Welcome to Chase your Dreams

In this episode, Mia Umanos, CEO and founder of Clickvoyant, illustrates the transformative potential of Google Analytics 4 (GA4) for e-commerce businesses. She explains how using data-driven strategies can significantly boost revenue, and she provides concrete examples of how the approach has helped her clients make more money without increasing their ad spend.

If you're navigating the complex world of e-commerce, this conversation will open your eyes to the often untapped potential of GA4. Understand what GA4 is, what it’s used for, and whether your business is ready to hire a GA4 specialist to configure your website, interpret the data, and offer strategic recommendations that drive growth.

About Mia Umanos

Mia Umanos is the CEO and founder of Clickvoyant, an AI-powered analytics firm for e-commerce companies. She is a 15-year veteran of marketing analytics who grew her career from Junior to Director of Analytics inside Omnicom and JWT Agencies.

Mia has a talent for breaking down complex data concepts, empowering e-commerce business owners to understand their customer behavior and intent to make data-informed decisions. Her passion for analytics extends to education, where she leads workshops on Google Analytics 4 (GA4), making advanced data insights accessible to both new and seasoned entrepreneurs.

Contact info

Email: mia@clickvoyant.com

LinkedIn: https://www.linkedin.com/in/miaumanos/

Website: https://www.clickvoyant.com/

Takeaways

• Adopt a data-driven approach to navigate growth and avoid plateauing in sales.
• Invest in foundational analytics knowledge to make informed e-commerce decisions.
• Use Google Analytics 4 to gain in-depth insights into customer interactions and behaviors.
• Use shopper behavior data instead of gut-feeling to enhance user experience and increase conversion rates.
• Track product efficiency ratios to focus on top-performing items and streamline inventory.
• Prioritize optimizing existing products and website design before adding new products.
• Design your website for your ideal customer.

Interview themes

What is Google Analytics 4 (GA4) and what role does it play in the sales funnel?

Google Analytics 4 (GA4) is an enterprise-grade tool that is free. It tracks shopper behavior when they are on your website. Mia explains that GA4 bridges the gap between the top and bottom of the sales funnel, focusing on insights in the middle funnel.

While GA4 can track initial traffic sources (like organic search, social media, or paid ads), it’s designed primarily for understanding user behaviors once they’re on the site. This middle-funnel focus allows brands to capture actions such as browsing product pages, adding items to carts, and navigating between categories—critical behaviors that indicate customer interest and intent.

GA4 also supports the bottom of the funnel by tracking key conversion metrics, such as purchase-to-detail rates and cart abandonment. These insights allow brands to address friction points in the checkout process and identify patterns in customer loyalty and repeat purchases. GA4’s comprehensive view of user engagement across the funnel helps e-commerce brands to increase conversions and optimize the entire customer journey.

Why is it important for e-commerce businesses to shift from gut-feeling decisions to data-driven strategies?

Mia notes that many business owners hesitate to engage deeply with analytics, feeling overwhelmed by the data. However, data can provide a clearer picture of customer behavior and site performance, enabling more informed decision-making. For example, data can reveal product efficiency ratios, shopper behavior, and engagement metrics—which help business owners determine what’s working and what’s not on their e-commerce website.

Mia believes that embracing data analytics helps businesses grow sustainably by aligning strategies with actual customer preferences and behavior rather than assumptions. This shift not only minimizes risks but also creates a foundation for scalable growth.

How can Google Analytics 4 help businesses understand customer behavior and optimize conversion rates?

GA4 captures user interactions, such as clicks, swipes, and product views, giving business owners insight into which items customers engage with most. This data can guide improvements in site structure and product pages to boost conversions. For example, understanding which products are frequently viewed but not purchased allows brands to tweak those specific pages, improving descriptions or images, to encourage a sale.

Mia stresses that GA4 is not just another dashboard, but a source of actionable insights that help optimize the user experience, directly impacting sales and growth.

What role does data analytics play in identifying and enhancing product efficiency ratios?

In GA4, the "cart-to-detail" and "purchase-to-detail" metrics provide insights that can be indirectly related to a product efficiency ratio by showing how efficiently products move through the sales funnel from viewing to purchasing. Mia often looks at the “cart-to-detail” and the “purchase-to-detail” to see how many shoppers view a product and proceed to add it to their cart or complete a purchase. If a product has high views but low add-to-cart rates, there may be issues with the product itself or how it’s presented on the page.

This data helps e-commerce brands understand which items are truly performing well, so they don’t waste resources on producing items that don’t sell. Focusing on these ratios allows businesses to avoid the common mistake of simply adding more products to drive revenue, instead enhancing what’s already working.

How can adjusting navigation elements on a website influence customer shopping behavior and average order value?

