Here’s the math that should make every small to mid-sized Shopify brand stop scrolling.
You’re about to launch a new product or campaign and you’ve got two choices. Option one: spend roughly $15,000 and wait four to six weeks for an agency to build and test ad variants. Option two: spend about $50, get 20 AI-generated, consumer-validated ad concepts in under 15 minutes, and see which ones are likely to convert before you spend a single dollar on media. A year ago, option two didn’t exist. For brands doing $10K months or scaling toward $1M, this kind of sophisticated testing used to be locked behind eight-figure budgets.
Today’s guest is Armen Mkrtchyan, CEO, Board Member, and Co-Founder of Extuitive, a company that emerged from Flagship Pioneering (the same innovation lab behind Moderna). Armen and his team built something genuinely unique: a proprietary database of 150,000 AI consumer agents, all trained on real, anonymized people living in the US, that can predict purchase intent, price sensitivity, and skepticism triggers before you launch. This isn’t a souped-up image generator spitting out “AI-looking” creative. It’s the methodology behind Nielsen’s enterprise testing tools, brought down to a price point a growing Shopify store can actually afford.
In this conversation, Armen breaks down the full system: how Extuitive builds five psychological consumer profiles from nothing but your store URL, applies what he calls “selective pressure” across thousands of AI consumers, and then drops launch-ready campaigns straight into your Meta account with targeting pre-populated. Whether you’re prepping for Black Friday or planning your Q1 launches, this is a playbook for validated creative testing that used to be out of reach for most Shopify brands.
Let’s dive in. 👇
✅ Why most “AI ad” tools miss the mark: the real breakthrough isn’t cranking out more images, and Armen explains the depth-and-breadth gap between a true consumer model and a basic ChatGPT prompt.
✅ How 150,000 AI consumer agents pressure-test your creative before you spend a dollar, scoring price sensitivity, skepticism, and purchase propensity like a 150,000-person research panel would.
✅ The enterprise method now priced for growing brands: the testing lineage from Affinnova to Nielsen, and why that same methodology is suddenly accessible whether you’re at $10K months or $1M months.
✅ How to validate the ads you already have: upload existing creative, let the AI consumers rank it, and find out which assets are most likely to convert before BFCM—rather than after you’ve burned through budget.
✅ Where the human still wins: the difference between being “in the loop” and “on the loop,” and how brand owners keep voice, guardrails, and final approval firmly in their hands.
✅ Whether to start now or wait: Armen’s honest take on Q4 timing, share of wallet, and what a three-second URL paste actually reveals about who’s ready to buy from your brand.
MAI Fulfillment is a 3PL partner for brands that have outgrown basic fulfillment but do not want to get lost inside a massive, inflexible network. We specialize in ecommerce fulfillment, B2B distribution, retail-ready shipping, inventory management, returns, and value-added services that help brands operate with more control and confidence. Our value is in the combination of technology, execution, and real partnership.
We give you real-time visibility, reliable workflows, and operational support that helps prevent small fulfillment issues from becoming expensive customer problems. Whether you are managing online orders, wholesale growth, seasonal spikes, or multi-channel complexity, MAI gives you the structure to scale without losing accuracy or service quality. We are practical, responsive, and built around your growth. That is what makes us different from 3PLs that treat every brand the same.
Most founders already know ad creative is a moat. The brands that crack it scale; the ones that don’t keep pouring money into Meta and hoping. What they haven’t had is a way to know which creative will work before the spend. That’s the gap Armen Mkrtchyan set out to close, and the path he took to get there is a big part of why this episode is worth your time.
Extuitive came out of Flagship Pioneering, the lab that originated Moderna, and builds directly on the legacy of Affinnova—a Flagship company that pioneered evolutionary, consumer-driven product design two decades ago and was acquired by Nielsen in 2014, with its DNA now living inside Nielsen’s BASES testing platform. Armen breaks down how his team took that enterprise-grade rigor and rebuilt it for the AI era. When a merchant shares their Shopify URL, Extuitive analyzes the store and products, then constructs detailed psychological profiles for five distinct consumer groups—people you should be targeting but might not even realize are in your audience.
