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Bonus: Got AI Anxiety? Learn How 875 Brands Are Using AI: DTC x Triple Whale’s State of AI Report
00:35:37
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- [00:00]
A
Most people don't know the majority of what Triple Whale is doing. They started as really a data platform to centralize your data. That was all in service of being able to automate tasks across your business. We recently, about a month and a half ago released MMM in our MOBI chat feature. A brand of ours was running a giveaway they asked Moby to look at on the last day of our giveaway how should we adjust our spend? MOBI then gave them a plan to do so that had channel by channel specific allocations to look at and then an action plan of what to do strategically to do that. And the brand saw their highest revenue day in history.
- [00:45]
B
Welcome to the DTC podcast. Today we have a very special bonus episode where we are diving deep into the state of AI in D2C marketing with one of our long term partners, Triple Whale. Anthony Del Pizzo. Welcome to the podcast. What's your role is? Director of Product Marketing.
- [01:04]
A
That's correct. Eric. Yeah.
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B
What does your day to day consist of at Triple Whale?
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A
Yeah, so I own and my team owns all of our product positioning or messaging bringing products to market. So when we're developing say new AI features or new features across our measurement tooling, basically understanding what are the problems that we're looking to solve for our customers and then how can we ensure that we bring those to market in a way that effectively solves those problems.
- [01:29]
B
So we're going to dive into the results of our survey where we surveyed 875, over 875 D2C operators to get a clear picture of how they're using AI. I feel like there's a bit of anxiety in the audience about whether you're doing enough with AI what you should be focusing on with AI. So that's what we're going to dove into. But I wanted to start just with a little because a little catch up with Triple Whale. It's been a long time since I've kind of chatted with someone from your side and I know your suite of tools has evolved quite a bit and I think it makes for a good sort of intro to what we're talking about today. So maybe give me a little overview on how Triple Whale's evolved over the years.
- [02:05]
A
Yeah, totally. And so I joined Triple Whale about nine months ago and when I joined Triple Whale I had a very singular view of what the company did that they did. Attribution. I had previously come from spending four years at Klaviyo and admired Triple Whale at how quickly they were growing. I joined Triple Whale and quickly realized that most people don't know the majority of what Triple Whale is doing. So Triple Whale, while they started as really a data platform to centralize your data, we had a mobile app we still do, that's widely used to centralize your data across every single tool in your stack to give you real time insights so you can make real time decisions. That was all in service of being able to automate tasks across your business because then once we had your data in one place, we then built measurement models on top of that so that data was trustworthy. So best in class and multi touch attribution is what the team started with. We've since expanded to a marketing mix model and mmm and adding incrementality testing really so that brands can just triangulate, hey, what of my marketing is actually driving revenue and then how can I lean into that more? And then we've built over the years ways to send that really robust data that we've built outbound. So like a lot of data enrichment capabilities and then all of our AI capabilities sit on top of that. So I have access to all of these data, this data layer that then now we have Moby Chat, which is our generative AI chat. You can chat with mobi, ask any questions about your business. It's like chatgpt but with real time context into every single layer of your business. And then we have Mobi agents, which is what we launched this year, which are really our AI agents that are essentially superhuman teammates that we're giving to all of our brands across different functions in their brand, really trying to be the one stop shop where they can actually leverage AI and feel confident that they can expand with Triple Whale and AI in their business concurrently.
- [03:58]
B
Agentic AI. Agentic is such a cool word. I still use AI every day, but just as sort of like an in in an individual. Like each task is unique kind of thing. And I'm building, you know, built, I've built a few GPTs and stuff like that, but I haven't got to that agentic level. So I'm interested to dive into how that's working with brands. So we surveyed 875 operators, 93 and a half percent of them are using AI. So what are, what are those other six and a half percent of people doing? And I think the main learning is right now the entry point for AI is creative. What are you guys seeing in the data on that, on that side?
