- A metric that is more and more used in UX
is the net promoter score or the NPS.
It was first introduced by Fred Reichheld
in an article in Harvard Business Review.
That is why many executives are familiar with it
and will want you to collect it.
Even though NPS is technically meant
to measure customer loyalty,
it is fairly well correlated with user experience.
NPS has, however, several disadvantages.
To understand what they are,
let's review the definition first.
The NPS is based on just one question.
How likely are you to recommend this site
to a friend or colleague?
The answer ranges from zero to 10.
The way the score is computed is very peculiar.
You look at all the people who gave you a rating
of zero through six and put them all in one bucket.
Those are called detractors.
You look at all the people who gave you a rating
of nine or 10 and put them in the promoters bucket.
The rest, who gave you something
in between a seven or an eight, are called passives.
The NPS is the percentage
of promoters minus the percentage of detractors.
While the NPS is based on a single,
relatively straightforward question,
it does have a few downsides.
First, some people will refrain from giving a high score,
not because they don't like the product,
but because they don't know anybody
who would benefit from it.
For example, if you were the only statistician working
at a company, you may feel that there's no one you know
to whom you may recommend a statistics course.
Second, the NPS is a single metric,
and if it is assigned too much weight or meaning,
it might be gamed.
For instance, customers might be pressured
into giving a high score
if the NPS question is prefaced
with an explanation that an employee's income
or promotion depends on the rating
or employees may send the NPS only
to those customers who had a smooth interaction
with a company and experienced no pain points.
Last and most important, you will notice that the way
in which the NPS is computed ignores a lot of information.
First, we ignore the ratings in the middle,
the seven and the eights, the passives.
Second, we treat fairly different scores in the same way.
Zero is very different from six.
Six is middling.
Zero means they hated a site.
Yet both these scores are considered detractors.
With NPS, like with any quantitative metric,
we have to compute a margin of error
when we report it.
The margin of error will tell us
how well the NPS that we obtained
from our study predicts the attitude
of our whole population.
But because we ignore so much information
when computing the NPS, to get a decent margin of error,
we need a higher number of users than with regular ratings.
For example, we need about 88 users instead
of 40 to obtain a 15% margin of error.
For this reason, we recommend that if you're using the NPS,
you also track the rating that participants gave
in response to that question.
In other words,
average all the individual numbers that you got,
whether they came from detractors, passives, or promoters
and compute the margin of error for that number.
Doing so will allow you to get a decent predictive power
with only a relatively small number of users.
Last but not least, make sure
that you do not rely exclusively on NPS.
Like any metric,
it won't capture the whole picture.
Supplement it with other performance-based
quantitative metrics, such as task completion, task time
and analytics data to understand the overall user experience
of your digital product.
- Thanks for watching.
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