The Definitive View of the State of the Ecommerce Industry

Oh, I'm excited about this one. This is going to be fun. We are joined here by the keepers of endless amounts of information across our industry. And we're here to provide you the best insight into what on earth is going on in e commerce. We're going to cause a little chaos, maybe even a little mayhem.

Some may say in providing this information. But I'm excited and to start us off, we're going to go through and just meet each of our team here in this partnership. And if we've got to start with, I screwed up in the intro. I actually undersold. You may not believe that because I was selling pretty hard, but I actually undersold the information.

We're actually sitting on 6, 000 stores when it comes to e commerce related ad data, et cetera. And that comes out of the team at Veros. So kicking us off, Yarden, co founder and CEO of Veros. Who are you and what is this data you're sitting on, sir? Hey, yeah, I'm a co founder CEO at Veros. Basically we're one big data co op.

Our businesses, our clients are e com and SaaS businesses. They share data with us. We aggregate, we anonymize that data. We tag it in a bunch of ways and then we show it back to them so they can see. How they stack up and they can see how they're trending in real time compared to their competitors. And this is data from med ads, Google ads, Tik TOK ads, Shopify, GA4, LinkedIn ads.

Yeah. 6, 000 brands, about 6 billion of ad spend going through the platform annually. So when I came across Veros, it was both the most exciting and discouraging moment of my life because. I liked the idea that we were providing a big dataset and that we could provide all this cool insight and view. And then I went, Oh, dang it.

Like we are useless now. And this is the primary source for all of the information and their business model is right for going after it. And so I went at that moment, I went, I'm not doing this anymore. I am not trying to go out and aggregate data. I'm going to go meet Yarden. I'm going to figure out how we can part with them because.

They're sitting on a mountain, and it's no point for me to build a molehill. So, I was like, we've gotta do this together. So, if you aren't on Verus yet, you're crazy. Because if you have ever tweeted Oh, what's happening on Meta? Is anybody CPM rising? You are wasting your time. Just go look. It's there for you and it's available.

So stoked to have these guys. It's gonna feed models and give us better insight. It's just, it's so cool what they're have built. And I'm excited to bring them in as our first leg in our data consortium here. And the second leg is the man, the survey man. You know him on Twitter. He is active. He's got a new haircut for maybe his profile picture, as I've stated before.

But he's sitting on. What I consider to be the best set of qualitative information in all of EECOM. And this is where. When I say we have the information that no one else has meta Shopify, et cetera, because what Jeremiah brings to the table is a set of proprietary data that isn't just about volume of aggregation.

It's answers that nobody else has. This is truly unique data that provides a point of view that nobody else in the world has. And so I'm excited to add Jeremiah. Founder and CEO at NoCommerce to the team. Jeremiah, give us a little breakdown. Yeah, thanks Taylor. Really excited about all this. Obviously we've been working together for about a year now on the DTCCI, the direct to consumer confidence index.

And we've seen some really cool things come from that. But just a little bit about NoCommerce. We are, we've always talked about ourselves as a survey platform and I'll break this down a little bit more in a moment, but we work with 3, 300 plus brands at this point, who do billions a year in online revenue, spend billions a year in paid ads.

We don't see a lot of the data that Yarden sees. What we're really focused on is asking customers questions and helping brands understand what that means. But specifically in the context of surveys, we do some of what Yarden and team do in terms of helping brands better understand where they stack up and how they fit into the mix.

And so it's one thing to ask questions and to get answers. It's another to know if those responses are actually meaningful and showing that data in comparison to the rest of the ecosystem is helpful in that way. And if you are familiar with us, you probably know of us as a survey platform, but I think the, that really was the foundation for our bigger vision.

And I'm really excited about this because our vision has been to build a customer insights platform, the first of its kind, leveraging that data that you talked about. Taylor, so they're the subset of the data that we collect, 44 million questions answered last year. It'll probably be 50 to a hundred million plus this year that kind of fit into this group of data where we can see the patterns across an industry and we can help brands better understand what that actually means for their business.

So that's really what we're trying to bring to the table here. Yeah. The power and the flexibility to be able to distribute. Any question to the customer base that you now serve has brought us is so powerful. It opens up so many possibilities of exploring really unique, qualitative information that when then layered and combined with other ads, it's just the opportunity is super exciting.

And I think. That leg of the stool, this like flexible, qualitative view into our industry has just been super exciting since the day we met and we were about to come across one year of being able to look at the DTCCI in a comparative fashion, which is exciting. So we've been in this project together and our vision was always like, Hey, what else could we add?

