The future of product analytics, less like Google more like Facebook
Google is a search engine, Facebook is a social network, which is why the experience a user gets while using each product is completely different.
When we have a question we will use Google to search for the answer, one search query at a time. We almost never come back and search for the same question to find if anything has changed.
On the other hand, when using Facebook or any other social network, we add our friends and are constantly catching up on what’s new with this specific group of people.
We don’t need to actively seek this information out, Facebook knows who our friends are and provides us with a relevant data feed without us needing to ask for it.
Imagine that we had to use Facebook like we use Google, which would mean that we would need to actively search for each of our friends to see their status updates, photos etc.
Doesn’t make any sense right?
Now let’s take a look at the analytics world, specifically at product analytics which analyzes how your product is used by your users.
When building your product features, you put a lot of thought into each feature. Talking with users beforehand, maintaining a backlog, writing requirements, working on the UI/UX and finally investing valuable development resources into building the feature.
After all this hard work, you need to make sure you did a good job and built the right feature for the right audience.
Ideally you need to ask the following questions:
- What are the adoption rates for the new feature?
- Which user groups are using it more than others?
- What is the retention of the feature? Is it used just once or continuously ?
- How usage of the feature influences other KPIs? For example, the conversion from free to premium users?
And that’s only the tip of the iceberg…
I used the word “ideally”, because when you’re in a rush to move on to the next feature, the chances that you will find the time to search for answers to all those questions is low.
Even more so, the chances that you will “catch up” and look for those answers again once in awhile is even lower.
Analytics are tough because there too many questions to be answered, and not enough time to look for answers.
You would probably never use Facebook if you needed to search for every friend’s profile to see what’s new with them. The same principle makes companies use gut feelings instead of analytics to make decisions.
All the existing analytic tools provide a “Google like” experience, but in fact they already know who your friends are. Or features in this case. So why not just tell you what’s going on? Why make you search?
The future of analytics — Answers without questions
I believe that in the future we will see more and more tools that are both asking the questions and providing answers, without making users ask the questions.
Data scientists are by definition people who can dig into data, ask questions and analyze answers all day long. But busy people like product manager and marketers don’t have the time to do this, they need insights and they need them fast so that they will be able to focus on their real job.
We’re in a middle of a process that makes analytics more accessible to busy people. The only way to achieve this is to rethink the way an analytics tool operates, providing an experience that looks more like a social network, rather than a search engine.
How we see the future of analytics
The ideas I outlined in this post represents my vision for Product Analytics, and they are being implemented in the tool we are building — Kilometer.io.
We’re working on out of the box feature dashboards, automatic product insights, scheduled reports, smart trend alerts and much more. You can also take part in our journey, by joining our beta.