Expert Voice: Ad Revenue per User with Sofia Gilyazova
Today we listen to the Expert Voice of Sofia Gilyazova who will share her opinion on Ad Revenue per User and how to take advantage of it.
“Ad revenue per user” has been a hot topic lately. Is it something you are looking into at the moment from an ad monetization manager perspective?
Yes, but as a mid-core publisher, we are looking into this primarily due to the AB tests to help see the revenue impact from different ad experiences. The next step would be another “hot topic” – user segmentation into “ad whales“ etc. and I am only mentioning the ad monetization side of business.
What would the goal of having ad revenue per user be? Calculating LTV or more as info UA oriented to target new clusters with ad whales? Or to create a different UX for each type of user (for example having different placements for different types of users)?
I think it is a great question that can be answered by different types of publishers differently. For us, it can be important to do more intelligent UA as you mentioned, do user segmentation (with largest apps by DAU) and eventually treat our ad whales differently from low-quality users that do not yield any revenue.
As your work experience covers both publisher side and ad network, how easy is it to get this data from the ad network? If hard, why? Which are the bottlenecks?
The existing solutions on the market are great but they are not 100% precise because of the use of the aggregated data.
Each network works with their advertisers using various business models – with the most popular being CPCV, CPM or CPI. To understand how many installs our users generated with each ad provider, you would need to request this data from each network separately – and not everyone is ready to share this – which I consider the biggest bottleneck. Moreover, some users may end up watching a portion of ads on CPM basis and another portion on CPI – which doubles the complexity of this data.
If you eventually gather the data, it would be a lot of work to assign it to the users’ IDs – and I am not confident this can be easily done on the daily basis. With primarily performance networks, this data will be key to having a legitimate segmentation; however, if you work with networks on a purely CPM basis/programmatically, your ad revenue per user will be pretty accurate.
There are tools on the market that claim to be able to be accurate on this info, do you have any direct experience with any of those tools?
We have some experience with one and hoping to explore it more.
Why have companies only recently started to look into this? Is it for the tech barriers or because just now ad monetization has become more mainstream? (even Candy Crush has rewarded videos now!)
Mostly due to the development of the market, where the competition pushes mobile developers to learn more about efficient monetization and SAS companies to offer more advanced and intelligent solutions to tackle the challenge. Ad revenue was historically deprioritized in favor of IAP, but the approach has changed.
Do you think this data will eventually be available and precise at one point?
I believe that with the header bidding era we will be finally able to use the data we are offered now.
Header bidding, a trendy word during 2018 that seems to be the solution to finally reduce the dependency to a waterfall setup. Do you think this tech is sharing the same entry barriers with ad revenue by user calculation? (lacking of S2S integrations or in general ad networks being reluctant to share such a granular info) Or on the contrary, it would help to get this info correctly?
Header bidding will imply everyone to work on CPM basis towards the publisher – which in the ideal case scenario will tremendously help to get an accurate ad revenue per user apart from abandoning traditional waterfall structure and all the hassle around it. However, it implies a marginal risk on performance ad networks that will have to make bids on a specific user without knowing the value of that user. In my opinion, this fact alone might slightly hinder the overall market transition to header bidding for some industry players.
Sofia has been leading Ad Monetization at Social Point for the past 6 months. Before joining Social Point, she worked for one of the quality-focused video networks – Vungle – for almost 4 years. Being able to combine the knowledge from both sides, her objective is to ensure that Social Point’s apps have the most engaging and rewarding video integrations and quality partnerships when it comes to in-app advertising.