In a previous post I wrote about why $ is the only CX metric that really matters. So, how do you make it the centerpiece of your CX measurement program? The basic principle is about as simple and compelling as it gets.
Let’s start with something that everyone in business already accepts: Some customers are more valuable than others. These customers are more valuable because they buy additional goods and services from a brand, stay with the brand longer (as opposed to switching business to a competitor), and cost less to serve because they complain less.
To the surprise of no one, those valuable customers are also the customers who are having a better experience, i.e., your happy customers are more profitable than your unhappy customers. This makes intuitive sense and, far more importantly, it’s something that data science proves conclusively. In fact, every year principal analyst Maxie Schmidt and data scientist Laura Garvin Tramm publish a report that shows the $ impact of improving CX for the industries in Forrester’s Customer Experience Index.
To get funding and not get fired, CX professionals must demonstrate this relationship between better quality CX and $ for their own brands. They can do that by setting up a measurement program that quantifies and reports how much more valuable these happier customers are, and how many of these customers a brand has. How? To keep things simple for now, let’s zero in on just the relationship between CX and revenue growth.
To quantify and prove the ability of CX to drive revenue growth in 5 steps, CX professionals should:
- Measure the quality of experience that each customer is having. Do this through a survey that asks customers to rate their satisfaction – using whatever methodology and scale you prefer. Which metric and scale you pick isn’t critical as long as you make sure that each survey is tied to a unique customer identifier.
- In your quality survey, also ask customers about their loyalty intentions, e.g., how likely they are to a) stick with your brand (versus switching business) and b) buy incremental goods and services from your brand. To make your life easier, use the same scale for loyalty as you did for quality.
- Use either assumptions or CRM data to tie a dollar amount to customer loyalty. For example, if a customer gives a 4 out of 7 on their likelihood to remain a customer, you could assume that customer is 50% likely to stay with you. So, if you know (or can estimate) that the customer is worth $400/year, you could assign them an estimated future retention revenue value of 50% * $400 = $200. You can do the same kind of calculation for enrichment (incremental purchases) and then add up the cumulative revenue value per customer for both types of loyalty.
- Use regression analysis to derive an equation that lets you estimate the average per customer revenue at any CX score. That will let you input a CX score (with whatever metric and scale you use) and output the revenue for the average customer with that CX score.
- Use the equation from Step 4 to predict how much improving CX will bring in incremental revenue per customer by subtracting the estimated revenue at your projected CX score (post improvement project) minus the estimated revenue at your current CX score. For example, if your customers at the 4/7 level are worth an average of $200 per year (because they’re 50% likely to leave) and your customers at the 6/7 level are worth $320 (because they’re just 20% likely to leave), then each customer you move from a 4 to a 7 through CX improvement will be worth an incremental $120 per year.
Once your CX measurement program is set up to capture and report this linkage you will be able to:
- Prove that CX improvements have a revenue impact that you can put a $ in front of (and therefore keep your job and maybe even grow your influence and your career)
- Model the expected return from new investments in CX improvement projects (and therefore get funding for bigger and better projects)
- Pick the most important projects to do first (and therefore make smarter prioritization decisions)
And you have options:
- If you don’t know how to do regression analysis, you could just look at tables to see what the average spend looks like for customers at different levels of CX metric. In other words, the average revenue for customers who give a 7/7 on satisfaction vs 1/7 on satisfaction (and every score in between). Because the table can look a little strange when there are small sample sizes in any of the boxes (for example, if very few people gave a 5/7), it can be helpful to look at bigger “buckets” of the CX metric (for example, top 2 box vs bottom 5 box on a 7 point scale).
- Instead of making projections from intended loyalty, you can use actual revenue numbers pulled from your CRM system to put a $ value on each type of customer (from unhappy to delighted). In general, that’s useful if your outcome metrics are easy to measure in the short-term (e.g., repeat purchases) and you have multiple years’ worth of data on the same customers that lets you see how changes in their CX scores affect their spend. If you don’t have that data, a customer’s self-reported intentions about remaining loyal to your brand are the best way to determine how CX impacts customers’ forward-looking loyalty.
Special thanks to Laura and Maxie for helping me explain these concepts. I’ll be writing more about all of them in future posts. In the meantime, if you’re a Forrester client and have questions, feel free to reach out to me, Maxie, or our colleague Faith Adams.