Calculating Customer Lifetime Value for online retail businesses

Business leaders have two main challenges when it comes to improving customer experience. The first is getting an objective view of what really matters to customers. The second is deciding how much it is worth to improve any given item. Unfortunately, most leaders decide based on their personal intuition, rather than using a rational ROI calculation. That's not optimal. What I want to discuss and demonstrate today is that the second challenge is not as difficult as most leaders believe.

New technology has simply made the work easier. The method below should work for most operational and CX measurement systems. I could use any customer-centric Key Performance Indicator for this, and I have chosen to use NPS in this example. As distinct from other types of companies, eCommerce retailers tend to have real-time brand-level NPS results. While they only cover customers who have responded to surveys, they are still good enough for our purposes, especially if you do not yet have an AI solution that allows you to accurately determine which operational performance metrics matter most. More on this below.

What sort of NPS are we talking about?

?For the calculation that follows it is critical to know the identity of the customers that we are measuring. We don’t mean their name; any unique identifier will do. The reason is that the calculation method requires us to know what Customer X, who provides specific satisfaction ratings, actually does in terms of purchasing.

The NPS numbers that work for the calculation are those that represent a significant proportion of the overall customer experience. In e-commerce, for example, customer feedback given just after order confirmation would work. So should feedback obtained several weeks after the order was delivered.

NPS ratings from contact centers are not useful, as most customers probably never need support. (If all of your customers need to phone for help, you probably have deeper issues.)?

Unique customer ID needed

You need to be able to match customers between your survey system and your ordering system. You must be able to see whether a particular customer has only ordered once, or multiple times. If you also have data on order value that is helpful, but not essential.

The premise of the calculation is simple: unhappy customers are less likely to order multiple times. Unless your measurement system has been biased in some way, your own results should confirm this logic right away.

Segment by NPS category

To make the results easy to communicate we suggest doing the calculations by NPS category. The number we are looking for is the proportion of customers who place repeat orders, broken down by Promoter, Passive and Detractor.

Here is an example adapted from a real-world eCommerce case. The company sent the feedback request just after order confirmation. They had a 32% response rate, with 3,958 survey responses.

To clarify, the figures mean, for example, that 2,948 customers gave a 9 or 10 rating to the “How likely are you to recommend…” question. Of these, 63% were repeat customers and 37% had just ordered for the first time.

Calculate

In the real-life case, the values of repeat orders and first orders were similar. Furthermore, the value of repeat orders did not vary significantly by NPS category. Your situation may vary, and you may need to adjust your sums.

?Here are the calculations:

Possible imperfections in the calculation

If you have particularly low response rates, say less than 10%, the results become biased. The proportions of Promoters and Detractors in your sample will be greater than that in your general customer population. This is because in a low-response situation it is those who have extreme feelings that are most likely to respond. You can of course eliminate this type of bias by using an AI solution (such as Spectrum AI from OCX Cognition) to generate a Predictive NPS number for all of your customers.

There is another obvious imperfection in the calculation, and it makes the results conservative. A customer who has only ordered once could be a new customer. If yours is a new company with low response rates, we suggest you explicitly assume that the low response rates and the newness of your company balance each other out.

That's a value for one year. What about the entire customer lifetime?

?The table above gives you a number for a period of 12 months. Hopefully your customers will stay with you for longer. However, your customers are not just worth 12 months of their business. In an eCommerce company, the value of your customers and the value of your business are one and the same. If your company is quoted on the stock market, it is easy to calculate the ‘revenue multiple’ that your company is worth. Apply that same multiple to each individual customer's annual purchases. That's how much they are worth.

If your company is not quoted on a stock exchange you should still be able to do the calculation. Simply do an internet search to find out what is being paid to acquire companies in your industry sector in your country. Look for the term ‘revenue multiples‘. If the range you find is very wide, I suggest discussing your findings with your CFO.

OK, you know what it is worth to improve your NPS. Now, what should you improve?

While it is best to use an AI system that will avoid human bias, it is possible to do this work manually, just to get started. The analysis is quite sophisticated, though not too difficult to explain. You need to do a multiple regression analysis to determine the relationship between every available operational metric and actual customer behavior. If you don't have solid enough customer behavior data, you can use NPS as a proxy to get started.

eCommerce is a gigantic industry. Return rates might come out as the top KPI for clothing, for example. Delivering on the promised delivery date might be among the top items for a business. So could order abandonment rates, or even website visits. Once you have used the regression analysis and customer lifetime value numbers, it should be easy to work out the ROI of (for example) making improvements to your logistics solution to improve the accuracy of the promised delivery dates.

Conclusion

If you have a common customer ID that is shared between your feedback system and your ordering system, you may be in luck. At the very least you should be able to determine the relationship between survey responses and actual customer buying behavior.

If there is no particular relationship, your feedback system has major issues. If the relationship is as expected, you should be able to use the resulting calculations to justify improvement investments.

Over time you will build knowledge and track record. You will also gain better and better understanding of the impact of each of your operational KPIs on repeat purchases, basket value, and therefore customer lifetime value. These will enable you to accurately forecast the impact of a given CX project on real-world customer behavior.

Soon enough, you can stop tearing your hair out.

Bonus - A new webcast that covers Customer Lifetime Value

Last week, Elissa Quinby of Quantum Metric asked our CEO, Richard Owen, to do a Quantify This webcast with her. The subject was The rise and rise of customer lifetime value in retail. It's quite short and to the point. I enjoyed it, and Richard emphasized a number of the points made above, particularly as they apply to online retailers. I particularly liked when he says “The more digital you are, the closer you are to getting things right.”

Notes

OCX Cognition predicts customer futures. Our breakthrough SaaS solution, Spectrum AI, lets enterprises transform what’s possible in customer experience. Reduce your customer risk, break down silos, and drive speedy action – when you can see what’s coming, you can change the outcome. Building on more that 15 years of CX-focused expertise, we’ve harnessed today’s advances in AI, elastic computing, and data science to deliver on the promise of customer-driven financial results. Learn more at?www.ocxcognition.com.

Maurice FitzGerald is a retired VP of Customer Experience for HP's $4 billion software business and was previously VP of Strategy and Customer Experience as well as Chief of Staff for HP in EMEA. He and his brother Peter, an Oxford D.Phil in Cognitive Psychology, have written three books on customer experience strategy and NPS, and a fourth book that focuses on Peter's cartoon illustrations for the first three. All are available from Amazon.

The author can be reached here on LinkedIn or [email protected]. Please let me know what you think and what sort of content you would like to see here.