Mia shared a powerful example of how a small change in website navigation led to a significant increase in average order value. For a luxury brand, Mia’s team removed the “Sale” option from the top-level navigation, replacing it with “Just In.” This stopped customers from seeking out sales immediately and instead directed them to the latest, high-ticket inventory. The result? The average order value increased from $300 to $750 within 45 days—without any additional advertising spend.

By strategically adjusting navigation, e-commerce brands can influence where customers focus their attention, encouraging them to explore full-priced, higher-value items rather than defaulting to discounts.

Through data-driven insights, e-commerce brands can create a personalized experience that aligns with the expectations and motivations of their core customer base, ultimately enhancing user engagement and conversions.

Why and when would an e-commerce business hire a GA4 specialist?

Hiring a GA4 data analyst brings expertise in setting up, interpreting, and acting on data in ways that maximize the tool’s potential. This professional guidance helps e-commerce brands make more informed decisions that align with their growth goals, using data beyond what a standard GA4 setup would provide.

Investing in GA4 entails a few critical steps and resources to fully leverage its capabilities for e-commerce. First, while GA4 is a free tool, integrating it to capture detailed shopper behavior requires a setup beyond simply installing a pixel or a basic Shopify integration. This setup might include configuring the "data layer," a technical component that gathers metadata about shopper actions like product views, add-to-cart actions, and purchase flows.

According to Mia, for many e-commerce brands, the initial setup can range from a $2,000 to $10,000 investment, depending on the complexity of the website and the level of detail desired in tracking. More extensive analytics services for businesses ready to optimize conversion rates scientifically could go up to $6,000 per month.

The benefit of this upfront cost is that it provides a foundation for ongoing insights and adjustments to the site, making it a cost-effective long-term investment compared to monthly fees for other analytics tools that may not offer as robust or tailored a dataset as GA4.

At what point should an e-commerce business invest in using GA4 to drive more revenue?

Mia advises that businesses should focus on using GA4 when they have moved past the initial stages of e-commerce growth and are beginning to see steady website traffic and sales. Specifically, when an e-commerce brand is generating consistent revenue and has around 500 to 1,000 monthly visitors, GA4 can provide valuable insights to help understand shopper behavior and optimize the site for conversions.

This stage is also when businesses often see customer acquisition costs rise, revenue growth plateau, and return on ad spend (ROAS) begin to dip—indicators that a deeper understanding of user behavior is needed to drive further growth.

For businesses that are pre-revenue or just starting, Mia suggests holding off on extensive analytics investments until there is sufficient data to analyze.

Chapters

00:00 Transforming E-commerce with Data-Driven Decisions

07:13 Understanding Google Analytics 4: A Game Changer

14:01 Leveraging User Behavior for Business Growth

20:58 The Power of Product Efficiency Ratios

27:47 Building a Data-Driven E-commerce Strategy

34:56 Navigating GA4: Simplifying Data Analysis

41:56 The Importance of Data Architecture and Layering

Transcript

Glynis Tao

Hey, fashion entrepreneur, are you basing your business decisions on gut feeling versus using real data? Well, it's time to get over your arithmophobia. That's fear of numbers. I have GA4 expert Mia Umanos here with me today to help you feel more comfortable and confident using Google Analytics 4 for your e-commerce business.

Mia Umanos is a 15 year veteran of marketing analytics who grew her career from Junior to Director of Analytics inside Omnicom and JWT agencies. Her talent for balancing math and human empathy turns her projects into gold. She lifted revenue up $4 million in 90 days through conversion rate optimization, created a sustained 40% increase in ad revenue for a major publisher, and won a Google News Initiative data grant for a Nobel Peace Prize winner. She now leads Clickvoyant, an AI-powered analytics firm for e-commerce companies.

Please join me in welcoming my guest, Clickvoyant CEO and pioneer in using Google Analytics for sharper insight, Mia Umanos. Welcome, Mia. It's so nice to have you here today. Thanks for joining me on the podcast.

Mia Umanos

Of course, thank you for inviting me, Glynis. I really appreciate the invite.

Glynis Tao

Yes, absolutely. I'm happy to have you here. We had met through a mutual friend, Carol Shih of Qode Space, whom I had interviewed in a previous episode talking about the importance of community to overcome entrepreneurial challenges. So how did you first meet Carol?

Mia Umanos

Well, we were actually in San Diego at an event for female founders and I was pitching Clickvoyant artificial intelligence on stage and Carol immediately found me and was like, I work with hundreds of e-commerce companies. They all need this. Who are you? Let's be friends. And we proceeded to be inseparable the entire conference.

Glynis Tao

Amazing. Yeah my first impression of Carol was that she was just a very friendly, approachable person.

Mia Umanos

Yeah. And hilarious. Just I can't stop laughing when we're together.

Glynis Tao

That's so great. So Qode Space is also a partner agency with Clickvoyant, right? Can you tell us a bit about that and how you work together?

