Inside the Blurb with Insighta

Speaker A: Marketers are sitting on more customer data than ever. But most of it never turns into real insight or action on inside the blurb. We skip the decks and go straight to how technology actually works in practice. I'm, um, Sean Simon and today we're diving into customer intelligence, behavioral data and how brands turn signals into decisions with Matthew Liu, founder and president of Insider. Matthew, welcome into the matrix.

Speaker B: Thanks Sean. Thanks for having me. Really excited to be here.

Speaker A: Yeah, likewise. So let's read your blurb. Here's how we describe Insider on blurbs. Insider helps marketers understand their data to drive growth by combining precise multi touch attribution and predictive lifetime value. It identifies profitable customer behaviors and optimal ad spend. This clarity lets marketers stop guessing and focus on strategies that actually work, adapting to rapid shifts with real time performance insights. Yeah, so you just heard the blur. How do you expand on that in practice? Like what does inside and actually look like day to day for teams using it?

Speaker B: Yeah, I mean I think to really talk about the value inside, uh, a. It really goes down to the methodology. Every single marketer is trying to understand what their return on their ad spend is. That's probably one of the most common measurements of how are my ads performing. But the way you get to that ad spend is critical. And if you think of traditional marketing measurement, the way they look at roas is they look at how much benefit was generated in a given given period of time. So let's say last week I, uh, generated a hundred thousand dollars in revenue. How much did I spend last week? Typically it's what they ask. Let's say they spend 50,000. So they have a $2 ROAs. Right. The issue is if you think of when you spend advertising dollars today, you're not necessarily buying an order today. Like when you like, you know, ask yourself the last time you saw an ad, Sean, did you make a purchase immediately after seeing it? Right. And the answer typically is no. Especially if the product is like a very highly considered purchase. Like maybe a high $1,000 product. Like you're not going to see an ad and then impulse buy that. But the way we measure benefit and cost, it's in windows of time. Um, I don't know if you knew, but recently Facebook and Meta, they just announced that they're actually deprecating certain attribution views on the 12th. So they are removing their 7 day click 28 day view and only relying on a 1 day click 1 day view attribution window. Which is crazy because like it's very difficult to especially if it's a, uh, upper funnel campaign to be able to evaluate, um, that cost and benefit in the same period of time. Um, so what insider really does is instead of saying, hey, how much did I, how much did I generate in revenue? What was my cost? It's saying of the orders landed today, what did it cost me to, to get that? Um, and that's a very different question. That cost could have come from 90 days ago, 120 days ago, you know, up to. It could have been 13 days ago. Right. But if you're using a last click, one day view, one day click, you're never going to get that correct signal to see how much you're actually driving in profitability and revenue.

Speaker A: So you're reverse. I don't see reverse engineering. But you're looking at it in the reverse compared to other solutions where they're saying, okay, here's what you spent. What did you get? You're saying, what did you get and how much did you spend on those people? And so you have to map that back. Right. So you say, okay, Sean Simon converted today. How many times did we touch him and what did those impressions cost me in order to get that conversion? Is that.

Speaker B: So it's like if for finance people out there, it's like activity based costing. Uh, right. You're tying the cost to the actual order that was generated. Exactly.

Speaker A: Makes sense. And then on the, on the Facebook thing, I hadn't heard that. So doesn't isn't that a negative for Facebook themselves? Like won't they get, or won't they be attributing less credit to themselves?

Speaker B: Yeah, and I think I'm curious on exactly why I didn't look into the reason on why they're doing that. Um, I do know though, from there are, you know, various windows of attribution. I think they're just trying to simplify the process and just create one single source of attribution. Um, now the challenge there though is with traditional measurement, there's going to be duplication in benefit. So Facebook's taking credit for an order and Google's taking credit for that same order. If you go into the separate platforms and try to sum up the revenue and you're using a Shopify ecosystem, that revenue that you're summing up across the different platform is never going to match one to one with Shopify. So inherently have this methodology to try to track the revenue, but it's hard to actually see what's going on if you don't have a single source of truth of Revenue. And I think that's what we do at Insider. Not only do we get the costing model correctly, but we're tying it back to the source of truth revenue, whether it's Shopify, Magento, Homegrown System, you know,

Speaker A: what's in the bank, Right? Yeah, exactly. And what's in the bank? All right, so at what point in the company's journey does Insider really make sense? Right. Because if they're only in one channel, doesn't make sense. So when do teams usually realize they need to spend or uh, need to subscribe to something like Insider?

