On this episode, Taylor talks to CTC’s Senior Ecommerce Data Analyst Steve Rekuc and Kno Commerce CEO Jeremiah Prummer about the launch of the Direct-to-Consumer Confidence Index (DTCCI) — a real-time view of the macroeconomic sentiment of your customers.
Learn about the origins of the DTCCI and how to use it to help grow your ecommerce business.
- Want Custom Insights On Your Customers? Sign up for the Direct to Consumer Confidence Index - DTCCI.co
- The Ecommerce Playbook mailbag is open — email us at email@example.com to ask us any questions you might have about the world of ecomm
- Early bird pricing is still available on the Ecommerce Diagnostic Toolkit for podcast listeners only — just use code at checkout to get the lower price.
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[00:00:00] Taylor: welcome back to another episode of the E-Commerce Playbook podcast. You'll notice right off the bat it's me, it's not Richard, and that's cuz I'm taking over the host chair today to interview two brilliant minds and to release a really, really cool product that we've been working on for a while.
All right, well, the last four months we have spent deep in the bowels.
This really actually goes back a year with these two gentlemen in trying to create a really, really cool. Tool for you all in the community of the DTC world. To use to help plan and build better, more durable, more awesome e-commerce businesses. And today we get to debut. But first, before we do that, let me introduce my guests, the brains behind the operation here I am nothing more than the face and the voice.
These are the brains right here. So first up, from CTC, you know him as r Steve. Data on Twitter. The man behind the D two C Index newsletter and so much of the modeling that happens at ctc. Mr. Steve Recook. Steve, how are you doing, man? Excellent. Happy to be on again. Yeah. Do you wanna give us a, I, that was my overview of what you do at CTC and, but is that, is that fair?
Well, what else would you add to that?
[00:01:12] Steve: Yeah, I would add, I do a lot of modeling, especially the CAC spend modeling a lot of correlations for our brands, a lot of different data analysis. And this. Particular topic? Cred my interest, yeah. About a year ago.
[00:01:24] Taylor: Yep. Awesome. And then additionally from outside the C T C ecosystem, but someone that I have been a fan of for a distance for a while and getting to work closely with now for the last year, for last few months on this project, you know him as the c e O of Kno Commerce, Mr.
Jeremiah Prummer. Jeremiah, who are you and what is Kno commerce?
[00:01:45] Jeremiah: Thanks for having me. Yeah, it's this has been really fun. So we, we simplest way to describe it is we have an a platform that brands use to survey their customers in the, the do see space. So, about 2,500 brands we work with I think last month, six and a half million questions answered on our platforms.
Lots of data coming in and what we're doing is just trying to help brands better understand who their customers are. Why they purchase and get diving in here into something that's pretty interesting, I think, which is, what does it look like beyond just an individual brand and looking at the broader
[00:02:17] Taylor: ecosystem.
I like to say that Jeremiah and Kno Commerce represent the voice of the people. While all of us are in our data platforms looking at results from a business or advertising perspective, he every day is powering, what is it now? Tens of thousands of questions every day with customers that we all seek to serve.
[00:02:37] Jeremiah: 200, 250,000 questions a day. So good. That's incredible
[00:02:42] Taylor: number. So, so, so much insight to be gleaned about the customers that we all want to be buying our products, and he is the man with that information. So without any further ado though, we wanna jump right into it. What are we here today, bu today, for the last year, we have been working on this product that as of right now is live dtcci.co
and that is the direct to consumer confidence. Index this is intended to be a macroeconomic indicator of the sentiment of the customers that are buying all across the e-commerce landscape and their view of their own financial wellbeing and their sentiment of the larger economic environment of the United States.
And this is an incredibly powerful tool that we think is going to be. A great additional element in your consideration for planning and assessment of the conditions in which we exist as businesses and business owners. And it is live now for you. And there is data that is live and able to be interacted with right now on DTC c i.co.
