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SuperAwesome's Awesome Content Targeting (ACT) explained
- 1. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 1
- 2. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Awesome Content Targeting
- 3. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Targeting a year ago - Picking Publishers
- 4. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Contextual targeting via demographic segments Gender Age PRE-SCHOOL 3-6 KIDS 7-9 TWEENS 10-12 TEENS 13-16
- 5. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Auto-optimisation Time-of-day Connection typeCreative KPI Interests
- 6. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Auto-optimisation Time-of-day Connection typeCreative KPI Interests
- 7. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Building a kids interest graph
- 8. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 How does ACT work? Gaming Lion King Mickey Mouse Disney Donald Duck etc... Books CatsGaming Disney Nintendo ACT Dancing Ponies Football Marvel Batman Interest analysis Interest analysis Content analysis
- 9. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 AwesomeAds dashboard
- 10. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Content targeting by creative analysis
- 11. Strictly Confidential | www.superawesome.tv Copyright SuperAwesome Ltd 2017 Tech driving performance for clients Ad performance when using ACT vs manual targeting:
Editor's Notes
- Intro to me & the team
- What I want to tell you about today is something we’ve been working hard on for the last few months, which we call Awesome Content Targeting (or ACT). ACT is all about using machine learning to make sure we show the right ad to the right child at the right time. As with many other areas we tackle, this is already a solved problem in the over 13 space, which comes with it’s own unique set of challenges in the U13 space. You might have read about this in the press over the last few weeks, and I’d like take you through how it works, so you can understand some of the technology behind the scenes.
- I’ll start by giving you a little context on the journey we’ve been on. If you look back 12-18 months, the vast majority of campaigns were bought & run on an individual Publisher or narrow site list. Now, there’s nothing inherently wrong with that - if you want to target kids on X publisher, you target that publisher. But this approach does come with one very significant drawback, namely that every single ad impression served in that campaign did so because it had to for the campaign to deliver, not necessarily because it resonated with that particular child.
- Campaigns run across demographic segments rather than specific Publishers / site lists The great benefit here is flexible campaign delivery. Each ad can serve on a far wider pool of destinations, which means, essentially, the ad can choose where is best to serve.
- And the way the ad chooses where to serve today is via our real-time auto-optimisation algorithm, which is constantly learning, adjusting and redistributing to ensure the optimum outcome for that campaign. Today, the algorithm optimises for many things: KPI (eg 75% VCR) & campaign goals (impression numbers) Interests Margin Time-of-day Connection type Creative
- And the way the ad chooses where to serve today is via our real-time auto-optimisation algorithm, which is constantly learning, adjusting and redistributing to ensure the optimum outcome for that campaign. Today, the algorithm optimises for many things: KPI (eg 75% VCR) & campaign goals (impression numbers) Interests Margin Time-of-day Connection type Creative
- We start from a position of great strength in terms of the enormous volume of unique high-quality data available to us. Firstly, we run the largest kids marketplace in the world - hundreds of publishers, thousands of apps, hundreds of millions of kids. Secondly, we’ve accrued a huge amount of historical campaign data - we have literally billions of historical campaign data points, so we can see that brand X performed best on content Y. And lastly, but perhaps most importantly, we have Popjam, which is effectively a real time panel of kids interests & sentiments evolving in real time. What we’re building here is a social graph, similar to Facebook’s social graph, but using zero personally identifiable information.
- Starting with the Publisher’s app or site - we’ve developed advanced crawlers which perform deep content analysis of apps & sites, dynamically generating a list of interests based on the content of that app/site. With this technology, we are effectively indexing the kids digital universe. We then layer in our data from our campaign performance history (brand x performed best with content y, and poorly on content z), and train the system with Popjam association data (kids who are interested in x are also interested in y). Potentially available to third parties as a DMP
- You can now also target interests on a multi-select basis. You click in the interests box and you’re presented with a list of the most popular interests across the SuperAwesome marketplace at that given moment. Select an interest and our system automatically suggests additional interests to you. For example, here we’re looking to target kids who are interested in ponies, but our system knows that kids who like ponies also like magic, riding and unicorns. The real value here is in the counterintuitive suggestions which you’d never arrive at without access to this social graph {insert valuable counterintuitive suggestion}.
- Once ads are uploaded into our system, we also perform an analysis of them using the same crawler technology, and provide targeting suggestions. For example, if our system detects your ad is about ponies, it will suggest additional interests for you to target that we know resonate with kids who love ponies, as well as making recommendations on the demographic targeting options you have suggested. The aim here is to provide you with the most targeted niche possible that still allows your campaign to reach it’s goal (eg 1m impressions).
- You can see the difference that this technology has made - performance improves drastically, all without any human intervention whatsoever. The reason for this improvement is that each ad shows not because it has to for the campaign to deliver, but because our algorithm reliability predicts that the ad will resonate with that particular audience. This is still just the beginning - the longer we run this technology, and the greater the volume we run it over, the more the system learns & improves You can of course still target to individual Publishers, but I hope I’ve shown the impact that this technology can have on your ROI