From there, the hard-to-replicate work begins. For each group, the platform generates at least 20 ad variants and applies what Armen calls “selective pressure”: 150,000 AI consumer agents, trained on real anonymized people across the US, rank the assets and signal what they respond to and what they don’t. The best-performing concepts are iterated again and again until you’re left with creative that’s been validated through multiple rounds. Underneath it all is a framework Armen calls Polyintelligence: human creativity, machine intelligence, and nature’s evolutionary logic working together. Because each agent carries real demographic markers—age, zip code, past purchases, household details—the system can predict not just whether an ad looks good, but whether the targeted shopper is likely to buy.
You’ll also hear the end-to-end workflow: how Extuitive overlays those consumer demographics, maps them to Meta’s targeting templates, and drops a ready-to-launch campaign as a draft in your business account—images, reels, ad spend, calendar, and audience all pre-populated, waiting on your click. After launch, it pulls performance data daily (and sometimes hourly) to optimize autonomously. Armen is refreshingly clear-eyed about the human’s role too. He’d rather you be “on the loop” than “in the loop”: let the system run where speed and iteration win, but keep brand voice, guardrails, and final sign-off firmly with the person who owns the store. As he puts it, this isn’t really about AI—it’s about giving the operator behind the business their time back.
This episode is a practical look at replacing ad-creative guesswork with validated, consumer-tested confidence, right when Q4 demand and BFCM performance are on the line.
👉 Validate before you spend, not after. The most expensive part of paid social isn’t the ad itself, it’s the weeks and dollars burned on creative that was never going to convert—pre-launch validation flips testing from a post-spend autopsy into a pre-spend advantage.
👉 Consumer clarity beats creative volume. Knowing exactly who you’re selling to—psychological profile, price sensitivity, real demographic markers—matters more than how many variants you can crank out. Extuitive starts by building five consumer profiles from your store before it generates a single ad, because targeting the right buyer is the harder, more valuable problem.
👉 “On the loop” beats “in the loop.” Let the system run autonomously where speed is the edge, like daily or hourly optimization on live campaigns, but keep brand voice, ethical guardrails, and final approval human—the strategic direction of your brand is the one thing the model should never own.
👉 AI’s real value is time, not novelty. The win isn’t that a tool is “AI,” it’s how much productivity it gives back to the person running the store. Judge any AI tool by the hours, focus, and headspace it returns to you, not by the buzzword on the box.
👉 Don’t confuse “AI-looking” with “AI-made.” The brain-rot problem is low-effort, generic output, not AI itself. Extuitive uses your actual product images and won’t hallucinate a different product, so the bar stays where it belongs: does the ad perform, and does it protect your product’s real image?
👉 Enterprise capability is finally democratized. Testing that used to require a big research budget is now a URL paste away, which means the edge is shifting from who has access to who executes well. Whether you’re at $10K months or $1M months, the question is no longer “can I test this?” but “what will I do with the answer?”
Armen Mkrtchyan
CEO, Board Member, and Co-Founder, Extuitive
Armen Mkrtchyan leads Extuitive, the AI-first company Flagship Pioneering launched in 2025 to bring enterprise-grade consumer intelligence to small and mid-sized businesses. Before Extuitive, he studied at MIT, earned a PhD focused on AI, and built autonomous drone technology for farmers—an unusually deep technical foundation for someone now solving ad-creative problems for Shopify merchants.
What makes his perspective valuable is the lineage behind the product. Extuitive builds on Affinnova, the Flagship-founded pioneer in algorithmic, consumer-driven design that Nielsen acquired in 2014 and folded into its BASES testing platform. Armen and his team took that 20-year methodology and reimagined it through agentic AI and the Polyintelligence framework, culminating in a database of 150,000 AI consumer agents trained on real US consumers—aimed squarely at brands that have historically been priced out of sophisticated testing.
Sitting alongside him on the Extuitive board are Flagship founder and CEO Noubar Afeyan and Nielsen Executive Chairman David Kenny, a signal of how seriously the consumer-research world is taking this shift. Armen’s throughline is consistent: technology should serve the operator, not replace them.
Over 9 seasons, I’ve been incredibly fortunate to chat with some of the brightest founders building amazing Shopify brands, as well as the partners shaping the app and marketing ecosystem. Every conversation has taught me something new, and I’m grateful for the chance to learn alongside you.
