“You wouldn’t believe it in China these days. No one uses cash anymore, everyone pays with QR codes in WeChat. Even the beggars beg with QR codes!”
Is this a problem for banks? Yes, it is, because of the growing importance of customer data in understanding customer preferences. Just within the last month, I’ve heard of two examples of major operators who brought potentially cannibalizing products to market, simply because these products would give the operator a customer data edge.
Are you analyzing data across both mobile & Wi-Fi?
The first example comes from an operator in Asia, who made the decision to launch a Wi-Fi network as part of their mobile offering. In the large citites, the availability of free Wi-Fi as part of their plan definitely reduces the amount of paid mobile data consumed, but the operator rolled out Wi-Fi because they felt it was more important to have the entire picture of how mobile customers were using their phones instead of only seeing the mobile data slice. Analyzing customer behavior across both the mobile and Wi-Fi networks gives the operator deeper information with which they can improve future rollout plans, network investment, marketing campaigns, and so on. This is particularly useful since the average global smartphone owner consumes only 20% of data on mobile, and 80% over Wi-Fi.1
The second example of an operator prioritizing the value of customer data over a cannibalization risk comes from the Americas. One operator there has rolled out a content streaming service that could potentially undercut their more traditional wireline-based home TV service. “What we lose in TV subscriptions, we gain in what we learn about multi-screen service delivery, customer experience and expectations, marketing effectiveness, and so many more key areas,” they told me. “We really see that content is the future, so we’re willing to take a loss on this first offering in order to gather the data that we need to learn how to get it right. This will save us millions down the line.”
And long term, customer data could be even more valuable than that. This excellent in-depth article from the New York Times about the development of Neural Network Artificial Intelligence at Google and elsewhere makes very clear that training neural networks requires huge amounts of real-world data. I agree that this isn’t a key consideration today, but within a decade it will be. And players who find themselves cut out of the data/knowledge value chain today (like our Chinese bank example) may find it very, very difficult to create compelling AI-based products and services in the future – well, unless they want to pay WeChat to find out if their customers prefer chicken or tofu when they travel to Xian.
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1 Mobidia (App Annie) Global Mobile consumption trends, Nov 2015