Speaker B: Yeah, I would say if you are in a growth phase and you're trying to um, optimize across multiple channels. So if you're in, you're trying to scale in TikTok or YouTube or CTV, I would say if you're generally spending larger budgets, like a million or more in ad spend across a diverse group of channels. Channels. I think that's when you need to start looking at like an omnichannel approach.

Speaker A: So like a million dollar ad budget across multiple channels. At least, at least two channels obviously. Right?

Speaker B: Yeah, yeah, Preferably more too, I think. Yeah. And then also a, uh, lot of people have this view of this uh, notion that like if it's offline channel, I want, I can't measure it in like a multi touch view. So for instance, we partner with like postpilot and lsdirect and all these other mailer companies. We can actually directly insert when Sean received a postcard in his mailbox and if that converted to an order, uh, a lot of people try to use QR codes, which is like a very small percentage of actual people who scan that. And so we work directly with the advertiser to say, hey, when was that mailer sent and did they land an order and then stitch that in that customer journey?

Speaker A: Yeah, yeah, no. I worked in the programmatic direct mouse space at the very beginning of its existence and it was frustrating because brands would want to use either QR codes or um, vanity URLs. Um, and it just isn't how shoppers think. And so we never got the fair credit for it. This is, this is going to be much more effective. So, so in terms of like your client size, do you focus more on Mid Market vs Enterprise? Like where's your sweet spot when it comes to company size?

Speaker B: Yeah, I would, I would say it's mid. Mid market. And the kind of realization, the whole reason why I built Insider was I worked for a mid size and a midsize, uh, Company kind of smaller end. But when, um, I first came in, I was the first kind of marketer and we scaled from 0 to 250 million in the course of four years. I was managing their $30 million ad budget. But, you know, I was looking at the different tools that were out there and it's like, you could try to build this in house, but most DTC companies don't have a tech team, they don't have a cto, they don't have. They don't have the infrastructure and they don't really need it, to be honest. And then the second alternative is to try to buy. So now it's a build versus buy. So now you're going to buy something. But again, all of the traditional pitfalls of, you know, is the attribution correct, is the revenue summing correctly? And then the LTV piece, which we haven't even gotten into, like, are you actually being able to capture for every ad that you spend, what's the incremental lifetime value of that? Like, those weren't being answered. And so basically out of necessity, um, I'm like, I know a lot of midsize companies need this type of solution, but it's cost prohibitive. They don't have the talent, need to build in house. And like, the solutions out there aren't, aren't doing what they, they're needing.

Speaker A: Yeah, I think building is, is strictly for the largest companies in the world that have extra resources they can afford. Otherwise, you're taking your eye off the ball. You're not focused on growing your own business and you're not experts in measurement in this case, and, uh, you know, you're not equipped to sort of evolve the solution as technology changes or the market changes. Right. So on, on the blurbs page, we, we talk about, like, what makes you remarkable, what makes you special compared to, say, your competitors or, or the ecosystem. I. What makes you remarkable. And then, uh, then we'll talk about it. So it says, insider brings cl. And by the way, this is written by Matthew. Insider brings clarity to one of the messiest areas of marketing measurement. Instead of relying on siloed, biased or slow tools, Insider unifies mmm, mixed media modeling, mta, multitouch attribution and product analytics and incrementality into a single truthful source of insight that shows what's actually driving growth. It moves brands from reactive reporting to predictive decision making, delivering fast, statistically sound guidance on what to scale, what to cut and what to test next. So, um, there's a strong philosophy behind that, obviously. So what makes, what makes that your remarkable statement? What, what does it mean in practice? I should say, like for customers. And, and in terms of how you built Insider?

Speaker B: Yeah, I, I think I really like the mantra, like for marketers, buy marketers. So I've been in the hot seat, you know, as one trying to convince the CEO, hey, we need to be spending, you know, x amount of money in this channel or we need to be dialing back. Uh, I think one of the biggest advantages is our team, our, you know, previous ad agency GMs are operators in the space when it comes to performance marketing. We've managed data teams, we've dealt with the problems. We've had to deal with investors who are looking to say, you know, well, what is your company valued at? And you can't do that without lifetime value. So I think a lot of it comes from experience of having to struggle through the problem that most marketers face to be able to grow, grow the, kind of grow the pie overall. And, uh, it goes beyond just having a dashboard with numbers on it. I think that actionability is a key component. If you don't have actionability, then what's the point? And one of the biggest things that we have is called the decision matrix, which allows you to evaluate campaigns and ads on a very forensic level to say, is this specific ad driving incremental lifetime value? If not, let's dial it back. Let's spend in other channels and opportunities. So it's a real tangible way for marketers to, uh, action on the data that they see.

