Steve Hutt:
Welcome back to eCommerce Fastlane. Today I’m with Ed Upton, the CEO and founder of a company called Littledata, and they’re at Littledata.io. Ed’s been on the show quite a few times — at least three or four — and every conversation I have with him, I learn something new about what’s unfortunately breaking under the hood with Shopify’s tracking. This whole data layer situation is really on a lot of people’s minds, and that’s the challenge Ed and his team have been working on.

Steve Hutt:
If you’ve ever looked at Facebook Ads Manager or Google Analytics, then your Shopify dashboard, and seen three completely different conversion numbers—and had no idea which one to trust—that’s what I want to unpack today. Ed’s company has rebuilt tracking literally from the ground up. Shopify does its thing, but what Ed has created is something different. We’re going to talk about why it matters to you as a founder, this whole new data layer, and what changes when you finally get your numbers aligned. So, hi Ed, welcome back to the show.

Edward Upton:
Thank you. Yes, it feels like I’m a regular guest, so thank you for having me back.

Steve Hutt:
My pleasure. Let’s start simple. First, I’d like you to walk me through what’s happening when a Shopify brand looks at their dashboard. Like I said at the top of the show, Facebook says one number, GA says another number, and there’s a big disparity. Even in my Shopify days, I’d say, “You have to be okay with that 20% or more differential between these different tools.” So talk a little about the original core of Littledata, and then we’ll move on to the data layer.

Edward Upton:
Yeah, sure. For most brands listening to the show and using Shopify, that is the source of truth. We all know that the order number we finally don’t cancel or refund is the revenue that’s going to hit our bank account after a few days of payout. The real problem is: why can’t we get that known, true Shopify revenue into the marketing channels or reporting tools we use to measure that marketing? If we drill down into what’s actually happening, it’s all reliant on what’s called client-side tracking. Google Analytics only knows someone purchased on your site because it gets a little ping from the thank-you page saying, “Hey, this person just bought for a hundred dollars.” If that ping never happens, if it never gets to Google Analytics or it’s corrupted, then that revenue is never tracked — and that leads to this basic problem.

Edward Upton:
Most stores have probably seen situations where 20% or even 30% of your revenue is just not visible. You know it’s happening in Shopify, you know you’re getting paid out for it — that’s real money — but it’s not visible in these platforms. And it’s not just Google Analytics. All of the other marketing tools you’re using — Facebook Ads, TikTok, Instagram, email and SMS platforms — are tracking based on a ping from the user’s browser saying they completed that action.

Edward Upton:
If you want to track a page view, that’s probably fine. But an order is something more formal than just viewing a page on a website. It’s a real transaction. In Littledata’s world, we don’t think of that as something that should rely on a ping from a thank-you page which, frankly, in modern eCommerce often doesn’t even get seen. Instead, we see it as a direct server-side or server-to-server integration from Shopify to the marketing channel. That means we can relay 100% of the orders and revenue, uninterrupted — not affected by Apple’s intelligent tracking prevention, ad blockers, or whether the customer consents to be tracked. It’s a direct one-to-one integration for every order into the marketing channels you care about.

Steve Hutt:
I see. That makes a lot of sense. In the early days, that was one of the core reasons I recommended Littledata. Now knowing that Littledata has expanded into what you’ve coined the data layer is really interesting, because it seems like you’re capturing almost everything happening on a Shopify store, not just syncing a few data points. We’re talking about product views, add-to-carts, checkouts, purchases, even customer identity.

Steve Hutt:
I think there’s also a connection into Klaviyo and others. Can you talk a bit about this data layer, why it’s important, and then some of the specifics? I want to make sure I’m clear too, because I know it’s fairly new but a really exciting upgrade.

Edward Upton:
Yeah. What we recognized is that most DTC brands are using the same core channels. It’s not really innovation to keep hunting for some obscure new channel. Sure, you can try Reddit Ads or LinkedIn, but there’s a relatively tiny consumer audience there. It really comes down to whether you can squeeze the best out of Google Ads, Meta, Klaviyo, and similar platforms. To maximize those channels, brands need a few things. First, they need consistent data between them all, and that’s the fundamental point of the data layer: the exact same events, and the exact same customer and product properties, get synced across all those channels. Second, you need to enrich that customer and product data.

