How to use analytics to prevent customer problems before they arise

Establishing customer loyalty is a necessity for any business, as acquiring a new customer is seven times more expensive than retaining an existing one.

But with an abundance of choice available to today’s consumers, getting them to stick with your company and not switch to your competitor makes establishing customer loyalty a difficult task.

If we take the mobile sales industry as an example, a high percentage of customers who purchase a smartphone return it within the “free return” window offered by companies.

Many of these customers claim they are returning as the device doesn’t work properly. But when looking into the data, it becomes apparent that this is often not the case.

More often than not, the real issue seems to surround the customer not knowing how to use the smartphone well enough. Either they simply do not realize this, or they are not willing to admit it.

And so, they return the phone, and in doing so, impact the profit achieved by both the smartphone manufacturer and the service provider

What if you could foresee a return, developing an intervention to preempt it and thus prevent these kind of problems from occurring in the first place?

This may involve engaging with the customer in the days after their purchase, checking in to ask how their smartphone experience is going and whether they have any issues they need help solving.

Many smartphone providers offer help and advice services at the time of purchase, for example Apple’s team of Apple Genius’ and BestBuy’s Geek Squad. But after the purchase is completed, this customer engagement often fizzles out.

What we need is proactive engagement. Yet, it is of course not profitable to arrange this kind of intervention with all new customers to avoid the problem of returns. Rather, we need to figure out which customers to implement this intervention with.

Analytics to identify the high-risk consumer

As the old adage goes, prevention is better than cure.

Some customer groups and behaviors are more predictive of churn that others. And now, many companies are using predictive analytics, machine learing algorithms, and journey analytics to help identify which customers are most likely to churn. This gives you with the valuable data needed to turn the situation around.

Customer churn is particularly a problem in the telecom industry due to saturated markets and slim margins. If a customer is unhappy with the level of service they are receiving, they are just one click away from switching to a competitor.

In order to combat this problem, telecom companies are now using analytics to pinpoint problem areas such as customer care calls and analyze how they can improve them or provide alternatives. When customers encounter a problem, predictive customer analytics can help businesses identify the right resources to solve the issue.

Another example of the intervention-related analytical model being used to address customer problems before they arise is in the healthcare industry.

In order to cut down on unnecessary hospital stays by patients considered likely to have a hospital stay in the near future, an intervention program can be harnessed.

This can take many forms, for example making a weekly call to remind individuals to take their medications.

Of course, with both examples, one must weigh the cost of the interventions versus the cost-savings in reduced hospital stays or returned smartphones that result from the intervention.

But using an analytics-driven intervention method, businesses from a range of industries can address issues before they become a real problem and hinder the bottom line.