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Behind the Numbers: an EMARKETER Podcast
AI-Driven Media Management, with Gigi and Amazon Ads (Part 1) | EMARKETER Miniseries
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- [00:00]
A
Today's episode is brought to you by Amazon Ads. Recognizing excellence in advertising innovation through the Amazon Ads Partner Awards. Discover how award winning technologies are helping brands achieve success across solutions including Amazon marketing, cloud and streaming TV. Visit advertising.Amazon.com partner-awards to learn more. Hey gang. It's Thursday, December 18th and welcome into a special Emarks podcast miniseries AI driven media management with GG made possible by Amazon Ads. I'm Marcus and here is episode one of this two part series with our Senior Director of Content Jeremy Goldman and Adam Epstein, Co founder and CEO of gigi. I hope you enjoy.
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B
So first off, Adam, really excited to talk to you today.
- [00:54]
C
Thanks Jeremy. Really excited to be here. Thanks for having me.
- [00:57]
B
Yeah, I have a lot of different things I'd love to dive into that I think our audience would really appreciate learning more about from you. You've said that Gigi started kind of as a non consensus bet that later acquired a major pivot. And I know we've got a lot of people who are builders in our audience who are very kind of curious about like what did you learn from that early CTV focused thesis and how did it lead you to rebuild GG as an AI native company?
- [01:24]
C
Yeah, it's a great question. So Jiji was initially founded on a premise that CTV advertising would be increasingly concentrated to a small group of large tech and big media companies, which is proving to be true. But the non consensus bet is that they were incentivized and had the means and were going to become increasingly walled gardens like Amazon and YouTube, Google, so companies like Disney, Netflix, Paramount, et cetera would become increasingly walled gardens. And that obviously didn't wait turn out to be true. Our bet was that if they became walled gardens there'd be an opportunity to unify the future fragmentation of CTV buying across those platforms. And so on account of that not being true, we kind of had to take a step back and they weren't true because they became increasingly open. All of those companies said we want you to buy the way that we can buy however you want to buy. And that ended up being on any other enterprise DSP of which Amazon DSP was a primary beneficiary. And so once that wasn't true, we kind of took a step back and we had a small group of customers with GIGI started on the Amazon DSP doing some really powerful work with data collaboration with Amazon Marketing Cloud AWS Clean Rooms to provide a unified buying experience across those two properties on Amazon's ad tech stack so brands could buy the best possible CTV Ads with the best possible audience and deterministically measure outcomes across Amazon signals and as first party signals as we did. Okay, but we definitely didn't hit product market fit. And all of our customers said, hey, like, it's really cool what you're doing on the Amazon dsp, but we want you to provide a full funnel solution and not just ctv. And this was around last year, a little over a year ago. And we said, hey, like how do we do this? But also we knew that we needed to be AI first or AI native. And we didn't really know what that meant at the time. So we totally took a step back. We said we want to continue to build in this space. We know we need to be AI first. What are the common characteristics of the most successful companies at the application layer in AI? AI and how can we architect those characteristics to media buying and measurement within Amazon's ad tech? So if you look at the most successful companies, many of them are able to identify a job in the workforce, whether that be software engineering and coding, outbound sales support, legal, in which that job has a lot of rote, manual and repetitive tasks in which there are a lot of people in that job. And so if we identify the job of the enterprise media manager, if you talk to any enterprise media manager, whatever DSP they operate in, there's a lot of buttons to push and there's a lot of rote, manual and repetitive tasks and there's over a million, there's over a million media managers at enterprise agencies and brands. And so we thought that there would be a really good opportunity to identify the ways in which those people worked in those jobs and create agentic AI workflows and automations to enhance the people in those jobs.
- [04:16]
B
And a follow up question for that because I'm really interested in that. Obviously everybody knows that there is kind of like a lot of waste and there are a lot of people in that media management function that are doing things that they don't necessarily want to do. Right. But they, they just, it's part of the job. So how did you break that roll down into workflows that could be automated or augmented by agentic AI? Maybe you could talk more about that.
- [04:40]
C
Yeah, and happy to get really nerdy here. So we would literally spend time with media managers that we worked with and we said, how do you spend your days? What are the buttons that you press in the Amazon DSP that you press the most? And, and how can we augment that with agentic AI and a really good anecdote is there's a history tab at the order and line item level in the Amazon DSP in which one of our customers said, look, I could tell you what I do, but just look at my history and just see what buttons we press. And so we were able to identify across a small group of large independent agencies, the buttons that media managers press the most often and most frequently. And then we were able to, for agentic AI terms, create tool calling with the GG agent. So GG had those buttons. So those buttons are API endpoints within the Amazon DSP in which those API endpoints became tools that the GG agent could call. And then we would set, create a set of rag retrieval, augment generation content around the GG agent in the tool calling to instruct GG how to properly execute this task on a frequent basis for each one of those individual advertisers. So a really simple example of this would be updating budget flights to extend a campaign for a month. So you need to update budgets in a variety of different places at the order line item and creative level. Our customers can just say to Gigi, hey, Gigi, can you update these budgets across all of these campaigns based on this? And we trained Gigi to understand that the places that she needs to update those budgets are across these six dimensions. In the Amazon dsp, GG would press all of those buttons on behalf of their customer, say, hey, this is done. And the customer would accept that plan from Gigi.
