India is one of the world’s biggest telecom markets, but it also has worryingly high churn rates. Can data analytics techniques stem the rising tide of customer churn?
India posts some impressive telecom numbers: it has 1,198.89 million subscribers [Source: The Indian Express] and 16 major providers; it is the second-largest telecommunication market in the world, and it is set to become the fourth-largest smartphone market in the next few years. [Source: IBEF.org]
India also puts up some equally concerning churn numbers:
- 96% of mobile subscribers are constantly shifting service providers in search of a better deal
- 6% is the average monthly churn rate for Indian telecom customers
Since it’s far more expensive to acquire a new customer than to keep a current one, Indian telecom companies have a real interest in stopping this trend. By using data analytics, they can incentivize and retain customers, increase their customer base and reduce churn.
Five Data Analytics Techniques to Control Churn
The following five techniques should be considered by any company looking to retain its customer base:
- Customer Lifetime Value Analytics: CLTV estimates how much value a customer will bring to a business over the customer’s lifecycle. Knowing whether this value is high or low lets businesses prioritize which accounts to target. It can also identify which customers are most likely to leave, what kind of plan to offer these customers (based on their usage and priority status), and which accounts might require special attention (i.e. personalized sales approaches).
- Predictive Analytics: Churn Prediction Modeling, Customer Retention, and Next Best Offer Recommendation: Taken together, these three techniques not only show which accounts are at high risk of churn, they also pinpoint when they will switch and what offers will most likely encourage them to stay. These offers can be further customized based on the CLTV value and the customer’s behavior and past actions.
- Campaign Management & Analytics: These can be deployed pre- and post-campaign to increase its effectiveness. The pre-campaign phase observes sales and response in a defined time, computes promotional benefit, and assigns customers into test and control groups. After the stimulus is applied to the test groups, the campaign can be adjusted on the fly. Post-campaign analytics show the actual promotional benefit and the difference between control and test group results; they also create a profile comparison. All this data can be used to refine churn-prevention techniques.
- Cross-Sell & Upsell Analysis: Increasing revenue from current customers by means of cross-selling and upselling services is a common tactic to offset churn-based loss. Using analytics to create and fine-tune these campaigns is more productive because these campaigns are based on customers’ transaction histories. Plus, cross-sell and upsell offers have been shown to increase customer retention.
- Optimized Pricing Strategy: Creating new service plans and packages has been a high priority for telecom By using a combination of pricing analytics and market research, companies can uncover optimal pricing plans for their customers.
To survive in India’s ultra-competitive telecom landscape, providers must use data-backed decision strategies. Not only will this help reduce churn, it will enable them respond to changes in the market, manage risk, and identify new opportunities.
Authored by Suhale Kapoor, EVP & Co-founder at Absolutdata Analytics