Analytics add color to the customer conversation

Customers require lots of color. They are fickle. Their behavior is subtle and driven by many things that are not immediately apparent. Our choices are infinite. It’s no longer a matter of two buckets chocolate and vanilla, coffee and tea, black and white.  Blurring the lines even further, social media allows each and every customer to have their own unique shade.

With that comes the need to have it your way, which means whenever and however you choose. No one wants to be offered ice cream in the middle of winter or hot soup on a summer day, or a credit card or an add-on TV package they already have, for example.  

Without predictive analytics, any type of brand/consumer communication can feel irrelevant. It’s not enough to know your customers, whether it’s to 180 or 360 degrees, because you don’t just need to know who they are, but also what they are going to do. If you can anticipate your customers’ behavior, you can optimize your processes. There’s a lot of money in that kind of color.

In a customer-centric world, channels are just channels, not entities in their own right. All decisions are centralized and consistent across channels and over time. So what’s the value of  adding decisioning and analytics as a core part of your business strategy?  What’s the value of adding color?  In today’s business world, it could be the difference between success and failure. 

In addition, if the customer is the rare breed that isn’t annoyed by inappropriate offers, precious mindshare would still be wasted on irrelevant communications. This affects the company’s sales performance, productivity and the bottom line. And that’s just the customer side of the equation. What if the company could predict the probability that their payment for that broadband TV contract is likely to default? Imagine the possibilities of being able to not only collect that data but analyze to truly understand customer behavior.

Often, the decisions that we make are based on explicit knowledge. Those decisions are typically captured in rules. You can do that if the pattern is clear. If it’s black and white. If all the rules are in the handbook and if the handbook is correct.

Explicit knowledge and deterministic processes will handle many situations, if not most. They work for policies, for eligibility, and for legislation. But a good customer experience calls for more than that. Some decisions require help. A very powerful way to boost the quality of decisions comes in the form of predictive analytics.

Predictions give us a sneak preview of the future and therefore allow us to make better process flow choices. Choices that are customer-centric and add color – and adding color makes for better customer differentiation.  So now, in addition to the black and white behavior, we can handle implicit patterns. Patterns that are not in the handbook. We see this a lot, but not exclusively, when we deal with customer behavior.

It’s easy to see that the world is no longer black and white. However, why are businesses still treating customers that way?  Businesses and their processes cannot be two-toned – especially when customers are involved.

Take churn for example. Businesses need to anticipate if and when a customer is going to close an account or cancel a subscription? It’s not a deterministic yes or no question. It’s only black and white after the effect.  Before that, it’s a probability. And it’s a probability that can change at the drop of a hat. After every response, after a click, a dropped call, a dispute, the likelihood of churn may go up or down, and that’s just the probability.

What a company needs to determine is how much this customer is worth to retain.  Of course, you may have only a split second to decide all this. These are all questions best answered using predictive analytics.  Why make assumptions when you have the data? You can’t just go with the averages and hope for the best. Hope is not a strategy.

And it’s not just retention either. You need to consider collections, sales, and underwriting. All of those processes depend heavily on customer behavior.

The effectiveness in determining the right cross-sell or up-sell offer as well as the customer experience and related satisfaction play a big role in this formula. A relatively small change in the retention rate has a huge impact on customer lifetime value. Later on, we’ll consider the business contribution of predictive analytics and focus on improvements in customer insight related to the cases that drive the business.

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