What else could we go get? And one of the things that we talked about and have been dreaming about showed up randomly in my DMS one day and I was talking about the DTCCI and. Someone slid it, slid in, kind of a lurker on Twitter, not posting a lot of information. And I was like, who is this character? And he said, Hey, I actually have about 6 percent of all consumer transactions at a customer or at a credit card level.

Would that be interesting to add to the data consortium? Would having 40, 40 billion transactions and trillions of dollars in revenue at a credit card level that we could assign and understand it an individual brand basis be useful to you? I said, excuse me? Yes, I'd love to hear more. And that's where I met.

Mr. Wyatt Mayhem, the co founder of Castmetrics, who's adding in our third leg of our data stool here, which is customer or consumer transaction level credit card data. That's going to give us visibility into how people are purchasing across the entirety of the United States. Wyatt, who are you? What is Castmetrics?

And what is the data you're sitting on? Yeah, thanks Taylor. Super excited, but I'm the, yeah, Wyatt Mayhem. No, that is not a joke or my wrestling name. That is my last name, Mayhem. I'm the founder of Castmetrics. We're partnered with banks and credit card issuers, and we're getting anonymized purchase data.

And it's primarily from, like you said, credit and debit card spending. To get insights into how consumers are spending in the market, similar methodology that kind of like second measure at Bloomberg uses. We just took that similar dataset to our industry, which I don't think. Has quite been done yet. So yeah, like you were saying, we have a hundred million unique card holders.

See about 1. 3 trillion of consumer spending processed over 42 billion transactions, and it's about eight years of historical data. So we can really start to see how these revenue trends for. These companies are going year over year and the data is refreshed every day. So it's great. And then, yeah, with our data, we're looking at revenue trends amongst DTC companies, Amazon, Timu, keep our finger on the pulse of how the industry is moving.

The opportunities and the kind of information we're going to be able to provide you a lot of this is going to be for some good fodder for conversation. So I'm excited to have Wyatt involved and. He's going to help us continue to answer this question, which is like the context of the macro performance of our industry.

And are we facing headwinds? Are there tailwinds coming? And it's going to be really powerful to add that information in. And last but not least is the man tasked with interpreting it all. He is the soothsayer of the data. He has to receive it all and tie together and knit together the story. Where we've got a bunch of data nerds here that kind of just hand it to him and go, I don't know, Steve, here's what happened.

Here's the objective truth of what is. Now you help us to sort it together. And he takes a little paddle on his kayak. And he goes out and he allows the vibes of the data sit over him and crafts the stories that you enjoy reading so much. He is the Senior Data Analyst here at Common Thread Collective.

Mr. Steve Recook. Oh, thank you, Taylor. Yeah, I. Aggregate all our data, look at status dot IO data, which is a smaller set than what Yarden has over at Veros. And I'm pulling in the data that Jeremiah sends to us from the DTCCI. And I calculate a consumer confidence index based on that. And also publish our weekly newsletter for Common Thread Collective's DTC index.

And manage that data. So that is the team that we're starting with. And I've already, since I posted a little teaser tweet about this group, received more people trying to get their data into this consortium, but for now we're like, Hey, we've got a lot and it's going to be incredible. And so here's what you're going to be getting out of this group.

Okay. One, right now, you should go to DTCindex. com, DTCindex. com, and you should sign up for receiving information from this consortium. And it's going to begin with this podcast on a monthly basis where we're going to look back at the previous month and we're going to give you the state of the industry from these three different angles.

That is going to be paired with a monthly newsletter where you're going to get deeper insights on the data that we're sharing in this podcast. We're going to give you a little teaser. And then from there. Who knows? We are going to continue to build more and more ways to put this data to use on your behalf.

Watch out CB Insights, watch out Statista and everyone else. We are going to try to provide you the best visibility and information into what is happening in your industry. d2cindex. com, sign up to get the newsletter every month and be the first to receive any additional information about the insights that we're generating.

And if you're a DTC index subscriber at CTC, good news is you're already going to receive the newsletter while still getting your weekly report. So no conflict with that as well. With that said, we want to give you today a little bit of a teaser of what is this information entail? What can you expect from each of these people given?

The data sets that they represent. So we're going to do this. We're going to look back at January. It's the beginning of February right now, and we're going to go back and answer what the heck happened. What is the state of our industry for January? And we're going to give each of these men a chance to tease you with the kind of information and insight that we're going to be generating as a group.

