The first metric of interest is Net Promoter Score or NPS.
NPS is a customer loyalty metric that is determined by asking users one question.
On a scale of zero to ten, how likely are you to recommend this product or
service to a friend.
Customers who respond with a number between zero and
six are called your detractors.
Customers who score seven or eight are your passives, and
anyone ranking with a nine or ten is a promoter.
And to actually calculate your NPS you take their percent of promoters and
then subtract out the percent of detractors.
This actually means that NPS can fall between negative 100 and positive 100.
You could, for example, have everyone vote you between zero and six.
A good Net Promoter Score will vary by industry.
As you can tell from this chart, many industries have a wide range for
net promoter scores.
And you can see their averages in these squares along the graph.
If you'd like to take a closer look at this chart,
you can refer to it in the instructor notes.
Click Through Rate is another metric that you'll find associated with website
buttons, links, and e-mail campaigns.
With CTR, you're measuring whether or not a user took action.
CTRs are helpful for
determining how customers advance through steps of a purchase.
For example, one checkout flow could be on Udacity's website.
We could collect data on how many users check out a homepage,
go to a description page for a product, how many clicked to start a free trial,
and how many actually completed the payment information form to register for
that free trial.
Daily active users, or DAU, is another common metric, and
it can take on multiple meanings.
The trickiest part is defining what active means for your product.
It could be as simple as logging in, performing specific tasks, or
making a purchase, depending on your business.
You can track the number of users over time, and you'll likely see some
variation from day to day, maybe even a plateau, and then hopefully you'll see
an uptick where you get a lot of adoptions from new users.
By relating product launches, new features, press releases, and
even market research to this data, you can get a sense for
what might be driving your traffic.
These next three metrics are similar to daily active users.
Except you're tracking active users,
in whichever way you define active over some defined period of time.
So instead of having daily active users, you might have weekly active users,
or monthly active users.
Here's a bar chart showing monthly active users for WhatsApp.
WhatsApp is a popular mobile messaging application.
Now, you might be wondering why some of these bars are missing, and
it's just because the source of the data didn't have this information.
It'd probably be better to show this as a line chart over time, but I
didn't want to give the impression that these points were actually in the data.
To calculate any x day active users, you simply total the number of
unique users who initiated sessions on your website or
mobile application from a starting date all the way until the ending date.
You might also look at patterns over weekday or
weekend activity to spot trends.
For example, Twitter determined that if a user opened Twitter seven times in one
month then the user was likely to return.
By attracting active users and possibly what features they use,
you might determine what is driving users back to the product.
And finally, another useful metric is retention.
It's the opposite of churn or attrition.
Retention describes the number or
the percent of users you keep over a certain period of time.
While not counting any new users you acquired during that same time interval.
Now, this example wouldn't really be that great, because I'm only keeping,
let's say, about 1% of my users after 12 weeks.
Even better, you can monitor cohorts of users
based on when they start using the product or service.
For example, you can create retention curves for two groups of users.
Let's say one group of users starts on January 1st,
while the other starts on January 8th.
To learn more about cohort analysis, check out this article
on Nir Ariel's blog, which is linked in the instructor notes.
The article discusses how to measure retention and use cohort analysis,
while looking at user retention for Pinterest and Twitter.
Now, these are some of the common baseline metrics.
And whatever metric you choose, the metric needs to signal
that users are using the product in an expected way,
and that the same users are likely to come back and use the product more.
This certainly isn't every metric that you might want, but
these are some common ones that you'll use in most products.
In the next video, we'll look at a framework for
how you can come up with other metrics.