- [04:36]
A
Yeah, it's really similar in that we, I mean we surveyed all of our customers just to get an AI perspective too around like everyone is using some notion of hey can I use copy generation creative generation. They're feeding feeding AI. Okay hey we had this creative that works. Can you generate something similar? What we're seeing is that brands find that copy is a really quick thing they can iterate from and trust it. And then they've tapped into creative mainly on like the creative elements perspective as AI has evolved to view images, even generate images. And that's why we at well have actually invested a lot in our creative analysis and generation agents where we actually can look at all of a brand's data. See hey, these are the elements we used AI vision to actually view the creative across every platform and say hey, across all your channels this is what's working and this what isn't. And here's an example of what would would work best that you can actually hand off to your creative team. So what we're seeing is people are excited about creative AI quite isn't. Isn't really there from like a creative generation standpoint, but it can cut the creative briefing process, the creative ideation process down 80 plus percent because your AI is able to basically take analyze all your data across platforms and then give you those ideas to then work with your creative team on how to execute on. And so it's been a really good way to at least start from and that you can feel confident in that. Hey, this is a use case that is something that our creative team just spends so much time ideating that we can cut.
- [06:08]
B
And there are some things I guess like some product shots or product orientation like some, some simple things that AI I think can do well now but it's like. But there also is that really uncanny valley thing that you can. Whenever an image is glowing in a certain way you can just tell that it's that it's AI. And there's probably a little bit of a reaction to that at this point.
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A
Totally. And that's why we're seeing it's like our best brands who are leveraging our creative agents are using it to get 80, 90% of the way there and then make the final touches to put it in their brand ethos and make sure it's on on really on brand. Because that's kind of the, the ways to to leverage AI to its most effectiveness right now without making it seem inauthentic.
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B
I think copy is something that everyone's using it for. Any AI tells out that my, my big. I have two big AI tells for when it's AI copy one is the use of the double dash. I love the M Dash, the double dash to like break up two ideas in text. But I have to, I've had to go back entirely to semicolons because the M dash is a total AI tell. And the other thing I find is when it, when it always says the thing you're thinking about isn't this, it's actually this and it does that sort of constantly. It's not this, but it does that so much. So those are, those are my two big AI tells and any, any on your side.
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A
Yeah, I will say the classic one that comes up is the EM dash. And as, as a marketer I have loved the M dash for years and I, and I still like to use it. But to your point it's, it's sad. It's getting all of the, the AI force right now. We haven't really seen anything too much on the generation side. I think when you think about like headlines sometimes the overuse of emojis can be a really big giveaway. We have AI, we have agents that produce email and email copy email subject lines, previews and sometimes when we look at some of the emails they produce, they're all back to data. But we really have been looking to see, hey, if we take this emoji out, if we cut that, will it actually have an impact? Do like a thorough a B test there.
- [08:07]
B
I think one of the things that I loved in the report was the data that showed that as brands get larger so brands that are like over 2 or over 10 million their shift, I'm sure they're still using it on the creative side but their shift focuses to include a lot more like higher level operations when it comes to budgeting for the business, sales targets, things like that. I assume that's, that's your bread and butter. What are you guys seeing?
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A
Yeah, it's exactly that. It was super validating because we're seeing a lot of our bigger brands basically have. Have two perspectives where they'll have the day to day execution or they'll be looking at things like their creative because the creative just the requirements now from platforms to, to constantly create new creative is, is so vicious at this rate. But we're seeing so many of brands also think about okay hey how can I use forecasting agents? We can use like we have agents that look at our marketing mix model which are just things that are more strategic. So things that you're going to be doing monthly things that you're going to be doing maybe quarterly to think about. Okay. Hey, how can we have AI start kind of planning ahead for us and then use the real time data and real time tools like the. Your anomaly detection agents, your agents that focus on your daily insights of your marketing performance or even your weekly insights to then of converge those ideas of like hey, how is our daily perform and weekly performance kind of mapping up to like these create the, the longer term strategic agents that are looking at models that just are thinking more ahead or are very much used for. For strategic thinking like an mmm. And so it's been a really interesting way of. I think it's just also like a bandwidth perspective when you have the team that can kind of handle both the strategic and the day to day. It's a good way to kind of bring those two together.
- [09:56]
B
Can you walk me through kind of an example where just so I understand how like using Triple Whale to understand my media mix model, what does that look like and what would be a really or do you have any examples of like pot? Really great cases where people looked at that and determined. Because everyone's looking for halo effects, right? Everyone is looking for their marketing to create flywheels and quite often what happens is the reverse happens where you end up spending way too much on the bottom of your funnel. You're not bringing enough new customers in. Can you give me an example of how yeah like Triple Whales unlocked some MMM ins sites for brands?
