A telco provider deployed recommendation engine based solution to recommend best recharge packs.
The telco is a large UK based provider having its presence in the south-east Asian markets. They are one of the Top 3 providers by subscriber base, but facing challenges from the competitors to retain customers. One of the strategies was to offer the existing and new customers with the best recharge option plan – to boost recharge rates and minimize churn. And since the subscriber could recharge on multiple platforms, the problem was as much a technology problem.
Similar subscribers prefer similar recharge packs
The telco used deep learning based recommendation algorithm to identify the inefficiencies in recharge plan and usage, and recommend best subscription plans.
- Average increase of ~4.2 days in the lifetime of a subscribers
- Reduced churn rates of about 1.8% on the entire subscriber base
- 3% higher recharge rates for <30 day subscriber segment
The initial analysis pointed to a large percentage of customers – both pre-paid and post-paid – who could switch to a more suitable recharge plan.
A customer awareness problem.
The telco was running aggressive customer acquisition campaigns with attractive subscription plans to cater to all segment of subscribers. They are able to capture a significant proportion of new subscribers on a monthly basis. However, they were also facing churn rate that was higher than the target volumes. Retention of a new subscriber was a big problem, also because the churn rate after first month of usage dropped significantly.
More than 96% subscribers do not change plans after the first subscription. With changing tariffs, the telco would launch new recharge plans and subscription rates – and a careful selection of recharge packs would allow the subscribers to save more, or provide more talk time, data usage etc.
It was observed from another study that an optimized recharge plan boosted retention and recharge rates.
Money saved is money spent
The telco is facing a lot of pressure from competitors to attract new customers and retain the existing ones.
One strategy that the client was looking at was to improve the recharge plan suitability. They believed that an optimized recharge pack recommendation leads to lower churn, improved life time and higher satisfaction levels.
Another typical problem for them is the high percentage of one-time users – who never recharge. These subscribers have a significantly lower life-time and generally prefer plans that provide them value for money. One of their key focus areas was to use recharge pack optimization to reduce churn rates in these one time use subscribers. The client deployed the solution – with both components – best recharge plan for new and existing subscribers.