Get the most out of your media investments with Adobe’s Real-Time CDP Collaboration

Transcript

Hello everyone, welcome to the Skill Exchange program. I’m Giuseppe, I’m going to be talking to you about Adobe’s real-time customer data platform with collaboration.

Jumping into it, a little bit about me, and you can scan the little QR code in the corner to connect with me on LinkedIn. I’m Giuseppe, I’m the client investment lead here at WaveMaker, a WPP media brand, and I help out clients like Adobe bring their dollars into the media space to deliver on brand KPIs and their goals. The last five years I’ve been heavily specialized in the CTV space, really focusing on the tech management of the entire media by different targeting solutions that come with that, and then the measurement opportunities provided.

So over the past 10 years, I’ve worked on a lot of different verticals, crossing CPG, retail, auto, and lately, tech with Adobe. So let’s just jump into it, and I’ll tell you a little bit about what we have in store for today.

Okay, jumping into the agenda for today, we’re going to first talk about the value of the data cleanroom. If you’re not familiar with what a data cleanroom can provide, we just want to overall showcase the landscape and what they are from our fundamentals. Then jumping into the specifics of Adobe’s real-time CDP platform and their data cleanroom opportunities there.

Then third, touching on ways, we have WaveMaker utilize the real-time CDP and collaboration for Adobe and really jump into a couple use cases of how we’ve looked at it across a couple different products of theirs. Lastly, touching on the case study and leaving it with some key takeaways for you all to go back to your Adobe teams with. So jumping into the value of the data cleanroom, living in a world where finding, growing, and measuring impacts of audiences is harder than ever, we want to find ways to help brand steward the changing landscapes, and first touching on signal loss. Whether the cookies become fully deprecated or not, it’s continuing to become more fragmented and making advertisers decisions harder in terms of what data is right for them. So cleanrooms definitely provide a solution for that. And we want to do that in a data privacy way. So the importance of stewarding the trust of our clients with their first-party data. We spend a lot of time capturing this first-party data, and we don’t want to be seen as someone who doesn’t manage it in a thoughtful and caring way to ensure it’s not leaked, and we hold their privacy to utmost accountability. Then last, complex tech.

Tech at times can seem and be sold as pretty streamlined and straightforward, but when actuality of activating, it can become very challenging, nuanced, and require multiple different solutions from different teams. So starting things off with the data cleanroom promise. You want to unlock targeting, measurements, and insights, and all that starts with your advertiser and your data. So you spent a lot of time collecting different first-party data signals, whether that be emails, phone numbers, addresses, and so forth, and managing that within your DMP. And you want to activate that with a publisher, where they have their first-party data, and being enriched in a data cleanroom environment, this can be done so, typically through a scoping agreement between you, the advertiser, and the publisher, as you both onboard your data within a cleanroom instance to gain customer insights. And we’re going to be learning about who your audience is and how you can reach them from media activation, as well as understanding who they might be on the publisher end, and think about whether there might be sponsorship opportunities or custom activations that you yourself want to explore, or different marketing teams within your organizations would like to explore.

Then we talk about marketing measurement, enriching your measurement framework with first-party data from your campaign activations, and potentially first-party data from your publishers.

Then, thirdly, jumping into audience analysis and creation, so as you’re building out these audiences, you can analyze their overlap, as well as maybe where they might index well against publisher data. And then lastly, touching on your customer journey. I don’t know how your organization is built out, but typically there’s different media that’s being spent towards brand goals versus performance goals, and through this customer journey analysis, you might see how brand really drives a lot of those performance and lower funnel KPIs through maybe some incrementality learnings or multi-touch learnings.

And we want to do this all through a privacy-centric way, through data obfuscation. And then, we want to control that data movement in one siloed environment to protect all your data assets, as they’re being pushed and pulled in different ways, and cut and spliced to your likings.

So jumping in, how does Adobe Realtime CDP and collaboration work? You have your brand data, as we talked about earlier, and then the publisher data. And you both have instances set up with Realtime CDP, which provides you access to data cleanroom and cloud storage.