- [10:27]
A
Yeah, I think there's two examples that I think there's one specific to MMM that comes to mind that I'll give but there's another that I think is really relevant to your question of just like how am I able to understand like where to invest in my spend and how things correlate. So first is that we recently, about a month and a half ago released MMM in our MOBI chat feature. So basically you can ask Moby in chat just like chatgpt. Hey, using mmm, can you look at my previous quarter's performance, look across all of my channels and identify where I should invest more in what tests I should run and where, how I should basically readjust my budget allocation. And so we saw a really cool example of that being used with a brand of ours who was running a giveaway. It was on the last day of their giveaway they asked Moby to look at, hey, you know what, on the last day of our giveaway, how should we adjust our spend using MMM model? Moby then gave them a plan to do so that had channel by channel specific allocations to look at and then an action plan of what to do strategically to do that. And the brand saw their highest revenue day in history by following the exact step by step plan. We've also seen brands take the MMM plan and say hey, let me plan my next quarter and you basically use that to influence their quarterly planning. So there's like both the near term and long term components. An interesting use case that I think has been also really interesting was we had a user who asked Moby to understand the correlation between meta spend and Amazon purchases. And Moby was able to basically give them a really strong understanding of hey, how, when I put a dollar into meta, how does that translate downstream to Amazon, to Amazon purchases to understand like hey, actually our meta is impacting our meta spend and meta ads are impacting Amazon. And so they invested more in meta in specific ad sets that actually were having an impact downstream on Amazon correlation. And that was really only possible because we're bringing in all the data together.
- [12:35]
B
Strategy is the key word across the entire pilot house organization these days is figuring it because tactics without strategy is generally going to waste your money. And so we, one of the, one of the things we asked the 875 marketers was how they're using AI, whether they're focusing on efficiency or they're focusing on ROAS. And it seems a lot of people cite 80, 83 and a half percent people cite efficiency as their top goal with AI that's sort of like the lowest hanging fruit in a way where you feel like you, you've got to take advantage of these tools to get more work done. What are you guys seeing on your side?
- [13:11]
A
Yeah, that's consistent. I think saving time is truly step one that is most tangible for brands is like hey, I used to run. An example that we have here is Dixon Flannel, an early adopter of Moby agents. They used to run creative analysis across all of their channels would take about 10 hours a week. I think we cut it down to 10 minutes and immediately you can see the output within a couple weeks. So it was really profound. I think what we're seeing is the long term value of being able to have that time back to be more strategic and have insights that you perhaps couldn't uncover because you didn't have the resources on your team. Your analysts were just too backed up. We're seeing that agents have impact on things like roas, where lskd, a global retailer in Australia, they even saw like a significant impact on their ROAS by leveraging our marketing channel performance agent because they were just able to find things like whether it was recommendations, channels that were performing better, ad sets that they could switch out earlier a lot faster because they had an agent doing that versus having to ask someone on their team to run a query. And so it's like the, the time savings is probably most immediate, but then pretty soon after it's like that time gives you a lot of more time to be strategic that then you can actually make smarter decisions that have an impact on your bottom line.
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B
Who are, who are your cast of agents at this point? Who, which, which discrete agents have you guys built out?
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A
Yeah, so we actually have a library of about 70 pre built agents, which maybe sounds overwhelming, but the way that we've cate or agents are all around the jobs that we know our customers they're looking to get done. Whether that's creative analysis, that's retention marketing, that's acquisition marketing, that's operations conversion rate optimization. So we have a, basically a cohort of 10 or so agents in each of those that are very supportive on like hey, this specific job. So maybe it's analyzing my meta performance week over week. Creative understanding what creative is working. In Google, we have this library that lives inside the Triple well app. We've also externalized a lot of it on our website just to provide some color into like the things that we're thinking about. And we're constantly getting feedback from our customers of like, what are the jobs that we're not solving well and like what are the things that you would love to automate? Because then let us automate that for you.