Speaker A: Sometimes on this show, we'll talk about gut versus data, right? And, um, you get that any kind of pushback from brands where they're like, I see what you're saying. Like, I see the data, but my gut tells me something different just based on my experience.

Speaker B: Experience.

Speaker A: Do you get that at all?

Speaker B: Yeah, I mean, yeah, we get that a lot. Uh, so, for example, we integrate affiliate marketing as well. And the most common question we get asked is, you know, would they have purchased anyway? Right. And, um, that's a common question. But what we see in the data is when you stitch all together affiliate marketing with social and mailers, we see a large percentage of those customers that are new, um, or large percentage of affiliate customers come in as new customers. So, for example, one of our clients, which we can talk About Obagi like 50% week over week, 50% of the customers that are coming in through affiliate channels are net m new customers that don't touch any other channel. You know, so in this case it's like, well, their gut feeling is like, well we can't scale in this channel. Like there's nothing to scale and it's extremely difficult to do. Um, and so in this case it's like, well, we can tell them that the data shows that it's not hurting you, you know, so you should continue to spend where, where it allows. But to your point, yeah, like you can't really scale. So their gut feeling is like, we can't scale in this channel.

Speaker A: Right.

Speaker B: There's, there's not enough publishers, there's not enough resources to be able to do that. So their gut feeling is kind of validating that. But what the data enforces, it gives them insights to say, well, it's actually good, it's not hurting you, it's bringing more new customers from, you know, where you otherwise wouldn't have.

Speaker A: Yeah, dig in further. So when you say that it brings in new customers that haven't been impacted by or influenced by other channels, are you also tracking whether that user has actually been to the website already? Because I think that's the biggest concern with affiliate is like, oh, they've been to my site, they're ready to check out now, they're going to go get an affiliate code and come back and get a discount. Are you able to track that as well?

Speaker B: Yeah, yeah. So, um, we track every single event that happens on the website and initially it's anonymized. Right. Until that person identifies, we wouldn't know that, you know, it's Sean that came to that visit. But whether it's cross device, whether it's cross platform, whether using, you know, Safari or Chrome, we were able to stitch with high certainty individuals that are coming to the site but haven't yet made a purchase. Something that's really astounding is that we can see customer journeys back 400 days before they even make a purchase. And this could be in a Google campaign or a Facebook campaign. So what's eye opening is that, you know, some of these, um, journeys are a very long tail, but people just don't have the capability to, to see that because of the technology that they're using or they're just not familiar with the space.

Speaker A: 400 days, is that like the life of a third party cookie?

Speaker B: So third party cookies actually get deprecated or reset depending if it's Safari, it's like the same day, Google can be up to seven days. So we actually don't rely on third party cookies to create the customer graph, we rely on first party cookies. So we actually implement a server side pixel which issues a unique identifier for that individual. Um, any time they hit the website persists regardless if you erase your cookies or not. Think of it like a gym membership. We issue it to you and so we've identified that and that can't be erased unless you go back in on the backside and just delete it.

Speaker A: Right. And then every time they come back, it restarts the 400 days.

Speaker B: Um, yeah, yeah. Anytime they come back, it reidentifies. So it could be even more as long as they continue to come to the website.

Speaker A: So, so it's no secret this is a really noisy space. Right. We've had multiple companies on this show alone. So when buyers compare Insider, uh, to alternatives, how do you help them understand where you truly fit and when they should choose you versus someone else?