Mia Umanos

Yes. So Clickvoyant is an analytics only agency. All we do is data analysis for e-commerce companies. And so a lot of times Carol will refer to us analytics and the development as the plumbing of all e-commerce shopping, which is that we're kind of, you know, chummy because a lot of times it's the last thing that merchants and designers think about when, you know, you're very busy thinking about the business, the lines, planning the logistics, sourcing materials, all of this stuff.

And when it comes to this underbelly of code that lives, it's often because you can't see it or touch it, the last thing to think about. And yet the entire business is dependent on what we do. And so we feel like, you know, the invisible superheroes of e-commerce because if none of it works, the data nor the development, then all of the, you know, line planning products, it's for not.

So that's how we work together. And she's the CEO of a company which does the code for Shopify stores. And I am the CEO of a company that does the analytics for Shopify stores. 

Glynis Tao

Okay. That's great. Thanks for explaining that. So what made you decide to become a data analyst and like, how did you first get into this industry?

Mia Umanos

Well, I mean, the first getting into this industry was actually the bio is a little old. It was almost 20 years ago. So I first got into the industry around the time that MySpace was the primary social media platform to talk about. So it was a long while ago. And I think that my path begins really in journalism school.

I wanted to be a science journalist. And what I really liked about that was, I like to write and I like to distill complex concepts into something that is more palatable for the layperson to understand. So I like to do the digging to figure out what is mathematically, scientifically going on and what's in the soup so that I can level up and tell the story about how it all works to the people who need to know about it.

And so my transition from broke journalist into data analyst was very natural because I could take the data from these websites and explain how behaviors are being impacted. And people understood it. Not a lot of data people can do that. If you've ever worked with any data people, lots of times you're opening up these different dashboards that you have access to and your eyes kind of gloss over and you just want to put it down. So I like to be the person to say, well, magically, here's what it says.

Glynis Tao

Right. So we're covering a big topic today and that's Google Analytics 4. So I just want to jump right into it. We'll be focusing on using GA4 for e-commerce sites as our listeners are mostly e-commerce store owners.

For those unfamiliar with GA4, can you explain some of the key differences between GA4 and Universal Analytics?

Mia Umanos

Sure. Well, I think I might start a little bit further back and say, you know, in the world of e-commerce, there's usually three things that you need to look at the data to know if your business is healthy or unhealthy. And the first part is some kind of paid media dashboard. So if you're at the point where you can start spending money to try to get people to your store, then you're going to want to know how much are you spending? What is your return on ad spend? Like how many dollars do you get for every dollar that you spend? And you're going to want to see the revenue at the end of that. So the revenue is usually something that you get from a Shopify or maybe some of you are in commerce even or big commerce, but that's data on the backend. It's like what comes out, what products are being sold? What is the margin? What coupons were being generated or used? And that's what we call marketing upper funnel data as the ads and then the lowest part of the funnel, which is what is happening on the checkout.

But what is missing between those two is your middle part is like, what are people doing actually in the store itself? So all of the ways that we're comporting ourselves, like we’re making the sale, we're showing the slide, we're showing the season, that's merchandising. And the merchandising you know why your grocery store gives you that tag, right? They want to know where you go. They want to know what you're picking up, what you're putting back, because they study your behaviors to see like, well, what do you want to buy? What are you considering and what do you want to buy? Not many merchants fill in that gap, but that's where Google Analytics comes in.

So Google Analytics is the place where you get all of your e-commerce website data, but it's not out of the box. And that's one of the real differences between Google Analytics Universal, which some people might know as the old Google Analytics. I actually know it as the second Google Analytics, because I've been doing this a long time. There used to be a method of tracking data and a data architecture, which is kind of a big vocabulary word, but data architecture used to be just how do we access the data and how do we store it.

So the data architecture of Google used to be on a thing called Urchin. Then it moved to a thing called Universal Analytics. And now it's moved to a thing called Google Analytics 4. And Google Analytics 4 is free. Google has always been free. But what Google Analytics 4 is is an enterprise-grade tool for free. So that just means that there's a lot more that you have to do to get the data in there than just

put on a pixel on Shopify or turn on the Shopify integration and I'm done. There's a lot more to get that shopper behavior clicks and swipes and gallery views. So, you when I say a lot more, I'm talking, okay, this can be anywhere from a $2,000 to a $5,000 to a $10,000 investment, depending on how big your site is and how complex it is. So when we're talking about a $2,000 investment to get this information inside Google Analytics, let's compare that to what sometimes companies of like a larger size are paying, a Triple Whale who has many clients who are under one million in annual revenue, charges seven, minimum $700 a month.