So we're going to start it off by going to Yarden and we're going to say, okay, Hey, take us inside the platform. The question that every media buyer likes to ask on Twitter, the question that everybody's wondering, how was performance in the month of January across the core platforms? What are you seeing from spend?

Give us the view from the mountaintop. Yeah, for sure. So I'll walk through some of the highlights here, and then all the data is going to be in the newsletter. So in January post holiday spend is down about 10%. Across the platforms, Meta and Google, interestingly, spend is actually up on, on TikTok in the month of January versus the month of December.

With spend decreasing, CPMs are also decreasing. January went down around 20 percent CPMs across the board, but conversion rates were also down. So ROAS was basically flat. Makes sense. There's, there's less people buying. They just bought over the holidays and there's less people spending. So the auctions prices are down.

Can I ask a question here? Because I think this is where I'm curious how we all would respond to some of this. And this is Steve, maybe where we bring you into this is when I hear that TikTok in particular was able to increase spend from December to January. That is like pretty novel in terms of a signal that there is scaling investment into a channel where coming out of Q4, you'd expect almost every dollar to be down.

Do you think that speaks to the kinds of brands that are on the platform? Does it speak to the overall trend of the platform? How do you guys see that as a signal? Cause that's pretty interesting to me. Yeah, yeah. That, I think that's extremely interesting. I think it also might lend to brands trying to branch out into different platforms, and this is something that we've seen in our data set as well.

And increase, we probably saw, I think, double the spend in other platforms, not Meta and Google over the last 12 months. Brands are looking to diversify their ad channels and TikTok is certainly a hot one that many brands are pushing more spend into and those January numbers lend to that. Yeah, I was, I was going to say something.

I need to check, I need to check these numbers in various, but I was going to say also a lot of times with holiday spend, it's more straight up, let's get the sale, like people need to buy gifts and let's get the sale and Meta and Google are a lot better at that. Then TikTok is kind, it's better at brand, getting a lot of eyeballs.

Like hopefully it, it helps people get purchases, but it's oftentimes less direct than the other channels. So it can make sense that over there, the spend. Shifted back into it, but, uh, but over the holiday period, Meta and Google pushing. One of the other interesting things, Yarden, and maybe you can jump into this next that I've seen in the data that we're analyzing for this month was a substantial placement change in terms of where some of the dollars are increasing.

On Veros across Meta's media or placement mix. So if we think about when I say placement, the difference between IG stories versus IG feed versus Facebook feed, and there was one placement in particular where there was a multiple double digit increase in placement change. You want to talk about that?

Yeah. So this is a cool stats. We looked at a percent change across all the dimensions of placement, both in Meta and in Google, and everything was down. I think everything was even down double digits except. For Facebook Reels Overlay, which is still, I don't know what that word is, like, uh, in a big underdog, it's not a, it's not a big player, but it was up 50 percent spend in January, uh, and it keeps, it keeps pushing the people that are cashing on to this one are pushing it.

And pushing it pretty hard. This is so definitely going to keep, uh, keep an eye on that one. And one of the things that I think is an under appreciated thing that Meta has done for 10 years is to find additional inventory to control pricing. So if you think about this sort of the most like Neanderthal base brain thing that I hear all the time is like CPMs are just going up forever.

And it's like actually not remotely true. There's actually been full annual years of CPM decrease on platform, and it has to do with Facebook's and MetaBroadly's unbelievable ability to unlock new inventory placements. That happened with IG obviously, then it happened with IG stories and then it was Reels and then it, but this idea of Reels overlay gives you both an interstitial ad.

So the traditional, like I interrupt your ad to give you a 15 second spot, but also, Hey, we're just going to take while a Reel is playing and we're going to create an overlay. YouTube has a similar ad product, but it's just more inventory to drive ad volume without having to increase the price. And so I think this is the thing that, and also they do it with class.

It's not obnoxious. Like you could think about one of those listicle websites that has like ads everywhere, you can't even read the site, but here. It's still, it's still done relatively cleanly, which is a huge part. Right. And if they are going to continue to get especially longer form video content, the ability to say, Hey, this is somebody watching a video on reviews of product XYZ and serve ads related to that with conversion optimization, like it's just a really powerful placement.

The other one that I know they're doing and maybe next month we can see if there's sudden change in this is profile feed is now an inventory, right? As people scroll in your profile, there's actually ads in that feed layout. The same thing that they've done with the for you page and other things. We actually had a few brands like panic.

Sending organizational obligation to turn off profile feed, which means you remove people's ability to use your feed as an inventory placement because they were worried that competitors would show up in there. But yeah, this is, this is like an underlying thing that Meta is just world class at. It's just finding new inventory to control price.

