Thanks to big data and the Internet of Things (IoT), we are closer now than ever to really achieving the “360 degree” view of the customer that businesses have been seeking. If your own big data initiatives can’t do it, you can subscribe to cloud-based services that can harvest “off premises” data about customers, such as how often they visit your web sales outlets, what they purchase, and what their buying preferences are. You can also monitor what is being said about your company and its products in the social media. When companies combine this with information their internal systems track from call centers and other corporate touch points from customers, they can obtain a comprehensive picture of general customer behavior, with additional capability to drill down into buying patterns and relational data on individual consumers.

But with knowledge comes the responsibility for acting on this information to improve results — and that’s where old patterns of corporate resistance begin to appear.

Here are some “use case” examples:

Call center

Complaint calls overflow the call center — so much so that some of the calls cannot be answered in a timely way, and there is a high “abandonment” rate in the call center, generated because many callers out of frustration with prolonged “wait times” just hang up. At the end of the month, the call center supervisor reviews the monthly call report and sees the high abandonment rate, but the report explains the situation as a one-time response to an unfavorable product offering that likely won’t reoccur and that is the end of the discussion. No connections are made from this information to consumers who could make a future decision to shop somewhere else out of frustration and disappointment.

Dormant accounts

A community bank pats itself on the back after a successful promotion of its branded credit card, but one year later, records in card services reveal that over half of its cardholders are not using the cards that they were issued. These “dormant accounts” cost the bank time and money to maintain. The fact that the cards aren’t being used is a clear indication that someone else (not the bank) is getting these customers’ card business. Data that is collected from the outside ATM network and from point of sales (POS) activities are marshaled together in big data reporting and can reveal at the level of the individual cardholder how credit is being used and where — but it doesn’t do a thing to explain why cards aren’t being used. Consequently, the bank’s marketing department designs its campaigns around the subset of “using” cardholders it is receiving card usage data from — and the over 50% of non-using cardholders are ignored as a “dormant” category that is buried in a back office card services report.


A large retailer has crisp information on sales that comes in from brick and mortar, e-commerce, and social media channels. Thanks to its big data gathering and reporting, the retailer has improved results in its marketing efforts and sales. But back in the warehouse, returns from these sales pile up because of merchandise issues, and they eat into profits. The return process is cumbersome and time-consuming. Both customers and the warehouse dread it. The number of times that returns are poorly executed is haphazardly reported and is never tied back to how individual consumers having to endure this process feel about the company. One day, a salesperson in a telemarketing campaign calls a customer to introduce the customer to a new product promotion. He’s told, “I tried this product once, and it never worked. It took me six months to return it and to get my money back. I’m not doing business with your company anymore.” “Wow,” thinks the salesperson, “I wonder how that happened?”

The bottom line is that many companies are still a long way from the “360 degree” understanding of their customers that they seek — although big data can now make this possible. Over the past two years, companies have made a great deal of progress in linking IoT and other forms of outside data to that of internal systems to improve customer visibility. Now, the task remains to elevate the value that back office functions like warehouses, call centers, and card services departments can bring to the 360-degree customer vision — and to make the necessary internal investments of time, energy, and money to link these systems into customer intelligence.