So we want to take this time today to explain what this is, where it came from, how to use it and why we think it's gonna be an incredibly impactful. Part of our ecosystem going forward. And so I'm gonna begin by giving you a little context for how we got here, why we went after this project. And then Steve and Jeremiah are gonna talk about how we bring it to life and how we actually built it.
And this story really goes back a year. It goes back to July of 2022, which was for, I'll say for C T C. And for a lot of our customers, maybe the most challenging short-term period that I've encountered in a really, really long time. We came out of 2020 and 2021 with all these head or all this tailwind, all this momentum, all this growth, and then iOS hits.
And then all of a sudden the recession hits and then inflation. And all of a sudden the news is just full of doomsday scenarios and it starts to show up in the performance of our businesses and our partners. And so we wanted to explore more, and there was a slack conversation that began this project.
It was from our VP of Paid media. His name's Tony Chopp, and he sent Steve a question, and that question was very simple. It was, Hey, Steve, do you think that all this PR around gas prices is affecting consumer's perception of the economy? And that set Steve, as it often does down a rabbit trail that led to where we are today.
But Steve, I want you to pick it up from there and tell me when you got that tweet, what did you do and where did you start pulling on that thread to get to where we are today?
[00:05:08] Steve: Well, I like Tony's idea and that like we're affected by the, even though we think of ourselves in this own little world with our own data, we're affected by like the broader economic trends.
And that was what Tony was kind of hinting at by like, Thinking about the gas prices. And so gas prices was one of the first things I looked at and how that correlated to our performance. And there was some kind of relationship. It was a little bit weaker than I'd like, but it led down the path of me.
Wait a minute. Maybe we should look at broader economic. Indicators to see how that impacts our overall e-commerce performance that we're looking at. And that led me to look at Consumer Price Index because that, that's a little bit more tied to what we're selling. We don't sell gas, I don't think any of our clients are doing direct to consumer gas.
But looked at consumer price index. Specifically for a lot of the goods peril and food and beverage, health and wellness to see if any of those correlated and there were better than gas, but even more so than that, I've looked into consumer confidence index. And specifically the one published by the O E C D and I found a pretty good correlation.
I think it was 0.8 88 at the
[00:06:19] Taylor: time. Okay. I'm gonna stop you before you jump to m e r. What the heck is I've heard of c p I. We all have had that beaten into our head for the last year because of inflation. What is the Consumer Confidence Index?
[00:06:32] Steve: So the Consumer Confidence index dates all the way back to the 1960s.
And at the time it was a bunch of, Economists that were thinking that it would be useful for businesses to have an understanding of the propensity of consumers to buy things. How ready were they to purchase? If we're gonna produce a lot of washing machines, a lot of new automobiles, telephones, televisions, we should have an understanding
[00:06:57] Taylor: of.
[00:06:58] Steve: How likely people are actually to purchase these things. How ready they are, how disposable is their income at this time, and they started a survey back in the 1960s, calling 5,000 people a month and tabulating that data and publishing a monthly leave report and it's a much slower time like that. That made sense at the time.
That's how you had to do things. And they're probably calculating all this by hand. And sitting there like going through all and tallying all the data and like call speaking to everyone in person. And that made sense at that time. I think we're in a far more modern era though.
[00:07:36] Jeremiah: They were, they weren't even using Excel back then.
[00:07:39] Taylor: Yeah, I know. Gosh. How did they accomplish anything? But nonetheless, like, so Steve, you sort of had this theory that this indication that consumers declaration about their own belief about the economic. Reality that they existed in could potentially be a signal about their propensity to buy, right? And so this 60 year old indicator, which is a monthly single data point that gets published about two weeks at the end of each month looking backwards, could possibly tell us something about the environment that we existed in and how it might affect.
Our businesses more directly than maybe the s and p 500 could, or gas prices could. So when you went and explored this, what did you actually find about the Consumer Confidence Index and its relationship to our brand's performance?
[00:08:23] Steve: So it correlated extremely well for the month that had occurred. So October, m e r was correlated highly to the consumer confidence Index that we saw in October.
