Measuring the return on investment for customer insights is notoriously difficult. Search online for articles on the topic and you will find more than one reference to ROI measurement as the “holy grail” of customer insights.
Some applications of customer data have impacts that are concrete and obvious. If you are monetizing your data by selling it to suppliers, add up the revenue generated and you are done. If you are using customer data to deliver personalization promotions, control groups and A/B testing can help you measure the sales lift you are generating. But if you are deploying a tool that makes customer data accessible to your category managers to help them make better trading decisions…calculating a precise impact is not so easy.
It is no surprise, then, that measurement of the ROI for customer insights activities is hit and miss. A study by Boston Consulting Group reports that over half of consumer-facing companies do not regularly measure the ROI from their customer analytics efforts.
There is not really any debate about whether customer insights are valuable. It is almost universally agreed that they are. As the chart below shows, top-performing companies consistently place a higher value on insights and analytics than underperformers. The challenge is not in deciding if it works – the challenge is in quantifying exactly how much impact it has.
While there is no single perfect approach, the methods below are a good starting place for organizations looking to measure the results of their customer insights efforts.
1. Before and After
This is the most basic and commonly used approach. The method is simple. What was our sales before we began using customer insights? What was our sales after we implemented it? Numerous case studies show that retailers are getting sales growth of anywhere between +1% and +6% when they begin using customer data to optimize their assortment, promotions, and pricing.
Of course there are problems with this approach. At any given time, a retailer will have dozens of large initiatives – and possibly hundreds of other smaller initiatives at the category level – that are all designed to impact sales. Tell a category manager that your new customer data portal drove the +3% sales lift she got, and she will tell you nineteen other things she was doing that contributed to the growth.
This method is simple and is a good starting point. But on its own it is probably not sufficient. Let’s explore further options.
2. Specific Projects
One solution is to drill down from the broad to the specific. Forget total company sales for a minute. Let’s look at one category that is doing an initiative that specifically came out of a customer insight. You did some analysis and discovered that 75% of your customers are only buying organic products when they are displayed with the mainstream products, but never visit a dedicated organic section. So you added a hundred of your best-selling SKUs to the list of products that make in on the main shelf beside their non-organic counterparts. The result? Overall sales in organics grew +10%.
In cases like this, it is easier to draw a direct line between the insight and the result. It is not quite a structured test with a control group, but it is harder to argue that the result was driven by something else. Add up a few of these stories, and you start to get a picture of the monetary value your customer insights are driving.
3. Tracking Usage
One of the metrics we find very helpful when working with our clients is tracking report usage. At the simplest level, increasing usage signals that people are getting value from a customer insights tool. If it wasn’t providing at least perceived value, people would simply stop logging in. A steady growth in the number of people accessing customer insights reports over time, as well as growth in the range of reports and time spent with them, is a clear sign that customer insights are adding value.
4. Depth of Use
Tracking reports usage on its own can help demonstrate that customer insights tools are viewed as useful. But they do not get us closer to an actual dollar impact. One solution is to combine the methods above by correlating usage with results. Start by classifying different category teams as light, medium, or heavy users of customer insights tools. Then examine the category growth and see how it correlates with insights usage.
Naturally, it could be argued that category managers who make better use of insights tools may also be better category managers overall. And we are still left with the problem of teasing out whether the results are directly driven by customer insights. But if the best category managers are the heaviest users of insights tools, that is certainly a signal that they are working – and an incentive to promote heavier usage among other category teams as well.
Ultimately there is no perfect method. Trying to determine the benefit of your customer analytics program down to the penny is an exercise that will end in frustration. But in spite of that, the world’s best companies are still relentless in making the effort. Using the tools above may only get you part of the way there. But you will be dramatically better off than those who do nothing at all.