- [15:33]
B
I think everyone has a different approach to the way they use AI on their team. And I think a lot of people, you know, I use ChatGPT and I've built a few GPTs that help me, you know, do do things with the podcast I have whenever we're working on a new project, I have a new folder. And I'm finding it's getting better and better at remembering all of the data that I put into it, which must be just a huge amount of memory that, that they're, they're storing for us. But I think one of the cool things about what Triple Whale represents is it's, it's that you're building off of a library that doesn't the whole company gets based on the data from your Shopify store, the data from your meta accounts, and it becomes an ongoing learning library where the learnings are persistent and kind of always building. Is that how you guys see it as well, that you're just sort of building this incredible database of actionable data.
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A
Absolutely, Eric. I mean I think that's the biggest thing with like why well and not ChatGPT. It's like when you compare the same prompt, you're going to get two different answers because we ultimately have access to all of your real time data and that means like updated live. And so like ChatGPT doesn't have access to the campaigns that you just ran an hour ago or your real time roas. When you think about what makes triple whales AI like so powerful, it's the data. It all goes to the data that sits on top like below it. Because we're actually, we're working with ChatGPT, we're working with cloud, we're working with all of the models because they're really building the best in class models and intelligence. It's how can we make sure to build the fastest and most effective way to get your data into that model. And that requires building a unified schema. So understanding how things are mapped and what data points are mapping effectively so that purchases, website, browser, browsers all get mapped to the correct place and are fed to the AI models with the correct context. And then on secondarily it's like we have all of these. We now 45,000 brands that use triple well and so that feeds in and trains our well models and our triple data and the AI models that we're working with on the fact that hey, we know what good looks like for your industry, we have real time benchmarks. So when something's underperforming, we know why it's underperforming and what is working best. And so being that E commerce and retail expert really has significant output on our AI output. Not to use the output buzzword too much, but really because it's like hey, MOBI speaks roas cac LTV out of the box. And you can even customize it for what ROAS means for your business, how you want to focus on NC roas versus regular customer roas. Like it's really, really, really just like the E commerce expert that can sit working closely with all of the top AI tools.
- [18:18]
B
Do you use Moby to set up like ongoing reports that are made all the time or is it more of just like as you think of things, you fire off questions.
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A
Yeah, great question. So really it's two ways. So Moby consists of Moby Chat, which are, which is exactly that latter use case you mentioned. Like I have a question, I need an answer. MobyChat can also build you reports, it can build you dashboards, build you Plans. You can also then turn those chats into an agent. But we also have what's called moby agents, which are those recurring, basically reports and questions. MOBI agents can also now take actions for you. So if you tell an agent, say, hey, if my roas dips below 3 on this channel, like, I need you to shut off this ad set, Moby. Moby agents can now do that for you. And so they're autonomous, they're recurring, and they're leveraging the same data that sits all under triple L. One of the.
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B
Cool findings that I like hearing as a marketer was that there doesn't appear to be a lot of displacement of marketers yet. People are not replacing full team members with AI. They're not. It's not taking our. These clankers aren't taking our jobs yet. That's my new favorite thing that I'm hearing on social media is people calling AI clankers.
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A
That's great.
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B
And that was reassuring to me, where people are just using this to become better marketers, to have more time. But I'm wondering, like, is the next stage of AI agents is like, are we going to have AI employees in the next three years where instead of using the agents in these discrete individual ways, we'll just have an ongoing person that knows how to use an ongoing AI employee that knows how to use all the different kinds of agents and just becomes one layer removed from having to manage it even in.
- [19:58]
A
Yeah, it's a great question. And we're seeing a couple different things. We're seeing benefit of both where, like, hey, we view AI as giving your team superpowers, allowing you to do more with the resources that you have, and really allowing you to uncover things that just transparently aren't humanly possible. And we also have seen. And what we're seeing more of is exactly that, Eric, where we actually had a. A brand under outfit. They're a global woman's apparel brand based in Israel, that they actually were looking to hire five data analysts. They onboarded MOBI agents. They then instead of hiring those five analysts, they hired one more senior analyst who could manage all of the MOBI agents. And so I do think we're evolving in a world where AI and working with AI agents will not become a nice to have. It's a need to have. And it will be more strategic to be able to, hey, how can we think about team structure in my org chart as both human and agents?