- [06:38]
B
Yeah, I mean, the cool thing to me about this, and I definitely want to talk about the human in the loop component that is clearly important to this. Right? But you're making people more effective. But, you know, in a lot of ways, like it boils down to, it sounds like being really close to figuring out, like, what your perfect customer profile is and getting very close to your users and just figuring out what is their pain point. And not just introducing agentic AI for, you know, technology's sake, but actually figuring out where it's going to add some value.
- [07:07]
C
Yeah, 100%. And look like many of the customers that we work with, um, many of them did not, are passionate about their job. They love working in media, they love working in advertising, they love being strategic. They love helping their clients or the brands that they work for achieve the business outcomes that they desire. But no one signed up to press a million buttons to extend a flight. No one signed up for on a quarterly basis, updating creative tags for a week. So new campaigns can run like these. These are genuinely tasks that we, we've identified as ideal use cases for agentic AI so that the people that are in those seats, the people that are providing that human judgment and strategic vision to achieve those outcomes, are able to do that and we can remove them from the mundane task that they don't enjoy doing.
- [08:00]
B
You said something to me once, I thought it was really interesting. Everybody's thinking about like AI is going to be reviewing the humans work. And I don't want to get this wrong, but I feel like you kind of phrased it as no, actually the human is reviewing the AI's work. I thought that was a really interesting way of framing it.
- [08:15]
C
Yeah. So I think, I think we're all becoming a little bit more sophisticated in the benefits and trade offs of working with LLMs. We all now have. I think ChatGPT has its 3 year anniversary a couple of weeks ago and we've been working with LLMs on both a personal and professional level for a while now. And so we all had this belief that we would do stuff and I would, would review it and say like, hey Adam, like this should be better, that should be better. And, and invariably these models are super smart, but they're not that smart. And the work that these models are replacing in the lens of gigi is not the work of the most senior folks, the people that are touching clients, but rather the lower level work that people don't particularly enjoy doing that requires less reasoning and less intelligence and can be repetitive and trained for AI to do that on an ongoing basis. And there was a really critical design decision that we made in building this product that I think has allowed us to be relatively successful, which is we've very intentionally made Jiji work so that Jiji does not change a bid, budget, flight or campaign without human direction. The mechanism with which we've architected the product is such that you assign Jiji work. Jiji says, here's the work that I'm presenting to you. From the assignment that you presented to me. Here's a natural language rationale of the work that I've done. And this is my chain of thought and this is the math that I've done to get to the outcome of this work. And now it's ultimately up for you to press accept. And I think that that is actually how, if we think about agentic AI as hiring a junior employee, that's how junior employees work. You're not just going to assign a new hire the ability to draft a media plan and own a client meeting automatically. They need to gain trust. They need to demonstrate that they're able to show value on the relatively mundane tasks that a senior doesn't particularly enjoy. And then once you start to do that, you can assign that person other more meaningful tasks. Or again, similar to a junior employee environment, if Gigi begins to repetitively do the same task and you're good with her work, you can say, hey Gigi, next time you do this, you don't need to ask me for approval. Just say that it's done and allow me to acknowledge that it's done. And so I think that it's really important to understand how we think about identifying the benefits that these alarms can have in our day to day work. Understanding the trade offs, because a common trade off is that these are incredibly magical but incredibly imperfect humans that we're hiring to do jobs. And if we let these humans or if we let these artificial intelligence degrees of intelligence do the task without human supervision, that's when we have bad outcomes.
- [11:16]
B
Yeah, like a really fast intern, but you have to check their work, but they're really fast, so it's worth it. I mean, I think that you're right that the human roles will absolutely change. And I think also there's another component to this which is the necessity. Like you've seen the agency world change so much over the last year. There's been consolidation, there are some concerns about certain types of work that are going to dry up and I imagine the type of efficiencies that you're enabling in a perfect world, I can see agencies benefiting from this and then having that contribute to margin just because you know it's going to, it's going to be difficult for them to thrive otherwise totally.
- [11:53]
C
So, so we've very intentionally focused on building this product for agencies. And I believe at least in the near term, let's call it one to four years, agentic AI is going to be a massive tailwind for agencies for a variety of reasons. One, like many services businesses or more broadly, there are many vertical AI agents that are tackling services businesses. Because historically services businesses have always operated on a fixed ratio of head heads on the team to customers and revenue. And the magic of agentic AI that if used appropriately, given that you're able to assign AI the work that the humans don't necessarily want to do, an agency or any services business should be able to exponentially increase customers and revenue while maintaining the same. Thereby, if used successfully, an agency's operating margin should fundamentally change. And the degree to which agencies might be valued on a EBITDA multiple might fundamentally Change as well, and they might get rewarded by being valued on a software multiple to prospective stakeholders and investors. So that's one aspect from a financial standpoint. But so we have two North Stars and this is in our deck that we send to all of our customers. One North Star is can we fundamentally change the positively change the operating margin of our customers and can we positively change the way in which we work? And I think that that's a better question. Again, getting back to what are the things that people signed up for to do in this industry and how can we get them back to doing those tasks? How can people be more strategic? How can people spend more time with their clients and how can they spend less time pressing buttons in an enterprise DSP and more time delivering and honing in on the right message at the right time to their clients in order to achieve the optimal outcome for them? So those are the North Stars that we hope to cultivate in working with Gigi.