Speaker B: Yeah, that's a good question. I mean there are a lot of great tools out there that exist that are more affordable and for that are more simplistic. I think Insider is good for those who, where lifetime value is a really critical component of their business. If you are a company that is more of like a one and done or like a high purchase like cars or e bikes, you know, those things may, may not be as relevant because lifetime value isn't a key component. I think those tend to fall off if you, you know, your budgets are very limited, you're only in one or two channels. You, you basically know it's either organic or those two channels that are feeding your revenue, even though you may not have like deduplication or a sophisticated attribution model. One other thing that I think does shine though is outside of commerce, like we have clients that are using it for lead gen. So we have, we work with banks and uh, insurance companies, you know, and we're talking with even schools for applications to see, you know, is this individual who saw this specific ad, which may have been like two semesters ago, you know, are they coming back? Whether the convert, if the converting event isn't an order, if it's a form fill or a uh, survey, we really shine in that space as well. But it begins to fall off if you're in Amazon. Even so, like if we can't apply Pixel on your website for MTA, then we can't get that visibility. So Amazon, TikTok shop, those are harder from an MTA perspective. But that's where we start relying on the nnn. Right. But again, there's pitfalls of, of those different types of methodologies and just, just to recap if you're an Amazon or TikTok Shop Meta Shop or smaller budgets, don't need something super sophisticated, you probably aren't a good fit for us.

Speaker A: Yeah, it's, it's a challenge, right? Cause there's, there's a lot of players out there. I, uh, mean, given that you were, you know, you were brand side. Right. How do you, how do you help? Let's, uh, say you're talking to a friend who's on brand side. Like, how do you help them figure out how to navigate the space? Right. I'm not asking you to. Like, you know, obviously you want brands to use you over, over the competitors, but at the same time, you don't want to, you don't want to be the wrong fit. Right. So how do you, how would you instruct somebody that, that sits on the brand side to think about the types of measurement partners they work with?

Speaker B: Yeah, I think one, probably the key thing that I encourage people to do is just to get educated. Look for a partner that's transparent. We, you know, I think closed models, black box models, there's a reason why they're hiding it from you. There's a reason why Facebook doesn't give you the view through data. Right. Or they don't give you that customer data is because they know for some way or reason that, uh, not, not to say that they're hiding anything, but I think the best type of partnerships are ones that are open and transparent. So first and foremost, find a partner that, you know, you're comfortable with and transparent with and that you believe in the methodology. You know, some people, I worked at a place that, uh, they didn't really believe in mta. That's just like, I don't, that's just not my wheelhouse. And that's totally fine. There's no perfect marketing solution. If there was, then we wouldn't be having this conversation. But find something that works best for you, that you believe in, and you can have a transparent, open relationship with

Speaker A: how you value the numbers. There are some solutions that have been on the show M that seem to me to be more customized. In other words, some are more out of the box. Like, here's our product. We're going to put up our pixel and we're going to measure and we're going to do our thing. Then there's others that actually are tweaking it to be customized for their specific business vertical size, whatever it might be. What is your approach? Is it more custom or does it lean more out of the box?

Speaker B: Yeah, I Think um, like any great solution probably needs to have a combination of both. Even like the thinking or the thought process of how we do measurement is completely different than what people are used to. And so I do believe that there is an education piece of that you need to be able to use a platform to its fullest. So we do have a guided solution for those that are you know, looking for a solution but don't maybe have the, the know how or the analytic, analytical like horsepower to be able to make those driven decisions. So we offer that to people who, who want more of a involved kind of relationship. But we also offer you know, if they just want to use the platform and make their own decisions, they work with their advert, their agent, marketing agencies as well and kind of have this symbiotic relationship as well as I think it's important to have like resources available. So we provide a way for individuals to have like, we have like active Slack channel where we connect with them. They can ask questions on the daily, you know, and so I think the education piece is really important because if you don't have that then they don't know how to use the platform and so forth. So I think it's a fine balance, but I think both are important.

Speaker A: Yeah, I think all marketers need to be really savvy on the numbers these days. Yeah, I'm glad you, you mentioned LTV a few times and one of the claims that you make on well I guess on your website and also on blurbs is that your predictive LTV feature helps brands invest in channels driving long term growth. So can you give us an example of where this kind of changed a real decision that a brand was making?