- [20:54]
B
Yeah, human in the loop. Yeah, love that expression. Because I think, because even just when it comes to writing like we recently wrote a landing page for a new project. Our agency community that we're building out and it takes so much working with it still to get it the way you want it still by the end of it was a full AI landing page and it's done extremely well. But the amount of guidance that it still needs to get it the way you want it is, is critical.
- [21:17]
A
Oh totally. And understanding how to prompt effectively is so critical now. It's, I think we're, we've seen even with GPT5 we've, we've gone away from just like oh ye use any prompt. It's like the prompt, the, the way you prompt has direct impacts on the output. And so understanding that and something that we're trying to become as a company, AI fluent, which takes a lot of education and re. Education and rethinking how workflows exist is definitely critical as we continue to evolve.
- [21:47]
B
One of the questions that Pilot House was trying to tackle recently was like, how do we make sure that AI is like an imperative across the entire organization? How do we. Because one of the, one of the key results here was that 40% of people say that nobody like owns AI in the company. Nobody owns AI Development as an initiative. It's like entirely decentralized where everyone across. Because you never know where these insights are going to come from. If that's the case where you have all of these people using AI in different ways, what's your advice to organizations that are looking for ways to either wrangle it or just to get, get, get the best results out of it.
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A
Yeah. So I mean we're seeing brand our best users start small, like start with a, start with a use case. But that use case spans across maybe a few different people because we, what we don't want to happen and what we found from our, our, our brands who have tried 10 different AI tools is that those AI tools aren't speaking to each other and they're running off of different data sets. And so they're giving you different insights that you perhaps can't trust. So it's like establishing that data foundation first. Hey, we're going to be looking at the real time same thing. And the AI is going to be looking at that too is like rule number one and then rule number two that we're seeing is like find an agent or a use case that you can feel really confident in and then you can scale that use case across the org because you really don't want to just limit this to just one person. Or team. Our goal at Triple Whale is like, we have agents that fit all different Personas. Maybe you start with one Persona, but that Persona can allow you to can kind of let their operations team or their retention marketing team know that, hey, we've been using AAA for these use cases. They have agents built just for your, for your organization. And we're seeing that you can expand using the same platform without having to. The kind of the concern of hey, is that can we trust this output versus another? Because we don't know the data that it's looking at. And so it really all goes back down to the data. But starting small and evolving that and defining like what are the key questions we want to solve for or key tasks we want to automate. We've seen brands just be most successful with and trying to bite off too much to chew.
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B
Walk me through some examples of how brands use either Mobi or these agents on the platform. Like when they first. What are these small things that you suggest brands sort of bite off to get a real sense for how good the tool is.
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A
Yeah, I mean, I think as we're, we're gearing up for Black Friday Cyber Monday, it's, there's, there's no shortage of questions brands are asking to, to basically pull their data. So we have Moby that has been really critical in Moby Chat of hey, how did I perform last bfcm and what are some growth levers I can implement based on what's been working this year? Really good way to take what would be a ton of analysis and comparing. Okay, hey, not even last bfcm, but what quarter one of this year, quarter two of this year, Understanding what's working. Mubby can basically amalgamate that and then put that into a report that you can use and then to basically centralize that report across your business. Hey, how can we get everyone aligned that this is the comparison set that we're going to be looking at for bfcm. That's been a pretty immediate use case we've seen most recently. But we're seeing brands really start with like, okay, hey, identifying what is the task or the most immediate business problem, whether it's they're looking to grow their revenue 30% year over year. They're looking to be more efficient. So hey, we've been spending more in meta. We've been seeing roas continue to decline like what are other levers for us? And asking Mobi that and then diving deeper. Because the great thing with Mobi and Moby Chat is that you can ask it as Many questions as you want and have follow up conversations, it will retain that memory of those conversations and get smarter over time. And then you can then, hey, we found a question that we're going to need answered over and over again. Turn that, that question into an agent that can deliver that analysis for you on any cadence. And so starting with that one problem though is so critical because that's kind of otherwise you've, there's so much at your fingertips, you can ask it any question you can look at all these agents. Knowing what that first problem is is pretty much gold for seeing success.