Edward Upton:
That means creating smarter segmentation and more granular audiences: people who purchased a particular product or category, or customers who bought a subscription product versus a one-time product. These are all things you need to sync with your marketing channels easily, without code and without a custom Google Tag Manager setup. That’s what Littledata enables. The data layer syncs all of the information you have in Shopify — not just what’s visible on the page, but all the behind-the-scenes data like product metafields and customer loyalty tiers — into your marketing channels. Then you can build the right audiences, retarget the right people, or trigger the right campaign at the right time for the right segment.

Steve Hutt:
So it almost sounds like it’s the plumbing for all the other tools so they can work together properly.

Edward Upton:
Yeah, we chose the term “data layer,” but we also think about “data pipeline” or “data infrastructure.” Some competitors lean more into the “infrastructure” term. Personally, I think that sounds a bit heavy. It’s not that what we’re doing behind the scenes isn’t complex, but “data layer” felt more accessible when we repositioned the brand last year. Everyone understands they need a layer between their commerce platform — their Shopify store — and their marketing channels.

Steve Hutt:
I see. Maybe let’s talk about some of these ad platforms and get a bit tactical. Let’s say a brand installs Littledata — what actually changes as soon as it’s installed? Walk me through the impact it can have on Meta, on Google Enhanced Conversions, and on Klaviyo. What really happens with that new data layer?

Edward Upton:
Let’s take it channel by channel. Starting with Meta, since it’s generally the most important acquisition channel. You probably have the Facebook and Instagram app that Shopify built with Meta, which is free to install. It does use the Conversions API, so there is a server-side connection into Meta. But what we find is that it’s lacking a lot of detail. Not only are you missing key funnel events that let you retarget people who abandoned cart or checkout, but even for the purchase itself, Meta’s ability to match that event back to a user in their ad network depends on having not just one identifier, but a whole heap of them. Meta calls this the “event match quality score.”

Edward Upton:
The better your event match quality, the better they can attribute those events back to an ad. At a simple level, if the algorithm can’t understand that a specific creative or placement is driving sales, it can’t automatically allocate more budget to it. The example I always use is: let’s say you have two ad creatives, version A and version B. Version A gets four orders attributed, and version B gets two. Facebook won’t just slightly favor version A — it will massively favor it, maybe giving 90% of the budget to A because it thinks it’s twice as good at converting.

Edward Upton:
The problem is: what if version B is actually missing an attributed conversion, and one of A’s conversions is duplicated? So it’s really three versus three. The targeting is extremely sensitive to the signal being sent into Meta. If we can send better signals — both in terms of event accuracy (actual orders, checkouts, add-to-carts) in real time, and better attribution of those events back to the right ad — then Meta’s algorithm, which they invest heavily in, can really go to work to optimize your budget.

Steve Hutt:
I have one question about Meta’s Conversions API. I find there’s a lot of conversion data out there, but imagine — and I’m hoping you do this — if you could send conversion events back to Meta that help with net new customer acquisition versus just repeat purchases. Repeat is a bit easier to manage on your own, but what about using this data for true new customer acquisition? I’d love your thoughts, because I think there’s an opportunity there.

Edward Upton:
Absolutely. One of the things we do is send offline or non-web orders back to Meta. In a normal setup, those orders would be unattributable because there’s no click journey from the ad through to purchase. But we all know that if we’re doing lots of display or creative work on Instagram, for example, it will drive sales across many channels. People see an ad, then go purchase in-store, on Amazon, through TikTok Shop, or elsewhere. The brilliant thing is we can bring all those purchases from all channels — not just the online store — back into Meta with the customer’s email, phone number, and sometimes other identifiers like IP address.

Edward Upton:
Meta can then figure out that some of those non-web orders were driven by Meta ads, which is critical for understanding your true return on ad spend. There are certainly cases where Meta is genuinely expensive, but often the value is underappreciated because brands aren’t attributing all orders across all channels back to the ads that influenced them.

Steve Hutt:
Right. So what about Google? Does it fit into the same bucket?

Edward Upton:
Yes. Google launched a similar product to Meta’s Conversions API called Enhanced Conversions for leads and for eCommerce. It enables us to send, server-side, not just the order, but all of the customer details. The “enhanced” part is really about who this person is — their email, physical address, and so on — so Google can match them back to an ad across their device graph. Google and Meta have huge device graphs. They know who’s using different websites across many devices. They’re in a brilliant position to use the first-party data we share from your Shopify store to match those orders back to the right campaigns. Again, more accurate matching leads to more efficient ad spend. Similar to Meta, Google can then auto-optimize Performance Max campaigns.

