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Predictive And Real-Time Customer Analytics ... And Mrs. Johnson's Mother-In-Law

By Tracey E. Schelmetic
Editorial Director, Customer Inter@ction Solutions

 

 

Customer analytics are all the rage these days. Exceptionally popular are predictive and real-time analytics, which allow companies to make decisions on-the-fly, rather than finding out after the fact what's going on. Why apply knowledge to the next call when you can apply it to this call?

Predictive analytics are attractive to companies. As anyone who runs a large call center knows, small improvements in metrics, when carried across the entire customer population, can lead to big savings. Why, if my checking account is out of whack by 10 cents, I generally just write it off. Even if it's not in my favor, it's not worth my time to go hunting down the arithmetic error. If it is in my favor, well ... I've just made 10 cents.

But imagine the difference 10 cents can make if applied to each customer interaction. Ten cents times 50 million customer interactions per year? That's ... [counting out on fingers] ... five million dollars per year. (I think. I'm an editor and took 'Math For People Who Want To Be Writers And Other Idiots' in college.)

There are many tools to help call centers shave precious seconds and pennies off each customer interaction; and many more that help with customer retention and loyalty to minimize churn and keep customers joyous and preferably off the phone. Customer analytics can do both. They can be used to predict which customers are more likely to depart and run to the competition; which customers would be most likely to benefit from, and be interested in, new programs, products or services (see Austin Logics: www.austinlogistics.com); which group is most likely to default on loans; and in some cases, analytics can help curb fraud. (By identifying customers that, for example, repeatedly make up new 'identities' to take advantage of 'new customer' offers. (See the Aperio CI product for more information on this function: www.aperioci.com.)

In terms of improving metrics, some analytics programs help predict the best time to call certain customers; and some predict when customers will call, allowing you to adjust staffing levels. (See SAS solutions: www.sas.com.)

Many of these products allow you to do modeling and simulations. 'If we introduce a new pay-as-you-go cellular service model, what will happen? Will we win customers from our competitors, will we pick up customers who have never had cell phones before, or will we cannibalize our own customer base?' Alternatively, 'If we lower the prices on David Hasselhoff action figures, will we increase sales and come out ahead, or will the public's inexplicable apathy toward David Hasselhoff collectibles continue?'

As with most technologies or machines, analytics get better every year. (The only exception to this technological and mechanical progress rule is glass or screen doors that slide. Why can't anyone perfect the sliding door? Why must it always get stuck in mid-position, threatening to fall off its tracks, while you're carrying a plate of barbecued chicken and a bowl of potato salad out to the deck?) I foresee customer analytics and predictive analytics becoming so sharp that one day you'll hear a conversation like this in a call center between a new employee and a supervisor:

Bob, the New Call Center Agent: OK, Sue. What do I say to a customer who wants to open a new savings account?

Sue, the Supervisor: It doesn't matter at the moment, because Mrs. Johnson is about to call next.

Bob: Who?

Sue: Mrs. Johnson. She'll be calling to tell us that her mother-in-law fell off a Vespa in Rome last week and has to move in to recuperate, so the Johnsons need a home equity loan to build another bedroom and bath on the ground floor.

Bob: How do you know that?

Sue: Software says so.
[Phone rings]

Bob: Acme Bank, how can I help you?

Mrs. Johnson: You can help me by telling me why an 82-year-old woman would get on the back of a Vespa in Milan anyway?!

Bob, to Sue, in a whisper: It was Milan, not Rome.

Sue: Damn software. The IT department should get our money back.

Think how we could apply advanced analytics to other areas of life. If an in-depth analysis of traffic patterns in our city determines that, at any given time, 28 percent of the drivers on the road are inconsiderate idiots, then the software could determine that, based on the amount of traffic on the road and the current speed at which we're traveling, a jerk in a German car has a 92 percent chance of cutting us off ' now. You can brake in advance, and prepare the hand gesture of your choice with time to spare!

Unfortunately, I'm not seeing how analytics will be able to advance the technological improvement of sliding screen doors in the near future. I wait and hope. CIS

The author may be contacted at tschelmetic@tmcnet.com.

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