Audiences and Advertisers Better Together

Advertising agencies often ask audiences whether or not they like a campaign creative - So, what's the big deal about voting?  TikTok is entirely based on opinion, YouTube occasionally requests a vote and Facebook, Instagram or Twitter convert single sentiments into multidimensional data-points. 

When AdFreeway started paying rewards to people, for opinions on its ads many wondered whether that data could be amplified to improve the performance of ad campaigns and justify the cost of rewards paid to participating audiences.

After running pilots for a few years AdFreeway extended its media buy trough The Trade Desk to prove the feasibility of this win-win model. In its most recent correlation, from multiple ad campaigns on three continents, its become clear that data can be applied in campaign-time to re-allocate ad spends to preferential ad creatives retargeted at the campaign audience.   

The fact is the more people like vs. dislike an ad the better it will perform. However, to obtain the performance enhancement media budgets must be reallocated to amplify sentiments to expanded look-alike audiences who will also like the ad and de-amplified to contracted audiences who will dislike the ad. Audience determination and reallocation in campaign-time now works with AdFreeway and The Trade Desk.

As demonstrated in the info-graphic, outperforming media served with no targeting proves that audience opinions are valuable and that put to proper effect they enhance campaign media performance. That enhancement is an economic benefit which AdFreeway is re-distributing to participating users from the advertisers campaign audience. 

Like ad budget reallocation, reward allocation is also dynamic - more likes dilute the value of each reward share. Therefore, tension in the ratio between the reward value of like and dislike is real and competitive. Optimal click volume (always excluding your own clicks) yields a maximum gross reward value to be shared among the least number of users in each look-alike audience.  Game on! 



 




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