So if you think about these other tools here that are $700 a month, you make a $2,000 investment over the long term, your $2,000 investment upfront will give you data forever for free. I mean, forever is a superlative. But I'm being fantastic to make a point. So that's it. Google analytics is enterprise grade. It takes a little bit more muscle to get that shopper information in there. But when it's in there, you can use this free tool forever more to learn about how people shop. In your store, what do they look at? What do they put back? Cause that's not data that's in either of the other platforms.

Glynis Tao

Okay. So the Google Analytics 4’s job primary function is to serve like the user behavior, right? It's to give you that data?

Mia Umanos

That's how I liken it. I mean, there are stages, honestly, Glenis, of analytics maturity. So in the stage where you have, you know, you're doing a data analysis and you're subscribing to a Triple Whale to $700, you're probably past a couple thresholds.

In the very beginning, Google Analytics can actually function as the only tool, right? Or that plus Shopify. So it's like, I've got, there is a component of Google Analytics that will allow you to do paid media, ROAS analysis, how are these channels performing? And when a company matures, there are some limitations to those reporting that.

Basically if you've passed a certain amount of spend that you're going to want a bigger gun, guess that'd be a bad analogy, but you know, so there are stages of knowing when to invest in analytics. Google analytics can do a lot of these things. Think revenue items sold forever should always come out of Shopify, like the most direct data point that should come out of Shopify. But anything beyond that, like, okay what are the campaigns? Google tracks that too. How much are we spending? If it's in Google ads, you can track that too.

Google analytics can also take your spend from your other platforms, Pinterest or TikTok and put it into there. And then you can see how your campaigns are performing. So it can do a lot of things. Is that a nebulous answer?

Glynis Tao

Scratch the surface right now or even like what it's capable of doing. Level of knowledge, would just say I, you know, it's the tip of the iceberg. I don’t know that much about it's full capabilities of what it can do. And it can be a very powerful tool if you know how to use it properly. And I think that's kind of the challenge that a lot of business owners have is they don't even know where to start to look.

Mia Umanos

Sure. And I mean, this is why I love talking about this to merchants and even startup founders, like I do a lot of, you know, webinars speaking, do the class, obviously I have it. I digitized that clash just last week, so it could be on demand anytime. So if anybody wants to use that class, the coupon code is girlboss and you can get in there for $7. At the end of the day, Glynis, like we talk about when a data driven company and the education that I do for these, we were talking about some big brands are not even using it in the way that I use it.

So I'll give you an example of how helpful this actually is. One company apparel, you know, when you're making apparel, dresses are always like number one. I feel like it's like dresses are so number one, lot of the time, unless you're like jeans, right? This company, a big company, was only using these products that I was telling you, like they have a Triple Whale, they're looking at Shopify. After the class, they said, I think we realized that we were just looking at dashboards and then going about our business. We weren't really thinking about shopper behavior in any way, and we weren't really changing any of our strategies as a result.

So in this instance, what we saw were people because we implemented for them all of these user experience, navigation, merchandising variables. And then they could see people who were shopping on sale and clicking there actually had an average order value of half the average order value for the whole site. And it was in their top level navigation. So you get into the site, some of these sites before and you could see Sale like, you know, if you're Forever 21, that probably makes sense. But if you're a luxury brand, why would we be putting Sale at the very top level navigation?

So we took that out. This is a luxury brand. We took it out of the navigation. We stopped training people to shop for sales and their average order value went from $300 to 750 in 45 days. No more ad spend. So, right? Like that's basically doubling the revenue without spending any more on ads with one small thing that we don't even think about.

Glynis Tao

That is incredible. And they wouldn't even have known that, I guess, if you hadn't come in to take a look at the data in terms of what people are clicking on, right?

Mia Umanos

Right. Because it was just like, you know, if we do a sale and this is very, I think, you know, fashion stores are very like... And I think that there is a true need for a sale, but I think that sales should be strategic pillars and a revenue model versus desperation. Right. Or as like, you know, it's you're training people to shop for sale when they're going on your site, what are they looking at? Top level navigation. So it's Dresses, Tops, Just In. So we actually replaced it, we didn't just take it out but we replaced the word sale from the top level navigation with Just In. So we're training you to come to shop for our high ticket, most recent inventory, and for you to want the latest from us, not to come here and shop for whatever's discounted. I mean, I myself do that. I'm an immigrant's daughter. I'm very motivated by getting it for 75 % off. That to me is a badge. But you know in some instances it doesn't matter. So when you're like, when you're changing your website and you're making these design choices, you're making the design choices for your best customer.

Glynis Tao

And that's where the data starts to lead you. like, the other thing is like we had another swimwear company come to us and say, we've been around for 10 years. We do not know. We knew who we started out with and we don't know who our customer is now. Like we started out with this like surfers, we're surfer girls making the swimwear brand for surfers and outdoor women. And then 10 years later, after so many Wunderkind apps and so many things and you're like, who are we attracting? We have no idea because the algorithms, the ads are just sending your, it's an algorithm, sending people your ad and then people are buying and you don't know who they are. So that's why it's critical to have the behavior, because the behavior starts to signal affinities for certain products, price ranges and so forth.