As the inventory becomes so diversified, like obviously creative needs to be relevant. You don't want to, you don't want to push it too much in terms of like. Uh, setting it for a specific placement, but every different placement does oftentimes have specialized creative. And I think as this keeps happening, hopefully people will use Veros and seeing it on their own too of like becoming dynamic with it, right?

Because the auction prices are going to change across. And once there's so many, some things will be very cheap and some things will be very expensive. And. You, you need to be dynamic in the sense of, Oh, here, the inventory is really cheap. Let me go make creative specifically for that and keep the finger on the pulse.

When it's just two platforms, it doesn't really matter, but when it's so many, That's right. There's arbitrage. That's right. That's right. And the people that take advantage of that tend to win in the early pockets of those placements. If you can be aggressive early about creative to match the new placement.

The other thing that's really interesting, and Steve, this I read in reading the most recent version of the DDC index, where you were talking about some of the trends in new customer acquisition efficiency and discounting. There's also another data point that you have Yard in, which I don't think people maybe know that you're not just advertising data.

You also have platform data about revenue, new customer revenue, LTV data, all sorts of cool stuff inside of Veros. But one of the things that you're showing that is like tends to be a thing that triggers me and makes me nervous is that new customer revenue is down substantially. Existing customer revenue is up substantially, which tends to mean one that people are coming out of a period of high new customer acquisition, but also efficiencies down, I pull back on my new customer acquisition, I squeeze the sponge a little to try and survive profitability, but that tends to be a little bit of a canary in the coal mine that I worry about.

So tell me what you're seeing on revenue from Shopify in your data. Yeah, yeah, yeah, overall revenue is down, I guess, as expected, uh, it's down 30%. New revenue, as you say, new revenue is down. We are seeing a 50 percent drop in the percent of, of new revenue and a significant increase in the percent of increasing of returning customers.

And yeah, that's the point, right? It's like. You can maybe keep the numbers flat, right? Maybe grow it a little with the returning customers, but at some point you need to keep feeding, feeding that, that funnel, have more new returning customers. Cause you can't rely on that same cohort in a few months or a year or something.

If that trend continues. Steve, how does that map with what you're seeing in terms of what you're calling the continued winter here a little bit in January as it relates to both discounting and AMER or efficiency of that acquisition? Yeah, I've certainly seen a decrease in new customer revenue, not quite as steep in our dataset, but yeah, it was reflected in AMER or our acquisition marketing efficiency ratio.

When we were looking at those numbers, it was. Yeah. That was down about 10 percent or 9. 98 percent year over year for the tramway 12 months. And January was in line with that around 15 percent a year over year. I think it was down, but we've also seen an increase in discounts year over year. We, the tramway 12 months, we've seen like 13 percent increase in discount as a percentage of order value.

And that kind of is concerning and it lends to that, that new customer revenue being down and are concerned with that, that. Essentially, that's becoming more difficult and that brands are leaning a little bit too hard, perhaps, on the returning customers. That's great. Um, Yarden, one of the things I think I've seen come up, I think it's worth us continually clarifying the sample set that we're referring to with this data.

So when you say something is up or down a percentage, are you comparing averages, median, what are you looking at when you make that inference in the comparison, and why do you choose that specific set for the sake of comparison? Yeah, we, the numbers I've been, I've been quoting are median month over month percent change.

Why averages are, I don't know, not to get too in the data, but averages are oftentimes just moved by like huge companies or sometimes companies have really weird metrics and it just pushes it up and down and it's a big enough set. When I quote numbers, this is based off thousands of companies. So it's a big enough set that the median is a very clean and representative number.

And if you, if we, if I would have taken like the cumulative data, like total revenue, it's going to be so weighted towards some of those big brands that are in Verus, that are in Verus database. And I think the most interesting is like what's generally happening across the market. And, but we could definitely look at year over year as well.

I choose month over month a lot of times just because we're in like a black swan market, things, there's wars, pandemics, like the market is crashing, interest rates are all over the place. It's really just like a messy time globally. And so year over year starts becoming even a little bit stale. And that's why like month over month has its issues.

Like obviously January and December are two different months from like a buying pattern perspective, but it's, it's hard to compare to like a completely different year that was 2023 and yeah, but there's decisions to be made. But the beautiful thing is if you're using Verus, you can look at both of those.

Right. So if you have a preference, it's there. And now I think this is one of the things I love about. The three data sources we have here. So Wyatt, I'm coming to you next. And so what Wyatt can do is, and this is what I loved is. So you heard Steve go in our data set. It was down this much. And then Yarden says, okay, Shopify median outcome down 28 percent median store revenue month over month with Wyatt.

