User Acquisition and Attribution: The Basics (Part 1)

Previously, I’ve written series on game analytics and prediction, and now I want to turn to a topic that is increasingly important, yet often misunderstood by game developers: user acquisition and attribution.

As it relates to advertising, the debate on user attribution models is usually one that takes place between the CMO and the CFO. The CFO wants to cut the marketing budget, and the CMO tries to fight back and use metrics to justify the company’s ad spend.  Anyone who’s had to explain their numbers in a board meeting knows just how important it is that those numbers are accurate, and how important it is that you can prove them out.
 

Marketing budgets are volatile beasts. However, as advertising models become more data-driven, it’s important that people outside the marketing team also understand attribution and its implications for games.

So, let’s start with the very beginning. As users click on ads to reach your game, attribution gives credit where credit is due. It takes sales, leads, new customers, and other favorable outcomes and figures out where those customers came from, which allows marketers to determine what sources are more valuable to them. The current debate for attribution models is what’s the best way to divvy up the credit among the sources, but the point of all attribution is to look at different ad sources and say, “Source A gave us a bigger return on investment (ROI) than Source B, so we should probably be focusing on placing ads in Source A.” Your ROI is always based on some measure of lifetime value compared with your cost of acquisition (CoA, or CPI for cost per install).

Now also consider the fact that Source A may be an ad publisher, but at another level of detail it can be an actual creative ad. If you’re looking at publishers, that’s a fairly gross-level way to look at ROI, but we consistently find that there are in fact real differences in performance, and that’s even before we add our own social special sauce. If you’re looking within a publisher, you’re considering the ROI of the particular creative or format (video, banner, etc.). That’s looking at messaging rather than reseller. Both are equally important.

To make this a real-life example, a good attribution model should be able to tell you whether to place more ads on Facebook or Google Search -- based on what ads are bringing in the most players and money. With the rise in online ads and their strong links to online games, this is more important than ever.

The big problem with current attribution models is that they’re not very precise. Attribution today involves a lot of guesstimation and not a lot of data. In the worst case, they are a reminder of the old joke in advertising: 50% works, but we don’t know which 50%.

A good way of attributing ads is through multi-touch data, which means we’re looking at the decision making process as multiple steps. Think about how you find things online: You follow link chains and see multiple advertisements and get recommendations from friends and read reviews on Amazon before deciding to buy. Multi-touch attribution looks at the whole process as a series of touch points, then distributes credit for the buy to each source along the process.

As you might have guessed, there are many models for this, and a lot of debate on which is the most accurate. Let’s start with the simple ones, like last-click modeling. It seems like a simple solution to a very complex problem: Whatever the user last clicked on is what is attributed to bringing them in. This is the typical default of most attribution companies, so if you’re using one and never asked, there’s a good chance that this is what you’re getting. It’s not flat-out wrong, but it may be too simple -- there are many steps and influences in the decision-making process, and yet only the last one of those sources is what gets the credit for bringing the customer in. The approach under-rewards campaigns that sow seeds and build up steam in a longer sales cycle.

The opposite of last click is first click. In the chain, this is the first place you’ve heard about a product: the recommendation from a friend or the first advertisement you saw. It’s widely used because of the awareness generator theory (what initially built awareness is what brings the customer in), but again, it doesn’t take into account all of the other steps in the process.

There’s also linear attribution, which attributes to each of the steps in the chain equally. That’s good because it takes into account multiple sources. However, it doesn’t take into account that different ad sources might elicit different responses (and therefore interest) from users.

As attribution has evolved, models have become a more advanced as well. We can start considering models that weight based on theories that are better than first, last or all-equal. Time decay starts to take into account cognition and the decision making process, and says that interest (and therefore attribution) grows as time goes on. The first click doesn’t have too much credit assigned, but it grows with each step in the chain. However, this model might overvalue the last click that “seals the deal.”

Another slightly more complex model is position-based attribution. The first and the last click are assigned the most credit, and the rest of the credit is distributed evenly on all the touchpoints in between. Again, better: but all the values are ultimately arbitrary.

Lastly, there’s a data-driven approach. A machine learning model can take the different sequences in the touch stream and start to base attribution not on your theory of which matters, but on which pattern actually yielded the best results. As with most machine learning model approaches, the results are sometimes harder to understand, but they tend to be the most accurate. They also require rare skillsets to execute against. My intuition is that this is the long-term future of the field, but it will take some time for the tools and the expertise to catch up.

So, what’s the solution? You might say the right strategy is just to blanket the web in ads, but who’s to say you’re not overpaying for all the sources that did work -- and, if you’re pricing ads, who’s to say your ads aren’t undervalued? Without an accurate gauge of advertising value, click-throughs can be underreported, and the ad will be devalued, leading to inaccurate ROI reporting. Even worse, the process can go the other way, and you could be buying up overvalued ads and sending your company over budget, much to the chagrin of that CFO. You may sometimes hear the term “optimizing,” and this is what it means in the most generic sense: making sure you are spending efficiently given the information and tools at hand.

Now, this series isn’t meant to be a crash course in how to get the most bang for your advertising buck; it’s simply a look at the ways you can figure that out on your own. And the next article will delve into that more.