And then within collaboration, our three pillars we really like to touch on is the discover, activate and measurement. And all these sub boxes kind of really fill within that, in terms of audience planning, overlay, overlap insights, audience expansions, secure activations and campaign performance, where you or your agency partners can really lean in and gain those learnings, because you’ll all have access to collaboration in your seat. So jumping into it, it’s empowering your media teams to engage with collaboration. Agency teams can discover new audiences and seamlessly activate across partners, or you can work with your Adobe account team to turn measurement solutions and capabilities that fill right for your brand, in terms of incremental frameworks. So first, discover. As you can see, there’s some huge publishers on this slide, looking at Roku, NBCU, Warner Brothers, and you can then instantly set up within collaboration, CDP, in instance with one of these partners, along with many more, are to understand those overlap analysis, and we’ll dive into that more. Then you can push it over to activate it. And then lastly, touching on measurement, in terms of you have a bunch of data that sits in with an AAP, which might also entail both online and offline measurement that most frameworks might not entail for, as well as incrementality that you’ll also have within measurement capabilities, as I mentioned earlier. Her Adobe is a beta and alpha partner for collaboration, so some of these tools might not be available in your instance just yet, but I would talk to your different Adobe account teams to figure out when that will be available to you and how you can fold that into your existing frameworks.

So, how do we at WaveMaker use a real-time data platform with collaboration? Let’s dive into it. First, discover. Dive into your first-party data and instantly see their match sides of your audiences across the publishers. So, as you can see in the chart on the left, we’re looking at two different Adobe audiences here for our Lightroom product. One was a propensity audience that we set up and built within Adobe’s DMP that then we pushed over to collaboration. And as you can see, it doesn’t overlap at all with our existing Lightroom subscribers. So, where’s the opportunity here? You definitely want to lean into this propensity audience and knowing that it’s not overlapping with our current Lightroom subscriber audience is all green lights to move forward there. But then Lightroom subscriber audience, that’s a really powerful data segment. These are people who are actively utilizing our product and paying for it. How can we build opportunities with them? We can A, simply build out a look-like audience within collaboration or B, we can push that Lightroom subscriber audience to a partner like NBCU and then have them build a look-like for us or tell us what sort of different audiences highly match against our Lightroom subscribers.

So, as you look to the chart on the right, this is an example of the propensity audience. As you can see, there’s a pretty decent overlap of identities between our data and TVTube’s data where we have a 30% overlap. And then also we’re able to see that there’s a high propensity of our Lightroom propensity audiences, sorry for the double dip there, are against college grads. So that’s one way we can think about it. I think a really relatable example is for one of our recent upcoming campaigns we’re looking at with Adobe is we were exploring an Adobe audience with NBCU and we noticed that it highly indexed against comedies. So in terms of comedies, we pulled a subset of that audience and saw was there any specific audience or comedies that highly indexed and we saw that the show The Office highly indexed against this audience.

So we looked at different media activations with The Office as well as, I don’t know if any of you guys have seen, The Office is showing, coming up with a new show or the show writer The Office, Greg Daniels, is coming up with a new show called The Paper where we saw a huge opportunity in terms of sponsorships as well as exploring custom content integrations with that program to really have a robust opportunity to reach our audience that is very well leaned in in a high profile format. So it’s not just for seamless activation as well as it can create white space for sponsorships, custom content and contextual targeting. So jumping into activate, this is a big opportunity since it’s not always easy to get your first party data to a publisher in a trusted way.

Adobe’s CDP platform really seamlessly provides an opportunity for you to push your first party data to a publisher all within one space. You don’t have to worry about your campaign being delayed through bringing in a third party or having to bring in another tech platform that has a match rate that maybe not be the same place where your first party data is stored. And it just creates complications. This is seamlessly integrated because it’s all staying within the Adobe Experience Cloud platform for targeting, building lookalikes and suppressing your audience for personalized CTV and then pushing it directly to that publisher. Then jumping into measurement, we always believe in leaning into your existing measurement framework. We’re not going to come here and tell you that collaboration should override any sort of existing framework you have. We at Adobe, we utilize quite a few different data partners and measurement capabilities such as iSpot for overlap analysis, frequency analysis, as well as conversion data.

Then we utilize Disco for ad measurement and brand tracking, as well as our different ad servers and measurements. We have our existing framework set up where we understand where our ads are going and how our existing framework is driving brand growth and brand health.

Where collaboration comes in is it provides additional opportunities for you to pull on offline measurement data, online measurement data that might not be existing in your regular framework as well as incrementality through AEP to enrich your framework, to allow you to have more deep conversations with your measurement teams and your finance teams to showcase the power of your advertising and how it’s stewarding the brand. So, how do we learn from these results? Let’s jump into it.