What we get to do is we now get to say aggregate total spend volume, December to January. In stores that we're tagging as our industry. So we've gone through their data and we've identified a large set of the biggest players that are what we call DTC brands. To represent the industry and he can say, okay, was transaction volume across those stores in totality up or down?

And how does that map to yarded? So rather than just having to sort out one single data point, which is never going to give anyone visibility into the macro state of anything, we get to triangulate with our three data sources here, Wyatt coming to you, tell me a little bit about what you're seeing in total transaction volume for our industry.

From January to December, and how does it map to what Yarden's seeing? Yeah, it's really interesting because we have a little bit of a different set that we're looking at, right? So with Yarden, he has 6, 000 companies across D2C that he's looking at. What we did for this exercise is looked at 60 D2C powerhouses.

So folks that if you said the name to anyone in DTC, you'd be like, Oh yeah, okay. I know that brand. The interesting thing that happens when we roll it up into these 60 powerhouses is when Yarden was saying he's seeing like a 30 percent drop month over month. We're actually seeing that almost get cut in half to minus 17 percent amongst these 60 that that we're looking at.

Which I don't know, I'm sure Taylor, you can provide some commentary on that, but I found that extremely interesting that when we're taking these top 60 that maybe have some firepower marketing teams, or maybe a little bit more seasoned in their prep from December to January, seeing that difference really stuck out to me within the 60 companies that we're looking at in this index.

Yeah, so two things that I think are really important. One is we move from a median consideration. To an upper bound consideration, right? So you take 60 of the top performers and you say, okay, what happens in that cohort on a volume basis? Here's important things both down. Okay. So directionally, we have signal January is down in revenue relative to December.

We start to hone in on a more definitive sense. That is true. Now that maps to what anybody would logically believe about December to January for industry. So that's good, right? There's not incongruence there, but we're now looking at the highest performers down 18%. Again, highest performers. We're just saying largest volume of business size in our industry.

Down about 18 percent in total transaction volume. Right. Tell us a little bit about how you're triangulating that. Like, how are you getting to the numbers that you're getting to for the credit card data? Cause you said you have a bunch of transactions. Are you seeing every transaction for these companies or how are you actually honing in on this number?

Yeah, great question. So with our dataset, we're seeing roughly 6%. Of all credit and debit spend that happens in the U. S. So it's important to know also that when we're quoting our numbers, we are just talking about U. S. revenue and the way that we're sourcing these transactions is primarily through their Shopify markers that would come up on your credit card.

That is the way that we're sourcing a lot of these transactions is we're going through our data set to find, okay, what are these pathways that we can identify that, okay, this is. The transaction footprint that we can ultimately follow to get revenue for all of these companies. So that's how we're typically building out these sets.

It's also important to know that we're not necessarily seeing PayPal, Amazon, Google paid and Apple pay, but we will see the Shopify market. So that's important. U S only we're seeing roughly 6 percent of spend. And likely on, on these companies that we're looking at in the set is a Shopify marker that we found that you would see on your credit card statement or your bank statement.

And that's how we're sourcing these. So what we're going to be, I think the 17 percent down and 30 percent down makes total sense to me because these are larger, I'm actually surprised it's as much as 17 percent because larger companies are just a lot more stable. I think on an up month, they'd also be up.

Like a lot less once you have a hundred million dollars of revenue You're not dropping 30 million in a year and up 30 million in another year It's a lot more more stable than that versus these upward coming brands that that are just A lot more dynamic and 30 percent is less than in an actual dollar amount.

Yeah, that's exactly right. Is that it's just bigger ships. And so when we move in percentages, the percentages are going to be smaller, but still represent tons of dollars. So what I want to interpret for you, because this is what we're going to be able to provide. And I want to make it clear because we have people here with real revenue data for their customers, and we care a lot about protecting that.

And privacy of the data consistent with the terms of service for each of us is like of the utmost importance, but what you're going to see and what you do get access to this. Is why it is able to approximate actual store revenue of these top 60 brands using this transaction data. And so that velocity change, we're going to publish the top 60 lists.

We're going to call it the power list. And we're going to give you a view into from Castmetrics, what these brands are doing on a monthly revenue basis from the biggest brands in e com. In a way that is distinct from anything that happens in Verus or anything that happens in NoCommerce or anything that happens in CTC, but that's what the transaction level data gives us visibility into.

