In Telecom, maintaining the loyalty of customers is difficult, as there are alternatives available with customers and the switching cost is low. In Telecom, the nature of the business is designed to be self-serve for the Customers. Therefore, each touchpoint where the Customer needs a service from the operator should be seamless. Prepaid customers contribute a significant part of revenue for telecom operators.

The need for anomaly detection for monitoring Prepaid Recharge
For offering convenience to the customer, there are too many channels to do the recharges such as online, through IVR, at Telecom stores, at departmental stores, etc. Moreover, there are several payment methods involved such as Credit Cards, Debit Cards, Net banking, various wallet offerings, through the redemption of loyalty points.

To retain and engage prepaid customers, telecom operators must keep a close watch on their buying behavior and promote customized offerings to match their needs. Anomaly detection powered by artificial intelligence and machine learning can empower telcos with the data insights they need for individualization of offers and optimization of revenue from prepaid customers. 

Prepaid Recharge Monitoring powered by AI and ML
Here are ways in which operators can use the AI-driven anomaly detection tool to monitor recharges and plug revenue leakages: 

1.Monitor transactions at a granular level to minimize recharge failure

With AI-driven anomaly detection, operators can monitor and track anomalies across multiple payment channels such as payment gateways, IVR recharges, over the counter recharges, etc. to actively find and respond to issues that lead to transaction failures.

Real-time alerts for any issues in the payment channels will allow the operators to look into the issues immediately and resolve it minimizing recharge failures.

2. Understand the behavioral data of prepaid users

Monitoring transaction data can also reveal new opportunities for maximizing revenue such as driving more efforts on merchant sites where customers recharge the most etc.  By looking for abnormal changes to the data across dimensions such as popular recharge plans, recharge dates, customer demographics, locations, data usage etc operators can spot incidents that impact customer experience.

With real-time behavioral analysis on how customers use their monthly balance, companies can send contextual, effective marketing messages. 

3. Drive customized marketing efforts

When it comes to prepaid customers, it is all about personalization. With real-time autonomous analytics, operators can not only forecast customer behavior but also drive revenue-generating marketing campaigns aligned to the needs of specific buyer segments.

This includes adjusting the recharge amount based on the popularity of recharge packs, adapting marketing messages based on campaign performance, and upselling relevant network services. 

4. Protect users against security threats

With real-time anomaly detection, operators can accurately detect threats and issues such as failure of recharge codes, possible customer fraud, etc and respond quickly with corrective action. 

5. Forecast trends in demand and market

With high competition, operators must keep a close watch on prepaid recharge metrics to spot any deviation from the normal stats that impact recharging behavior such as cheaper offers from competition or new market need for better data coverage. 

6. Drive customer loyalty

With anomaly detection software, operators can also monitor subscribers who engage in a large number of recharge transactions and understand the factors that influence higher recharge patterns such as timely and relevant marketing messages. With these insights, network operators can predict future recharge patterns and initiate steps to prevent churn and drive loyalty such as offering incentives like free talk time for a timely recharge, etc. 

Thus, with AI-driven anomaly detection, operators can monitor massive volumes of telecom data and identify new opportunities to increase revenue from prepaid customers. 

This article is originally published at CrunchMetrics

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