Edward Upton:
For example, if Google can see that a particular campaign actually drove 20–30% more orders than previously thought, it can be more confident in budget allocation and auto-scaling. You can import conversions from Google Analytics into Google Ads — and we recommend doing both — but Enhanced Conversions directly into Google Ads is the only way to do this matching based on actual customer data like email and phone.

Steve Hutt:
I see. Then I’m hypothesizing that the Klaviyo integration — they’re the elephant in the room for email and SMS — becomes really interesting. Imagine having product views, cart activity, purchase information, and proper customer identity sent into Klaviyo flows.

Edward Upton:
Yeah. Klaviyo already does a much better job, because they specialize in Shopify and their main customer base. They track more customer events like add-to-cart, and they track purchases server-side. We improve it in two main ways. First, we can better identify people on your email list who are active on your storefront right now, so you can trigger abandoned cart or browse flows when they actually matter. Those flows tend to convert very well because these people are actively shopping. We’ve all had clumsy add-to-cart flows that trigger after you’ve already purchased — that’s bad data. If we can get more people into those audiences by correctly identifying them, we can win back more revenue. Often the limitation is simply that brands don’t trigger enough of those emails. We can trigger 50–100% more.

Edward Upton:
We do that by “joining the dots.” We’re not using any shady third-party data sources or big pooled identity graphs. I don’t love the phrase “identity resolution,” because it often implies tools that aren’t compliant. What we do is simply connect the people already on your list with their activity on your storefront. You can take the exact same abandonment flows you’ve already set up in Klaviyo, clone them to use the Littledata trigger, and you immediately see an uplift. It’s literally a 10-minute fix to improve data quality flowing into Klaviyo. There are two more benefits: first, adding the Klaviyo integration boosts the ad channels because we can use Klaviyo’s identity to tell Meta, “This is Edward who is purchasing or about to purchase,” and retarget them in ads as well.

Edward Upton:
If we know who they are in Klaviyo and we know we’re emailing them, we can generally match them better back to the ad network and allow targeting that way too. It’s a good workaround for the fact that cookies are increasingly unreliable for tracking. Using actual email and phone numbers is a much better way to identify customers.

Steve Hutt:
I see. Let’s talk about implementation. It’s important for people to realize that you talk about it as a 10-minute install and fairly straightforward — and that can sound a bit too easy. What’s actually involved? Do brands typically need any help, or are there snags along the way when implementing Littledata?

Edward Upton:
We’ve tried to make the app as self-serve as possible, and we’ve gone through several iterations. We just launched a new embedded app following Shopify’s latest design guidelines to make it really easy. You don’t need a developer to install Littledata, and you don’t need to change any theme code. The install has two main steps: first, you add an app embed, which is a standard snippet you toggle on in your theme. Second, you add what Shopify calls a customer event or web pixel to your checkout so we can track there. Everything else is automatic. When you want to add an ad channel or data destination, you just go in and authorize it. So you might log in with your Google Ads account, pick which account you want to connect, and we do the rest. It’s absolutely plug-and-play.

Edward Upton:
The cool thing is there’s a lot of depth if you want it. If you have custom needs — say you want to exclude recurring orders or add a particular metafield — you can drill into the settings. We’ve tried to make it super easy for first-time users but powerful for power users. There’s a 30-day free trial for all customers. Typically, people see a big uplift within a couple of weeks, but we like to give a month so you can be absolutely sure the data quality has improved. It’s often not obvious at first glance that you’re missing data in any given channel.

Steve Hutt:
Right. You don’t know what you don’t know.

Edward Upton:
Exactly. To that point, we’re proud of offering excellent technical support. We have a great help center and we’re happy to dig into a brand’s data to figure out what’s going on. As for common snags: not really. The great thing about specializing in Shopify stores is that we’ve seen most edge cases many times and already have workarounds and guides.

Steve Hutt:
I’ve spoken to a lot of agency partners that serve brands, and I know you also work with agencies. For the agency partners listening, how does Littledata affect their workflow and how they serve clients?