Speaker B: Yeah, so we had a brand that they were, before they engaged with us, they were down new customers about 10% month over month, year over year. So they were struggling to try to generate new customers and then also the retention was down. So what we were able to help them do is identify, you know, which, which campaigns are, are not, are not driving future growth. So if we look the first thing we did was we evaluated how their budget was being spe event and it was like 80, 20 meaning like 80% of their spend was in like lower funnel channels. And right there in my mind that's a pretty red flag, pretty big red flag. If you're spend, if your, most of your spend is in lower funnel, what you're doing is you're just hitting the same people and over, over and you're just cannibalizing your own, your own business because you're not reaching a further audience to then drive down to the, to the lower part. So we've identified, you know, hey, which campaigns are you running? Are they efficient from a uh, lifetime value perspective? And what we found is that most of those campaigns they were driving a high AOV actually. But when it came to were you retaining those customers, they were falling off, they were not coming back and purchasing. And the way we combated that was because we have this forensic view of attribution where it's on the order level, we know everything, every single touch point that led to it. We can pivot that on the product level. And so now we know on the SKU level which marketing campaigns are driving cac, uh, AOV and even LTV to CAC ratios. So what we did simply was we pivoted that data on product and then uh, we just like in an Excel sheet you just sort by the lowest CAC product. And those aren't necessarily like your hero products. These are like products that are driving new customer growth. And we took the top nine products and we put it on a hidden landing page, you know, obagi.com newcustomeroffer and you could only access that through a social media paid link. And they did a campaign, they spent X amount of dollars specifically to drive traffic to that site. So when we talk about actionability, right, like how do you get a platform that's actually like clear and helping marketers be successful? Well we've identified products that are low, low hanging fruit from a new customer perspective. They have camp working with the ad agency to create some type of prospective new customer strategy and then working with the brand, they created that landing page where you could see the direct impact of is this specific campaign driving new customer growth. And what we saw is that over the course of three months they received record breaking new customer growth year over year. So in July was their of uh, last year was their highest in customer growth up until that point which was on par with November of the previous year. So basically Christmas in July is what is what we coined it because their new customers are almost as high as the prior year, November. And it was driven specifically from this campaign. And we could see it like we, we can look at all the orders in Shopify and we can see which campaigns they touched. And the majority of the new customers that were coming in were coming from this specific campaign. It was remarkable to see.

Speaker A: So tell us more about Obagi. You have a case study on the blurbs page. And so what is it? First of all, I Never heard of the brand. And talk uh, to us about the problem they came to you with and how you help them solve it.

Speaker B: Yeah, so yeah, we kind of maybe jumped the gun a little bit. But Obagi, they are a cosmeceutical company. So they're health and wellness, uh, they are based in California, they are owned by a private equity company. And so again like you're talking like mid sized company. They're spending you know, eight figures in marketing annually. They have nine figures in revenue. And so this is not like your mom and pop shop that's trying to have like uh, trying to scale. But again they're, they're facing growth issues at their stage. Right. So they came with us because they were, you know, they realized their new customer and retention growth year, year over year was failing. And we came in there, we looked at their products, we looked at the health of their campaigns from an LTV perspective. And when we were able to do that it was funny because um, their investors had set um, goals for Q4 of last year and the goals that they had set were like, oh, we need to hit X amount of new customers for December and we exceeded that by 25%. And for them that was a stretch goal. They're like, I think this is a stretch goal, we should hit this benchmark. Um, and we were able to exceed that. So I think especially if your company is backed or if you have VCs or investors, it's all the more important to have lifetime value, a really sophisticated attribution model to be able to identify problems and opportunities and scale your company.

Speaker A: So I'm curious about lifetime value because I understand the metric, I know what it's about. How do you think about lifetime value with a brand that is fairly new. Right. And maybe it's a product that you don't even buy, um, maybe once or twice a year. Right. So how do you, do you know what the lifetime value is early on until you know, I get, I get it. If you're established you can do some backtrack of the math and kind of figure it out. But how do you do it? Sort of forecasting.

Speaker B: Yeah. So I think that's one of the limitations of lifetime value and why mid sized enterprises or mid sized market is probably better because you've been around for longer. But uh, there's really two types of lifetime value models. There's historical or heuristic which is, you know, you take your past year, you look at how, how many of them came back from the prior year. So that's Your multiplier, let's say 20% of them came back. So you can assume this year 20% of them will come back and you multiply that. So that's more of like a very you know, kind of a simplistic way of thinking of lifetime value. Um, and the second way is um, a, it's a Bayesian statistic way of thinking of lifetime value. So it's a machine learning model that trains on historic data to then make a prediction, accurate prediction of hey, if Matthew came in today as a new customer, what will he be worth in one year from today? And being able to answer that question requires. It requires more data and more technicality. Right. And so I think with the limitations of if you are uh, like advice for younger companies that don't have a lot of data, use a historical lifetime value model to start. But once you start getting more sophisticated, once you get more data, more SKUs, you're selling in multiple channels, you're applying discounts and getting really, really, you're trying to dial in optimization. That's when you should start looking into more of a machine learning predictive lifetime value model.

