How Many People Actually Recommend?
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Purchases and referrals are two closely tracked company metrics. If you can predict the extent to which people will purchase and recommend, then you will have some idea about future revenue and growth. Much of that forecasting is done through surveys using behavioral intention questions.
The behavior for purchasing is, of course, actually buying something. The behavior for recommendations is recommending (for example, verbally in conversation or via text), and the intention is what someone states they will do. So, how do you measure the intention of a future behavior? You ask people about their intentions.
But if people say they’re going to recommend a product or company to someone, will they actually recommend it?
In this article, we review data on how well measures of intention to recommend match future recommendation behavior.
Read the full article on MeasuringU's Blog
Discussion and Summary
Aggregating across four longitudinal data sources (two external publications and two of our datasets) we found:
Those most likely to recommend mostly recommend. Across the studies that reported this data, about 60% of people who selected the most extreme response option (10 on a 0–10-point scale) reported recommending. A similar percentage (55%) of promoters (those selecting an LTR of 9 or 10) also reported recommending. For extreme responders (who selected a 10) and promoters (who selected 9 or 10), the estimates of recommendation behavior ranged from 42% to 73% (only one estimate was less than 50%). It looks like between 50% and 60% of promoters ultimately make recommendations. We can also see that promoters were about three times as likely to recommend than detractors (55% vs. 18%).
Those expressing any intention to recommend have a surprisingly high rate of recommendation behavior. Across the four studies and seven estimates of recommendation behavior, 44% of participants classified as intenders recommended. Intenders were defined as the combination of NPS passives and promoters indicated by selecting from 7 to 10 on a 0–10-point recommendation scale or yes on a yes/no likely-to-recommend question. This recommendation rate is less than the estimates for extreme responders but still substantial. The recommendation rates across all seven estimates ranged from 30% to 63%. Even when the strength of the intention to recommend is diffuse, it’s reasonable to expect the percentage of recommendations from intenders to be less than half but more than a third.
Recommendation rates were comparable for tracked versus self-reported. An interesting secondary finding from aggregating this data was that the average tracked recommendation rate of 44% for intenders (Kumar et al., 2007) was the same as the self-reported recommendation rate averaged across the other three studies.
Those least likely to recommend generally don’t. Across these studies, there were two ways to identify those who were unlikely to recommend. One was the NPS designation of detractor (those selecting 0–6 on the LTR item) and the other was non-intenders (those selecting 0–4 on the LTR or Juster items). The average over four estimates of recommendation rates from detractors was 18% and from five non-intender estimates was 16%.
Recommendation rates for recent purchases were unexpectedly high. One context of recommendation explored in Sauro (2019) was for recent purchases. Even for detractors and non-intenders, about 40% reported recommending their most recent purchase. For now, we can only speculate about potential causes for this spike in recommendations. It’s possible that, having made a purchase, the attitude toward that purchase is affected by the resolution of cognitive dissonance over time (e.g., “Even though I don’t usually make recommendations, in this case, I must have made a good purchase decision so when the opportunity presented itself, I recommended the product”).
It isn’t realistic to expect recommendation behavior to equal recommendation intention. Some researchers have negatively framed the key finding as “only” about a third to a half of those who express an intention to recommend follow through with a recommendation behavior. Even the lowest estimate of 30% from Romaniuk et al. (2011) is far from zero. Once someone expresses an intention to recommend, there are obstacles to actual recommendation including never having an opportunity to recommend or not having a strong enough intention to exert the effort to recommend (Burnham & Leary, 2018). A more positive framing of the key finding from these studies is that there is a strong relationship between the intensity of the intention to recommend and future recommendation behavior, so changes to the quality of UX and CX that affect the likelihood to recommend are important.
Read the full article on MeasuringU's Blog
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