Glynis Tao

Okay. It's so powerful. Just this example that you've given me, you know, by removing the sale banner off, how it just increased your average order from 300 to 750 instead of like making it a sale right off the bat on the top. People just go on and click on that. Right. And then that's what they're trained to do. But if you actually switch it out and I guess you're using that data to understand like, okay, what types of things are people looking or looking on, I guess, when people land on the site? And then understand and then change that out to your new products, right? Is that what you changed the banner from the sale to new or just in? Is that what you ended up doing?

Mia Umanos

Right. It wasn't technically a banner. There were no banners on this particular site because it's luxury, you know. And so it was in the navigation. Like when you go and you first go to a website and you see, all in the navigation, it was at the top. And so they switched that out with new, just in, just in or new arrivals, new lines. And then somebody you train them to, to look there. And, know, I mean, people who are sales motivated are also good customers. And we, and they will find it. They will find the sale. And this is the thing that we, this is what data does.

When you get shopper behavior, it starts to elevate the different stories of who she is. Who is she? Who is this kind of shopper? How do I need to comport this page for her? You know, the wedding dress shopper, what is she like? The event, the occasion shopper, what does she care about? Or the price sensitive shopper, what does she care about? And you can make these different featured edits to comport, to call to them and be more empathetic to like comport your pages and your store to be more empathetic to that person. And that's the power of digital that you could never do in a department, right? Or, you know, everything is like the best line. If you walk into in-store, it's like just whatever we think is the most stylish thing.

But on digital, you can really start to comport the merchandising to the motivation of that person who's there that day. And it's just by creating these trails and on a homepage, you know, here's this for you, right? Or there's a navigation that's clue one. Where am I going? Mia is going to go to sales. Mia is going to go to clearance, right? But somebody else might go, I'm a lover of this brand. I want to see what's the new line. They're going to click there. So you find these like different clues on the homepage. There's a lot of things there as well.

I mean, we, you know, people like carefully, artfully craft home pages. And sometimes they don't realize some of the things that they might be doing to basically change behavior when you're creating all of these things. So here's another example. Some of the things that we're discovering about the behavior data in a merchandise store is like, you see this all the time, Shopify home hero, then some headline and then some triptych. Right? It's like.

Glynis Tao

And a triptych is three images, right?

Mia Umanos

Yes. When we see three images across, we often see the most clicks in the middle.

And if you see two, a diptych, two images across, we see fewer clicks to something like that. So, and this is just a hypothesis, but it's like, the data is going to tell you one thing and then what you decide about how shoppers behave is another. So what I'm going to say to you is my hypothesis. I see the data. I'm making a hypothesis. My hypothesis is that a triptych allows for an eye to be drawn somewhere in the middle. Humans see with their brain, not with their eyes. So it's taking shortcuts. the one in the middle, right? I don't know which one to pick. I'm going to click the one in the middle. When there's a diptych, it's often used as a comparison. It's like there's, these two are equal. If they're both equal, neither of them are important. So these are the hypotheses that I start to think of.

You know, I'm, I'm, I might, I might be a data person, but I am at the root, just interested in people. I'm interested in people. I'm interested in behaviors. This is why I wanted to be a science journalist at the beginning. Like I'm most interested in why they do that? That's so interesting. So when I say, you know, data and scientific method, they're very intertwined. And so I'm always like, very, very obsessed about the shopper behavior, why they are clicking on things that they're doing. And you know, can I help get them to the places that they actually want to go?

So that's why, you know, when people take a look at what I put in Google Analytics, they're like, holy crap. This is a lot of stuff.

Glynis Tao

So they may not necessarily need to be tracking all that stuff. Is that what you're saying?

Mia Umanos

No, I'm saying that most companies don't, that they don't track all that stuff. Our clients do this because they now have seen the power of like, well, when I understand how shoppers behave, now I can change my site according to what is converting better, which leads to a transaction. 

Glynis Tao

Yeah. So what do you start with first, like maybe kind of walk us through your process a little bit when somebody comes to you, right? When they come to the store and they're like, I'm, what's the number one problem that people have?

Mia Umanos

Yeah. So, this is great. Actually the number one problem, the real trigger to become a Clickvoyant client is that a company can get to a million dollars in annual revenue just by slugging it out and dragging through the mud and sleepless nights. It can get to a million in a year it can, but it doesn't have to.

Getting to a million dollars in annual revenue and now you're spending ads, what's happening with the companies that come to me is like, Mia, we've been doing it like this forever. And now our growth is plateaued. We're flatlining. And not only are we flatlining on revenue, we're also increasing the cost per customer acquisition. And our ROAS is going down. Like a ROAS, when we started this company, it was like a four a five and now we're at like a two. It's terrible. Like we're barely breaking even. Right. And so they come to me and it's like, well, I have these two pieces. I got Shopify, I have Triple Whale. I have an upper funnel and a bottom of the funnel. I have nowhere else to go, but now start looking at my site.