Through initial tests with collaboration, we’ve seen the power of our first-party data. One of our biggest partnerships to date was with NBCU where we looked at two different Photoshop audiences and we looked at existing Photoshop subscribers and then built a lookalike on them. And then people who’ve purchased other Adobe products but have yet to purchase Photoshop.

And we were able to set this up and test it against other data from NBCU, which we have historically been buying, as well as a lot of other different CTV providers. And what we noticed was through enriching our campaign with this first-party data, we saw increases in aided awareness, increases in consideration. And lastly, the increases in perception of our products. So, we drove all the different brand impact pillars that we would like to see with this campaign. And then how did it perform versus the baseline? NBCU performed 25% higher in terms of driving site traffic than the baseline, showcasing the power of this data. Since we were able to tag it individually, we were able to get individual audience insights. We were able to get individual perception insights. You know, the perception ones are a little bit more challenging and we might not have that stat sig on every different targeting detail, but we were able to holistically see the stat sig of NBCU versus other partners, as well as seeing the conversion insights at the different line item levels.

So, overall showcasing strong results for our brand.

Then lastly, jumping into the key takeaways, Adobe’s collaboration solution interface allows for brands to understand, you know, like we talked about, the audience overlap and the opportunities that, you know, are created there from not just an activation standpoint, but also from providing custom activations to enriching insights to share with your different marketing teams.

The scale against different publishers, whether that be like we talked about, an NBCU or, you know, maybe a Roku and looking at TV interfaces, as well as, you know, we didn’t talk about this too much, but it can also be pushed to your different programmatic platforms, such as ADSP, as well as social platforms. So, if you want to activate on meta, this data is able to be ported there as well, without having to, you know, set up another instance.

And how that audience is indexed against partners can drive strong brand results for your media executions.

And one key takeaway here would be, as you want to lean in, and if you find this content really insightful, that you should jump in, have the conversations with your Adobe account team and highlight which partners you’d like to test first, since there is a legal scoping agreement required for that data sharing.

Then second, collaboration allows brands to seamlessly activate that first-party data, like we’ve talked about, for precise targeting and lookalike modeling. And I can’t tell you enough, like, the flexibility of being able to build out lookalikes within the platform, or, you know, maybe you don’t have enough time or the bandwidth, you can push that directly to your publisher partners, and they can build out those lookalikes for you, and that’s all lived within collaboration, and it can be seamlessly activated upon, which is a nice feature of theirs.

And then lastly, in terms of measurement, you know, depending on your vertical and depending on your brand, like, offline measurement might be the key driver of success for you, and you have all that sitting, you know, within AAP, being able to match that back is a huge unlock, or maybe match back online measurement to incrementality or frequency management, like, there’s tons of white spaces that this can unlock that, you know, we definitely think it’s worth having conversations with your internal measurement as well as the Adobe account team to figure out how to get this set up for you to best enrich your media buys and showcase success, especially because, you know, a lot of this CTV space has not been as measurable as we’d like in the past. There’s been, you know, a lot of limitations, and we really find opportunities here with collaboration and CDP to providing those unlocks in these emerging spaces as they continue to grow.

Then last but not least, jumping in for any Q&As that you all would have. Wow, Giuseppe, what a thorough session. I know your insights will be a big help to many of our listeners. Thank you for joining in. Yes, thank you for having me. I’m excited to be here and excited to jump into the questions that the listeners have.

Awesome. So we have this time for questions.

So guys, if you have any questions, post them in your Q&A chat and we have some line of questions now.

Giuseppe, user asks, what recommendations would you have for someone who is getting started with real time collaboration? Any best practices or tips? Yes. So as you jump into best practices, we definitely recommend starting up those publisher relationships, especially as you’re looking into the CTV landscape, since those have to be set up as a one on one integration. So working with getting those agreements in place with your different CTV partners would be my number one priority, because the last thing you want to do is sell them the excitement of doing this new test and then being stuck with the paperwork that’s involved with the data sharing agreement. So once you have the data sharing agreement set up in place, you know, you have a lot of green lights and opportunities to test this great and rich data that you all have compiled within your CTV platform.

