Common Business Metrics | Key Business Metrics | Product Design | Udacity

Common Business Metrics | Key Business Metrics | Product Design | Udacity

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.


  1. Business Strategies & Insights says:

    Great info—thanks for sharing!

  2. haris martan says:

    Must read related post here:

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