Member Since: 11th Sep 2018
Retired VP of Customer Experience for HP and HPE's $4B software division. Now Editor-in-Chief - Content, for OCX Cognition, the Customer AI company.
Author of four books on customer strategy, all available on Amazon.
Editor in Chief - Content OCX Cognition
17th Jun 2021
Please allow me to exaggerate to make my point: I agree with the conclusion, but with almost none of the reasoning.
The main reasoning provided is that an NPS number does not provide sufficient information on the actions that need to be taken. I agree with that. You goes on to say Customer Effort Scores, CSAT, and Value Enhancement Scores (which I have never heard of and is presumably something Gartner sells) do not suffer from the same defect.
The defect in the reasoning should be obvious: None of the scores tell you what you should do. None. No exceptions. To find out what to do, you need to ask more questions. No matter what your metric, I would suggest that you need exactly two more questions:
1) Why? (Why did you give us this score?)
2) What could we do better?
That's it. You don't need anything else. The trend in the metric will tell you whether you are making progress.
Now, I said I supported the conclusion. The reason is that the research behind the book 'The Effortless Experience' proves that Customer Effort Score is a far better predictor of revenue than NPS for customer support businesses. NPS is a brand-level metric.
I could go on... for example by pointing out that Bain and Satmetrix did a huge amount of research together back when Fred Reichheld published his seminal HBR article. That research proved that customers did indeed tend to do what they stated as their intention. Meaning they did indeed recommend, and so on. And the brand-level metric was proven to be the best single-question metric to predict revenue trend / market share of all those tested.
I could go on... but won't for now...
18th Oct 2018
Well, that's the nature of R2 Jeff. It is a measure of variation. It is to be expected that if you take two relatively homogenous subsets, the combination of the two will have more variation than each individual subset. More variation means a lower R2 number.
A random example occurs to me, and I have not checked it for validity. Lets suppose you have an R2 number for the relationship between iron levels in carrots for each month of the year, and another R2 number for the relationship between iron levels in broccoli and the months of the year. The R2 number would probably be relatively high for each, meaning low variation in numbers. Combine the two with similar scores for other vegetables to have a set of scores for 'vegetables' overall, and there will be far more variation, so a lower R2 number.
11th Sep 2018
Jeff, I have done a full study of the subject covering over 300 companies. I did it first in 2017 and repeated it earlier this year. You can find my article on it at the address below, and that includes access to the data set. In short, there is no relationship between the two for industries where employees have little or no direct customer contact, and quite a high relationship for industries like hotels where there is a lot of direct customer contact.