Speaker A: Just treat them well so they come back.

Speaker B: Yeah, sure.

Speaker A: What it comes down to. Right. Don't worry about the numbers, it'll uh, work itself out. So who, who at the client or the agency owns inside. Who do you work with mostly inside the organizations?

Speaker B: Yeah, um, I work closely with typically it's the um, the marketing agencies who are running the ads trying to understand like doing the keyword bidding or creating the ad set itself. So just trying to understand you know which one of these campaigns perform well. And then we work closely with the C suite so directly with head of marketing, head of E Comm. Um they're in the app uh daily trying to figure out you know where the best low hanging fruit is.

Speaker A: So it's, so it's obviously the agency size, more media, brand side, more marketing. Do you have situations where you're working with a brand that has or an agency that has an analytics team you have to work with?

Speaker B: Um, so we um, trying to think. Yeah at the moment no. Um, we have partnerships with um, both agencies, with agencies from that have ah, that have clients that are more sophisticated that like, let's say like one of them. They don't have the LTV piece but they have a really good reporting system. So we'll maybe assist in some ad hoc analysis for them. But typically the clients that we have are um, they don't have like a performance marketing engine and they're Looking for

Speaker A: one that makes sense. So what is, what does onboarding look like? How difficult is that?

Speaker B: Yeah, um, onboarding depends on the platform. The transaction system that they're using Shopify is pretty seamless in terms of integration. We can do that within about two weeks. If they're like a homegrown website, there's a little bit more heavy lifting and also depending on the channels that they're advertising in. But typically onboarding is between two and four weeks and to get actual insights is about an additional 30 days after we have onboarded. Because the nature of implementing a pistol, getting the data to feed and train the LTV models and other things, it usually takes about a month before we can get real insights in it.

Speaker A: What's involved? Sorry, involved? Like who, who on the brand side or client? I guess it's m. More on the brand side is responsible. Like what do they have to do to get.

Speaker B: Yeah, um, so first thing is they'll get access to the app. When they go into the app they'll get a login, it'll be basically empty. There'll be a, an a platform integration page where they can self serve and just integrate uh, which channels they're in and which transaction systems they're in. So that typically takes less than an hour depending. And then so getting all the integrations through API for the automated data feeds and then setting up the UTM so making sure all of their existing campaigns and ads have utms that we can track back to the customer journey. And then the last thing is getting that pixel placed on the website to track again. We don't rely on third party cookies, they're not as reliable. So usually they have like a dev team to implement a pixel.

Speaker A: What does support look like post launch? Like ongoing? Is it just a training or always there?

Speaker B: So um, we have the training is usually like a month and that comes free with you know, just um, we don't have any onboarding fees or anything. So typically we work with them pretty heavily with the first month and then we have on demand like Slack channels where they can message us. We uh, have resources on our website that they can look to. And so I think, you know, just having some type of immediate connection with Slack or email is typically how we continue to nurture that. And then if they have the guided solution we'll meet with them weekly or bi weekly to go over, you know, strategy, higher level things and how their company's been performing overall.

Speaker A: I'm going to assume since it's early 2026, I'm going to assume AI is still going to be a meaningful conversation, uh, although a very vague one for, for a lot of companies uh, this year. Where does AI fit into Insider?

Speaker B: Yeah, I think AI like we use pretty uh, deep machine learning models to help with the, the predictions. So the heavy lifting is done on machine learning. AI though we use it a lot with automation. You've heard like a lot of companies can 10x their production by the use of AI in terms of how we use that to help our brands, a lot of the onboarding processes can be automated. A lot of the uh, tagging and the kind of the operational things can be worked on. I think a lot of people think, think that AI is like uh, you know, it's this Pandora's box where I can ask you questions and it'll give me answers. I don't think we're quite there yet in terms of using AI to help you make consistent answers. You can ask it uh, one thing, you ask it again, it'll give you nuanced answer. So I don't think from an AI perspective we're quite there yet. But from an operational like doing the manual tasks, it's really good at that and it helps the brands use the app more seamlessly. It helps us be able to, you know, deploy features quickly. But again, marketing is just as much as an art as it is a science. And so until we get there, um, I've yet to see it really shine, uh, even in like um, like automatic bidding. Right. Like people are trying to create these tools to um, create the ad set or create the creative or the images. Like I have yet to see it really shine in that side of um, kind of creative side as well.