And so when you sign up, that's going to be part of what you're going to see is you're, we're going to be able to use that and highlight opportunities, show you as an example, how much freaking revenue Timu and Sheehan did this last month. Holy crap. You're not going to believe it when you see it relative to the people that we think about in our industry.

So that's worth signing up for a note alone is an estimation of that. We're able to look at Amazon level transaction data. So how much marketplace transactions are happening and look at that month over month. So, so many exciting things that are going to be coming out of that consumer data, as well as just a sense like, is the consumer spending?

Are they spending, like you said, we get to go back eight years. And we get to say, how does overall consumer spending look for us as a broader, both industry, and then we can look at the total data set and draw inference as well. We're going to layer that into the DTCCI for our modeling. There's just so much that we can do to say, okay, how does this transaction level data map to what we're seeing brands do and how the consumer spending so much insight to be had there.

All right. Now the last piece we're coming over to Jeremiah on the qualitative side, and we're going to give you. Now, the ability to take aggregate questions and look at more specific insights that we can generate that also still reflect behavioral patterns of people in moments in time. And Jeremiah, one of the things that you pulled out this month for us was the question of what are people, who are people buying for?

We know we're coming out. Of a season of November and December where gifting becomes more of a conversation into January, where it is more of seemingly would be like new year, new me, I'm buying for myself and proving me. The question is how much does that really change? Is that true? Is that just a trope?

You're the man with the answers. So what are you seeing as it relates to how people are actually buying and who they're buying for, and give us a little bit of that data tease. Yeah, absolutely. We are working, again, we work with 3, 000 plus brands and I want to be clear about something, too. Any data we share is fully anonymized, fully aggregated.

There's nothing that ties back to a specific brand. If you're working with us, I just want to be clear about that. And thank you, Taylor, for the call out on what Wyatt's doing, too. Because we just want to be clear, like, we're, there's never anything that we would share that's sensitive to the individual brands we work with.

In, in light of that, though, there are This year, we'll see 5 million plus people answer the question of who is this purchase for. And that's happening across all different product categories, AOVs, lots of data is coming through on that. And what's really cool about that is we can actually track trends over time.

Just to give you kind of, and you'll see some of this if you sign up for the report, but we saw purchasing for family member and for significant other peaking to pretty high levels. Essentially like half of purchases were coming through that were being purchased for those people in the peak season, gifting season in December, which makes sense, right?

And so from that, you see a big drop down to what's normal. And then in January, and then you actually start to see an uptick in significant other purchases in particular in February. So again, that's not much of a surprise. You would expect that, but what it is cool is we can actually track some of those numbers.

So you'll see those trends in there. If you go and take a look at this chart, what we're doing is we're actually showing you the, we're showing you the actual numbers from the last three months. And then the expected based on last year's data over the next month or so. So you'll see all of that data through February on a weekly basis in terms of where, who we expect the purchases to go towards.

And again, it's, it's half a million plus responses a month, essentially. So lots of data there and really representative of the industry. So here's the fun game, because I think that what's beautiful about this data is that it takes a general idea, like saying. Yes, there's gifting that happens in December and it helps you to think about how much because when I think about my media mix, one of the things I think for December is easy to do is to just swing the pendulum to be like all my ads should be about gifting and I think this actually helps us get much more specific about the question.

So we're going to play a guess that data point game and youth non Jeremiah people get to answer to test how much we think about this. Okay. In the month of January, what percentage of purchases, so not December, we're talking January now, what percentages of purchases do you think were for people, for themselves, people buying for themselves?

Zero to 100%, what percent do you think were, and if you've looked at the data, don't answer, but what do you think it is? What do you guys, Wyatt, you're a guess first. Oh man, I'm gonna guess 68%. Okay, Yarden? 80. Steve? I cheated. I would have guessed around 75. Yeah, Wyatt cheated too, because he got it exactly freaking right.

It was 69. No, I swear, I swear I did not read it. 69. 3 percent Okay, which this to me is actually like, when I saw this, I thought that was really low. I was shocked to see, man, in any given period of time, there's still 30 percent of purchasing that's happening for someone else that really like actually shapes how I would think about ad creative.

Like I've got to every month, not just in peak seasons, think about the buying path that is someone buying for somebody else and make sure that a significant portion of my messaging still fits that narrative. And in December, it only, the like, the, it dropped all the way down to 43 percent was still myself, but that's still really high.

That still means that almost half of purchasing, even in peak gifting moment, is still for people's buying for themselves. That's like really like counterintuitive, I think, in a way that's really helpful to see. Yeah. Yeah, and it's really cool that Jeremiah, like you guys are getting into, it sounds like, uh, not just significant other, but if it's for your kids and things like that, because obviously that's a different creative.

