Marketers are currently evaluating how to effectively target and measure digital advertising at scale while adjusting for a whole bunch of ad industry shifts. And, how to do that with the same efficiency we’ve all gotten used to.

In this episode, show host Jake Moskowitz talks with Todd Touesnard, EVP of Product and Data Science at Ericsson Emodo, about a number of AI-related topics, including potential applications of machine learning for targeting advertising audiences without device IDs.

Jake’s FIVE list:

Five ways AI can help the ad industry adjust for the changes to Identity:

  1. Make up for lost scale
  2. Make wiser decisions in the face of rising costs for ID-based inventory
  3. Make contextual targeting more flexible by incorporating a wider definition of context
  4. Allow marketers to focus on data quality rather than quantity
  5. Be more privacy compliant

Jake and Jeremy Lockhorn meet up again to explore some really interesting ways to experience AI personally. They share four browser-based applications that are free, easy to access and easy to use. And, they’re really fun.

Referenced in the segment:

https://quillbot.com/summarize

https://teachablemachine.withgoogle.com/

https://research.google.com/semantris

https://quickdraw.withgoogle.com/


Transcript of Episode 8: AI’s Role in Identity

Jake Moskowitz 0:01
Problem is, you don’t have this ID to be able to sort of target users and locate the intended audience the same way that you did before. And I think that that’s exactly where machine learning models can help.

Let’s talk AI. Welcome to FIVE, the podcast that breaks down AI for markers. This is episode 8: AI’s role and identity. I’m Jake Moskowitz.

Unless you’ve been living under a rock for the last six months, you know that the advertising industry is undergoing an identity crisis. I’m not talking about an industry getting in touch with its inner self, though that wouldn’t be a misstatement. I’m talking about the deprecation of device IDs and cookies. Two important steps when learning about a new technology like AI, or one understanding exactly how to apply it in a specific use case, is getting your hands on it, and trying it yourself. This episode will help you do both. The future of identity is a timely industry issue that can serve as our AI use case, in fact, has become one of the most prominent and uncertain topics in marketing today. In this episode, I’ll talk with a product leader about a number of AI related things, including potential applications of machine learning, for targeting audiences without device IDs. As for getting your hands dirty experiencing AI, personally, Jeremy Lockhorn and I will explore some really interesting ways to do so a little bit later in the show. They’re free, easy to access and easy to use. And they’re really fun.

But let’s start with identity. It’s kind of a big deal. It’s hard to imagine digital advertising. Without the sophisticated targeting we as an industry have developed and sharpened and made the very core of our marketing strategies. The ability to link an exposure on a device to a specific outcome has enabled digital ad measurement to go far beyond that of traditional media. I mean, think about multi touch attribution, and controlled lift studies that measure in store sales and traffic. Or how about the ability to find devices again, just that capability ignited the explosion of retargeting. And the ability to aggregate data over time for a particular device enabled all kinds of capabilities we sort of take for granted now. Stuff like frequency capping and building behavior based audiences, custom audiences and look alike audiences.

But identity is changing. And we’re facing a future in which there’s less ability to accumulate massive amounts of device level data, and less ability to use the data we do have. We have privacy regulations, like ccpa and GDPR. And sweeping policy changes, like those in Apple’s iOS, the deprecate the mobile device ID. There’s the deprecation of third party cookies in Google’s and Apple’s browsers. And of course, there’s the changing consumer who’s becoming much more aware and sensitive about data tracking. That’s a lot. So marketers are currently evaluating how to effectively target and measure digital advertising at scale while adjusting for all these changes, and how to do that with the same efficiency we’ve all gotten used to.

The issue of identity is a really timely, relevant example to get into. In order to dive in deep and early, we kind of need to go behind the scenes. One way to do that is to open our own kimono. So I’ve invited Todd who’s in ARD to join me. Todd is the head of product and data science at my company, Ericsson Emodo. Todd, thank you for joining me, I’ve noticed just from watching how we work as a company, that so much of your product work and building AI products, connects you to every other part of the company.