Speaker A: I think it's generationally too. I think it's going to take people some time to trust it enough to allow it to spend money. Right. It's one thing to, to take a recommendation for a restaurant or a recipe. It's another thing to say, here's a million dollars, do it, you know, spend it. That's a scary proposition today. That'll take some time. How should brands think about pricing? Right. Like I'm not asking for like your pricing, but like how do you think about it in terms of the model? What are you based on?

Speaker B: Yeah, um, so what I've seen in the space, and this is pretty consistent whether you're looking at other competitors or not, or even in the measurement space, it's typically like some percentage of ad spend or gmv. And the reason why is because there is a real cost when it comes to data movement. So larger, uh, brands will have more data to move. And so they need to be able to scale that somehow. And the way it usually goes is here's three tiered buckets, right? If you're in this spend or this gmv, then you're going to be spending this. So you can expect as your company grows that the marketing platforms will kind of, they'll grow with you. Right? And if you know you are in expense control and I think this is where we decided to use like ad spend instead of gmv. Because if you're in expense control and you cut back your spend, like we're going to incur that risk with you as well. Right. We don't want to be overcharging on services that we're not providing.

Speaker A: So we covered a lot, a lot of great insights here. Is there anything else that we should know about Insider that we didn't cover here?

Speaker B: Uh, yeah, I think when it comes to mta, which, you know, is our bread and butter, like I would just really emphasize on are you getting the source of revenue correct? Is it one to one? Are you doing the attribution? But where most traditional MTA falls off, it's only the clickbase journey, right? But in reality there are more things that are in that journey that you can't capture with a click based view. Right. For instance, like if an order was placed today, you know, there may have been eight clicks to land that order, but they could have viewed an ad or seen something without interacting with it. Like there could have been 22 different interactions that weren't click based. Right. And so I think that's where Insider really shines is that we're not only just getting that click based journey, but we work with the advertisers to give you the view through. Even if you saw a YouTube ad and didn't convert, we know that the advertisers served you an ad and we can attribute that back to an order being able to get that whole view of. I just encourage people to think more critically about mta. Not just the clicks, but like View through ctv, you know, Programmatic has a lot of display ads and then offline mailers. Like any touch point that or interaction that should and can be captured should be included in the mta.

Speaker A: I think a lot of the pushback around MTA has been multiple devices, multiple browsers, right. I'm on my phone, I go to my computer, I'm a different browser. And so you can't make those connections from a click perspective. How do you, how do you handle that? Is it, is it, you handle it the same way.

Speaker B: Yeah. So it's the deterministic piece. If they don't identify, then it's hard to, I mean you simply can't. Right. And that's where it falls off. MTA is not a perfect solution, but there are ways around that. So there's probabilistic stitching based on your journey and your click behavior. You can assign, you know, these sessions with high probability below. Matt, even though he hasn't self identified, so a lot of people are now looking outside of deterministic stitching, which is basically, you know, Matt has accepted the cookie. Matthew has um, entered his email and we can use that to identify him. Now if he doesn't accept the cookies and we can't identify him, then we don't know if that's who that is. Right. So that's the pitfall of first party tracking. But using probabilistic stitching is a way around it. And then looking outside of mta, that's where you really need to rely on like an mmm or increment incrementality testing I would call it like there's like a triangle, like the golden triangle. If you're a company that's really sophisticated, really want to grow, if you could use MTA mmm and incrementality testing in tandem, then um, you can get a really good read on, you know, how to scale the business.

Speaker A: Yeah, incrementality isn't something we didn't really talk about today, but that is something you do.

Speaker B: That's another thing as well, but definitely for another time.

Speaker A: Yeah, for sure. But it's important, I think I 100% agree that being able to take everything at your disposable, in this case nnta incrementality, you know that that's where the art comes in. Right. Understanding the data and then applying it a certain way and testing it. Right. That's, that's, that's the whole, the whole game. Another source comes up in the future. Uh, you know, don't, don't throw out the data. Matthew, thanks for taking us inside the insight of blurb. You can explore Insider's full profile, including the first 20 questions you should be asking. And you can ask questions anonymously, all@trustblurbs.com insight IO that's I n S I G H T A I O I'm um, Sean Simon signing off.

Speaker B: Thank you.