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B
One of the questions we kind of ask as a, as a canned question in the, in the forum when you join the TSC podcast is if we were to give you $50,000, I guess it scales for the, for the amount that's relevant to the brand, scales to the, to the size of the brand. And you kind of had an interesting answer. How, like how would you approach that? If we were to give you $50,000 and you were running a DTC brand, you were on triple whale, how would you approach that thought experiment?
- [26:33]
A
So very literally I would go into Moby chat and ask Moby that question because we've seen brands do that. Like that example I gave around the four day giveaway where the brand walked Mobi through that and Mobi gave an answer that then elicited their highest day in revenue, like really, really measurable and impactful results. And see what Moby comes out with. Because I think the biggest thing is that with $50,000, do we invest that in meta, do we invest that in Google? Do we actually lean more into retention and focus more on Klaviyo efforts or run a direct mail campaign? You don't know unless you have access to all of your data. And Moby, since Moby does it can help you get there a lot faster. So I would start by asking Moby and then see where to go from there and then understand, okay, what are the tests we can run? Whether that's on your website, whether that's in an ad channel and let Moby be the determinant of that test. And then maybe you can work with your performance team and your budget team around like okay, how much money? Hey, Moby recommended this but how much do we want to test with? It's always a good, I think you human in the loop hands on type of experience with, with AI.
- [27:40]
B
Was there anything else in the report? By the way? The link to the report will be available in, in the show notes to this. So make sure. You find them. It'll be on our website, probably on yours as well. Were there any other takeaways or insights from this report that you thought were, were interesting or really telling for, for what you guys are seeing?
- [27:58]
A
Yeah, I think there was a couple things that were exciting. I think one thing that was like, super exciting was like, almost everyone said that their AI usage is going to dramatically increase over the next 12 months. And so I think there's, it's becoming no longer a nice to have, it's a need to have. And brands are really leaning in, which is awesome. And we see software leaning in, but it's like brands finding that software that really works for them, that they can trust, which really bleeds into kind of my, my second that like, point that they, that the survey really validated and made made us kind of realize, oh, wow, we need to ensure that brands can trust our insights. Because I think it was like almost 50% of the reason why brands aren't really leveraging AI is they can't trust that AI is actually going to deliver measurable outcomes. I saw like McKinsey just recently came out with a study that said like 80% of generative AI tools aren't delivering measurable outcomes to a bottom line. And like, that all makes sense why a brand wouldn't trust it. And so for us, it's how, really, how can we focus on delivering the most trustworthy AI tools? And that all starts with your data. And so it was really, really validating and almost like, hey, how can we develop our product roadmap and narrow the scope of what we're doing, really around how can we build trust in what we're delivering?
- [29:14]
B
I just loved the decentralized nature, how it really isn't relying on like designated AI leaders to figure out how to use these tools effectively. And I think that balances with this fact that you do. You can't be totally scattered at the same time. You don't want just every person running off in different directions using different tools, using different data sets. Sets. Because that could lead to hallucination, to confusion. What you do want to do is make sure you're a decentralized approach, but where the data is centralized a little bit more.
- [29:41]
A
Right, Exactly. And that is totally, I think everyone like should experiment and should test of what's working best for them and what we're, what, what use cases they want to start with. But it's the centralization of, of the data because the AI is only as good as the data that you're feeding it and if you're comparing, you can't compare apples to apples. If you're looking at two different data sets, the insights and output are just going to not be possible to compare. So yeah, it's exactly that Eric. And I think part of our ethos has always been to democratize access to data and we have so many users across an org using Triple Whale. And so it's now it's how can we extend that further and democratize access to AI insights?
- [30:20]
B
I think the next trillion. I've said this for a long time on the podcast, I think the next trillion dollar product is something like a persistent AI butler. So it's like, it's like we need Triple Whale for like our personal lives in a way. Right. Where you could look at all, you put in all your data and just start querying it about. About questions. I feel like that's the next trillion dollar app.
- [30:38]
A
Oh, I would sign me up. That would be amazing. I just think there's, there's so much that that could be done there and so many things I can think of now that I'm like, I would love to just optimize or automate that.
- [30:48]
B
Yeah. Are there any. I just, I just randomly curious, are there any interesting ways you're personally using AI in your life to see a good result of results?