What matters most is that this podcast helps you solve real challenges and discover new ways to grow. Your support, feedback, and stories have made this journey truly special. Thanks for tuning in, sharing your wins and losses, and being part of the eCommerce Fastlane community.
Stay Connected: Leave an Honest Rating/Review on Apple Podcasts or Spotify. Follow & Subscribe on YouTube for new episodes.
Steve Hutt:
Welcome back to eCommerce Fastlane. I’m your host, Steve Hutt. Now imagine you’re about to launch a new product or an ad campaign and you’ve got a couple of different choices. You can spend $15,000 and wait four to six weeks for an agency to create and maybe test some different ad variants. Or you could spend $50 and get around 20 AI-generated, consumer-validated ad concepts in under 15 minutes, with data showing exactly which ones will convert before you spend a single dollar. A year ago, that second option did not exist. Today, the game is changing for small to mid-sized brands that have been, I would argue, locked out of the kind of sophisticated testing that larger, eight-figure brands have had access to or the bankroll to support.
Steve Hutt:
Today I’m joined by Armen, who is the CEO and Co-Founder of a company called Extuitive — that’s spelled E X T U I T I V E, Extuitive. They emerged from Flagship Pioneering, which is the original lab behind Moderna. Armen and his team have built something very unique. I’d call it remarkable: a proprietary database of AI consumer agents that can accurately predict purchase intent, skepticism triggers, and price sensitivity. All of these signals have been organized into this very proprietary database.
Steve Hutt:
This is now available to Shopify merchants, which is really interesting. I’m excited to jump into it. I don’t want to make this preamble too long because we need to get into the meat and potatoes of what makes this so unique. There are other competitors in the market attempting to do AI-generated ads, but the challenge is they don’t have the depth and breadth that Armen and his company are bringing to the table. So, hi Armen, welcome to eCommerce Fastlane.
Armen Mkrtchyan:
Thank you, Steve. It’s great to be with you.
Steve Hutt:
Yeah, this is really fun. Let’s talk about your background because it’s fascinating. You come from Flagship Pioneering — deep tech, biotech, and healthcare. It’s not the typical path for someone who ends up building a tool for Shopify merchants. Can you share the moment when you realized there was an opportunity to bring this kind of enterprise-level consumer intelligence down to small and mid-sized brands? What made you think something was broken, and how did you believe AI could fix it?
Armen Mkrtchyan:
Yeah, happy to. Thank you, Steve. As you said, Flagship is a company that creates and originates companies generally in human health and planet health. Many won’t know this, but there was a company about 20 years ago, founded and originated by Flagship, called Affinnova, that was doing genetic, evolutionary design of consumer products. This company was sold to Nielsen, if I’m not mistaken, in 2014, and I think most of your listeners will know Nielsen. Nielsen acquired the company, and what is now called BASES — which is used to test new concepts and new products — is mostly based on Affinnova.
Armen Mkrtchyan:
About three years ago, when ChatGPT was just coming out — the consumer version wasn’t out yet, but the API was, and we were working with it — it occurred to us that the power of multiple different AI systems working together could be revolutionary for new consumer products, and potentially for ads and creative assets as well.
Armen Mkrtchyan:
What I mean by this, Steve, is: imagine a world where you don’t just have one ChatGPT-like system that you ask to come up with a new image, but instead you have the power of 100,000 designers coming up with new concepts and debating among themselves what’s a good concept and what isn’t before they show it to you. That’s what we set out to build about three years ago, and that culminated in the company we now call Extuitive, which is doing this work. We’re focusing on Shopify merchants and Shopify sellers as our initial market. That’s how we got started.
Steve Hutt:
Yeah, this is amazing. Let’s talk about the whole ChatGPT thing, because a lot of people are thinking about it. There’s some interesting activity with Shopify and ChatGPT right now. Sellers can actually buy from within the app, and OpenAI has made that relationship happen. ChatGPT is now part of many brands’ and marketers’ day-to-day workflows. It’s almost a necessary part of the job to have access to it.
Steve Hutt:
So, with that in mind, what’s the difference between using ChatGPT right now to make ads or write ad copy, and what your system is doing instead?
Armen Mkrtchyan:
Yeah, great question. It’s actually very different from ChatGPT. When a partner or merchant shares their URL with us — which is generally all we need to start — we analyze their store and products, and then we begin with consumer profiles. We don’t just jump into generating an ad. We need to know who we’re generating the ad for: who is the consumer of this product, and who are we going to target?