It's then like for same age group, different gender is like different creative than like different age group, same gender, for example. The, the one comment I'll make on this too, is that it's very different for each brand, right? Every brand has their own mix. And so that, that is something I always encourage brands to try to understand.

I don't care what tool you use to try to learn that, but you should try to learn that. And, and if you can track trends over time, that is really helpful too, because. Your brand's gifting behavior can look very different from Mother's Day, Father's Day, Valentine's Day, Christmas, than, than other brands. And that is actually one of the benefits of our platform is we will show you how that compares.

But at the end of the day, like you should understand those things and understand how that should inform your market messaging and even diving into demographics and looking at, like what one of the things we see just. Not really, it's something we're showing in this report, but one of the things we see is that women 35 to 54 are more likely to buy things for family members than any other demographic kind of makes sense.

It's usually mom buying for her kids, right? That kind of behavior is just so different based on the brand. So this is definitely an average and aggregate and doesn't tell you the full story, but it is very interesting and does do a really good job of showing trends and seasonality. What I'd like to say about benchmarking, it's generally useful, specifically useless, but what is generally useful for is the kinds of questions you should ask of your own data, right?

So rather than taking it and applying it to your brand, it sets the question up, Oh, I wonder what percentage of my customers are buying for themselves. That's what it helps to evoke is a way for you to walk through a door, to get a good hypothesis, to learn about the general community that you can go find out how your brand is, what's uniquely true about your brand.

And that's, I think, the way to just keep encouraging people to use this information. Okay. The next thing we're going to share in the newsletter. Is about social media usage for DTC shoppers. See, this is like everywhere you go to read about, Oh, what app usage they do not specifically focus on our customers.

And this is why this data consortium that we're putting together is going to be so much more powerful than the general data is because Jeremiah is going to ask questions of people who are buying things on e commerce websites. So when we talk about social media usage, what you read on the general blogs out there are not going to be reflective of our customer.

So the question you asked, and I love this slice of data. It's a fun one. So this is a question of social media usage by gender for D2C shoppers, 65 plus. Come on, mom and dad, where are you at? What are we seeing in terms of which platform they find to use the most? And I was shocked by this for women and men, but tell us a little bit about this data and some of the things that stand out to you.

Don't give it all away though, cause they got to go sign up. Yeah, absolutely. We'll not give it all away. Yeah. So just a little bit more context on this data. Uh, we have an experience that you, some of you are probably using. If not, you can use it. And this is the same experience that's been powering the direct to consumer confidence index in the last year.

And basically what we're doing is we're asking a set of questions that are remarkable sort of questions or questions that may or may not have relevance to your brands. But we can show that to you in aggregate. We can help you understand. Where there may be differences. And I think Taylor's point a moment ago, the differences are what matters because that is areas of strength or weaknesses that you should be understanding about your business.

That's a little bit simplistic, but the point is understanding what the differences between your customers and the aggregate is where there are triage opportunities. What's really interesting in this context though. So we look, this is 2023 data. So we're going to break this down every month and looking at a few different cohorts.

And we'll refresh it over time as well, but for 2023, we had over 40, 000 people answer age question, gender question, and what are up to three social networks where you're spending time on. Unsurprising, most likely younger people are using more social networks than older people. And so we have this breakdown of 18 to 65 plus, depending on what you're looking at.

So we decided to start with the data that everybody, at least I've thought was most interesting, which is 65 plus nobody ever talks about these people, but guess what? They have the most disposable money to spend and they do buy products online. Sure. Magazine shopping was the thing for them 20 years ago, but that's really gone away.

Right. And so the people are going and they are shopping online and we don't think about them very much at all, but they are there and they have money to spend. It's very interesting data. So I'll just cover a couple of highlights here. This is probably not a surprise, but Facebook is the number one platform for both men and women.

And where it gets really interesting though, and I'll just give a highlight here, Instagram is number two for women, but YouTube is number two for men. So in terms of where people are spending their time, there's a pretty massive difference. Even in the same age grouping for men versus women, and I would definitely encourage you all to go look at that.

There's a few surprises in there that I was shocked by when it comes to, I'll just highlight a couple of things like TikTok and Twitter and some of those platforms, so I would go definitely take a look at that, but I'm really excited about this data again, we'll be breaking it down. Each month giving a little bit of a different view of it, but this, the 65 plus, I think it's really compelling and true information.