Todd 4:13
Yeah, I think that’s right. Achieving success with this type of product goes well beyond, you know, engineering, data science and product teams. I think it requires alignment across most functions of the company. So you call that a few BD and working in tandem with, you know, potentially legal and data privacy to understand and go secure data marketing that cross functional discussions with marketing to communicate value, sales need to engage with customers and also for on the front line, providing feedback on the market. And then Ops, the operations team, at least in a marketing context here, ensuring that the algorithm is being leveraged to its full capacity also, which is very, very important to help measure success in the wild. Once you deployed, I knew, you know, ai type of product. So it is absolutely key. And yes, it goes well beyond the technical components of building a product like this, all functions seem to be lined up and going in the same direction.

Jake Moskowitz 5:13
That might be the best depiction I’ve heard of the cross company nature of an AI effort. So AI is not a product thing or a data science thing. But to do it, right, you need support and involvement across the company. When a company is focused on machine learning as a differentiator, or as a way to go to market, the entire company needs to be behind that effort all working together, as one

Todd 5:37
Absolutely agree, 100%.

Jake Moskowitz 5:39
What have you learned the hard way about building AI products that you could never have possibly known without personal experience?

Todd 5:47
I’ll call it two different areas. One is on the implementation side, it can certainly be a challenge. I’ve spent a lot of time on this particular one. So an example within implementation where you can have a challenge is about performance. So we have several models that we’ve had to implement over time that are highly computationally expensive. And there’s a huge cost to running them in his full scale production environment that just really, you know, wasn’t obvious in our lab. So that’s something I would I would note.

Another example is effectively rolling out a real world testing program, it can be much tougher to do that in the lab. So really, I was just thinking this through pulling a production focused plan to verify that your model is doing what it’s supposed to be doing. That is a smart approach, and something that I learned. So those are two pieces around implementation. And for starters, it’s not always obvious what’s going to be useful or what’s going to work. And domain expertise and hands on experience are very key in this particular step. So an example from from my work is we work with really large mobile operator datasets, where we have a comprehensive view of device and data consumption patterns, it’s huge data set and to take that data into engineer intelligent inputs, can be very, very difficult than a tremendous amount of work. It can also change based off of geography.

Another huge problem that I’d highlight is just getting good data may be a tremendous challenge. In my experience, getting your hands on data that fits all the key criteria to build great models is really hard to come by. I can’t be so you know, a huge data set, data set that represents all of the geographies that you need to if that’s the case, and all the classifications of data, if that’s relevant, but also trustworthiness in that data set. The accuracy is huge. But then, you know, and what’s really important, especially today in marketing, is data, privacy considerations and data usage rights. Those are very complicated problems that really, they aren’t technical in nature, but they are huge challenges that need to be dealt with.

Jake Moskowitz 8:00
Do you think the industry mistakenly thinks of gathering training data as a checkbox? Like, yes, we have it or no, we don’t have it. That there’s an under appreciation for the characteristics that make for successful AI, like trustworthiness of training data, or consistency or accuracy or usage rights of your training data?

Todd 8:25
Absolutely. They can often be the most difficult problems to solve.

Jake Moskowitz 8:31
I want to reflect back on something you said that models can be computationally heavy, and thus I assume expensive. It got me thinking building an AI product is a delicate balancing act, because you obviously want to maximize performance. But you also have to ensure you’re keeping costs in line so that the model provides more value to the client than it cost you to build, and thus is financially sustainable. Some market needs presumably can’t ever find that balance. So that’s part of the trickiness of using machine learning to solve industry needs that sometimes it just can’t solve them. Because you can’t find that balance. Does that sound right?

Todd 9:08
Yeah, that’s right spot on.

Jake Moskowitz 9:10
Where do you see the biggest use cases for machine learning within marketing in the next year or two?