Armen Mkrtchyan:
We don’t even need merchants to tell us that. If they want to share, they can, but by just looking at their products and descriptions, we’ll figure out consumer profiles in a very detailed way. We build almost psychological profiles for different consumer groups.
Armen Mkrtchyan:
Generally, we’ll start with five consumer groups. For each of these five groups, we’ll create assets — we’ll create ads. What we do here is almost impossible to do with any other system. It’s based on a concept we call Polyintelligence: combining multiple intelligences — human intelligence, machine intelligence — and being inspired by Flagship’s work in human health and planet health, using nature’s intelligence, by creating many different ads.
Armen Mkrtchyan:
The minimum number of ads we’ll probably create is 20. We’ll take those 20 and apply selective pressure. In our case, selective pressure comes from all these consumer agents we’ve trained ourselves.
Armen Mkrtchyan:
At this point, you mentioned 100,000; we are now at 150,000 consumer agents that we’ve developed, all based on real people who live in the US. The data is fully anonymized, but we collected data from 150,000 real people. They provide selective pressure by looking at the 20 ads we’ve created and ranking them — telling us what they like and what they don’t like about those assets.
Armen Mkrtchyan:
We’ll take a subset of those ads — generally four to six — the ones these consumer agents like the most, and we’ll keep iterating on them, making them even better. After multiple rounds of iteration and new ad creation, applying selective pressure from these consumer agents, you end up with much better ads than you could ever construct with a ChatGPT-like system where you just say, “Create me an ad that is X, Y, and Z.” The system is very specific to your customer. It’s iterative in a way that aligns with what your consumer is going to like. And it validates at the end before it helps you launch that specific ad.
Steve Hutt:
Hmm, very interesting. Ad creative is a competitive moat in a lot of cases. When brands get it right, they can really start scaling. Even UGC content can be powerful, especially in the influencer world.
Steve Hutt:
What I find, and this happens a lot because I work with many businesses, is that purchase intent is crucial. It’s about the types of ads created based on where people are in the marketing funnel, their touchpoints, their search history, and where they’ve been around the web. Price sensitivity is another big one. Some people are clearly price shopping or looking for deals; you can tell by the products they’re viewing.
Steve Hutt:
What’s the underlying technology around purchase intent and price sensitivity as it relates to ad creative?
Armen Mkrtchyan:
Yeah, good question. Imagine you had a panel of 150,000 people. You and I could ask them a whole bunch of questions. One question could be about price sensitivity. Another could be about a specific ad.
Armen Mkrtchyan:
You could ask these consumers whether they like the ad — whether they’d be excited to pause on it if it showed up in their Facebook feed. You could also ask about their propensity to purchase, which is a deeper question. It’s not just about how good the ad asset is visually — how colorful it is, how great it looks, whether the content is presented the right way, whether the text matches what a content person wants to see.
Armen Mkrtchyan:
You also want to know: is this something the person would actually want to buy?
Steve Hutt:
Right.
Armen Mkrtchyan:
That goes much deeper because it has to be connected with a whole bunch of other demographic markers we capture with our AI consumers — their age, zip code, what they’ve purchased before, how many kids they have, what kind of car they drive. Imagine combining all of this, not just predicting whether an ad is visually appealing, but whether something in your ad is a product your targeted consumer would want to buy. That’s what we’re trying to predict with our purchase propensity score that we generate for our partners.
Steve Hutt:
I see. This is really interesting. Once you have all of this ad creative organized and ready to go, what’s typically the next step to get these ads into Meta? The ad creative is one part, and proper testing is another to see which ads will be winners. There’s also the targeting side.
Steve Hutt:
You mentioned consumer research. Can you walk us through what happens when you have a group of ads you believe will be successful? What’s the process into Meta, and how do you set up the appropriate targeting so those ads succeed?
Armen Mkrtchyan:
Yeah, happy to. A seller, merchant, or partner uses the platform to create a campaign. They tell us what the campaign is for. We generate the ad assets and validate them against our AI consumers. We iterate if needed.
Armen Mkrtchyan:
At the end, we present everything to the seller or whoever manages their profile. They confirm: “Yes, this is what I’d want to launch. These are the static images, these are the reels, this is the number of assets.”
