What I Wish I Knew Before Using Google Analytics for Firebase for Mobile Product Analytics
Understanding your users is the best way to continue building a product that they want and ultimately cannot live without. One way to better understand your users and how they experience your product is to talk to them or survey them; another way is to dig into data on how they’re using your product–what actions are they taking, how much do they come back to use your product–to gain insight into how you might be able to improve their experience. This is often called “product analytics”. While preparing my first iOS app for release, I thought about how I might track user behavior in my app so that I’d have the data needed to explore how they’re using it, such as what buttons they’re pressing, what screens they’re visiting, etc. In web development projects, I’ve traditionally relied on tools like Mixpanel to track events easily and explore and visualize user behavior in different ways, but Mixpanel has been too limiting and expensive, so I decided to go with a cheaper and more flexible solution (but less user friendly on the visualization side of things) for mobile app event tracking and analytics, Google Analytics for Firebase. We all make mistakes when using any new tool, but I came across some nuances of Google Analytics for Firebase (Firebase Analytics for short) that I wish I knew about before I started using it. Here is a list and short description of each, which will hopefully help new users of Firebase Analytics learn from the mistakes I made.
- “Turn on” parameter reporting from the start if you have dimensions in your events that you want to see numbers for, at a glance, in Firebase.
- Link Firebase to BigQuery from the start if you want access to your raw event data.
- Firebase’s default Funnel reports are “open” funnels, not “closed” funnels.
“Turn on” parameter reporting from the start if you have dimensions in your events that you want to see numbers for, at a glance, in Firebase Analytics.
Firebase Analytics gives you some basic visualizations out of the box, like how many times a certain event fires, over time. I had an event that would fire whenever an upgrade popup was shown to a user, and I specified a parameter called “source” which would note which action preceded the upgrade screen, so I could see the most common paid features that free-tier users tried to access. However, Firebase Analytics did not report on this “source” dimension at all until I manually set up “parameter reporting” for it. So don’t forget to enable “parameter reporting” for important event parameters/dimensions that you care about!
- In the Event view, click the three vertical dots to the far right of your event, then add a parameter of your event to the table by clicking and dragging
- Firebase Analytics will start collecting numbers for that parameter (here, “source”), which you’ll be able to see in the report for the parent event (here, “upgrade_popup_show”)
Link Firebase to BigQuery from the start if you want access to your raw event data.
By default, your raw event data is collected and made available to you only after you link Firebase to BigQuery. When I first implemented Firebase, launched my app, and got a handful of users, I could see a high level picture of their behavior via Firebase Analytics’ basic visualizations. A few weeks later, I found out that I had to link Firebase to BigQuery explicitly to start telling Firebase to “save” my raw event data, and only after doing so did I see that raw data coming in (and saved into tables in BigQuery). So I had “lost” the first several weeks of raw event data, which isn’t bad for my small app, but could be more costly for a high profile, heavily marketed app launch where mobile analytics and being able to mine insights from the data matters more.
Note that when you link Firebase to BigQuery, you’ll need to upgrade to Google Cloud Platform’s Blaze plan, which is a pay-as-you-go, or pay only for the bandwidth, storage, etc. that you use, plan. You can visit their calculator to estimate your costs, but so far, collecting the data and running infrequent BigQuery SQL queries for my app has been free.
Firebase’s default Funnel reports are “open” funnels, not “closed” funnels.
If you go into Firebase Analytics’ Funnels page, you’ll see an area where you can create a funnel easily. After trying to do so, I found out that the funnels Firebase creates are “open” funnels, meaning that at each step of the funnel, a user doesn’t have to have completed the previous step of the funnel to be included in the count of that step. In my opinion, “closed” funnels, where at each step of a funnel a user at that step has to have completed the preceding step, are much more informative; it’s also a core feature of other event analytics tools like Mixpanel and Heap. Several others are also confused about Google’s decision to have Firebase only report open funnels.
For example, I created a funnel in Firebase Analytics to report on what percentage of users who open my app for the first time go on to take their 1st photo with my app, then what percentage of those go on to take their 2nd photo, etc. I expected fewer and fewer users to make it to each step of the funnel, so was surprised when I saw what appeared to be 100% of users who take one photo take two, 100% of users who take two photos take three, etc. Until I found out that Firebase had constructed an open funnel:
There isn’t a setting in Firebase Analytics to see closed funnels yet, so I decided to create a closed funnel in BigQuery with SQL, on my raw event data.
I won’t go into the details here, but I tested a few different kinds of SQL queries for constructing closed funnels, and the following “LEFT JOIN”-based one had much better performance than a “subqueries”-based one that you may find elsewhere on the internet. You too can create closed funnels to better understand the flow of your users, if your event data is in BigQuery: here’s my SQL query for the closed funnel “first open -> take 1st photo -> take 2nd photo -> take 3rd photo” (using UNNEST to flatten arrays because BigQuery stores stuff like that):
count(distinct e0.user_dim.app_info.app_instance_id) as first_openers
, count(distinct e1_user) as photo_taken_1
, count(distinct e2_user) as photo_taken_2
, count(distinct e3_user) as photo_taken_3
FROM `youday_IOS.app_events_*` as e0, UNNEST (e0.event_dim) as e0_events
LEFT JOIN (
SELECT events.name as e1_eventname
, e.user_dim.app_info.app_instance_id as e1_user
, events.timestamp_micros as e1_ts
FROM `youday_IOS.app_events_*` as e, UNNEST (e.event_dim) as events ) ON e0.user_dim.app_info.app_instance_id = e1_user
AND e1_eventname = 'add_photo_from_camera'
LEFT JOIN (
SELECT events.name as e2_eventname
, e.user_dim.app_info.app_instance_id as e2_user
, events.timestamp_micros as e2_ts
FROM `youday_IOS.app_events_*` as e, UNNEST (e.event_dim) as events ) ON e1_user = e2_user
AND e2_eventname = 'add_photo_from_camera'
AND e2_ts > e1_ts -- 2nd photo taken after 1st
LEFT JOIN (
SELECT events.name as e3_eventname
, e.user_dim.app_info.app_instance_id as e3_user
, events.timestamp_micros as e3_ts
FROM `youday_IOS.app_events_*` as e, UNNEST (e.event_dim) as events ) ON e2_user = e3_user
AND e3_eventname = 'add_photo_from_camera'
AND e3_ts > e2_ts -- 3rd photo taken after 2nd
WHERE e0_events.name = 'first_open'
Firebase makes it easy to track events and collect all of them into a datastore, so you have the data you need to (quantitatively) understand how users are using your mobile app. There are just a few “manual switches” that someone using Firebase Analytics should know about, to ensure that they’re collecting complete behavioral data from the start. Firebase can also improve its visualizations to be more informative and insightful, so users don’t have to write SQL as much. Firebase certainly has the potential to get there, with its relatively affordable “utility” or “pay-as-you-go” pricing model and its superior data storage and querying capabilities (good luck trying to get your raw data out of the other event analytics platforms). I enjoy learning from my users to build a better product, and having the data to do so, and am excited to see what Firebase Analytics can do over time for the advancement of product analytics.
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Originally published at blog.troyshu.com on November 25, 2017.