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09 Feb 16 The Importance of Analytics for your Web Application

Last month, we were excited to announce the formation of the Gmail Meter team. Since then, our team jumped right into the analytics (or lack thereof) and we immediately realized how important it was to have these analytics in place before considering things like growth or new product features.

We have a pretty strong commitment to freedom of information and transparency, so in this first series of blog posts, we plan on sharing some of the lessons that we are learning as we progress in our endeavor to bring you the best Gmail analytics tool possible!

Some of these posts will be more technical, others will be more related to customer development, and still others will be focused almost entirely on numbers.

From the title, it should be obvious that today we will be thinking about numbers – many, many numbers. Don’t expect this post to have any detailed how-to’s on things like how to setup Google Analytics, filter out ghost spam, or use Google Tag Manager to track button clicks. These will come later; in the meantime, please feel free to signup below to stay updated!

Instead, please view this as a broad overview on why in-app analytics are absolutely important to building a good product and how these analytics will help inform you on important product decisions.

How are users behaving?

Even with all the time we’ve already spent over the course of the last couple of months, we still feel that we have barely scratched the surface of Google Analytics.

Setting up Google Analytics is very simple and straightforward. All that is required is the addition of a simple Javascript snippet in between the tags to get started, but there are just so many ways to customize analytic triggers and data presentation. It can be difficult to even see from a glance the actual potential of Google Analytics.

Beyond the baseline metrics that are calculated by default, the real power of Google Analytics is the ability to find out what users are doing at every step of their experience with the app. Getting these statistics does require a bit of additional time and set up, but have no worry we’ll be covering those steps in a later post. In addition, there are many articles, both Google- and third-party-provided, that can assist with this process.

I’ll be breaking down the rest of this post according to the different stages of our user flow, roughly correlated to the AARRR method for simplicity’s sake.

Optimizing signup flow and activation friction

Before we took the time to set up these additional metrics, we had only been logging the email addresses of every user who had initially authorized the Gmail Meter script. While this method is the obvious choice for simple implementation, it did not provide an accurate or full picture of our users’ initial signup experience.

One of the first things we did was to link our Google Analytics account with Google Tag Manager and we set up a custom event trigger that tracked the clicks to our signup button. After just one day of data collection, we were quite shocked to discover that there was a large discrepancy between the number of users who initially clicked that button and the number of email addresses collected during our signup process.

Realizing just how little we knew about how our users progress through the signup flow, we decided that we need to take it a step further. We set up yet another custom event to capture the actual number of users who complete the entire signup process and land on our “Thank You” page.

Once we got this data, we knew that we had to go even one final step further. Originally, before getting any of this data, our “Thank You” page had actually included a button for first time users to generate their initial Gmail Meter report. Suspicious that this additional step may have actually created friction for activation, we decided to also track how many users actually requested their first Gmail Meter report.

As you can probably already guess by now, the results were a bit shocking.

The data presented below is just a sample of the total data, specifically this is for the period between January 20th through January 26th.

Of the total users who clicked the “Get My Report” button –

  • 82% of users completed the initial authorization and entered into our internal database
  • 67% of users actually requested their first report to be generated

So this means that from first step of our signup process (“Get My Report” click) to activation (actually being generated their first report), 29% of our users were dropping off.

Conclusions drawn based on analytics

After making these discoveries, we knew that we had to immediately shift priorities to address the shortcomings in our signup and initial activation flow. Previously, we had been focusing on different growth strategies like SEM and additional distribution channels to bring more users to our landing page.

With this data collection set up, we knew that we could make a significant impact in the number of our activated users by focusing on reducing friction for signup and initial activation. In addition, by digging deeper into the data, and separating out each step of our signup flow, we were able to identify and pinpoint the exact steps that needed reworking.

For example, one very easy decision to make was to automate the generation of a user’s first Gmail Meter report. Only 82.1% of users who had completed our signup process actually clicked to generate their initial report. With automating this one step, we are now guaranteeing that 100% of our signed up users are also activated users (barring any bugs or glitches, which is a story for another day).

This decision may seem intuitive and obvious to us now, but without the right analytics or data we would still be in the dark about our signup flow. Now, if there’s a single takeaway for you, reader, it’s that the right data can and should quickly inform your decision making process, and change priorities.

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