What Makes a Good Customer Experience Metric?
I’m asked this question a lot. And while we have written other posts related to the topic, I feel it’s necessary to revisit it every once in a while since it could be one of the most important question to ask when evaluating the need for measuring the customer experience, and researching the ways to do so.
There are a lot of choices out there, ranging from the over-simplified to the extremely complex – neither of which are (or should be) considered criteria when evaluating customer experience metrics.
It is important to understand that any measure worth tracking should meet all of the following criteria:
- Credible: How widely accepted is the measure? Does it have a good track record of results? Is it based on a scientifically and academically rigorous methodology? Can we trust it to make decisions? Will management trust it?
- Reliable: Is it a consistent standard that can be applied across the customer life cycle and multiple channels? When all factors remain the same, are the results the same with every measurement?
- Precise: Is it specific enough to provide insight? Is it specific enough to allow us to make business decisions based on it? Does it use multiple related questions to deliver greater accuracy and insight? (Remember that a watch without a minute hand may be accurate but it’s not precise. Likewise, customer experience data need to be precise to be useful.)
- Accurate: Is the measurement correct? Is it representative of the entire customer base or just an outspoken minority? Do the questions capture self-reported importance, or can they derive importance based on what customers say? (For example, most customers say price is important to them, but in practice price reductions typically do not inspire them to make purchases. Other factors—such as product information—usually have a much greater impact on purchasing behavior.) Does it have an acceptable margin of error and realistic sample sizes?
- Actionable: Does it provide any insight into what can be done to encourage customers to return, buy again, or recommend? Does it prioritize improvements according to the biggest predicted impacts? A score without actionable insights helps keep score but does not help with improvements.
- Predictive: Can it project the future behaviors of customers based on their satisfaction with the experience?
If your customer experience analytics do not have these qualities – even just one – you run the risk of collecting insufficient data that can lead you down the wrong path when it comes to making strategic, tactical, and/or operational business decisions. And nobody wants that, especially your customers.