But the companies who look at their site and are obsessed with shopper behavior from the very beginning, they're not going to have to go through that pain. Right. Again, like for a small company, like a $2,000 investment. Lord knows you're spending way more on other things, right? It's like, okay, I'm getting the data. I can understand how shoppers behave. I see what they add to cart. I mean, it's probably not appropriate if it's like a pre-revenue company, obviously. But when you start to get to a place where like, all right, I've got like a thousand visitors a month, you know, I've got a thousand people looking here a month. Like, why aren't they buying? That's enough data. Or even like five or 600 people a month. It's enough to start looking at.

Why aren't they buying? Why are they buying? And how do I comport the site design to make sure that it's easy for them to navigate, easy for them to understand why I'm the one that they want to buy from.

I mean, it is like a little bit of an easy process, maybe it's easy to me, but I know that you just took the course, so maybe you have a different opinion. But it's like looking at all of the things that we have, like all the plugins, the wunderkind, the quizzes, all these things, the size function, like accordions on a product, you tell the page and look and see it. Do you have that data? And if you don't, those are usually the places that we start.

It's like navigation. What are people doing in a product detail page? Where is the add to cart coming from? Is it from an upsell widget? Is it from a rebuy app? Is it bundling upselling like versus let's put in those data points. And when that doesn't become enough, you add more. So you're enriching the data as you're maturing your business.

Glynis Tao

Okay. I think one of the things that stuck with me, I remember the first time I took the course and you telling me the story about how a lot of companies think they want to increase revenue. So the first thing they do is, you know, adding more products. Right. But how you able to look, you know, kind of dig in deep and see, understand the customer behavior and help them to increase their average order value, increase sales in turn without even having to, you know, add more products, that sort of thing.

Mia Umanos

Yeah. I mean, I think it's like when you're a business and you want to grow, you're going to grow in the ways that you know what to do. So I know that when I make a product, people buy product. Right. And so I think that that can be one of these ways that, you know, most companies who are just maturing to understand analysis and their data. I mean, before they get to that stage, they'll just say, yeah, let's release more things and then product will start churning out. I mean, the real root of true business growth in an e-commerce setting is to understand your customer acquisition cost, your new customer, basically your customer acquisition cost, the product efficiency, meaning, of these products, what are my favorite metrics to look at? So we should get into that. My favorite metrics to look at are like a cart to detail and a purchase to detail rate. Those are the percentage of people who view a product or the percentage of people who add to cart or a percentage of people who purchase a product over the total amount of times that that product was viewed. So if you've got a high view and a low add to cart rate, it's either the product or the page that's not doing it. But what we like to see, if there's like an add to cart rate on all these pages or a purchase to, a good metric to get to is a 6% view, like a purchase to view rate.

If you get, if 6% of views end up in a purchase, it's a pretty good marker. If you're below that, I'd be concerned about that product. So a lot of companies don't know how to look at their product efficiency ratios on a site. Some of them are like, I've never even heard of this, even mature businesses. So if a business that's starting out, like a lot of your audience is like, okay, this is a metric I need to pay attention to. How much does it cost to pay, you know, how much does it cost to get a customer and what is my product efficiency ratio?

Because I'll tell you, we do a lot of analysis on here's all the revenue that you're creating and here, a lot of times, a metric that I see a lot is 98% of your revenue was generated by 5% of products. Think about that. Almost 100% of your revenue is generated by 5% of your SKUs.

Like think about all the churn expense overhead to make those other 95 products. Right. I mean, we're not all Forever 21. We can't just churn out garbage, right? Like we can't do that as a business. We have to pick. And so when we like, I think that the more merchants know and startup companies, anybody, whatever you're selling, start to look at product efficiency ratios. 

Like if you say, okay, this is going to set me free from having to just churn out products. There are other ways to grow my business.

Glynis Tao

Okay, so just to emphasize what we just talked about is an important metric to look at would be the product efficiency rate, which is something that I just, I don't hear about that often. I'm not sure if many people even know about it. I mean, is that what you come across with a lot of people? Don't even know about this? I guess it's like average order value, you know, customer acquisition, you hear conversions.

Mia Umanos

It's really not known. Actually I was just talking to another client of mine, a jewelry company, and she said, Mia, you really need to go out there and start talking to some of these accelerators for e-commerce accelerators, because we went through product, logistics, line, business model. There was nothing about a DTC analytic strategy.