- [30:56]
A
Yeah. So I have a few different AI tools that I'll use to optimize my calendar. So like scheduling personal time, scheduling time to go on a walk. If there's a meeting, it will shift to all of my meetings around based on basically inputs that I had set based on how much time I need in between meetings, how much time I want to eat lunch, all of these different criteria. And that's been really helpful. I will say I do use Claude in ChatGPT compare outputs pretty regularly. We use cloud projects on the product marketing team for messaging and positioning and like storytelling, brainstorming really just to understand like how can we build something unique and what are even the words that we're using that could be, could really push the boundaries that we're normally used to. And so it's been really, really, really fun to just like play around and test with things, especially as models continue to evolve.
- [31:49]
B
Well, you gotta go to DTC website, follow the link in the show notes here to download the state of AI and DTC marketing to get all these learnings and it help import them to your organization. And if you're not on Triple Whale already, you should really look at it. What do you recommend people do? There Anthony.
- [32:05]
A
Yeah. Head to our website. You can sign up for a free account. When you sign up for a free account, you can try our AI tools for free. You get 3,000 free credits to chat with Moby, connect your data, ask any question, or even run some agents. So definitely sign up for free, no strings attached and go from there.
- [32:22]
B
Nice. I think that's a great way to get. Because it is. I actually just saw a really interesting thing for an agency the other day. They had, on their landing page, they had like contact us and then they had a button that said Ask Chat GPT about us. And it preloaded, it preloaded a prompt to say, hey, look at this. This, this agency kind of in ChatGPT. And it's like we're starting to see chat like referrals come in from ChatGPT all the time for Pilot House. So it's like actually having people engage your, engage AI about your product. To hear it from that perspective, I think is something that you'll see more and more of these days.
- [32:58]
A
Oh, totally. No, I mean we're seeing it too. Even just the referral source of triple stores and shops, our customers, we're seeing month over month, the percentage continue to go skyrocket in comparison to where it was a year ago of just chatgpt Claude being the referral source.
- [33:17]
B
It's a brave new world. Any, Any wild predictions for where we're going to see this all go in the next three to five years?
- [33:24]
A
Yeah, I think the, the agent agent on team, the like the org chart as we're thinking about things is just going to become such a reality. You have to be thinking of yourself as human plus agent. And as we're, as you're hiring, as you're building the org, it's what tasks or even jobs can we automate to then open other jobs?
- [33:43]
B
Super exciting. I think about living through the, you know, I'm a bit older than you probably, but just living from the, from high school beyond, from grade eight beyond, like with the Internet. And just to think that this AI revolution is going to be 10 times bigger, a hundred times bigger potentially than what the Internet was.
- [33:59]
A
Yeah, it's, it's over. It feels overwhelming at times, but remarkably exciting and there's just so much opportunity and so it'll be, I mean in 10 years from now, I can't imagine.
- [34:10]
B
And it's so native. It's because, because you have things like Moby where you're literally just having conversations with things, it just becomes so accessible, even a lot more accessible than Dial up was back in the day when I started, right?
- [34:21]
A
Oh, totally. I mean, the barriers to entry are borderline nothing at this point. So it's, it's really nice.
- [34:27]
B
I think we're going to a Star Trek future where we'll just be able to say, like, computer, like what Trump said when he looked at the Tesla. He's like, everywhere is computer. I feel like that's pretty much where we're going. We're computer will be embedded everywhere and we'll just be able to chat with computer, chat with mobi, get better business results. It's easy. Why?
- [34:44]
A
You know. Yeah, no, exactly. I mean, that's. We're seeing brands do that today. It's. We always kind of joke around at triple or like, the future is now, but we wholeheartedly mean it. And becoming AI first is. Is a thing that brands are doing yesterday. So it's, it's really, really critical that everyone's thinking about their AI strategy.
- [35:02]
B
Nice. Well, thanks for coming on the DTC podcast. Go and download the state of AI and DTC marketing our report in partnership with Triple L. This was a lot of fun, Anthony. We'll have to have you again on soon.
- [35:12]
A
Appreciate it, Eric. Thanks so much for having me.
- [35:21]
B
Thanks so much for listening to today's episode. If you're not a subscriber to our newsletter, you can do that right now at directtoconsumeralloneword. Co. I'm Eric Dick and this has been the DTC Podcast. We'll see you next time.