Magazine article CRM Magazine

Insights Matter, the Data Proves It: Look to Uplift Modeling to Predict Customer Actions

Magazine article CRM Magazine

Insights Matter, the Data Proves It: Look to Uplift Modeling to Predict Customer Actions

Article excerpt

IN THE COLUMN "Big Data, Big Deal" (June 2012, CRM), I described what big data is all about and what to consider when it comes to its challenges. In an informal way, this is actually the second half of that article. In this half, I want to focus on how to get insights from big data since, when you boil it down, if you don't gain anything from big data, it's a big nothing.

Gaining insight from big data requires a lot of work and a lot of thinking prior to, during, and after the work. You'd be way off if you thought that the tools are going to give you the insights. What the tools do is give you the information you need to gain the insight. If we were dealing with a single customer, it might not be that difficult, but we are potentially dealing with insights about hundreds, even millions, of customers, since many of the customers are looking for personalized responses based on their individual expectations and behaviors.

I'm going to tell you a story that will make it all clear. Telenor is a Norwegian telecom with 203 million customers across Europe and Asia. It is the 17th largest mobile network operator in the world, with $17 billion in revenue in 2011.

Like any telecom, Telenor has churn problems. But the churn is a result as much from what's called promotional churn--low-cost offers from competitors--as it is from dissatisfaction with the services it provides. The difficulty with this kind of churn is figuring out what offers keep customers, which can be an expense unless you do some modeling with all the data you have on customers. A blanket campaign to effectively keep customers might be somewhat costly and generate churn itself.

Plus, the company was dealing with 203 million customers, of which 150 million were mobile, which is where most of the churn occurs.

So what could Telenor do? It had tons of data, an undifferentiated target group, and a definite problem to resolve.

Telenor did the smart thing. It applied what is called uplift modeling to the problem.

Unlike traditional modeling, which only focuses on a target group,

uplift modeling focuses on both a target group and a control group. Its purpose is to measure against the control group to predict both the likelihood of a particular customer performing an action and the change in the likelihood of that customer performing the action--meaning it looks at context, time, and the impact of other customers or influences and influencers on the target group. So, for example, it would not only look at the percentages of the customers likely to churn, but also at the impact of a campaign on those customers' likelihood to churn.

Uplift modeling identifies four groups.

* Sleeping Dogs: Those who would leave due to receiving an offer. They want to be left alone.

* Lost Causes: Those who are leaving regardless of anything.

* Sure Things: Those who are staying regardless of anything.

* Persuadables: Those who might stay due to an offer being made.

What Telenor did was run analytics to create models that identified the individual customers in each group. The company used data from billing systems, customer service data, and sales/purchase data. The variables they looked at were plan size, number of products, and call volume, among many others. Telenor also looked at the propensity to leave and the responsiveness to offers of each customer.

Note that all the data I've spoken about is traditional structured data, not social data. …

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