Marrying RTB and big data to maximize app monetization
Leaders from mobile, commerce and media converged in San Francisco to discuss the business of apps at Appnation.
Our CEO, Adam Foroughi, paired up with Yannis Dosios, a VP at Flurry, to discuss how the power of big data has allowed the mobile ad industry to move beyond contextual advertising to drive more relevant ads.
To many of you, this might be “ad arbitrage 101.” However, for an audience with varying levels of knowledge and expertise, this session served to explain Real Time Bidding for mobile advertising and how it can benefit advertisers, developers and customers.
To begin, they explained RTB (Real Time Bidding) for mobile ads. They outlined the basic principles of RTB: 1) SSPs (server side platforms) receive an ad request from a device. 2) The SSP makes a request to the RTB exchange for an ad. 3) The RTB sends out the request to DSPs (demand side platforms). 4) Based on what the DSP knows about the user and what it can predict about the user, it will bid on the ad request. 5) The best bid will fill the ad request and serve the ad creative.
Yannis demonstrated how an SSP like Flurry is better positioned to manage and maximize the value of its ad inventory because it has data on hundreds of millions of users via analytics integrations. The more data points the SSPs send the RTB, the more information the DSPs have to bid on the request.
Adam followed to explain how the DSP interacts with the RTB to determine the value of an ad request, whether or not to bid on it, and at what amount to bid at. He detailed what it takes for AppLovin to know when to bid on an ad request by highlighting the following points:
- Sophisticated Predictive Modeling: AppLovin’s algorithms must accurately predict the value of each ad. Via direct integrations with developers, we have built data profiles on over 750 million devices.
- Significant Reach: AppLovin reaches over 1bb devices over 200x per device a month.
- Process High Volumes of Data: AppLovin processes over 7 billion ad requests worldwide, daily.
Because AppLovin is able to ingest all this information, it knows when and what to bid on in order to deliver highly relevant ads to specific customer profiles.
In summary, Yannis and Adam showed that when a strong SSP (like Flurry) delivers more data points and detailed personas to the RTB, a sophisticated DSP (like AppLovin) can be better informed in the bidding process. This results in the generation of more relevant ads, leads to a stronger return on investment, and most importantly creates a better overall experience for both developers and their customers.