Todd 9:16
Yeah, one use case that is certainly top of mind for me in most companies in the digital advertising space is around identity. You mentioned this at the top of the discussion. So access to third party cookies and mobile ad identifiers will be to some extent deprecated or go away entirely. So most believe that this will have a huge impact on the ecosystem. And I agree, core capabilities such as ad targeting and retargeting. Frequency capping measurement will certainly be affected, and there will be a lot of change. So with this change, though, I think there’s a huge opportunity for AI to play a strong role in solving some of these problems.

Jake Moskowitz 9:58
Just to clarify, there’s a lot of industry chatter around alternative idx structures like UI d 2.0, as a replacement for cookies and mobile ad ideas, and about old school contextual targeting making a comeback. When you talk about AI playing a role in the future of identity, is that what you’re referring to?

Todd 10:18
Yes, absolutely. I mean, if you just drill into the problem around ID deprecation, if you look at what’s actually happening, users going to have to explicitly opt in site by site or app by app in order for their data to be leveraged in the way that it is today. So that poses a huge challenge in terms of, you know, advertisers having the ability to reach their customers. So for example, even if a publisher has a tremendous database of 12 years of data built up on a particular user, the user does not opt in, and that data can no longer be used in the way that it is directly use today. So this could provide a huge challenge. I think predictive modeling is an area that could certainly play a big role. And I believe that the industry will certainly look to adapt in that way.

Jake Moskowitz 11:08
To be clear, you’re saying databases won’t be as useful regardless of their size? Because there will be less inventory with an ID to match to. And we’ve got to make up for that loss of scale elsewhere.

Todd 11:21
Yeah. And I think another dynamic that could surface is the cost of IDs based inventory could probably rise. So cost of inventory and solving that problem. And how do you allocate marketing spend most effectively is certainly another area that’s ripe for some AI based innovation.

Jake Moskowitz 11:39
So as cookies and mobile ad IDs go away, you need a varied approach. There’s no silver bullet, you need alternative identifiers you need contextual targeting. And you also need machine learning based approaches.

Todd 11:51
Yeah, that’s right. And as far as contextual targeting goes, I think that’s something that’s really evolving, that used to mean, you know, a user’s on a particular website, so serve them a specific type of ad based off of that context. So contact doesn’t necessarily mean around the ad, or what the user has searched anymore. I think with AI driven approaches, we’re able to deliver a much more sophisticated understanding of what context is. So leveraging more than just the content the users interacting with, but also deriving context based off of, you know, time of day, day of week, the physical location of the device that the users holding, the orientation of the device, and the movement of the device, the weather, proximity to home, and it goes on and on. I just really think there’s a huge opportunity to take context to the next level.

Jake Moskowitz 12:46
That’s really interesting. So let me play that back. Contextual targeting is indeed coming back into vogue. But it’s not necessarily your parents contextual targeting, so to speak, the definition of context has expanded beyond just the words on the page you’re looking at, to include things like time of day or device location, inventory source, that are contextual to the moment in time in which the ad slot is available. That sounds exactly right. When it comes to the use of machine learning to fill the gap and scale resulting from cookie and Id loss, what will differentiate those that are successful at it versus those that aren’t?

Todd 13:25
Yeah, I think there are quite a few things. One I’d start with is access to high quality, accurate, and potentially unique datasets that exhibit the many of the characteristics that we talked about earlier around size, representation and trustworthiness, but also data privacy. So I think that’s a big piece.

Another maybe not so obvious characteristic that I think is important is just having domain expertise to straddle the art and the science of building great AI products. Just think that’s really important. And not something that’s obvious, or that people think about.

And a final thing that pops to mind is the ability and willingness to adapt is more of a business question. But for example, on the inventory side, marketing publishers may start exposing additional attributes that bidders can leverage in their decisioning models, exchanges may do something similar to with new, you know, categories of metadata. So adapting to that. And back to data privacy, I just faced quite a few challenges over the last few years, but there will be new ones. So for companies to be successful with data, they’re going to have to face those challenges and adapt.

Jake Moskowitz 14:36
How do I balance the need for g