Adazza Blog

Why Traditional Churn Prevention Initiatives are Failing for Telecoms

1/29/2016 0 Comments

In this post we’ll talk about why marketers have no control in preventing subscriber churn.

For the reasons outlined below, we believe that the future lies in empowering the marketer with powerful technology that eliminates their dependence on business intelligence or technical teams, putting marketers in control of preventing and lowering subscriber churn.

Marketers are overly dependent on technical resources
Every marketing organization is responsible for millions of dollars in marketing budget, subscriber growth and retention, and customer awareness.

However, in order for a marketer to make critical decisions that affect business goals, marketers have to turn to an analytics or business intelligence team for actionable data. Any marketer who has ever had to depend on technical teams for data, knows very well the series of challenges you run into.

For example if a retention marketer at a telecom company wants to create an effective campaign that prevents unhappy subscribers from canceling, they have to send custom requests to the BI team multiple times before launching a campaign.

Marketers face massive delays when acting on customers at risk of churning
When a BI team receives a custom request from a marketing team, they have to manually cleanse, model, and analyze data before sending it back. Any changes or additional requests from the marketing team means running through the whole process again.

This results in massive delays, causing marketers to execute campaigns way after most subscribers have already churned. In fact, by the time marketers are able to act on subscribers at risk, nearly half of them have already churned.

Tools today haven’t caught up with modern technology
The amount of data within telecoms is exploding; this includes user behavior data, call detail records, network performance, and customer service logs. However, today’s tools built by traditional enterprise software companies are predicated on archaic stacks that can’t handle the mounting volume of telecom data that’s being stored.

These tools that are designed for BI teams can only analyze only a fraction of the data which fail at accurately identifying churners.

On top of this, all of this unstructured data is strewn across several disconnected sources, further making it completely inaccessible to the marketer who is responsible for executing revenue saving retention campaigns.

No feedback loop: Campaign results aren’t fed back into predictive models
The worst thing any marketer can do is blindly launch a campaign and not learn from the results. That is what’s happening today when marketers launch retention campaigns, and the results from those campaigns aren’t fed back into the prediction models that were created by the BI team.

For example, if marketers are sending targeted campaigns towards subscribers ‘At Risk,’ but the BI team never sees the results from the campaigns, then their models never get better. That’s why subscribers are receiving irrelevant mail, service calls, and emails! The models aren’t learning.

A marketer's job has never been more challenging and multidimensional as it is today. They are expected to be data analysts, statisticians, creative magicians, and experts on the constantly evolving landscapes around social media & digital customer experiences.

All the while, the tools that are available today for identifying churners are built for business intelligence teams, not for the marketer executing campaigns. As a result of this dependence there is no true owner around churn management.

In the next post, we’ll discuss the changes over the past decade that has finally made it possible for marketers to instantly access these customers in just a few clicks, without any dependence on technical resources.