Keep your customers with big data

Luke McCormack
27 Jun 2013
00:00

With mobile phone penetration rates in Asia-Pacific standing at 89%, according to ITU data, large operators are shifting their sales and marketing focus from attracting new customers to maximizing the value of existing ones. As they do this, they are finding new ways to use the massive amounts of data at their disposal to make better decisions, and re-think their approach to customer segmentation and offers.

Today, leading operators are taking into consideration the exact value of the customer to their organization – represented by a life time value (LTV) or some other value metric. They then use real-time decisioning technology, combined with a guided interaction, to create and present a specific retention budget and the most appropriate offers for each subscriber at the moment of truth. This individual approach optimizes profitability by making each offer a perfect fit against retention value.

When operators first implemented their retention initiatives, they applied these against subscribers who were dissatisfied and trying to leave. This has emerged as the most expensive retention strategy, since it requires changing the subscriber’s mind.

It is far more cost-effective to make sure customers never get on the exit path in the first place by implementing two new types of retention strategy -- preemptive and proactive.

Preemptive strategies are those applied before there are any recognizable churn signals from the customer, and is meant to provide an excellent customer experience. Executing these requires the ability to effectively predict customer lifetime value since operators will likely want to execute more expensive preemptive strategies first against their highest value subscribers.

Proactive strategies are applied against early warning indicators of churn. They introduce a new requirement – predicting likelihood of churn by finding common behavior patterns that are reliable attrition indicators from historical data. Once this data is analyzed and patterns are discovered, it can be applied against current subscribers to find those that need proactive churn strategies applied.

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