A DTC analytics strategy in e-commerce startup accelerators. I was like, what the heck? That's insane. So I mean, that's why I'm out here now. I'm like, I'm going hard on content because she told me that literally over like a week ago. And I'm like, okay, I'm on a mission now. Like if that's true, you just went through a startup accelerator hosted by a very well known e-commerce startup accelerator and you did not have any analytics training, then I'm going to die on that hill. I don't care. That's my new thing. I'm going to die on that hill because there's so many great brands, right? So many. And like I'm particularly fond of female run startups. It will always be, I mean, I will always make myself available. I am a female founder myself. So I know how hard it is.

Glynis Tao

Yeah, I mean, they've got a million things going, right? They're juggling a lot and, you know, wearing a lot of hats and they're probably not focusing like, or even know, you know, they don't know what they don't know, right? So I think it's just having that awareness, first of all, which is what you're doing. But there's so many things that are taking away their attention, right? What they think they should be focusing on. You know, I think it's like social media is a big one. They think that they should be doing, spending their time on, but not really looking at a lot of the numbers. I find that that's what they're not looking at. And I think it's maybe because, you know, if they are even able to have the data, get the data, but they won't know what to do with it.

So what advice would you give to businesses that are starting out with GA4, or they feel overwhelmed by the data? How can GA4 be able to help them simplify and streamline the process of identifying meaningful trends and the data and that sort of thing?

Mia Umanos

Yeah, I mean, I would say like thing number one is like if you have really expensive tools that you're spending money on, to just stop that right away and start looking at Google Analytics because you're not mature enough for a $700 a month tool. It's like, and Lord knows you can't burn through cash, right? You cannot burn through cash. if you're spending, so that's like thing one, don't just go and start to like spend a ton of money on a tool that you're going to ignore. Cause frankly, that's what happens a lot. $100 a month is a lot of money.

So the second thing that I would say is take the GA4 class, wake up, get a grip on it. It's easy to take. I'm easy to talk to. The concepts are hard. But I mean, you say to me like, what did you get out of it? Like, if you get nothing but a better understanding of what it's for, I've done my job. 

Glynis Tao

Yeah, that's what I would say. I got out of that, your course and the course we're referring to is your four-day GA4 boot camp, which was amazing. I mean, you know, I wouldn't say I'm like an expert, a GA4, I'm not even close to that, right? Like I understand a bit, I use it as a tool and I do SEO, but you know, like I just know the basic basics really, but I think your course really just opened my eyes to what is possible out there and I feel like I know a little bit more about it now.

And at least that's sort of now on my radar, right? When I'm talking to clients and stuff and hearing about like certain problems that they're having with their websites, maybe not performing or whatever, you know, they're not hitting their goals and, OK, then I could perhaps prescribe something to help them do that or to an expert like you, if they want more help with it or something like that, at least I know about it.

Mia Umanos

Yeah, absolutely. I think getting it on the radar the most is like step one, know that it's there, know what it's for. It's for website shopping behavior. If you're having a problem there, you've got that in your bookmark. Right. And then I think the next thing is, you know, like taking the course is helpful, but as well, like take it in little bites, like don't try to, you know, boil the ocean. 

Just like if you're thinking about, okay an add to cart rate or Google Analytics called the cart to detail and the purchase to detail. Those two metrics and you're looking by product, it's going to help you understand what products are performing and what ones aren’t performing so good. And again, if they're not performing good in those two metrics, it's either the product or the page. So you get two things like one thing you could change easily, which is the page and experiment there. And if that doesn't work, then you know it's a product. Put that thing on sale. And move on.

Glynis Tao

Yeah. And don't wait too long to do that. Yeah.

Mia Umanos

Right. Absolutely. Yes.

Glynis Tao

It'll move fast enough, I think, and sit on it, hoping that, OK, maybe it will pick up or something. And then maybe they just lost that opportunity or something where they moved it a lot faster.

I know we're sort of running out of time, but there's some terminology I just want to cover when it comes to analytics and the two words that come up a lot often. In your course, you mentioned data architecture and layering.

Can you just please explain what those two things are?

Mia Umanos

Yeah, absolutely. So data architecture just refers to the structure of how the data is built in Google Analytics. So basically, Google Analytics, a lot of people turn it on and they put on the Shopify integration go, this isn't very helpful at all. And it's because they haven't put all the shopper data in there. Now, when you put the shopper data in there, there are some considerations for how you're going to get it out, which is, okay, I can do a CTA click, but I also want to know what that was, what it was, what did it say? Did it say shopped now? Did it say shop the season? What did it say? You want to know, because you want to know how you're talking to your customers.

