Today we’re talking about four of the biggest issues that make mobile attribution a challenge for businesses, along with four ways an attribution provider worth its salt can help you overcome them.
Attribution is tough to get right. It’s the mobile marketers who understand the journey their users take to install that are a step ahead of the game. Mobile app tracking enables marketers to make well-informed business decisions in real time – from optimizing on the best creatives to understanding ROI at a granular level, keeping ad spend low.
Dissimilarities between mobile and web attribution, a lack of industry-wide standards, competing models of attribution and rampant fraud present quite an obstacle course for mobile marketers. These differences can be a bit unnerving at first, especially for those who are new to the mobile ecosystem. That said, there are solid solutions to all of these challenges. Read ahead and we’ll walk you through them one by one.
If you’re new to mobile marketing and would like to first explore topics like what happens when you click on a mobile ad, or how a mobile attribution provider actually does the matching of one engagement to another, check out our beginner’s guide on what is attribution.
Problem #1: A fractured mobile ecosystem makes it difficult for apps to follow the user journey.
The mobile user journey can include mobile phones, televisions, tablets, and even desktop computers. Think about how you shop for something like furniture – you might watch a video ad on your tablet, browse in a few shopping apps or in mobile web on your smartphone, and make the purchase from your desktop.
The most common tracking tool for the web – cookies – can’t follow a user as they switch devices. Other standard methods that are used for web attribution – image pixel tags, and tracker links with custom parameters appended – cannot be used as a standard on mobile. They do not work at all. For example, if your campaign uses Facebook to send users from your ad into the App or Play Store, you can’t use a destination URL to figure out where the user was acquired. In this case, you’d need to work with an attribution vendor who has a partnership with the platform you’d like to advertise on.
Android allows you to track your marketing campaigns; however, if you only rely on Google Analytics or Firebase for your attribution, you are limited in the conversion data you can send to your partners to optimize your campaigns. As an example, Google Firebase is not integrated with Facebook, Twitter, Snap or Pinterest.In fact, they only have around 20 integrated partners. On iOS, users enter the App Store and fall into what’s more or less a ‘black hole’, where you cannot rely on tradition web attribution methods to understand what they’re up to at all.
On mobile, attribution methods must be agile, speedy and privacy compliant while jumping from device to device. They must also find a way to work with self-attributing networks – major publishers like Facebook, Instagram, or Google Adwords – who perform attribution on their own traffic instead of allowing the attribution provider direct access, rendering their corner of the mobile ecosystem a black box. A third-party attribution partner must allow marketers to understand what happens in both stores as well as at every step of the mobile user journey along the way, from first engagement to final purchase.
Implementing an attribution SDK gives app developers the ability to track every single data point – from a user’s login to the time of their most recent purchase – so their marketing team can tie that data together within their own CRM or business intelligence system. When shopping around for an attribution partner, make sure that the one you choose is fully integrated with the self-attributing partners you intend to work with. That way you won’t have to rely on those networks to provide you with data; your attribution partner should be able to ‘check their homework’ and independently confirm each claimed engagement they send.
Problem #2: There are no industry-wide standards for app tracking.
Each ad network has their own criteria for attribution, which can lead to a number of challenges. Most advertisers work with many different networks – if, for example, the attribution window setting differs between them, this can lead to multiple sources claiming the same install. The outcome is that the advertiser might pay for the same install twice.Some of the biggest networks report their own attribution data to clients – including Facebook, Google and Twitter – but this does not necessarily simplify things for advertisers. Because networks earn money from the data points attributed to them, many app developers prefer to receive attribution data from a neutral third party source, rather than the network itself.
Your attribution partner must be committed to automatically deduplicating users (to prevent you from paying for the same person twice) in real time. It should be simple to create a set of customizable standards across the networks you work with in your attribution provider’s dashboard.
Problem #3: There are competing models of attribution.
Attributing a user’s in-app activity to their ad engagement is not as easy as matching one click to one install. As an example, Adjust measures both clicks and impressions – an impression is when you see an ad but don’t click on it. Let’s say you see three different ads for the same app. After the third ad, you decide to install the app. Which ad/network gets the credit? How should that credit be divided up? Here are the most common ways to attribute users:
First touch attribution: This model awards credit for an advertising interaction (either an impression or click) to the first point of contact a user has with an ad campaign.
Last touch attribution: This model awards credit for an advertising interaction to the final point of contact a user has with an ad campaign.
Multi-touch attribution: This model assigns varying weights to different traffic sources for an advertising interaction, leading to multiple channels benefitting when a user interacts with a campaign.
Every attribution provider has their own way of resolving this issue. For example, Adjust’s attribution offering makes use of a last touch attribution model, where clicks receive priority over impressions. Adjust also offers an opt-in impression attribution model, which takes into account the difference between impressions and clicks.
Problem #4: Ad fraud is rampant in the mobile marketing industry.
There are two kinds of supply-side fraud that present major challenges. One is known as fake installs. The most prevalent way of creating fake installs is via bulk device emulation in a virtualized environment on rented hardware (data centers). Fraudsters use Tor networks, VPNs and public or private proxies to try and hide emulated installs in “incentivized” campaigns.
The other type of fraud is called organic poaching. The goal here is to poach attributions from your organic users. Fraudsters create a chance to randomly receive credit for the install via click spamming. A typical method for this fraud scheme is the scripted (not human) execution of tracking links (clicks) on mobile web pages e.g. in games or video players during page load or the execution of click links on banner view. This is also known as click spam, forced clicks and 1×1 pixel redirects. Installs are attributed to clicks that were executed without the user’s knowledge or intent, so the fraudsters cash in on the random chance of a user installing a popular app organically.
Why does mobile ad fraud happen? Mobile ad fraud presents a significant opportunity to make money quickly (though illegally). Mobile ad fraud has long been considered low-hanging fruit because it isn’t uncommon to see a campaign receive hundred of thousands of clicks and relatively few installs, providing a perfect cover for would-be fraudsters. Until recently, a lack of industry action in combating fraud means that a criminal is less likely to be caught and can act with somewhat more impunity.
Every attribution provider has a different way of approaching fraud. Adjust’s Fraud Protection Suite offers three different tools to remove fraud before an advertiser pays for it. Our Purchase Verification SDK confirms purchases made in the Apple Store and Google Play Store in real time – it is its own separate SDK, designed to reduce discrepancies between Adjust’s revenue data and app store revenue data. We also cross-check all IP addresses in real time to prevent illegitimate user data from entering and ruining a client’s data set; the result being that no client pays for these fake users. We combat both types of click spamming with a filtering method known as distribution modeling, which rejects installs based on a statistical model of when those installs are most likely to occur. Taken together, these measures all work to keep fraud out of clients’ data before it ever happens, rather than after the fact.
Those are just some of the main issues that mobile marketers grapple with every day. With the right technology behind you, you can navigate these challenges on your way to success in the App Store.
If you’re new to the world of mobile attribution and would like an even more in-depth overview of the entire mobile ecosystem, check out Adjust’s newest ebook, ‘Essentials of mobile app attribution: A guide from start to finish’