Edward Upton:
We work with three main types of agencies. For design and build agencies, we’re essentially a painkiller. The alternative to using Littledata is often setting up a server-side Google Tag Manager instance, which is painful, fragile, and breaks easily. The client comes back complaining the data is wrong or double-firing, and you’re stuck in a maintenance loop.

Steve Hutt:
Or double-firing — exactly.

Edward Upton:
Yeah, all of that. I know because I used to run one of those agencies before I started Littledata. We were basically a Google Tag Manager maintenance business, and it was painful, so we got out of that. The second type is performance marketing agencies. For them, we enable them to do more with their clients. The basic fix of better ad attribution makes them look better on return on ad spend, but we also unlock more sophisticated tactics — like building audiences of first-time subscribers, or new versus returning customers, so they can run more advanced campaigns.

Edward Upton:
For email and SMS agencies — retention marketing — it’s a simple uplift on whatever else they’re doing. The client still needs the agency for copy, strategy, and creative, but they can add Littledata on top and get a nice lift in abandonment flows and other triggered journeys.

Steve Hutt:
That’s amazing. I also peeked at the Littledata.io website this morning and there are a lot of case studies of successful campaigns and ongoing clients. Can you share one notable example — what life was like before Littledata and what changed after implementing it?

Edward Upton:
Sure. Since we were talking about agencies, let’s look at a brand called Skinfix, a skincare brand we worked with alongside the agency Verbal+Visual. It’s a great example of a dual win. Skinfix needed Verbal+Visual to set up the flows and guide them strategically on marketing automation. By implementing Littledata on top of that, they saw a huge boost: about 150% more revenue from checkout abandonment flows and nearly 400% more revenue from browse abandonment flows. Better data plus intelligent implementation delivered that result.

Steve Hutt:
I think they’re probably a subscription-based business too, so they’d have something like Ordergroove or Recharge connected.

Edward Upton:
Yeah, in that case it was Recharge, but Ordergroove would be similar.

Steve Hutt:
I did read the case study — I always do a bit of research before recording.

Edward Upton:
Right. Well done.

Steve Hutt:
That’s great. There are lots more on the site, and I’ll make sure to link them in the show notes so people can take a look. I know this is a bit of a side topic and not necessarily your core forte, but a lot of people are talking about AI right now. I don’t see AI heavily featured in Littledata’s story. What’s your mindset on where things are headed with AI, data, the data layer, and our near future?

Edward Upton:
I have no doubt AI is transforming online marketing. Some of the big winners are the large ad platforms that have massive datasets and AI engineering teams — Google and Meta in particular. They’re moving toward a world where more of the campaign planning and creative orchestration is done automatically. That’s great, but it’s all underpinned by having the right feedback loop with the right data. AI running blind is worse than a diligent PPC manager. It can be far better than a human, but only if it has a fast, accurate feedback loop on what’s working.

Edward Upton:
By using Littledata instead of free connectors, you’re giving the AI every chance to shine. There are a couple of other intersections. One is using machine learning for fuzzy matching of users. A lot of our attribution is deterministic — we don’t say it’s the same person unless we’re very sure, based on email, phone, etc. But there are cases where you can use pattern-matching techniques to say, “This probably is the same person.” Fuzzy matching has been around for decades, so I’m cautious about labeling everything AI.

Edward Upton:
I also push back on competitors claiming AI magic when it’s not really AI in the true sense. The third area where AI is interesting is in insight tools. Rather than logging into a dashboard with graphs, many people now want a chat interface they can query. Shopify does this with Sidekick, but Shopify’s own analytics don’t have all the top-funnel data — like what people did before they purchased. If you want to know how many orders you had last month, Sidekick is fine. If you want to know the incremental benefit of Meta ads, it’s not going to answer that.

Edward Upton:
Instead of building our own AI insights layer, we see players like Triple Whale and Polar Analytics doing that well, and we’re betting on Google too. Google has finally rolled out their own chat interface for Google Analytics. It took them longer than expected to iron out the challenges, because it’s thorny under the hood. I’m excited because the big barrier to using Google Analytics has always been the UI — it’s hard to build reports. If you can have a chat interface where all your accurate data flows in via Littledata, and you can just ask questions, a lot more people will be comfortable and get value from it.

Steve Hutt:
The other thing I’ve noticed — more of a side note — is the Shopify and ChatGPT connection. It’s interesting. I know that’s probably not a data layer you’re directly involved in, because it’s more Shopify’s existing data like orders, products, and customers, which then moves into ChatGPT to enable discovery and checkout.