So the data architecture is of all the things that we want to track. Where do I put it? It's like organizing a garage or organizing your kitchen drawers or your pantry. It's like, okay, well, you know, anything that's a button click is going to go here and it's going to go to things that are the add to cart type is going to go here. So are they POS? Are they user experiences? Is it messaging? That's what we refer to it with data architecture. The way that it's going to be familiar to most of your listeners is like, well, when you're deciding on the navigation, you're deciding on the architecture of a website. So it's going to be dresses. And then within dresses, it's going to be petites and regular or whatever, right? In pants, it's going to be jeans. It's going to be, that's also a data architecture, your navigation. So your data architecture is really just the way in which we structure the information so that people can access it. And your website navigation, that's your data architecture for how your customers can access your products.

In GA, the data architecture is referring to how are you or your marketing people going to access the information of every click and swipe that happens in the store.

Now, the second piece, which is the data layer, the data layer is a bit of code, actually, that lives on your site. It doesn't have anything to do with the front end, but it's all these little pieces that live in the code that is exchanging between Shopify, Google Analytics, and your ad platforms. 

So for example, in the data layer, there'll be things that when you're looking at a product detail page, inside the data layer will be things like product name, red dress, product category, dress. The name might be something snazier like Alyssa, the Alyssa, right? That's your product name. The category is dress. The item category two might Fall 2024 line. So there's this, it's the metadata that is passing so that other places can pick it up. That's the easiest way that I can think about it, like these secret messages that you're putting into your data that nobody's really seeing, but that allows you to know that when that product, the Alyssa dress goes from cart to view or viewing to a cart to a checkout, that all that metadata is passing so that you can do that analysis later and say, Hey, everything from season 2024, fall 2024, is tanking as a bad product efficiency. We had a bad line planner that season. So that's what those two are about.

Glynis Tao

Okay. Yeah, that makes a lot of sense now. When you're speaking meta tags and that's totally, you're speaking my language. Yeah SEO, we do look at site structure as part of the technical SEO checks that we do. Right. Making sure things are categorized properly, put in the proper collections folders, right? And products are named properly. And then I look into your URL structures and all that stuff as well, because it tells search engines what the product is.

So I think the same goes with Google. Well, it's, yeah, the same search engine that we're using. The better you're able to categorize, organize things on your website, not only does it make a better user experience for your customers, it also helps with Google.

Mia Umanos

Sure. And I mean, at the end of the day, there's like a data analyst human, and then there's the data analysis algorithms and we need the data to be in a certain structure to be able to do our jobs. So that's why if you have a bad structure, it's hard to show up. And as well, if you have a bad structure, it's hard to analyze shopper preferences because it's the same outcome, different products. But that's what metadata really, yeah, that data layer is the metadata on products and shopper experience that we need.

Glynis Tao

Yeah, absolutely. And so before we wrap up here, what size businesses do usually work with? You had mentioned some costs here in terms of like a $2,000 investment. Is that generally how it works? Like you have a package or like how can somebody get started if they want help from you?

Mia Umanos

Yeah, I mean, definitely not pre-revenue. I'm happy to get anybody into the classes. I think when  a company is starting to generate revenue and they really want to start, they want to make all the right moves and they don't want to make any mistakes and they want to be data driven and not product driven.

Actually a CFO of an e-commerce company told me recently this week, she said, well, a lot of these e-commerce companies are either building a business or they're building a brand. And one of the other two is neglected often. And a lot of times in the beginning, you're building a brand. You're like, I just want people to know who I am. Right. But to build a business you really need to be data driven. So if you're in a stage where you're building a business and you bought into that, then in the beginning, we start at $750 a month to help with support as a retainer.

We also scaled to $2,000 a month for deeper services. And we also do conversion reoptimization where we're actually scientifically testing websites. And that starts at $6,000 a month. So, you know, not for beginning, really beginning companies, but for companies who are you know, at the stages where we really don't want to make too many mistakes. If you want to take an engineering approach to our business, we're building a business. We're not just building a brand. If you're in that mindset, then it makes sense.

Glynis Tao

Amazing. So I know we just really scratched the surface of what GA4 can do. And really for a deeper dive, I recommend you take Mia's four-day GA4 bootcamp.

Mia Umanos

It's just an hour a day too. It's not like four days. It's an hour a day.

Glynis Tao

It's an hour a day over four days. But you'll learn how to harness the full potential of Google Analytics 4 to drive better business decisions.

Do you know when your next workshop is going to be?

Mia Umanos

Yes, it's November 18th. So Monday, November 18th to Thursday, November 22.

Okay, amazing. And maybe you can provide a link where people can sign up for that. Where can people find you if they want to get in touch with you?

Mia Umanos

Well, strangely, I'm the only Mia Umanos in all of the internet. I don't know how that happened, but I am the one.

So you can search for me. You can find me on LinkedIn, but my email address is mia@clickvoyant.com. I have an open door like most founders. I have a very soft spot for you. 

Glynis Tao

Amazing. Well, thank you so much Mia for being here today and sharing your expert insights with GA4 and data analysis with us.

Mia Umanos

Wonderful. I appreciate you. You're very welcome.