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WHAT EVERY PRODUCT MANAGER NEEDS TO KNOW ABOUT PRODUCT ANALYTICS

The data we glean from product analytics tells us how users actually use the
product.

By Sam Tardif

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Summary: Product analytics is the process of analyzing how users engage with a
product or service. It enables product teams to track, visualize, and analyze
user engagement and behavior data. Teams use this data to improve and optimize a
product or service.

As product managers, we take every opportunity we get to learn more about our
customers because understanding their needs is critical to building useful
products. This means conducting customer interviews, running surveys, and
examining in-product analytics. The data we glean from product analytics tells
us how users actually use the product – not what they want to do, how they think
they're using them, or even how we think they are using them.

Where software development differs, and home building could definitely benefit,
is the use of agile methodology. Agile allows multiple teams to respond to
changes, quickly. So how can agile, a method based on frequent, continuous
delivery exist with long-term, big-picture planning? Is it possible to create a
realistic forecast over a long period of time, knowing that the one constant is
change?

As a PM, questions like, "How much time do users spend with the product each
day?", "What actions do they take most?", and "Which features get used least?"
are incredibly valuable for understanding your users and give us clues as to how
to make their experience better. In this post, I'll explain what product
analytics are and why you should use them; how to gain a true understanding of
your users so you can pay off "empathy debt"; and how to use analytics to help
guide new feature development

Let’s get started!


WHAT IS PRODUCT ANALYTICS?

Product analytics is the process of analyzing how users engage with a product or
service. It enables product teams to track, visualize, and analyze user
engagement and behavior data. Teams use this data to improve and optimize a
product or service.

In order to get a quantitative understanding of what users are doing with your
product, the first step is instrumenting it with product analytics. The idea is
to fire an event for every action that a user can take in your product so you
get an aggregated view of how many users use a feature, and how often they're
using it. For example, if you want to track the number of times a user clicks a
specific button, you might fire an event called "big-red-button.click." From
there you can see which features need work, which are your most important, and
use that information to prioritize changes. 


ProTip:

There are a ton of solutions out there that give you a framework for adding
analytics events and tracking them. Check out Google Analytics or KISSmetrics as
a starting point.

At Atlassian, we've tried to make it as easy as possible for everyone to get at
data and be able to run their own queries and reports. We use some internally
developed tools to provide these services, but tools like Google Analytics will
get you started too. This has lead to everyone, from developers to PMs to
design, asking questions about usage and trying to understand the impact of what
we build.


“EMPATHY DEBT”: THE NEWEST KIND OF DEBT

Lets try out this new term, “empathy debt.”

Empathy debt: a gauge of whether you understand your users and how they use your
product.

In-product analytics can help you pay off empathy debt in two ways: with
qualitative feedback gathered through activities like concept testing and
customer interviews; and with quantitative data collected in-product with things
like product analytics and NPS surveys.

As an example, Confluence has been around for a fairly long time now and it has
a lot of features that have little to no analytics. One of those is the
dashboard, which is the beginning of most people's journey with Confluence. We
had some feedback about the dashboard from customer interviews, but we didn't
have all the product analytics needed to really understand usage from a
quantitative perspective. We had a lot of unanswered questions, like:

 * How much usage does the dashboard get? How many times do people visit the
   dashboard in a typical Confluence session?
 * What do people actually use the dashboard for? The All Updates feed? The
   Popular feed? Navigating to a space?
 * What do people want on the dashboard? Can we determine the best things to
   surface based on what people do after visiting the dashboard?

These are some pretty fundamental questions that we needed answers to before
embarking on a change to one of the most visited pages in Confluence. If you
don't have analytics in your product, or even a specific feature you're looking
to change, then you're in the same boat and should be very wary about making any
decisions. It's time to pay off that empathy debt!

In our dashboard testing, we learned that one of the most common actions taken
on the dashboard was viewing "favorite pages." This was a super important
finding and one that wasn't necessarily in our initial hypothesis. This brings
us to the main takeaway here: Pay off your empathy debt as soon as you can – if
you don't have analytics in your product, add them in ASAP and start using data
to help inform your product decisions. Otherwise, you'll make important
decisions in the dark. And remember that analytics don't lie! They show us
exactly what users do with the product, but try and dig a bit deeper and use
analytics to understand what users really want.


TESTING THE FUTURE BEFORE IT'S HERE

While adding product analytics can be valuable for understanding how users use
existing features, they're also extremely valuable for testing new features and
experiences. If you have a clear goal for how much you want your feature to be
used, having product analytics helps you work towards that old agile mantra of
failing fast and iterating until you succeed.

The process that we use generally looks like this:

 * Define a clear hypothesis for a product change – e.g. "By increasing the size
   of the comment box we expect to see a 5% increase in commenting."
 * Build the cheapest possible implementation of this change, loaded with any
   analytics events we need, that will allow us to test our hypothesis.
 * Deploy the change to a subset of customers in an A/B test.
 * Twiddle our thumbs while we wait for results.
 * Do a breakdown of the results, with the help of an analyst in the case of
   more complex changes, and decide whether the change was successful.

For our dashboard changes we ended up designing three very "opinionated"
dashboards, each promoting a different use case and set of behaviours. We ran
them through this process (though our hypothesis was somewhat more complicated)
and it worked worked really well for us. But there are some common gotchas we've
learned – sometimes the hard way – that you'll want to think about before
testing new features this way.

Some anti-patterns to watch out for:
 * There's nothing worse than getting to the end of an experiment and realizing
   you don't have all the events you need... Try to do your analysis before you
   run the experiment using some dummy data, you'll quickly see any gaps in what
   you're capturing.
 * Coming up with a hypothesis can be time consuming, but you need to make sure
   you have one and that you're confident you can prove or disprove it with the
   product analytics you have before you launch. Doing an analysis on dummy data
   before launching will help you test this, too.
 * Make sure you're testing on enough users and for a long enough period of
   time. You want to make sure your results are statistically significant.
 * Be prepared to throw away bad ideas! As I mentioned, you want to test
   features out as cheaply as possible and run these tests as quickly as
   possible. Failing fast is good. 


JUST DON’T FORGET TO LISTEN TO YOUR USERS, TOO

As I mentioned above, it’s great to be data-informed, but being entirely
data-driven can sometimes leave you blind to the overall experience that you’re
creating for users. Being dependent entirely on data can also be a bit crippling
when it comes time to make a decision and you don’t have all the data you need.



Product analytics expose the raw reality of how people use the product, or even
a particular feature, but it can be very one-dimensional. Combining what you
think you know from product analytics data with qualitative feedback in customer
interviews, concept testing workshops, and sparring will give you a more
complete picture of what's happening so you can build the best product possible.

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 * 
 * 
 * 

Sam Tardif

I'm a developer-cum-growth-hacker-cum-developer-again, now leading the
Confluence team. At work, my passions are data-driven product development and
simple, elegant products. Outside of work I stay away from data, spending more
time with puppies and making my way through every Japanese restaurant Sydney has
to offer.

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