So anecdote, this is fun. Corey got married, our producer, he's off camera right now. And I went to his wedding and he was, his grandparents were there and I sat next to his grandpa. And the whole time, this man is Korea 80, I don't know, he's elderly. And the man watches endless amounts of YouTube. Like he was showing me all of his favorite channels.

He was so dialed in on YouTube. It was unbelievable. And to see that, like that, the number two channel for men, 65 plus is YouTube more than Instagram and by a lot, almost twice as much as Instagram. And then it's funny. The third is LinkedIn. Like that's fascinating too. And then there's a channel here.

That is the number four for women, 65 plus. I'm not going to reveal this. You got to go sign up to get this that I think will shock you like that. For women, 65 plus, it is fascinating. And this is bigger than TikTok, bigger than Twitter, bigger than WhatsApp. There's another channel out there that is, if I was advertising to this demo, I'm like, Ooh, there's opportunity.

There's something really interesting there. So. Each month, we're going to use Jeremiah to cut some data. So we're going to use Yarden and Wyatt to give us big views. Yarden is giving us big views of all the platform and advertising. Why it's going to give us this consumer. And then Jeremiah is going to help us slice, and we're going to pull these insights out into unique stories and opportunities that eventually are going to compound into reports and databases and all sorts of things that we're going to be able to bring you.

So if you go sign up, here's what you're going to see in the newsletter. You're going to see. Yarden providing you the absolute best source for what happened on the ad platforms, on Shopify, across media mixes by percentages. You're going to get Wyatt giving you consumer data and we're going to publish the top 60 e commerce brands revenue for the month.

Yes, we're going to publish it. It's going to be his estimation. Take it up with him, flood his DMs with your complaints if you think he's wrong, but it's pretty dang good. Jeremiah is going to be slicing for you. And then Steve's going to be.

So Steve, with this, give us a little view of the DTCCI and how you've been thinking about how you're going to go about weaving these together. What are you going to do with this kind of information and what kind of insights can we expect from you? Yeah. It's a lot of great information. So many, so many different sources here.

We're looking at the, like credit card data versus the survey data versus all e commerce. It's amazing to pull this all together. The other point that we still have data from no commerce that we pull in for the DTCCI, and that's another bit of interesting data as well, where we'll have like consumer sentiment about the market, about the economy and how much they intend to spend in the future.

We're not spent how much they intend to save. So I think it all pulls together. I'm going to be adding those into the consumer confidence index I'm calculating, but I think they all lend great to the store. And as we pointed out in January, acquisition is down a little bit, depending on the source you're looking at larger company down a little bit less than the median data set.

Um, so I think there's a lot of great info here. I'm excited about this. Go sign up right now, dcindex. com. We're also going to try and do a Twitter Spaces. Live content and interaction is a thing that's a big on my agenda here for the near future, where we want you to come in because one of the things that we want to crowdsource are the questions you all are asking.

We wanna know, what do you want to know about the data? What kind of information can we pull together to help you answer the big questions that are burning a hole in your brain that will help inform the strategy as you go. And then the second thing I'm gonna ask for is if you have a unique data source, if you have something that you think provides a view into our industry in a way that would be really useful.

Some of the things that I'm interested in are financing data. So we're talking to some people on the lending and capital market side that I think is really interesting to understand. Where's the money coming from that's informing the growth of our industry? Other consumer level and creative insights I think are super interesting.

Some conversion rate optimization website utilization data I find very interesting. We would love to talk to you about adding to this group, adding to this consortium and seeing one of the things that I saw this week that I thought was really cool. Somebody pulled together. I don't know if you guys saw this, a public markets breakdown that does like a full regular update from all of the monthly 10 Ks that get published about how the brands and the public markets of our industry are performing.

So shout out. I think his name was Ben Trigoey. I don't know if I'm going to mess that up, but like we might, I might tap that to pull, to add to this. The point is we are going to build the best resource, and this is going to be home to the absolute bookmark it, save it, go there to answer your question.

Kind of information that you can get out of this group of people. And that doesn't prohibit you from signing up from each of them individually, because again, we want to give you a doorway to the questions, right, and they're going to help you get to the specific answers. So go sign up for Veros, go sign up for NoCommerce, go sign up for Castmetrics and use them to bring your individual data to life.

Of course, come see us at CTC. We'll help too. But in terms of answering the question about the market, d2cindex. com, this is going to be the place and we can't wait to keep building on it. So I appreciate you all listening. Yarden, Jeremiah, Wyatt, thanks for letting flying out one day and pulling this all together and dreaming about what could be, I'm excited for the future and keep doing great work.