Advanced Analytics, Customer Churn and the Appliance of Science

Advanced Analytics, Customer Churn and the Appliance of Science

In 2010 Eric Schmidt (former CEO of Google) said “Every two days now we create as much information as we did from the dawn of civilization up until 2003." That’s something like five exabytes of data. According to IBM, the build out of the “internet of things” will lead to the doubling of knowledge every 12 hours. Let that sink in for a moment.

We take the digital era for granted these days, we’ve normalised its existence but when you step back and think about its impact, it’s as remarkable as it is overwhelming.

With the collective knowledge of the entire history of civilisation available for dissection, human behaviour has been documented in its entirely.

We’ll leave the philosophical ramifications of all of this to others - this is a B2B article on advanced analytics after all, but it’s worth taking in the bigger picture of just how much data is out there.

If we leave aside the focus on big data and the internet of things and apply advanced analytics on just a tiny speck of this information - your customer database - the insights gleaned from their behaviour will be decisive in the future success or failure of your company.

Customer Intimacy

To start with, let’s get the most obvious learning out of the way - retained customers are way more valuable than new ones, due to the costs of acquiring new customers. Adobe once found that it takes seven new shoppers to equal the revenue of a single repeat customer.

So if your focus is on retention campaigns, then your focus needs to be on your existing customer base. The development of programs to improve customer experience has been a direct result of this understanding. By delivering an exceptional experience, customers will not defect – or so the theory goes. But despite throwing millions of euros at “experiences”, customers continue to defect. If anything, they leave even more quickly and easily than ever before.

So what has gone wrong?

Net Promoter Score

Customer experience is a nebulous concept, but there has to be a way to assess its success. And so the famous “net promoter score” (NPS) was born. For a while marketers felt they had a good way of understanding satisfaction levels by simply asking customers what they thought.

Surveys were sacrosanct.

But there is a problem with surveys and the NPS regarding churn prediction – what customers say and do are two different things. According to a report published in Bloomberg Businessweek 60% of defecting customers describe themselves as 'very satisfied' just before they leave.

To make matters worse, the evidence of their impending defection has always been available – if you know where to look.

The Appliance of Science - Applied Analytics

Your existing customer database is a veritable goldmine of data for analysing customer behaviours. Every interaction between brand and consumer creates a digital footprint, an indication of intent – if you know how to read them.

Applied analytics provide a way to spot trends and patterns based on past behaviours. By classifying and categorising customers based on commonalities, you can drill down into those behaviours and better understand customers as individuals.

By following the behavioural trail you can identify indicators of intent. A customer may not say they are leaving, but their behaviour provides clues about what they are thinking. Has there been an increase in calls to customer support? A use of increasingly negative language in their emails? A reduction in their use of your service? All the behavioural indicators are there in plain sight - but only if you know what to look for and how to analyse it.

Taking these indicators and comparing them to the behaviours of other customers, you can predict their next move.

And here is the thing - you can identify, understand, and predict behaviour right down to the individual.

You can uncover how any one customer feels about your service and your offering and confidently predict how likely they are to leave, when are they likely to leave, why are they likely to leave, and what offer will make them happy to stay.

Act Early, Reduce Costs

With refinement your analytics will begin to identify these behaviours much more quickly, allowing you to act earlier. The sooner you act, the easier it is to recover the relationship – and the cheaper the incentive you need to offer. Your analytics will even reveal which retention incentives have had the greatest success for similar customers previously, further increasing your chances of a positive outcome for both parties.

Instead of issuing surveys that can be ignored, or which capture inaccurate sentiment data, analytics use the actual behaviours of your existing customers to make extremely accurate inferences and predictions. Statistical patterns provide actionable insights in a way that the nebulous NPS scoring system cannot, which means that your attempts to improve customer experience will always be more effective because you better understand each customer as an individual.

Fads come and go, but predictive behaviour modelling is just that...predictable. All the answers are there, but very few have the expertise or the tools to spot them, track them, report on them and recommend actions.

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