a customer.
- Inefficient processes and undisciplined implementation: A customer who searched online for how to disconnect a line of service would be directed to call customer service, even if it was understood that the likelihood of saving the relationship with the customer was near zero. The result was a call to customer service (and the associated expense) that could have been prevented by allowing the customer to redeem a save offer online or perform a self service cancellation.
For customers who called the general customer service number, many reps would quickly suggest a save offer without first asking the customer why they wanted to disconnect and without describing the value of remaining a customer. Without this meaningful conversation, the rep could not adequately position the provider’s services or a save offer to the customer. The result was either providing a save offer that did not address the customer’s needs or losing the customer to the competition.
The wireless communications provider selected Pegasystems as the decisioning platform and Merkle as the implementation partner to define, develop, and implement a reactive retention solution, initially for the channels assisted by the call center and retail agents, and later for the online self-service channel. The reactive retention solution was the first large-scale agile development project for the provider, so an integrated project team was constructed with members from the base marketing, retail, and call center channels, the internal IT group, Pegasystems, and Merkle.
The team determined that the quickest approach to delivering a solution in different channels, each with a different primary agent support application, was to develop a user interface with Pega UI capabilities that could be “mashed up” in each channel application. The team then expanded on the vision and initial business requirements to define more than 350 user stories that could be developed, tested, and released over several enterprise touchpoints. In parallel, the team worked with Base Marketing, Finance, and channel subject matter experts to define the decisioning logic based on context gathering requirements, value statement definitions, and an expanded and richer set of offers that could be more effectively targeted and prioritized to maximize the likelihood of the right customers accepting the right offers and to ultimately reduce churn.