So far, we’ve been talking pretty generally about marketing with alternative data. I want to take this time to focus specifically on the Communications, Mobile and Media industries. Let’s start by checking out this whitepaper authored by one of my colleagues, “The Ways Communications & Media Services Companies Market Can Increase Risk to Their Business.” This whitepaper takes a deep dive at the challenge of balancing market growth through mass-marketing campaigns with evaluating the risk that some of those new customers might not pay the bill, or might even be using someone else’s identity.
The research LexisNexis Risk Solutions conducted examines the marketing and data tools which enable these companies to compete for customers with reduced risk. We found that signing huge numbers of customers doesn’t necessarily translate to success—and can lead to significant losses. It may seem obvious but the flipside is that limiting efforts to ‘good’ prospects can severely hamper growth.
The good news is that these challenges are solvable.
The report calls out three types of customer churn:
- Voluntary – when a customer switches to another service or moves.
- Involuntary – when a customer is unable to pay.
- Fraud – when a customer intentionally doesn’t pay.
“When we look for the ideal candidate, it’s looking for the lower credit risk customer who is going to pay their bill once a month, potentially go onto auto pay and stay with us,” says a Senior Vice President of Marketing for a broadband provider, that was quoted in the study.
As expected, competition for these lower risk customers is fierce, and marketers are using every tool/channel at their disposal to target them, including direct mail, digital ads, out of home and TV ads. When describing the challenges, one fraud specialist put it very colorfully, “When you put food out, it doesn’t mean you’re going to get one particular type of cat. You get all of the cats. And that’s basically what happens with publicly broad marketing campaigns.”
The LexisNexis Risk Solutions State of Risk report finds that sorting the good risk from the bad risk relies on having a clearer understanding of how new customers might act. Credit reports and a customer’s billing history don’t always give an accurate prediction of a customer’s future behavior. “We do a lot of modeling and look at lifetime value,” says a Vice President of Marketing for a wireless services provider, that was quoted in the study, “but we really need behavioral data about prospects instead of relying on our own customer data.”
The study also examines the layers of alternative data and predictive analytics that the communications and media services industry can use to better pinpoint higher-value, less risky prospects. By developing more in-depth identity profiles, companies can gain an understanding of how a new customer might behave before on-boarding them. Now many more “thin file” customers, such as Millennials, are included, and communications and media services companies can increase their market share while limiting the risk to their business.
Separate from the white paper, I also worked with a mobile virtual network operator (MVNO) last year on a very interesting study to profile high life-time-value customers. We created a model using our data that scores prospects who are likely high LTV. The results were very enlightening to the client – much more so than anyone expected – as we were able to teach them many new things about their “best” customers. We can talk about that offline – just send me a comment or email me at mailto:firstname.lastname@example.org.
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About the Author: John has a dual role consulting on using alternative data for marketing data analytics and consumer credit risk decisioning at LexisNexis. These roles intersect at many points throughout the customer lifecycle starting with marketing strategy (acquisition, cross-sell/upsell, customer service and retention) through credit underwriting and account management. The goal is to help companies increase organizational efficiencies and effectiveness using a pragmatic, empirical data-driven approach that allows for measuring, refining, and scaling customer insights across the enterprise.