Video: OTT Ad Personalization Done Right

Learn more about personalization and OTT at Streaming Media's next event.

Watch Nola Soloman's complete presentation, B103. Dailymotion--Hyper-Personalization & Viewer Experience Across Platforms, in the Streaming Media Conference Video Portal.

Read the complete transcript of this clip:

Nola Solomon: I want to talk a little bit about OTT, as I promised I would in the beginning. And personalization within this very challenging format. I'm gonna start with a fairly simple example and walk you guys through some of the positives and challenges of personalized targeting on OTT.

So, in this scenario, we have Connie, and we're gonna try and target her. She is 28, she is female, she's a travel enthusiast, she owns a connected television device, and she streams advertising video on demand whenever she feels like it, she doesn't pay for subscription.

If you're an advertiser and you wanna target Connie, you need to identify who's watching the connected television device, and to do that, you utilize a tool called an identity graph. And identity graph houses all sorts of information about users, and creates user profiles. It's completely anonymous, it's totally PII, but it's able to help you guys identify who the user is, whether they're male or female, what age range they fall under, other information you might know about their behaviors or credit card information, any kind of information you can get.

There's so much data out there, and the identity graph will take it all together to create a use profile that you'll use to target. So in this case, we've identified who Connie is. She's female, she's between 25 to 35 years old, and she's interested in travel. So we need to show her the right ad. Maybe there is a travel agency that's trying to get clients to book a trip to Fiji.

This could be a really perfect opportunity for them to target Connie. So she's watching a connected television device, which means that the advertiser needs to think about what device the user's on at the moment. This is a really engaging device because it's large screen, it's highly viewable, and usually really completion rates. You can't click out of it, you can't really engage with it the same way you can with other devices.

So they're gonna target Connie with a beautiful image of a beach in Fiji to try to entice her to go there. In fact, they're sort of bringing her there because of the size of the screen, and they really get the chance to be creative because of the way that this unit allows for them to really immerse the user in the advertising experience.

Next is leveraging other touchpoints. You've shown Connie this beautiful beach, and you want her to come buy a trip with your agency to go to Fiji. You gotta actually get her to a place where she can buy that. So you gotta think about how do you target her across her other devices because you just showed her the ad on connected television.

So you go back to your trusty identity graph, which also has cross-device solutions to it where it's mapped all of the different devices that it knows are Connie's, or at least it thinks it knows are Connie's. And you're able then to target her with an ad, potentially on her mobile phone. If she's sitting on her couch with her phone that's connected to the same IP because it's connected to the same wifi as her connected television device.

So you would target her. You would target her with a consistent message from the original message. You leverage sequential advertising to continue to tell the story and show her the next iteration of what her trip could look like, and then you give her a call to action. What it is that you want her to do. It could be signing up for a newsletter, it could booking this trip to Fiji.

Our end results, user satisfaction and conversion. Connie has an amazing time at the beach. So this is great. This is an example of where it works really well. But the problem is this is the ideal scenario. We all know that it's not quite this simple most of the time. And why is that? It's because connected television and usually OTT streaming is done at a household level, not at a individual user level.

So if we're looking at the scenario where Connie actually lives with roommates, John and Sarah, they're all about the same age, but they have very different interest, and obviously, they're not of the same gender, all of them. So how do yo know who's actually watching the connected television device at the time that you wanna serve the ad? So that's the challenge, who is watching?

In this scenario, Connie is watching, but she's using Sarah's streaming service, and all three roommates are home, all their other devices are connected to the same wifi. So you actually have a really hard time to identify that it's Connie that's watching the content. And therefore, you might target her using the data from Sarah's streaming login with an ad for sports, but Connie's not interested in sports, she's interested in travel.

So this is one of the major challenges of OTT. One of the other challenges that I'm gonna talk about is completely unrelated to this fictitious scenario of Connie, and Sarah, and John and their lovely roommate pad, but is the concept of frequency capping, which we are all very familiar with across mobile and desktop, but is a really challenge on OTT.

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