Organic Mobile Growth 101 – Measurement

When our customers ask us where to start in growing the user base of their mobile app, our answer sometimes comes as a surprise.

Measurement.

Responses include: “Yes, but that’s easy?” and “Don’t we already do that?”

Collecting and measuring data about visitors is a cornerstone of growth strategies on the traditional web. However, the reality is most companies, even many of the largest and most successful, cannot tell you where their new mobile app users come from, particularly for non-paid organic channels like email, SMS, social media and traffic from their mobile website. Recent research points out that only about 5% of users find an app by clicking on an ad. This leaves mobile teams with a huge gap in the data they need to grow their app user base.

The Mobile Organic Attribution Gap

This is often referred to as the “black box of the app store.” It means that a user’s pre-install data (like click history, channel, campaign, etc) cannot be passed through iOS and Android app stores with any degree of certainty or consistency (some is possible on Google, none whatsoever on the iOS App Store). This makes it nearly impossible for mobile growth teams to get answers to fundamental questions such as:

Who is this user?

How did this user find my app?

What channel triggered this user’s click?

What call-to-action resulted in more installs?

Which messages are resonating with my users?

Who referred this user?

Which channel and campaigns drive more long term engagement?

This data is a requirement for any growth team to identify, test and optimize their mobile app channels and messages. But until recently, most growth teams ignored this growing section of organically acquired users because there was no way to collect the data in order to answer these questions. Additionally, it’s important to have accurate attribution and measurements to answer these questions.

First-generation Fingerprinting: Advertising-focused

Because many of the initial advancements in mobile marketing came from the mobile ad networks, the tools that allowed mobile marketers to track and estimate clicks and installs were built and optimized for advertising. In-App Install Ads use Advertising IDs (Apple’s IDFA or Google’s Advertising ID) to match users who click an ad with those who install and launch an app.

When first-generation fingerprinting is used outside of the intended purpose, Advertising IDs cannot be used and its accuracy and reliability suffers. For example, when first generation fingerprinting is used to measure organic installs from the mobile web, email or SMS, a fallback method is used in which standard data points such as IP address, location and time are used to make a “best guess” match of the click to the install. A best guess is not adequate for organic channels which need accurate data to optimize, streamline and personalize the first-time user experience to maximize value.

Another limiting factor of ad-focused mobile attribution tools when used for organic tracking is time: the longer the time between a user’s initial click on a link to the time of the app install, the lower the accuracy of the match given by these products. As a result, the user matching data these tools deliver vary widely in accuracy (typically from 70-90%), directionally correct but not much more than an educated guess. Advertising companies focus is generating advertising revenue and are not incentivized to improve their organic attribution.

Next-generation Fingerprinting for Organic-First Attribution

When the app stores first launched, there was no way to measure where new app users came from. Then there was attribution created and optimized for advertising. Now, there is next-generation attribution designed for the specific needs of organic growth.

Because organic growth channels can’t access and leverage IDFA or Android ID, the technical requirements for matching a single click to a single install are different than those for mobile advertising. As a result, organic mobile attribution needs to go beyond the standard data points used in first generation attribution methods.

What is needed for organic attribution is:

1. An advanced algorithm that takes advantage of additional data points both standard and transformed and applies them contextually to increase accuracy and enable true one-to-one user matching.


2. A vast and growing dataset of anonymized user install behavior to improve the performance and accuracy of click-to-download matches over time.


3. Transparency of the accuracy of each click-to-install match, enabling the app developer to create additional value not possible with paid user acquisition. For example, with transparent accuracy, the first time user experience can be personalized with confidence to drive app activation based on organic campaign and channel details.

Next Steps for Organic Mobile Growth

In this post, we’ve discussed the importance of accurate attribution and visibility into where your new organic app installs are coming from. This is the first step to drive more organic app installs. The next steps to increase organic mobile growth are:

• Prioritize channels and campaigns based on growth potential


• Optimize and test priority channels and campaigns


• Personalize the first-time user experience to drive activations

We’ll be covering these topics in more detail over the next couple of weeks.

Organic Mobile Growth 101 – Measurement