Thank you. It was very practical tips. So here is the next question. Given your strong results in collaboration, are you thinking of any optimization strategies for your campaign? If so, how are you going to do about it? Yeah. So as we look to evolve our campaigns, you know, we definitely are off with this light room of campaign where we’re setting up the CDP audience with like different propensity layers. So that’s exciting. And then looking at lookalike audiences as well as, you know, light audiences that just have the free subscription and finding best ways to reach them and see how that trickles down into some lower funnel opportunities to remess it to them. So we’re really excited about that. We’re also exploring the CDP product across like new Adobe products such as Acrobat and going with the similar approach of building lookalikes within the platform. Since previously we’ve tested building lookalikes with the publisher directly to see how that varies in terms of driving campaign results as well as pulling in site engagement, which is new for us. You know, looking at our Adobe analytics analytics data to find site traffics and then build upon like a free messaging audience there. People visit the landing page and be able to reach them directly with the CTV publishers as well as like partner expansions. So, you know, right now we’re talking a lot about CTV and how you can utilize that there. You can also push this to different platforms such as, you know, Amazon DSP. So programmatically and then social platforms as well to, you know, have a more holistic approach with this audience setup.

That was thorough. So here is another question. A very, very right, very timely question. What kind of prep is required to talk to CTV partners and how much time typically it takes? Yes. So setting up with CTV partners, it really just depends on how quickly your legal team can move. Since it is a data sharing agreement that needs to get into place and you have to set up with each individual CTP partner. I would definitely understand from a media strategy standpoint who is your priority. We’ve had some pretty streamlined processes thus far working with like NBCU, Warner Brothers Discovery and Roku, just to name a couple in terms of getting that in place and being able to work with them within our CTV platform and them being pretty, you know, quick partners to sign agreements and expedite things on their end.

Awesome. Thank you. It was quite insightful. So do you have governance best practices that you can share when working with partners or any other stakeholders when it comes to audience overlap? Yes. The great part about CDP is like you can understand that audience overlap within the platform. So part of the discovery phase, you know, as you’re looking at what audience is right to test, it all exists within CDP before you push it over to the publisher to understand the degree of overlaps that you have to identify the right audience. You know, you can also get creative and, you know, try and do suppression within CDP, if that makes sense, depending on the scale of the audience. So the overlap opportunities there are really rich because you can really tell how much overlap there is and whether you should recommend suppressing an audience within CDP. I will say that is kind of a best practice if you’re looking to suppress an audience within CDP, because the publishers, once they have these audiences, might be a little shy to suppress them because they’d be fearful of scale.

Insightful thoughts. So our shy audience is opening up. We have a good line of questions. So how can we leverage our direct one-to-one collaboration with entities that are not Adobe partners and still use Adobe lookalike models to build audience? So if the audience is within your platform, you can then build the lookalike within your platform and the publisher is just accepting that matched audience. So that’s like the real key capability there is like that can all live within your CDP to build the lookalike off of your Adobe audiences and your first party data. However, you can also work with publishers. So you could just send them your first party data and ask them to model it out for you. We’ve tested both across the Adobe business and, you know, varying results. We haven’t done a head-to-head test yet, but that’s probably on the roadmap for our future to see, you know, what sort of tweaks we need to do within our CDP lookalike versus maybe, you know, an opportunity a publisher has at creating that lookalike model for us. But also if you think back, like lookalike modeling within DSPs and social platforms have become a little bit more limited. So you do have the ability to create the lookalike in CDP before pushing it out to a DSP platform or a social platform.

Thank you for sharing the approach. I hope you found the approach insightful. Here is one last question. So what is the best way to identify the right audience for data collaboration between publisher and advisor? So the best way is really just to go back to your media brief and your media objectives to understand the right audience. So I think one of the great part about having a rich, you know, build a first party audiences is you can understand and, you know, are you able to build out a propensity audience based off of the data you have? And propensity audiences are really helpful and insightful in finding that top of funnel messaging and building your brand, which CTV can be a really effective medium to do so. However, you also have the opportunity to build lookalike models based off of your, you know, conversion audiences and match that back to measurement. And so you have this measurement data that sits within AP. You have that conversion data and you can build lookalike models off of that for brand or mid funnel messaging to then help push consumers along further within your purchase cycle. So I think those are like two great ways that you can utilize your data and your audience and then push it directly to a publisher like NBCU to have a really strong and effective outcome.

Awesome. Stop on real time collaboration. Big thanks again to Giuseppe.

Yeah, thank you for having me. This has been great. Really appreciate sharing the insights on CDP and the success we’ve seen across the account. Thank you.