Armen Mkrtchyan:
We also ask about their calendar. We want to know if there’s a month where they want to be more active, or a specific date that’s significant for their industry and should be taken into account. Once all of that is set, we overlay our consumer demographics. These are demographics we’ve figured out through our AI consumers — who we should be targeting. For example, it might be moms in California between the ages of 32 and 45, within certain zip codes, with two or more kids. I’m just making that up for our conversation, but imagine that’s what our consumer model tells us.
Armen Mkrtchyan:
Then we take that and map it to Meta through their targeting templates. We create everything as a draft inside their Meta account. When someone logs into their Meta Business account, they’ll see the ad images, reels, ad spend (if they’ve provided it), the calendar that’s already set up, and the target demographics, all pre-populated, basically waiting for them to click “launch.” We try to make it that easy for someone to go from ad creation to “How do you launch this, and who do you launch it to?”
Steve Hutt:
I see. Once these are launched, the next job is analysis and measuring success. We get into campaign optimization and budget management, which are really important. I’ve spoken to lots of people who focus on bid management and performance monitoring.
Steve Hutt:
What’s your thought process around handling that and making the best decisions about what to do next?
Armen Mkrtchyan:
That’s an excellent question. We have pre-launch validation with our AI consumers, but once you launch, you get real-world feedback on the ads. We take that data daily, and in some cases hourly, and feed it back into our model. We identify what’s performing well and learn from it.
Armen Mkrtchyan:
If there’s a discrepancy between our AI consumer model and what’s happening in the real world, we adjust the model. We might stop running a specific ad and run a different one instead. We can do this almost autonomously — within hours or at least daily — optimizing and changing things.
Armen Mkrtchyan:
Talking about AI, this is where its power really shines. In many cases you need a person in the loop to help guide what the system does. But in some cases you don’t.
Armen Mkrtchyan:
In some cases, allowing the system to run autonomously can be much more powerful. If you’re already getting real-world data back on click-through rates and return on ad spend, you can quickly optimize and make sure what you’ve launched performs as well as it possibly can.
Steve Hutt:
That’s very interesting. Another question people will have is about being early adopters. Founders and marketers know they have to get involved with AI or risk being left behind, but being first doesn’t always mean you win. Sometimes things are still in their early days.
Steve Hutt:
I don’t want to use the term “brain rot,” but there’s a lot of garbage out there — especially in video: reels, TikTok, Shorts. There are a lot of low-quality, AI-ish looking pieces of content. What’s your thought process around generating ads that are actually good, that convert, and don’t have that unfortunate stereotype of “Oh, that’s an AI picture”?
Armen Mkrtchyan:
Yeah, I’m sure a lot of people are thinking about this. Ultimately, Steve, you have to deliver results. If you generate ads that don’t perform, you’re not doing something useful. You might not be communicating the message to the potential consumer very well, or generating images worthy of someone’s attention or time.
Armen Mkrtchyan:
What we’ve found is that our models have become so good that it’s hard to tell the difference. We’ve done tests within our team where we take a real image that a customer has and generate five variations of an ad based on that image. We’ll compare those to a real ad the customer launched, and people can’t tell the difference. It’s become so good that the lines between “AI” and “not AI” are blurred.
Armen Mkrtchyan:
I’d go further and say many things people generate in Photoshop today have some version of AI integrated. Whether someone does it manually or via a prompt is not that different. It’s like going from Kodak film cameras to digital cameras. There was a transition, but at the end, if you shot really good photos, both analog and digital versions were good. That’s where we’ve evolved to — we can generate with AI images that are just as good as non-AI images. The difference is that doing it without AI would be much more time-consuming.
Armen Mkrtchyan:
One more thing to highlight, Steve, is that the product images we use are the same ones our partners, our customers, have. We’re not recreating a different product. The setting may be different, the angle may be different, how it looks on a specific person may be different, but we don’t change the core product image. We don’t hallucinate a different product and show it to the consumer that’s different from what the merchant is actually selling.
Armen Mkrtchyan:
If we do all that and combine it with selective pressure — asking lots of AI consumers and real consumers to provide input on the ads — you end up generating superior ads that are both appealing and clearly convey the product message.
