Our results do not support the notion that likelihood of recommending is the best and sufficient measurement to evaluate business performance. Other indicators do well or even better than the Net-Promoter scales. Especially ‘liking’ ["How much do you like or dislike each of the following companies?"] seems to be a particularly strong and consistent measurement, while satisfaction might be mediated by the likelihood of recommending.
The traditional CSAT (Customer Satisfaction) and propensity-to-recommend questions come into play after someone has become a customer. ‘Liking’, on the other hand, typically comes before becoming a customer. “Liking — as the affective disposition towards the company, brand or product — should be predecessor to any purchase,” the authors point out. And, of course, liking should last throughout the experience of being a customer. And, alas, liking — or at least its opposite — can be measured among former customers.
All of which makes the ‘liking’ question an important metric that you can use in your surveys of prospects, current customers and past customers.
What scale should you use when you ask about ‘liking’? The authors tested three scales:
| Bipolar 7-point scale | Dislike a great deal
Dislike a moderate amount
Dislike a little
Neither like or dislike
Like a little
Like a moderate amount
Like a great deal |
|---|
| Unipolar 5-point scale (Like) | Do not like at all
Like a little
Like a moderate amount
Like a lot
Like a great deal |
|---|
| Unipolar 5-point scale (Dislike) | Do not dislike at all
Dislike a little
Dislike a moderate amount
Dislike a lot
Dislike a great deal |
|---|
Some respondents saw the bipolar scale, some saw the unipolar Like scale and some saw two questions: Like and Dislike, each using its own unipolar scale (a difference score was then calculated from the answers to these two questions).
Which scale worked best for predicting business performance? First, some background on the measure of business performance used in the research:
We also carefully selected the companies for our studies to compare the measurements to actual business performance by selecting those companies for which we could obtain accurate measures of business performance. At the same time, we picked companies that are well known enough that we would get a wide range of responses from a general population sample….
For airline companies we chose the number of passengers transported by each airline and for car companies we chose the number of cars sold for each brand. Both these variables are directly related to customer behavior, probably more so than revenue or profit, which are also depending on other factors (although Reichheld’s (2003, 2006) claims are extending to very general business indicators as well).
And a discussion of the results by scale:
Across all analyses, the results were quite consistent and confirmed the results found in study 1 [the scale test was in study 2]: liking emerged as the strongest predictor in most of the analyses…
Both the bipolar scale and the difference score [between liking and disliking] are the two dominant scales across all the different models. If anything, they are equally powerful predictors, indicating that perhaps the bipolar concept of liking and disliking can be measured both ways effectively – although using only one question would be more efficient for most applications.
Therefore, to use the ‘liking’ question to measure your company and a list of its competitors, ask “How much do you like or dislike each of the following companies?” (the wording used by the authors). To measure just your company, ask “How much do you like or dislike [COMPANY]?” For conciseness, and to spare respondents the feeling of answering the same question twice, use the bipolar scale: “Dislike a great deal, Dislike a moderate amount, Dislike a little, Neither like or dislike, Like a little, Like a moderate amount, Like a great deal.”
Why do I call ‘liking’ the penultimate (next-to-last) question? Because my ultimate question, following up on it immediately after, would be “Why?”
Not that I believe there is one question that fits all businesses. As Schneider et al write, “We agree with those researchers who have suggested using a variety of measures, rather than just simply one measure, would better capture the complexity underlying customer satisfaction and customer behaviors.” Like the authors of the study, I don’t believe in the ultimate question (or the no-win scenario, for that matter).
That said, if I were only ever allowed to ask two survey questions, I’d pick the ‘liking’ question and an open-ended followup. The answers to those two questions would unlock many ways for your organization to adapt and grow. And what’s not to like about that?
Wednesday, March 10th, 2010 by Sandro Saitta
Tuesday, March 9th, 2010 by JeffreyHenning
Monday, March 8th, 2010 by Tom De Ruyck
Thursday, March 4th, 2010 by Annie Pettit
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I’m currently reading two interesting books about analytics and data mining. I will review these two books in the near future. Meanwhile, here is a few information about these books:
