← Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Using SEO principles to optimize product feed visibility
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- [00:00]
A
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice then visit iheareverything.com welcome to the Voices of Search Podcast. A member of the I Hear Everything Podcast network, ready to expedite your company's organic growth efforts. Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search podcast, Jordan Cooney.
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B
I'm Jordan Cooney and joining me today is Katie Morrow, Director of managed services at ProductsUp. Katie, welcome to the Voice of Search Podcast.
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C
Thanks so much, Jordan. It's great to be here.
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B
Thrilled to have you here. Mainly because we don't get to talk about this level of detail in E commerce often. We're just kind of coming off the holiday shopping season and really I think this is the time of year where brands and companies are truly thinking about how to invest in a more structured way to scale their reach and their capabilities. As we get into the show, how about you tell us a little bit more about yourself, tell us a little bit about Products up and what you guys do and how you guys support the commerce market.
- [01:31]
D
Absolutely. Well, products up supports 2.2 trillion products a month worldwide in and out of the platform, out to search, social, affiliate, email, text message, agentic commerce platforms, order management, order sync, and we absolutely drive product data for the largest retailers, brands and manufacturers in the world. And we optimize it, normalize it, structure it for each of those individual channels. And I've been doing this for 15 years. So I got my start at an
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C
agency that did bid management and feed
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D
management, Channel Intelligence located in Celebration, Florida. And back then it was a celebration, right? To really understand product data, what it looked like, and then to spend a lot of money on behalf of these large retailers to achieve return on ad spend goals, to understand clicks, conversions. People were still kind of getting used to what does it look like to place pixels and to really follow the journey of those products and to identify what was working and what wasn't. So our responsibility was to do that, right? And to do that at a granular level by the sku, by the category and then by the channel. So did Bing drive more traffic than Google or get more revenue? We needed to know that on behalf of those retailers. Since then, yeah, since then I had the opportunity to be a shopping specialist at Google through the acquisition of Channel Intelligence. And that then helped drive and make decisions about the Google merchant center and what business rules they would put in there for small to medium businesses. And then I found this German software that said we can do it better, we can do it. So you visually see it low code, no code, launch net, new channels in a day and not a month and I was sold. So I've been with products up 11 years. This is my 11 year anniversary month. So, you know, self, self proclaimed data diva.
- [03:53]
B
Love it, love it, love it. Thank you for, for the intro to Products up yourself. And I think I really want to start our conversation around the concept of agents because AI agents is one of the hot conversations right now across all kinds of work, whether it be content production, whether it be fulfilling engineering tasks or development tasks. It's basically a universal conversation right now. And I want to understand your perspective on how agents are reshaping product discovery and then connecting that to what that means for shopping Personas.
- [04:30]
D
Yeah, I mean it's an extension of the sphere of influence over us as individual consumers. And so when you look at that, it's a whole new discovery phase that maybe those that didn't know the questions to ask are being given the keys to understanding how they're going to make decisions in the future. So the inception of this concept of someone making conscious decisions about what we consume is here. Whether or not we felt like we had that kind of control before, we did, but we weren't necessarily always taking advantage of it, knowing we still had to make our products discoverable. Now we have to influence something that is very intangible. And so it's bringing us all as marketers back to kind of brainstorm. Right. We need to brainstorm as marketers ideate on what does that look and feel like for the consumer to discover a product to allow these agents to I guess algorithmically see us.
- [05:47]
B
Yeah, absolutely. So one of the, one of the things I, I want to, I want to navigate towards more on this is, is Personas themselves. Like how does this, how does this, how does this help marketers better understand what's happening at the household level? Because that's where really purchases are done. Right. It's, it's, it's, it's at an individual level. But how do we use what's happening in the household to create better knowledge and better capabilities when it comes to our overall marketing and optimization?
- [06:19]
D
Yeah, I mean, I think we've taken into consideration the sphere of influence before as marketers when it comes to a household perspective, but not to this level. So you and I are both parents and we are in the sphere of influence of shoppers. My shoppers here probably spend 20% of the overall budget right in I want this snack or I need this new toy. And it's only becoming more and more prevalent as they are also obtaining devices. Right. Their devices are now starting to historically collect this information. And oftentimes I see their algorithms in my behavior on my platforms. And so then I have to consider, did I look at these shoes? Did I look at that E bike? You know, and Meta already is showcasing like for example, I'll see ads for E bikes. And I'm like, I'm not in the market for an E bike, but my
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C
household for sure is in the market and they're researching it and they're watching content about it. So how are we then, as those responsible, responsible to, to drive that revenue, going to be able to target all of those in a household?
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D
And that's what we're really looking at, right?
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C
That's that ideation. Like now I have an audience of
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D
five, not an audience of one.
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B
Yep, yep. And I want, I want to transition this from like understanding who we're targeting and where we're targeting to really the concept of like measurement. Right. Because measurement is something that's been under extreme amount of stress over the last two to three years. And now as we're looking at AI as a new vehicle for consumer discovery, it's even becoming more and more complex. Tell us and tell our listeners a little bit about what the readiness KPI is and what are the, some of the traps and what are some of the expectations that, that we should have as we see more and more of a push towards AI platforms and AI discovery for commerce.
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D
I love this question. So structured data, right? Let's start at the base. The journey of one product. What does that look and feel like? That's going to be my core message forever. So you need to have a solid product catalog. You need to understand your data sets, you need to make sure that things are multi noted.
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C
Right.
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D
So you have a multi noted structure as to what products live in categorically so that you can then represent them, number one. Number two, I know a lot of marketers are focused on building out Persona based information. So if that product now is going to expose itself to multi Personas, what does that look and feel like? So start with good structured data and then building out those Personas to understand where you're going to take that information. I would say another one for me is as we're Kind of starting at the horizon of this, I would not necessarily recommend sending all products. So if you're really trying to start at the base of an experiment, start small because this is really an experimental phase where, okay, so now I'm going to take my top 50 products or my top category, and that's what I'm going to expose. Using things like LLM Txt to validate what you actually want to expose to the agents. And then you're going to track from there. Because if you go and expose 50,000 SKUs and just release them into the ocean of robots, you're not going to be able to track that. So I really do think we need to think a little bit smaller at the initial stages so that we can understand impact as marketers and then release more and more from there. And I think that that's the way to do it, especially in Q1, Q2 of 2026, when we're really here at the forefront that way, then we're keeping a control group and we're saying, okay, now we can really see what does that feel like from a smaller segment and then expand from there.
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B
I want to double click on something which is like sending everything. Right. Like, this is a concept that I've been talking to a lot of different brands with and actually other providers too that exist in this new LLM discovery environment. So one of the examples that I'm hearing a lot about is like building a cache or building some sort of a repository of my pages that I want LLMs to prioritize or use, aka it's like the old sitemap of SEO. But you know, Katie, the funny thing,
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D
oh my gosh, full circle, full circle 20 years ago called how's your site map?
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B
Yep. And we're coming back to that. But to your point, this is the point that I want to dive into is it's not about everything. Sitemaps used to be about everything. But LLMs are not going to crawl the entire web the way Google has. And so this prioritization exercise, especially for commerce, becomes very complicated. And one of the main, I guess, pressure points that I want to test with you is how do you prioritize when you're an E commerce site? Where do you put brand versus product versus category versus informational type, pieces of content? And then how do you prioritize specifically your product catalog?
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D
Well, I can speak to the product catalog part. And if you aren't, if you don't have a good business intelligence, business analyst, good tool that you feel confident in when it comes to the representative categories. And even at the SKU level, that's stage one. So you need to feel confident. Even if there's an over under of, let's say less than 12% when it comes to traffic numbers. If you're at a 25%, then maybe you need to look at getting a new tool. But that being said, at this point, us as marketers, we as marketers should already understand our data set. What is my top performing category? What are my top performing skus? And what is that cadence? Is it every seven to 10 days? Is it every 20 days that that shifts? And that's where to get started. Really hyper focusing on those products, knowing that either or high revenue drivers, high margin drivers. I mean we really should be having our catalog segmented out. And I will say a lot of our 2.2 trillion products are still getting it wrong. And they are exposing everything. Every fuzzy sock, every, you know, toothpick. Why, you know, while I understand that the Walgreens has toothpicks, that's not getting me to come back to the store.
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B
Right.
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D
You know, so if we don't understand what's actually driving that, start there and look at your catalog holistically. Do you only want to expose net new products because you're looking for, you
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C
know, brand new people to your site? So what are your KPIs at their base? If it's high traffic, then you only expose high traffic skus, high traffic categories. So as a marketer, look at what your KPIs are internally and then say, okay, now that's how I'm in a segment my catalog. And I gave you this story at one point, you and I have done business together that we have merchants who are sending upwards towards 300 million rows of information for their local inventory ads. And I find it obscene. I find it absolutely obscene. No marketer wants to be looking at any of that, working towards that. And I get that it's a localized program and I get that you want to make sure that we can get to you for that one item. But at the end of the day we're searching for children's Tylenol and that's what's going to get us to brick and mortar. And, and it's not the fuzzy socks, right? The fuzzy socks don't need to be in your catalog. It just muddies the water and makes it much harder for all of us.
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B
Let's, let's dive in here on, on the concept of product catalog and products themselves. So there's, there's two components to this that I want to ask you questions about. The first one is what's the distinction between how we should be thinking about our refinement data? So color size variables in those. Those elements when it comes to our data and the structure of our data versus the prioritization of products themselves. Like your example here of the fuzzy socks, like, are we putting fuzzy socks and toothpicks above other products that have just much more meaning and purpose to consumers, like, say, you know, Tylenol and other products that would help a consumer who's looking for a brand like Walgreens that is very relevant from the pharmacy perspective. How do we think about those two aspects as it pertains to prioritization?
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D
Well, okay, so I think the way that I look at it is if, for example, I know that right now a lot of my revenue is being driven by search, right? So a majority of the revenue, 40% of my business's revenue is coming from search. It is important to have the party toothpicks, right? It is important to tell somebody that I have the Christmas fuzzy socks because I'm going to a sock exchange, although seasonal. So again, taking that one step further and saying fuzzy socks seasonally. But you know what is actually getting people to drive to store, that's a brick and mortar strategy and that's that search. And that I understand, but then complementary to that. And what you asked was, okay, so the same data set, I have a men's polo shirt. Is it always necessary to send every size? Right. So I know I'm a size medium. I'm going to go look for a blue men's polo and hope they have a medium. Right? But I'm spending my ad dollars on small, medium large, extra large. And for some channels like Social, that is absolutely unnecessary. It is a visual discovery stage. And I see people continuing to send every variant of every skew to Social, and they're just making their jobs harder. So, you know, I remind people, think about yourself, right? Are you like, oh, my gosh, I'm on Facebook, I need to look for a pleated front denim pant right now in 3,230s, no. But you might see them, right? You might see a pair of cool jeans, and that's the discovery. But when I'm on Google, yeah, I want my exact size. I don't want to mess around. I'm ready to buy. And AgentIQ needs that too, right? So I'm a man who has maybe a shorter inseam. I'm a 28 and I don't want to spend tailoring costs anymore. So what brand actually carries 28 size inseam? Are those more rare?
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C
Right.
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D
The 30s and the 32s are super common. So maybe that's not where I'm trying to discover it and that's how agents are going to come into play. Is I finally have somebody or a
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C
friend, an agent to tell me how
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D
to find those short pants that I've for sure gone to 20 retailers looking for?
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B
Yes, I love this and I think this is a really good segue to where we think about concepts around product data and the shift that we're seeing in the market now. Because the shift is much more extreme than just Google shopping today. And traditional search was very good at organizing these relatively binary data points, right? Your concept of inseam or color or other components, those are easily managed in a Google shopping traditional search like experience. But to your point, if you're trying to understand a complex set of data like is an inseam more popular than another and what are the brands associated to that size or fit? Those are much more complex and are not easily defined in a traditional search experience. So my question for you Katie is how should we be thinking about product data differently? How should we be taking this into account as we shift to this AI discovery world? And more importantly, how does this impact things like seasonality, use cases, best selling and all these other more complex discovery desires that we have as shoppers?
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D
Well, it's not a set it and forget it. That that is, that is absolutely true. And you're going to need to keep on top of the focus of your catalog and the tre and the seasonality and what's new and popular. And so the influence of TikTok shops is going to be something you need to be aware of or the influence of, you know, things that are happening in media needs to be really relevant. So a sporting good retailer, right, has a very big responsibility now to stay on top of what are we exposing on agentic for the agents when it comes to who's coming up, right? March Madness is coming. Who, who's going to win? When do they want the jersey? Where do I find the jersey Right now I need a jersey for tonight kind of experiences and they're going to hope that the agent's going to come in clutch, right? Oh, you can find that, you know, in this arbitrary store somewhere that you would have never thought of and you can go get it on your way to the bar where you're meeting your buddies. So being on top of those ever shifting changes is our Responsibility and how we expand expose it. So the product data is going to need to change much more often. The optimizations, the exposing of the information will need to change on a very regular cadence in an effort to keep up with those trends. And so I think about this, all of a sudden I can't anywhere find a neato. A neato is this squishy toy that my daughter is just insisting she has to have. And I said, well they always carry it at the store down the street. And she said no, I actually went there on my scooter and they told me next Tuesday there's going to be a delivery of these neatos. And I was like, wow, like that. That's how fast things are changing, right? Like they were on the shelves for months and months just sitting there and now she has to have it. And so much so that she's like asking the store when it's going to be in inventory. And she's 10. So I thought to myself, wow, that's a big responsibility on the marketers to say what's the up and coming, what's new? How are we going to sell a hundred thousand units of neatos this month, right? So so many things come into play where on the agentic side, a you know, what is the most popular? What is our web window update? What's new and upcoming? So the best new grill out there, right, that's connected to Bluetooth and has a speaker. Is that what's going to be hot this summer maybe? And so that needs to be discoverable. So if we want to drive certain things, we actually have kind of more
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C
power than we ever did before too, right?
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D
To say I'm going to expose these
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C
only net new products or new looks, right?
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D
We're, you know, each year you see
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C
on all these retailers websites, what's new? So maybe only the new things, right? Because you're wanting new customer acquisition or you want to drive what's been seen in New York Fashion Week. So if you don't have a tool that helps you make those decisions quickly and you're not set up for that,
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D
you're going to miss a lot of
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C
opportunity this year and ongoing because this is just the beginning.
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E
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- [24:35]
B
You tiptoed around a couple of really interesting concepts there and I want to, I want to look at these more specifically. One of them is this concept of how consumers are going to change the way they shop. So like this, this shift that we're seeing that's like, maybe you want to call it like the Pinterest, like shopping, the social shopping, this more like discovery based shopping versus what's traditionally been a keyword based shopping, a much more transactional thing. I need X, I need three of these, right? Versus this like emotional, connected, kind of more passive way that we may consume online. As we see this shift or change in trend, how does our data become more important in helping to orchestrate these shopping experiences?
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D
Well, I mean I have a little bit of concerns that the agents are going to start taking over who I am as an identity. So you know, I haven't spoken anything like on the con side, but it makes me think of the brand. And if the agent starts determining I'm only brand centric towards these five brands or this luxury retailer and the like, how is another brand going to get exposed to me? Right? So as they start to build your historic brand perspective, how will brands be able to then inject themselves? Is that a long string of text that is comma delimited that says they are like this brand? So how, you know, there, there is definitely some concerns because right now all of these things are being built into kind of Personas within the agent as well. And so at one point will the p, will the agent just Tell me she's never going to grill, she'll never buy a grill. I'm never going to expose any grills to her because she only bakes or she likes her air fryer. And so that's kind of the interesting part there. I do think that we have to decide again on a small scale what we want to expose and how we influence as we're exposing our catalogs. That's very important. Number two, how to get net new after. The agents are going to make decisions, right? So if the agent's going to make the decision, how am I going to get new customers? How do I influence the agency? You know, the agent, somebody told me to be polite to them. Like I have to say thank you to the agent for exposing this. Right. And so there are a lot of question marks still for me. I think we need to remember to keep our data structured, make sure that we understand where things live. Backfill with as much keyword information as we possibly can in very relevant attributes within your product data. Segment your catalogs in different ways.
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C
So maybe you do have several agentic commerce catalogs based on what you're trying to achieve. That's another element of this where in the past I've seen people segmenting out their social catalogs by vertical, by brand, by gender. And maybe that's going to give us a better understanding of how to move forward in this Persona based shopping, this behavioral shopping. And knowing that the sphere of influence is, is actually to your point, at a household level. That's really. I hadn't even gone there.
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D
So thank you for the big bam.
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C
As everyone is listening on all these devices in my house. That's huge.
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B
You brought up something I think we haven't really touched on very much. But you brought it up here. And I think it really pertains to AI and AI discovery when it comes to consumers. How does brand now play into this picture? Especially since so many products are now homogenized across platforms, right? So you can often find the same products in dozens, if not hundreds of retailers. So in theory and in principle, all of them have the same product data, all of them have the same product information, but the brand that's associated with that is different. Right? So if we have 10 hardware stores all selling that barbecue, it's the same barbecue, it's the same barbecue data. But hardware store A versus hardware store B just have a different brand and different brand perception. And maybe one of those hardware stores just happens to resonate better with bakers and another resonates better with construction Workers and thus may get seen or visible in AI in a different way. How does brand play into that and how do you associate brand to the data that is being built to help serve customers through commerce?
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D
Yeah, I mean will it be a price war?
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C
Will it be, you know, brand A,
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D
brand B like it has in the past?
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C
In a way, like we did see
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D
a lot of price comparisons over the grill that is available on five retailers. Is it available today? Is it available for pickup? So I think making sure that your structured data has that information because we're getting a little bit smarter on how we're talking to agents because not only do I want those pants, I want them today and I need them by five o' clock and I want, I don't want to get out of my car. So yeah, so if you aren't injecting that rich content into each sku. So is it the lowest price? Is it on sale? Is it available curbside? Is it available for delivery? Can I get that grill door dashed? Which you can't, but you know, which I found you can't. They don't, they don't really want someone with a truck picking up your, your grill. But that being said, I'm in the middle of a party and my grill dies, right? I mean am I going to go to that agent? So rich, rich content at the SKU level, will it be a price war? You know, we're all trying to save a dollar. So make sure that you're short up on your price because you may win out over the competition over a couple of bucks because gas is high where the cost of daycare, who knows, right? So if I'm going to save 20 bucks on that grill, I'm going to come to you and I may not worry that you're not where I typically go. So that's going to become a little bit less of a thing. And again that discovery maybe now I'm a loyal customer and I hadn't ever been to the tractor supply to go buy hay for my bunnies, right. I always thought I'm going to the, to the Petco, although they don't sell hay. But now I'm at the shopper supply and I see they have clothes and I see that they have a lot of other things that aren't just for a ranch. Right. And even though I don't have a ranch. So I will say price is going to be important. Making sure that you have all that content about availability. Can it be door dashed? So same day delivery type things needs to be in the feed and at the granular SKU level.
- [32:01]
B
Yeah. One of the things I'm hearing is completeness and readiness to meet customers at their moment of need is critical. And that's where brands should over invest is ensuring that you can meet every different type of Persona's expectations so that they can, they can be present in the shopping moment. Products UP has built a lot of different features around that. You guys have built a lot of different capabilities around that. I'm kind of curious to understand what is it that product supp is doing different in the market. How are you guys competing differently from everybody else or even from how in house teams can better manage their data?
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D
First and foremost, being first to market with the agentic commerce platforms. So it was a privilege to work with them.
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C
I know they're working fast and furiously to understand.
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D
Right.
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C
Because as much as it's an agent, it's also people trying to make sure
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D
that they're ready for the presence of
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C
SKUs and what does that process look like? So that was first we really came across as a leader and had a lot of content to provide. So that's number one, number two, building in platform AI data services. So for example, if you want to go build out those Personas using the example of a unisex sku, I now need that SKU to be a woman's sku, a man's sku. I need it to be good for hikers and runners and joggers and I need it to be good, good for
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D
wet weather and I need it to.
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C
Right. So how do I take that one product and expose it into multi Personas so that it then is ready for an agentic platform? Because I don't want to miss my runner just because this says it's a basketball shoe. Right. So in the past we've said definitely claim it as a basketball shoe. Oh, skew shoe. But now, you know, how do I then say, well it's good for running, it's good for baseball, it's good for sports, it's good for kids. Right. So our AI data services are already ready for any of the agentic platforms and building out those Personas. We have templates that are ready to build out. We also will be in the future talking about how you get a product data feed ready for LLM Txt. So we're going to be showcasing that there's some readiness there that you can be doing front loading to get your PDPs ready, your sitemaps ready. You brought that up. And so yeah, I think we are here to make sure that those conversations are happening. We, we talk about what the KPIs are, because please don't send all 300 million products. That, that's just not going to be the way to do this because you're never going to know the impact it has. And so how do you quickly then identify, isolate, and deliver product content to those agents? We're ready to have those conversations with you today.
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B
One thing that really comes to mind is in, in this area of readiness is the, this, the split between speed or how fast you can move to the market versus completeness. And I'm curious to get your perspective is how does that matter in terms of both what products up has built as well as how should our listeners take that into account as they think about the execution of their product data? Is it about being first and moving fast and being really, really rapid at deploying data and the access of that data to these AI systems? Is it about really completeness and wholeness that may reach all customers at every possible shopping moment?
