The ad spend conundrum
In the age of mobile, the question of what type of ads to run (and when) is no longer a decision to be made based on a gut feeling. Major players today prefer to base their strategies upon millions and millions of real-time data points which paint an accurate picture of the state of their existing users, their habits, and their advertising sources.
Despite this, there remains one element of the mobile advertising landscape mired in inaccuracy, which in turn makes every other decision just that little bit less objective.
That’s right - it’s time to talk about the poorly-reported elephant in the room: ad spend.
Defining “Ad Spend”
When people say ad spend, they’re talking about the cost of acquiring a single user from a paid source, be it CPI or some other buying model.
When Adjust talks about the kind of cost data we want to display on our dashboard, we’re talking about tracking and calculating the per-user, non-aggregated cost that’s paid at the time of acquisition. This includes even the tiniest shifts in bidding being reflected on our dashboard with complete accuracy, for each and every user that comes in via a paid source. However, this concept is not shared by all players in the scene.
So what’s the problem?
Getting the per-user, non-aggregated cost that’s paid at the time of acquisition seems pretty reasonable, right? Unfortunately (with the exception of a few networks) this data simply isn’t being made available. So instead, some attribution providers end up basing their statistics on aggregated data obtained from the network, which often means scraping data from network dashboards, or building out custom integrations which offer no control or uniformity over the data obtained. This means that users with wildly different acquisition costs end up sharing the same value for their ROI calculations, because the network bunched them all together under one campaign (or in some cases, network-wide) cost.
This isn’t just unfortunate - it’s dangerous, as it can lead to fantastic looking ROI numbers that don’t reflect reality. In fact, there’s a whole bunch of unfortunate possibilities that originate from this situation:
- As users from the same network/same campaign all end up with identical allocated costs, your break-even point for high-cost users will likely be too low, or you may see less revenue than forecast from users whose cost data made them look pricier than they really were.
- As things stand, you’ll never actually know exactly how much profit you made from a single user.
- You’ll also not be able to accurately compare your return on ad spend (ROAS) between two networks, or accurately be able to estimate how long it takes a particular network, campaign or creative to have positive ROAS.
- These blind spots in your analysis make it harder to optimize both marketing and engagement strategies. They can lead to poor decisions, like chasing the wrong users, or overemphasizing campaigns that don’t generate the returns you’ve been led to believe they do.
- Audiences or user segments based on profitability would also suffer from accuracy issues - as different networks aggregate their costs differently based on country/network/campaign. As such, there really is no way to know if the users in your targeting really belong in there or not.
Towards a solution
So what can be done to bring ad spend in line with the rest of mobile ad analytics? Well, the solution lies in decoupling a user’s cost from the campaign or network they were acquired from, and instead assigning a cost of acquisition to each user.
The reason for this is simple - modern attribution providers like Adjust do more than just report on the performance of campaigns. For instance, we allow you to break users down to the most granular levels, build segments and audiences based on their behavior, and so on. Each user is treated as a separate entity, which can then be analyzed in a vacuum or as part of a campaign, and for accuracy to be maintained, ad spend data needs to be equally granular.
If per-user ad spend data was to become industry standard, Adjust would be able to combine it with revenue data collected by their SDK, leading to 100 percent accurate ROI and ROAS calculations not restricted by campaign or user source.
This user level cost data can then be sent in real-time to any third-party or in-house BI or analytics tool for custom analysis and modeling, or you can see your results on the Adjust dashboard, also in real time. Plus, while a network may only display cost on a per-campaign level, we make all of our KPIs available right down to the creative level, meaning granular breakdowns of your users’ ROI and ROAS for even the largest of campaigns.
Retargeting efforts would benefit from this too, as per-user cost data would mean users obtained from the same campaign could be segmented into different audiences based on their ROI, allowing you to prioritize users who have already proven themselves profitable, but those who you haven’t retained.
The solutions discussed here currently don’t exist, but they really should. Our industry has matured in ways unimaginable in just a few years - in the way we advertise, in the way we analyze, and in the way we compete. It’s a shame that ad spend hasn’t seen the same drive for accuracy that other elements of our ecosystem have. We at Adjust believe in data transparency and accuracy, which is why we are currently working on changing this status quo for tracking ad spend.
To find out more about our Ad Spend Initiative please reach out to firstname.lastname@example.org.