Mobile ads are one of the biggest drivers of growth in the advertising industry. According to forecasts, global spending on mobile advertising is expected to reach 270 billion US dollars by 2020. However, many mobile marketers lack specific knowledge on how to leverage the full potential of their mobile ad campaigns. One of the solutions for this is machine learning-based mobile app attribution.
What is Mobile App Attribution?
In order to comprehensively identify marketing potential, attribution is also becoming more and more important for mobile app marketing, as it already is for the web world. Mobile app attribution is relevant for all those who have an app and spend marketing budgets on promoting it.
Mobile app attribution allows assigning mobile app conversion events to the responsible advertising campaigns. The goal is to quantify how big the influence of individual marketing touchpoints is on certain conversion events, such as app installs or transactions in the app. This gives mobile marketers insights into which mobile ads are effective and which are not.
Reasons Why Mobile App Attribution Matters
When the success of marketing campaigns is tracked, usually only the last touchpoint profits. Often because marketers don't know better or can't measure it differently. The consequences are distorted results about the campaign efficiency and burned marketing budgets. Here’s one prominent example:
Procter & Gamble cut its digital ad spend by more than USD 100 million – without any negative impact on growth. Because of the lack of visibility and transparency about the effectiveness of advertising activities, they simply wasted millions in marketing spend. This dilemma is similar to the mobile world.
"Google and Facebook have the decision-making power to attribute themselves the click that ultimately led to the conversion."
János Moldvay, Co-Founder & CEO of Adtriba
Lack of transparency in mobile user journey data and its measurement
Compared to the web there is still a huge lack of transparency regarding user journey data and the correct evaluation method for mobile data.
On the one hand, many mobile marketers use misleading attribution models such as last-click or first-click. But much more serious, however, is the fact that Google and Facebook do not allow independent tracking and measurement. Google and Facebook, which, amongst others, are also known as Self-Attributing Networks (SANs), have the decision-making power to attribute themselves the click that ultimately led to the conversion. The US tech giants are simply evaluating themselves, which necessarily gives the definition "SANs" an ironic touch.
This approach is very different from the web world, where an advertiser actually knows where the click comes from and doesn't have to rely on Google or Facebook.
Mobile App Attribution implicitly protects against Mobile Ad Fraud
Another challenge for the mobile industry is the rapid increase in ad fraud. W&V, Germany's leading magazine for marketing, advertising and media, recently published a number that probably will make every mobile marketer sit up and take notice: Mobile ads have ad fraud rates of around 30 percent. What does this number mean for mobile marketers?
Mobile performance metrics, such as clicks or app installs, are very susceptible to ad fraud. According to Appsflyer, 51 percent of the app download fraud is caused by DeviceID Reset Fraud. For companies, this means losses running into billions.
Even if there is no guarantee that one will not be a victim of ad fraud, mobile app attribution can implicitly protect against it. Because effective mobile app attribution focuses on long-term targets such as retention rate or customer lifetime value (CLV), instead of optimizing for short-term mobile marketing targets (e.g. clicks or app installs).
The Key to Success in App Marketing: Optimizing CLV with Mobile App Attribution
App providers also face the challenge of competing alongside the top 5 apps such as Facebook, WhatsApp, Instagram & Co. Apps seem to be one of the best ways to reach users: According to GfK, Germany's largest market research institute, users spend around 86 percent of their mobile time in apps. However, 85 percent are spent solely within the top five apps. Adding to the mobile engagement platform Localytics also found out that 71 percent of all app users no longer use the downloaded app after 90 days.
The Solution: Machine Learning-based Mobile App Attribution
To be successful with app marketing, a corresponding app marketing strategy, that evaluates which marketing activities increase the retention rate and CLV, is required. This means that mobile marketers should optimize their campaigns directly for maximizing CLV instead of aiming for short-term mobile marketing goals, such as increasing the numbers of installs.
This requires appropriate data insights. Attribution with the help of machine learning provides the necessary insights: By processing converting and non-converting user journeys, mobile marketers gain a holistic understanding of which mobile ads have a particularly positive effect on customers (and which do not). Synergies and interactions between the single touchpoints can be evaluated and patterns can be identified as to how the single touchpoints affect the CLV.
For mobile marketers this means: Specific mobile ad goals should be measured with mobile app attribution. Ideally cross-channel and cross-device, as well as machine learning-based. Because in this way customer journeys can be displayed much more comprehensively than static attribution models could ever do.
3 Reasons Why Mobile App Attribution with Machine Learning is Important
The following three summarize why machine learning-based mobile app attribution helps mobile marketers realize the full potential of their mobile advertising campaigns:
1. Mobile marketers can evaluate mobile ad campaigns much more comprehensively, optimize budget allocation and increase Return on Invest (ROI).
2. Mobile marketers can win the best customers. mobile app attribution identifies which mobile ads contribute to a high retention rate or which sustainably increase CLV.
3. Mobile marketers can eliminate their mobile ad fraud. By shifting the focus away from short-term mobile marketing goals and toward optimization to retention rate or CLV.