- [36:10]
D
So the pressure's on from the top down. So the top down says, what are we doing about AI? Right. What this year can you do about AI? Because everybody wants to know that somebody's doing something. So we feel that pressure from the top, and then we have to pause. We hear the pressure. Now we have to be decisive. We have to make good strategic decisions because we're already ready for you. We can give them that minute, that breath. Take a breath. Okay? So while you want to engage in that, and we understand from the top down you're getting pressure, let's pause, make some conscious decisions, and then launch. So, yes, that's what everyone's doing. They're feeling the pressure. We have readiness. But let's take a pause and be decisive and make sure that the data is structured and ready. Because the worst thing we can do is expose bad product content to the agents. Because at that point, you're building a bad historic presence with those that are making future buying decisions on our behalf.
- [37:21]
B
Yeah, super valid point. And I love that. And I think it's really resonant around concepts that we've talked about not only on this show, but you and I have talked about things like hallucinations and providing bad input. And if you're already putting in bad data, it's only going to exacerbate the problem. So let's, let's transition to my favorite time of our show, which is the lightning round. I'm gonna hit you with some questions from today's Episode and we're gonna. You're gonna be a 30 to 60 second response on these. Ready?
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C
Deal. Yes.
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B
Awesome. All right, what's the biggest mistake brands make when they try to be AI commerce ready?
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D
They don't know how people are gonna actually buy their products and they can't see that that's where it came from.
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B
And in this tracking ambiguity world we're living, like, how do you, how do you make good decisions?
- [38:18]
D
You expose just a small subset of your product catalog and you watch it every day.
- [38:25]
B
Love that, love that, love that. Okay, if you could only segment a catalog one way for AI platforms, what would you choose first and why?
- [38:37]
D
What's my KPI? If my KPI is revenue, it will be top revenue driving SKUs. If my KPI is bring new customers to the website, I would expose only net new products. If my KPI was traffic, I would only expose my high click SKUs. If my KPI was to sell more shoes, I would only expose shoes. So what's my KPI? Segment my catalog as granular as possible and grow.
- [39:12]
B
That's great advice. What's one thing that surprises you most about how AI is changing product discovery?
- [39:23]
D
My lack of autonomy on my decision making as well as my surprise and amazement on how quickly I can be influenced by something I don't know anything about.
- [39:38]
B
Are you ever concerned that there just isn't enough selection, that there isn't enough choice when you're going through AI product discovery versus maybe even traditional search?
- [39:49]
D
Absolutely. So I do recommend all of us go try to buy something and it is very limited. It's like the very early days of Google frugal shopping where I have five things to look at. And so I do find myself abandoning
- [40:08]
C
my agent's recommendation out of a lackluster experience.
- [40:13]
B
Yep. What's one metric you wish every E commerce team would stop using?
- [40:26]
D
That's tough. I love data. Jordan.
- [40:30]
B
I gotta take something away. Katie. I gotta take it away.
- [40:33]
D
Oh, man. That's the hard one at the end. One thing they would stop looking at. Stop reviewing.
- [40:40]
B
Yeah, Yep.
- [40:45]
C
Shoot.
- [40:49]
D
I. Nothing. Give me more. Give me more information. More granular. So I'm going to tell you nothing. I want to see it all and
- [40:58]
C
I want it at the SKU level, if not the variant level.
- [41:01]
B
All right, awesome. What's the fastest small test you'd run to prove feed driven AI revenue?
- [41:11]
C
Just send one SKU and see how long it takes.
- [41:15]
B
Okay. All right. Beautiful.
- [41:16]
C
Top performing product on the website.
- [41:19]
B
That's awesome. Okay, that wraps up this episode of the Voices of Search Podcast thank you to Katie Morrow, Director of managed services at ProductUp, for joining us. If you'd like to contact Katie, you can find a link to her LinkedIn profile in our show notes or on the voicesofsearch.com or you can visit our company website, productsup.com if you haven't subscribed yet and want a daily stream of SEO and content marketing knowledge in your podcast feed, hit the subscribe button in your podcast app or on YouTube and we'll be back in your feed every week. Okay, that's all for today, but until next time, remember, the answers are always in the data.