Steve Hutt:
Ah, I see. That makes a lot of sense. Let’s bring it back to Shopify. Most people listening are on Shopify. You do have a Shopify app. Can you talk about some of the technical integrations or workflow?
Steve Hutt:
So, someone installs your Shopify app. What happens next? Is it ingesting products, descriptions, titles? Does it understand the audience? I’m curious about inventory levels too. Does it bring all that in, and are those data points used to start the ad-creative process along with your consumer insights?
Armen Mkrtchyan:
Yeah, happy to. Initially, we’ve tried to make it even easier so you don’t even have to install an app. You just provide us with your URL.
Steve Hutt:
Oh, okay.
Armen Mkrtchyan:
Of your Shopify store. Once we have the URL, we can pull product descriptions and all images you’ve already uploaded to your Shopify store. That’s our starting point on the Shopify side.
Armen Mkrtchyan:
We then need your credentials for Meta, because if we want to go end-to-end — from creating and optimizing new ads to launching on Meta — we need a way to connect to your Meta account. Whether it’s Facebook or Instagram, or in some regions WhatsApp, we need credentials to integrate and launch there too. That’s all we need right now. We don’t really need anything else.
Armen Mkrtchyan:
We’ve tried to make the barrier to using what we have so low that it’s almost a no-brainer to say, “Let’s try this and see how it helps us navigate the world of AI uncertainty.”
Steve Hutt:
I’m in the midst of writing a book right now and I talk about AI, but also about human oversight and human creativity. AI is not replacing everybody’s job. There’s a lot of hyperbole online — 30% or 40% of jobs are going to be lost, and so on. I’m not sure that’s completely true.
Steve Hutt:
My question to you is: your AI agents and system are clearly growing as they learn about the brand and create better creatives. What’s your thought process around the human creativity that goes along with ad creation? Are there guardrails set up? Are there feedback loops from humans about what’s being created? Can you nudge the system — “Hey, I like what you did here, this worked well, but we want to try this because it’s more aligned with our brand ethos”? I’d love to hear your mindset about a truly AI-driven system versus how humans are involved.
Armen Mkrtchyan:
Yeah, I think this is shaped both by how I and our team think about it, and broadly how Flagship thinks about it. As I mentioned, we coined the term Polyintelligence.
Steve Hutt:
Right, yeah.
Armen Mkrtchyan:
It basically means we can take machine intelligence, AI, but to make it truly powerful, we need human intelligence and, potentially, nature’s intelligence. In this case, iteration gives us nature’s intelligence — that’s how evolution works.
Armen Mkrtchyan:
For what we’re launching, human intelligence is critical. Humans provide guardrails around what’s good for a specific brand and what would be exciting for that brand. Brand guidance is something we expect humans to provide.
Armen Mkrtchyan:
We also ingest visual brand guidelines when generating images. You don’t have to spell out every detail, but we want to know how the brand owner, the merchant, wants to be perceived — that needs to come from a person.
Armen Mkrtchyan:
We allow people to iterate on every ad we create. You can click an ad we’ve generated and say, “I love this, but I don’t like the background,” or “The feet are a bit too large for the shoes I’m advertising,” or “Can you have the person look more to the left or right?” Humans are always — I don’t want to say “in the loop” — I’d rather say “on the loop.” The human can always control what’s going on. They can let the system run by itself, but ultimately a human has to approve what goes live.
Armen Mkrtchyan:
We don’t want a system where you say, “My budget is $10K per month,” and then never touch anything from ad generation to launch, and at the end of the month you hear, “Your $10K has been spent, here are the results.” We might get there someday, but for now, we want the merchant and user involved in the process too, because we learn from them how to make it better for their specific store and brand in future iterations.
Steve Hutt:
Yeah, I love this. My takeaway about human intelligence and guardrails is that humans — the brand owners — set the strategic direction of the business, the brand voice, and even ethical constraints. It sounds like your team has built bias checks or calibration into the system, recognizing that AI can do certain things very well, while humans provide important feedback.
Steve Hutt:
It’s great that you’re harnessing what the brand owner has to say and letting them guide and refine what the AI produces.
Armen Mkrtchyan:
Yeah, and I think, Steve, sometimes we focus too much on AI. I say this as someone who did a PhD in AI. I don’t think it’s really about “AI” — it’s about machine learning, or whatever we call these tools.
