What if you could know in advance which of your customers are most likely to churn, and which will deliver the greatest return to your business if you stopped them walking out the door forever?
Once, we would have done this in the rearview; a retrospect of a retention campaign. Once we would have taken the learnings we’d made—what worked, what didn’t, who engaged, who didn’t—and applied them to the next retention push we did.
Customer data is providing us with a way to predict success before it happens. No more guesswork, no more umming and ahhing over the best targeting strategy, and most importantly, no more campaign flops. With the right data, machine learning and data science expertise, Perceptive can run simulations to reveal the approach that will generate the greatest ROI.
And they have the evidence to prove it.
In 2018, a New Zealand retail company—let’s call them Retailer X—had a problem. Close to 60 per cent of their loyalty programme members never walked back through their doors over the course of a year.
Given it costs 5x more to replace a churned customer than it does to retain them, this was a major problem. Retailer X faced not only a loss in repeat customers but by extension, a loss in revenue.
So they got in touch with Perceptive, providing a subset of their total customer database (52,000 loyalty programme members) and a mission: understand and then solve Retailer X’s customer churn issue.
Perceptive combed that data for gold, using advanced data modelling to grade customers according to their risk of churning and the amount they spend per visit. Off the back of the findings, an example retention campaign was created—with some remarkable results.
Thanks to automation technology, Perceptive’s theoretical email marketing campaign was scalable, low cost and flexible. And with the groundwork in grading customers by churn risk and spend done, data scientists used predictive modelling to uncover the most effective targeting strategy, which would:
- Re-engage 25 per cent of targeted customers, 67 per cent of whom would be customers who had already churned.
- Lift in annual revenue by 5 per cent.
- Save a total of $292,399 from walking out Retailer X’s door.
- Generate an ROI of $21.04 for every dollar spent on the campaign.
It still remains to be seen whether Retailer X will roll this campaign out, or upscale the data project to a nationwide sample. However, it’s clear that with customer data and data science guiding us, businesses can not only try and test a strategy before it hits the market but see the road ahead with laser focus.
To read the full report, click here.