Edward Upton:
I think it’s going to become an important order channel for brands. From what I understand of the partnership, people will be able to check out inside ChatGPT, and those orders will appear in your Shopify store, similar to connecting Amazon or another marketplace. It’s quite similar to Amazon in many ways. You won’t know much about the user journey beforehand. You’ll know the order came through ChatGPT, but not the exact prompts they used. Other tools might help you see what they were searching for. But going back to the point about feeding all your revenue into ad channels: if a ChatGPT sale was actually influenced by Facebook ads, feeding that order back to Facebook and telling them it happened is still crucial.

Edward Upton:
There’s another channel that’s arguably more important right now that people aren’t talking enough about: the Shop app and Shopify’s pay network. It has hundreds of millions of users and is becoming an order channel in its own right. One thing we support is tracking orders from there, because Shopify is clearly moving toward a universal catalog and a more Amazon-like experience.

Steve Hutt:
A super-app for sure, just like we see in China.

Edward Upton:
Exactly. So being able to understand, as much as possible, the customer journey that leads people to purchase via the Shop app is really important.

Steve Hutt:
What do you think the next steps are? There are a lot of different people listening. Littledata is a very easy self-serve tool, and I think the 30-day free trial is fantastic because it lets people kick the tires. For mid-market to enterprise, what do you see as their next steps?

Edward Upton:
We have a couple of roadmap items that are particularly relevant for larger brands. One is Shopify Markets. A lot of brands are moving from multi-store setups to Shopify Markets to simplify inventory, currencies, and operations. One major limitation of Markets is that when you use the basic integrations — say for Facebook Ads — all global data gets merged into one ad account. From both an analytics and ad management perspective, you need to split it by country or market into different destinations. Littledata will allow you to configure each market completely separately.

Edward Upton:
You can set entirely different pipeline rules, filters, and ad accounts if you like, and also tag data differently in Klaviyo or Google Analytics. That means you can build segments like “people in my Canadian market” or even “people in my Canadian B2B market.” Markets aren’t just geographic — they can represent different storefront experiences. So full support for Shopify Markets is critical, and that’s what we’re rolling out. We’re also launching an integration to Microsoft Ads. Bing Ads has been around for a while, but it’s having a resurgence through the OpenAI partnership, and we’re seeing more brands wanting accurate data there. The out-of-the-box Shopify–Microsoft Ads integration is pretty bare, and in some cases non-existent, so we’re filling that gap.

Edward Upton:
The final longer-term item is what we’re calling the Event Customizer. This will let brands configure their own events. Going back to the Klaviyo example, you might want to trigger a flow for people who bought a one-time product in a particular category and then later viewed another category. You’ll be able to do all sorts of advanced configurations, well beyond what Klaviyo’s own event-builder supports. That will also be true across other ad channels. Whatever marketing destination you’re using, you’ll have much more fine-grained control over which events you send where.

Steve Hutt:
That’s amazing. Ed, thanks so much for jumping on the show today. The 30-day free trial is very easy — just go to Littledata.io, and there’s a link there.

Edward Upton:
Install the Shopify app, start the trial, and reach out to us if you have any questions. We have a great support team. I think you’ll find it’s a seamless experience, and I really hope we can help you get more efficient ad spend and faster growth for your brand.

Steve Hutt:
I know I learn something new every time we chat. I love it. You’re going to help a lot of people who are listening right now — first to get their numbers straight, and now, with this data layer and ad platforms, to really level up. Thanks again for coming on the show and sharing today.

Edward Upton:
Thank you, Steve.

Steve Hutt:
All right, have yourself a great afternoon. Well, that’s it for today’s episode. I’d like to thank you personally for being a loyal listener of eCommerce Fastlane. It’s my hope this podcast offers you a ton of value through growth strategies, tactics, and exclusive insider tips on the best Shopify apps and marketing platforms — all with my personal goal to help you build, manage, grow, and scale a successful and thriving company powered by Shopify. Thanks for investing some time today and listening to the show. I’m so proud and excited that you have a growth mindset and are a constant learner. We truly appreciate you and your entrepreneurial journey.

Steve Hutt:
Enjoy the rest of the week, and keep thriving with Shopify.