Here’s How to Break down Reporting Options in Mobile Analytics

Whether you’re a novice or a seasoned mobile marketer, the reporting choices available to you might be somewhat overwhelming. More options can sometimes mean more confusion. You have everything from activity dashboards and cost per install (CPI) reports, to aggregate data, raw data, and much more. If you're wondering where you should focus your energy, this post will put things in order.

Your dashboard

Image Source: Google Analytics for Mobile Apps

If you’re just getting your feet wet, your first exposure to mobile analytics will usually be through a web-based dashboard. Platforms like Google Analytics (GA) typically display a home screen filled with charts of all kinds when your first log into your account. As the data is aggregated, these charts are great for providing you with an overview of how your campaigns are doing.

Key performance indicators (KPI) to look out for include:

• Impressions: The softest metric of them all, this is usually used by brand marketers to measure awareness and reach.

• Clicks: Another soft performance indicator that can signal intent and engagement, when tracked through the funnel. (It shouldn’t be looked at in-silo because of the fat fingers problem). If you’re paying by click as part of a CPC campaign, you need this metric.
• Organic installs: This is a measure of the number of installs from organic search in the app store or from social invites.
• Non-organic installs: You’ll monitor this metric to ensure your marketing and advertising user acquisition campaigns are driving app installs. Because of the still-dominant CPI business model, it is a key metric for measuring campaign success. (More and more, though, apps are measuring in-app events to ensure that the installs are delivering real value).
• Assisted installs: In general, non-organic installs are credited to the last click. However, there may be media sources that lead users down the funnel, thereby assisting down funnel conversions. This metric is meant to highlight their added value.
• Revenue-related: If your app is dependent on in-app purchases, look for data on total revenue per campaign and the average revenue per user (ARPU). Connecting these in-app events to actions higher up in the funnel (e.g., impressions, clicks, and installs) helps pinpoint which campaigns actually are driving value.

• Engagement-related: This is another group of in-app events centered around retention such as open rates, content views, level-ups (if it’s a gaming app), registrations, etc.

Image Source: Facebook Analytics for Apps

Even as a small/medium sized app, you’re probably A/B testing multiple campaigns on numerous networks. You’ll want to track the effectiveness of each campaign by media source. So look for an array of charts designed to help you achieve your goals.

Some will cut the data by installs if you’re looking to improve your app store rankings:

Some will sort by loyal users/installs to understand the quality of the installs:

Others will sort by a customized event like number of users who ordered a family package on a travel app:

As you can see, different networks deliver different value. It all depends on what your goals are.

Retention and cohort reporting

While installs are important, so is retaining those hard-earned users and maximizing their lifetime value. You’re going to closely monitor them using the retention and cohort reports.

Retention reports provide an overall snapshot of your users’ activity in your app, showing exactly how many users who installed the app actually opened it again on day one, day two, day seven, day 30, day 45, and so on.

Most analytics platforms will give you options to drill down into the data and filter it by install type (organic/non-organic), media source, or campaign type. These multiple dimensions will give you a more in-depth understanding of your users and which media source/campaign is helping you acquire the most loyal users.

Having said that, a retention report doesn’t make an apples to apples comparison because it lumps all users together, regardless of install date. That’s where cohort reports come in.

Cohort reports allow you to group users with common characteristics together and measure specific KPIs over different timeframes. For example, you can look at the segment of users who installed your app on September 1 and drill down again by campaign, media source, geography, and other parameters (depending on your analytics provider) to get a really good indication of the quality of the average customer and whether it’s increasing or decreasing over time.

The metric—whether sessions, revenue, or any other defined in-app event—is calculated per different time frames, which represent the first X activity days per user. It is then accumulated among all users, which explains why the graph never drops.

Raw data reports

Although the aggregated dashboard is a great way to visually inspect the results, soon enough you’ll want to drill down for more insights on your users. That’s where your raw data reports come in handy. Usually, these CSV files can be directly downloaded from the dashboard for your analytics account. It’s an important feature for app developers that don’t have their own business intelligence (BI) system. With these files, you’re in the driver’s seat, empowered by the ability to dissect your data as you see fit.

Raw data reports are where you’ll find user level data on...

• Acquiring/contributing media source
• Device type
• Device identifier (e.g., IDFA and Google Advertising ID)
• Operating system
• Device language
• Ad network
• IP address
• WiFi connection
• Country
• City
• And much, much more

Any Excel aficionado will be able to analyze the data from the CSV files in pivot tables and other charts to help you understand your users’ behavior so you can ultimately refine your campaigns and target them better. But bear in mind that there’s only so much data and crunching power that Excel can handle. Retention and cohort reports, for example, are beyond the scope of Excel.

You can also utilize user-level data as the basis of advanced audience campaigns like lookalike targeting, personalized retargeting and custom audiences. For example, you can send a network a list of identifiers for advertisers (IDFA) for users who added a women’s fashion item priced above $199 to the cart for the purpose of re-targeting.

Pull API

If you don’t want to manually download the data, then the Pull API is the right option for you. This will automatically send you a CSV file at a predetermined interval (with the most common pull happening every 24 hours). That way, you can ensure you have all the data when you need it. This is important, as analytics companies only make so much data readily available for download.

If you have a small or medium app and see the data pool growing over time, you may want to limit the type of data that is sent to you. Many platforms give you the option to toggle organic and non-organic data for installs and in-app events on/off. This is a great way to ensure that the data you receive is still relevant to your KPIs and is usable.

Push API

Excel has its shortcomings and there will likely come a day when it just won’t be good enough. This is especially true for large apps that are inundated by data. Every aspect of app use, including app sessions, install circumstances, and in-app activity needs to be analyzed.

Fortunately, there are companies that can solve this problem for you. They leverage an analytics platform’s API, which pushes the data from the server to the third-party service provider's BI system in real time as an event happens.

Real time can make a big difference, as it gives you the ability to optimize rapidly. When you're working with large budgets, every second counts.

What to keep in mind

As you can see, the reporting option that is right for you is largely dependent upon the size of your app, the amount of data you generate, whether you have a BI system or not, and—ultimately—your KPIs, which are going to vary.

If you're analyzing the performance of a new app, you’ll want to focus more of your energy on basic aggregated data. For a more established app with loads of data, though, you will want to focus on growing its user base as well as working to retain and monetize the full potential of app users.