In an earlier chapter in my career, I had the seemingly lofty title of “software and intelligence editor” at Telephony magazine. That gave me a lot of insight into the telecommunications industry. Even back in 1996, telecom was experiencing a “big data” problem. It wasn’t a problem of collection — no, telephone companies collect more data than almost any other industry, recording call times and durations, as well as the numbers their customers dialed.
The problem became one of how that data should be used. It was a problem that did not exist in a vacuum: The Telecommunications Act of 1996 threw open the gates to competition, and newly empowered customers began exploring their new options. Switching carriers became a national pastime.
With all that data, you’d think the carriers would have come up with great strategies to retain their most valuable customers. They had the data to know who those customers were — billing info, usage data and more — just waiting to be analyzed, evaluated and refined into a must-retain list.
In my job, I saw several startup companies that positioned themselves to provide technology or services to do just that.
The problem was not the data — or even analyzing the data, however. The problem was that the carriers did not care about retention. Their business models all seemed to favor a good offense (new customer acquisition) over a good defense (retention).
The data they could have used to hang on to profitable customers largely was ignored in an orgy of acquisition, resulting in a Wild West atmosphere with some carriers refusing to allow their customers to be switched to new providers (in violation of the law) and others switching customers without their knowledge or permission (REALLY in violation of the law).
U.S. monthly churn rates in 1998 were 2-3 percent of the customer base. At an average cost of US$400 to acquire a new subscriber, churn cost the industry nearly $6.3 billion in 1998, according to one industry study. The total cost rose to almost $9.8 billion when lost monthly revenues from subscriber cancellations were factored in.
However, retention could have made a major dent in those numbers, according to the same study. For a carrier with 1.5 million subscribers, reducing monthly churn by 1 percent would have meant an annual savings of at least $54 million.
So, where are the telecommunications industry’s great retention efforts today? As far as I can see, they’re still at the levels they were at in 1998 — which is to say, nearly nonexistent. Telecom still sees acquisition as the overwhelmingly critical key to success, and so existing customers are ignored — even when data flags them as churn risks.
The Confirmation Bias Trap
This is the real problem of the big data era: It’s not that the data is impossible to analyze or that it’s difficult to discover insights. It’s that insights only impact business leaders whose assumptions, beliefs and philosophies allow an impact.
Too many of us suffer from a debilitating form of confirmation bias, or the tendency to accept anything that confirms our beliefs and to ignore or even disparage information that challenges them. We see this in politics these days, but we also see it in business.
It makes sense in that context; a challenge to your beliefs about your business can seem like a challenge to your very competency and, ultimately, your livelihood. However, disregarding the reality reflected in data is a guaranteed path to a negative career outcome.
These days, we can discover all kinds of insights that challenge the way we approach customers. The problem is not embracing those insights that buttress your beliefs. Those are easy to accept. The hard ones to accept are the ones that suggest a need for change. These are often the insights we don’t look for, or that we don’t want to look for, and they’re almost always the insights that are most valuable.
So, telecom continues to choose to ignore the customers it knows the most about. It accepts a massive churn rate, which is created in part by the industry’s own behaviors. It refuses to challenge the status quo, to acknowledge the power of the customer, or to change even when it’s apparent that a change could be massively profitable. The data exists to retain customers, make them happier, and increase their bottom line revenues — it’s just waiting to be taken seriously.
Are you taking data seriously — and are you willing to use it to challenge your own assumptions?