Armen Mkrtchyan:
It’s about the productivity of the person behind the store and business. It’s about their performance and how much time we give back to them by using these tools. Whether it’s an AI tool, a data analytics tool, or another kind of tool, they’re all meant to make what we do more productive. I hope we treat them that way — as tools to boost productivity.
Steve Hutt:
Yeah, that’s a great point. It’s a good segue into timing. We’re in Q4 right now, pre-BFCM — the Super Bowl for eCommerce.
Steve Hutt:
What do you say to those listening who already have their Black Friday campaigns planned? We’re recording this about three weeks out. They may have some ad creative ready. Products and promotions are semi dialed in. What do you say to people who want to implement Extuitive now? Is now a good time?
Steve Hutt:
It sounds like you can ingest data quickly and start forming hypotheses about which ads will perform well. Is now a good time, both pre-BFCM and for those listening later in Q4, to get better ad creative into the market?
Armen Mkrtchyan:
Yeah, I think as it relates to Extuitive, Steve, for the next several months — probably until the end of the year — we’ll allow Shopify merchants to go to our website, extuitive.com, and simply copy and paste their URL. They’ll wait under three seconds, and we’ll show them a couple of examples of what we can do.
Armen Mkrtchyan:
We’ll show at least one or two examples of their target audience based on the products we’ve seen on their site: psychological profile, income ranges, age range, and so on. We’ll also create around three ads for them, based on the products they have and the images they already use.
Armen Mkrtchyan:
They can look at those examples and decide if it’s impressive enough to dive deeper. If it is, they can subscribe and use the full suite of options — including validating existing ads they already have and didn’t create through us. If they have existing ads, they can upload them to our system and ask our AI consumers to rank them and indicate which ones are likely to perform well and which are not, before they launch.
Steve Hutt:
That’s amazing. As I said, I’m connected with a lot of DTC founders, and I did exactly what you described. I went to extuitive.com, pasted a URL, and it said, “We can help identify who’s ready to buy from this particular brand.”
Steve Hutt:
You identified really interesting things, like one company being “modern renaissance enthusiasts.” You identified the size of potential buyers, who the audience is, historical aesthetics, and more. You broke down age range, income range, and gender split, and then went into psychological profiles and other details.
Steve Hutt:
Based on what I’m seeing — and I know the founder of that brand — what you’ve generated is very significant. I love the like/dislike indicators. It’s really impressive. I wish we were on video so I could show what I’m seeing live, but it’s very well done.
Steve Hutt:
Kudos to you and your team.
Armen Mkrtchyan:
Thank you — it’s a team effort.
Steve Hutt:
This is amazing. Armen, thank you so much for recording today. I joke on the show that when I’m on page two of notes, it means a lot — it means the founder is solving real problems.
Steve Hutt:
People clearly want to use AI, but they’re skeptical. They don’t want brain-rot, obviously AI-looking images. It sounds like your system is significantly wider in scope, with more data points, and still keeps humans involved. It’s very interesting. From what I’m seeing live, these ads would convert, and I’m going to contact that founder right after our recording.
Steve Hutt:
Thank you so much for coming on the show. Any parting notes for those who are going to visit the website? Any final takeaways for those listening today?
Armen Mkrtchyan:
Not necessarily, Steve. I just want to thank you, and thank your listeners for spending the last 30 minutes or so with us. If we’re able to help with anything, especially over the next several months, we’d be happy to. I’ll personally be involved in responding to prospective customers, so please reach out.
Steve Hutt:
Sounds good. So that’s extuitive.com. Once again, Armen, thanks so much for recording.
Armen Mkrtchyan:
Thank you.
Steve Hutt:
All right, take care. Well, that’s it for today’s episode. I’d like to thank you personally for being a loyal listener of eCommerce Fastlane. My hope is that this podcast gives you a ton of value through growth strategies, tactics, and exclusive insider tips on the best Shopify apps and marketing platforms — all with my goal of helping you build, manage, grow, and scale a successful and thriving company powered by Shopify.
Steve Hutt:
Thanks for investing some time today and listening to the show. I’m proud and excited that you have a growth mindset and are a constant learner. I truly appreciate you and your entrepreneurial journey. Enjoy the rest of the week and keep thriving with Shopify.