- [13:57]
B
And where do you think we are in an overall adoption lifecycle, let's say, because it's an interesting thing that you say, I think it's right that there is an advantage to leveraging a tool like Jiji to, you know, get these efficiencies to move faster and to be able to iterate. And then at the same time, I do imagine there probably will be a world where these are just table stakes at some point. So wondering how far, you know, do you think we are from that?
- [14:23]
C
We are unbelievably early in this journey together. As far as I know, we're one of the first companies that are approaching the problem that we're approaching in this vertical. And just by way of the fact that we're very early in our journey and we have a small subset of really awesome customers, but it's still like relatively small in the high dozens of customers. We are very early on in our journey. And I think there's been some really awesome learnings now that we've had this product in market for six months. And so one of those primary learnings that I'd say is the way in which people are approaching beginning to work with a product like us. So we typically begin working with an agency or brand on a defined pilot. And I think one of the biggest things that we learned is that, so I've been working in ad tech for eight years and typically we would have a pilot and for like six to eight weeks, maybe 10 weeks, when every time we'd onboard a new customer and people would like rigorously test the technology to see if the technology would work. I previously was co president of a large retail media company company that's currently owned by Omnicom. And so like when we had those pilots, it was testing the technology to see if the tech works. Now the tech is LLMs, the tech is generative AI in the models. And so I think we all recognize again that these are magical when used appropriately, but inherently imperfect. And the test isn't the tech itself, but the test is the change management required for an internal company to say, like, I want to be AI native or an AI first. This is the approach that we're taking. This is the hands on role that we're taking to training our GG agent like it would be a team member customized for us. And these are the steps that we're taking, assuming it's successful, to re architect our org and the way that we serve our clients in an AI first manner. And so that's super early and we're all figuring this out right now.
- [16:31]
B
Well, and speaking about figuring it out, you know, I'm glad that you gave me that transition because one of the things I was really curious is that you have a lot of agency folks who some, some are, you know, leaning in full, full steam ahead, you know, into AI. A lot of people also have some trepidations, you know, about the workforce changes or they've had a bad experience with a hallucination in the past and as a result maybe like that's coloring the way that they feel right now. So I'm wondering, how do you address that skepticism around AI touch live campaigns? Obviously there's the whole human supervising component of it. But from a change management standpoint, how difficult do you feel that is for the average organization just to address writ large?
- [17:18]
C
Again, we made some strategic product decisions so that we've mitigated as much AI off the rails, hallucination risk as possible. But again, a lot of these decisions get back to change management. So we were chatting with one agency that we're continuing to chat with and that agency. So campaign building is one of the features that Jiji offers. And so Jiji does this really cool thing where Jiji creates these things called agentic operating procedures, which are basically standard operating procedures for how someone wants to build a campaign, but has done so agentically, in which you're codifying all of your best practices and providing advertiser context so how each individual advertiser thinks about widely used targeting strategies within the DSP and then just simply writing a small order form that expresses advertising intent of that campaign and then Jiji will spit out order with five line items in a couple minutes, saving an agency anywhere between 30 to 40 minutes for each individual campaign. Multiply that by X number of campaigns. It's been a huge value for us. And so the reason why I say this anecdote is one of the agencies that we've spoken to said, hey, our process for QA and any campaign that gets built is downloading a bulk sheet and then uploading that bulk sheet into the Amazon dsp. And so how would gg, how would we be able to accommodate that process within gigi? And. And so I tried to be as respectful as possible to that agency leader and saying like, hey, like, I think that you need to rethink your QA process if you're going to begin adopting AI. Like I said to one of our colleagues after that call, downloading a bulk sheet to upload it into the DSP after ggically builds the campaigns is almost like printing out a fax to then print out an email or send an email. But I don't say any of this dismissively. I think that these are real human problems that everyone is starting to ask themselves of, like, hey, what are the processes that we currently have? And if we want to be AI first, how can we change those processes so that we can have those same checks and balances to make sure that we're providing the same guardrails against hallucinations or human error, but doing so in an optimal manager that leverages AI to the fullest extent possible rather than reverting back to the old ways of doing things.
- [19:57]
B
By the way, this is great. Hopefully we can revert back to this conversation in the near future because Adam, this was fantastic. I know our listeners will get a lot out of this, so I really appreciate you making the time.
- [20:09]
C
Of course. Happy to.
- [20:11]
A
That's it for today's episode. Thank you so much to Jeremy and Adam for the conversation. Thanks to the production crew, of course. And thank you to everyone for this listening to this special eMarketer podcast miniseries, AI Driven Media Management with Gigi made possible by Amazon. And tune into part two of this miniseries next week on Tuesday, December 23rd.