The true cost of churn

We are often asked how high the cost of churn to service providers really is. As often in economics in general, and costing in particular, it really depends how you look at costs and value, i.e. whether you focus on the direct costs only, or include the indirect costs as well.

The direct costs of churn mostly include:

  • the SIM card costs (not much these days, but you might want to consider the fully loaded costs i.e. the sourcing, logistics, packaging, and all other costs directly associated with the SIM card, including staff costs)
  • the SAC (subscriber acquisition costs), including marketing, advertising and commission of various sorts paid to the acquisition channel (indirect sales) as well as the fully loaded costs of the direct sales channel
  • a portion of the SRC (subscriber retention costs), as ‘retention’ money is certainly spent on subscribers who none-the-less decide to leave.

All in all, the direct costs of churn will typically be in the range of $20-$100 per churning customer (depending in which country you are, and how much you spend on acquiring customers).

IBM whitepaper quotes Investaura and the work that we are doing to reduce service provider churn

Investaura is pleased to appear in one of the most recent IBM whitepapers:

 Harness the power of data and analytics to maximize the value of each customer

Download “Smarter Communication through Analytics” 04-Smarter-Comms-through-Analytics-WirelessWireline.pdf – Downloaded 707 times – 3 MB

This excellent whitepaper highlights how mobile and fixed line telecoms service providers can leverage the data that they have about their customers to reduce Churn, boost ARPU, reduce Costs, and overall increase the Customer Lifetime Value (CLV) of their subscribers.

We also agree with IBM that four key components are required to make this enterprise a full success:

The 5 reasons why your churn reduction measures are not working, and what you can do about it

Truth be told, you don’t need a Ph.D. in data mining or statistical analysis to reduce churn. Which is good news.

Truth be also told: investing tons of money in data mining tools does not guarantee success – more often than not, it guarantees failure.

Churn reduction is a complex process.  There are many reasons why it can go wrong, and from our discussions with telecom service providers, it seems to go wrong most of the time. Why is that?

Winning the war against churn: how to use churn prediction techniques to improve customer retention

There is no question that churn is the plague of the telecom industry.  In the mobile business, annual churn rate of 20%-30% are standard.  In developing countries, churn can be as high as 60% per annum.  Do you know any other industry where companies lose 20%-60% of their customers between the 1st of January and the 31st of December?

High levels of churn are the results of both the supply-side and demand-side peculiarities of telecom service providers.  On the supply side, operators engage in intense marketing activities, launching new tariffs, handsets and promotions on a continuous basis to lure end-users to their own service offering. On the demand side, barriers to switch are very low: SIM cards are mostly free, prepaid customers usually do not have to provide much information, and in countries where Mobile Number Portability has been implemented, customers can take their phone number with them to their new service provider.

But if churn is the plague, you don’t have to be fatalistic. Tomorrow’s winners will be service providers that better understand what customers want and better anticipate how customers will behave. This applies to churn as well and raises questions such as: can we forecast which customers are likely to churn in the near future? Can we explain churn? Can we retain customers who are about to churn?

Yes, we can. Churn can be reduced, and should.

Impact of Churn prediction and Micro-campaigning (Source: Investaura, Lumata)