The number of people you need to observe in
order to identify the primary problems with your website or app depends on the
quality of your website or app to a significant extent. If your app is of very
low quality and an immature environment, then very few people are required—typically
no more than eight—to identify the key findings (I would define a key finding
as a pattern that has been observed three or more times in a group of no more
than 20 people.)
If you have an inefficiently managed
digital environment, then actually observing large quantities of people can be counterproductive.
This is because as you observe more people, you start locating more minor
problems. I have found that if you want to see action taken, you should not
have more than three key findings and recommendations. If you report twenty
findings and recommendations, then many teams will simply throw up their arms
or just politely ignore you because there’s too much to do.
If you observe six people and locate a
problem three times then that problem is affecting 50% of your sample. If you
observe 15 people and you see something three times, then that’s affecting 20%
of your sample. If it’s 100 people, then observing the same problem three times
represents only 3%. If you decide that a finding is three or more occurrences
of the same thing, then you will have a considerably higher number of findings
and recommendations with 100 people than with 15. Furthermore, as mentioned
previously, lots of recommendations may make for a big report but they rarely
lead to genuine progress and improvement. Because it’s not enough to merely make
a recommendation; you also need to understand the capacity and motivation of
the people to make a change.
Anyways, in a typical environment, there are
no more than three big things that if fixed, would make things significantly better.
Fix those things and you discover that many other problems fade away.
What got me thinking about this was reading
an informative article by Roy Ballantine, where he does a statistical analysis
on the probability of different types of problems occurring, depending on the sample
size. Roy showed that at 14 people, a problem that would affect 20% or more of
the population has a 55% chance of occurring. However, a problem that only affects
5% of the population has only a 3% chance of occurring. At 30 people, a 20%
problem has an almost 100% chance of occurring. However, a 5% problem has an
almost 20% chance of occurring.
If you have a mature environment and you have solved the major 20% problems, then you will need much more people to identify the smaller, infrequent usability issues. However, if you haven’t solved the major issues for your customers, then observing lots and lots of people could flood you with data. This can lead to data paralysis and could even make things worse by solving minor problems whose solutions actually worsen the major problems.