Or in the case of when I looked at it first, like July and August, it correlated extremely well with those months. But the problem is it lags significantly. It's reported like on a Monday or Tuesday, two weeks into the, the next month. So we are looking at July's consumer confidence Index. In like early August, early to mid-August by the time we see it.
So it tells us what happened in the past but was not a pretty good, not that great of an indicator of the
[00:09:04] Taylor: future. So we, to clarify a couple terms, so marketing efficiency rating, m e r. Is just total revenue divided by total ad spend. And the reason we like that measure as sort of a good relationship to consumer beliefs is because it sort of reflects the amount of convincing it takes to get someone to buy something.
Is the way I like to talk about it is that. If people have a high propensity for purchase, then the amount of marketing dollars required to com required to complete that sale is lower. Whereas the more marketing dollars required, the more convincing it's taking. In some ways you could think of it. And so this, the hypothesis was that these things would be related.
And when we went back and looked at it, and I remember Steve and I would be like, did it come out yet? Like, and, and we'd like sort of be slacking each other, wondering what the number was gonna be for the previous month. Cuz we're like, it was really crappy. I hope it's, was the number down or was it up like in.
It was sort of this game, it, but it didn't really have much use to us, right? Like there wasn't really an action that you could take because the metric was so lagging. And so I started thinking like, man, what could we do? How could we get this information more real time? And, and to Steve's point, it was kind of this rudimentary process.
They call 5,000 people on the phone and ask them five questions a month. Like that's the whole thing. It's sort of like classic political polling methodology to get to a representative sample of a broader audience and their sentiment of the nation, right? Like that's their goal. And that's when one day I was scrolling Twitter and I saw a man with some beautiful curly hair in his profile picture, and I thought there there is the solution to all that ails me and all this lagging data.
Mr. Jeremiah Prummer and Kno commerce, the company deploying every day, thousands of attribution are not even just attribution. Thousands of survey questions on behalf of the D two C industry. That could be the way that we could get realtime view of this sentiment. So I dmd, Jeremiah, and I said, Jeremiah, here's my idea.
This might be completely off your product roadmap. And he responded and said, no, we are actually thinking about how we can get data from across our network. Let's talk. So we met up in Manhattan Beach and we sat down and we clearly had a shared V view of the world and how they could use their incredibly powerful tool.
To help it bring to life. So, Jeremiah, why don't you tell us a little bit about sort of your vision for how surveys play a role in bringing this kind of data to life and then what we're doing specifically with the D T C C I and New
[00:11:28] Jeremiah: Commerce. Yeah, for sure. I apologize for getting a haircut too. I know I ruined everything, but yeah.
So this is I mean obviously I, I live in the world of surveys, right? See thousands and thousands of questions every day. Lots of different things being asked. And I think what's really interesting about the type of data that you get from surveys is that it actually fits really well into what we're talking about here, which is that at the end of the day, what you're not necessarily looking for I is what is objective truth?
What you're looking for is what do people believe to be true? And I think that's a really important distinction and get into conversations a about that all the time on Twitter. But it doesn't actually matter where, like if you talk about attribution for example, it doesn't actually matter where somebody actually saw your ad for the first time.
Where're looking for where do they remember seeing your ad in the same sort of way. With this it doesn't actually matter if somebody spent less last the last 30 days. Then normal. It matters whether they think they spent less the last 30 days. And it matters whether they think they'll spend less in the future.
And it matters how they feel about the economy because at the end of the day, the feeling and what people believe to be true is what actually is going to impact Buying decisions. And so that is really what we're looking at here. And I, I think that's what makes surveys so, so cool and so interesting for this.
Obviously in the context of, of what we do the, the nice thing about having a network of so many brands who are trying to understand so many different things is that it gives us the ability to look at, at data across the network. And so we do a little bit of that with things like attribution, but This is going to be much more exciting, I think, in, in a lot of ways to be able to actually dive into something that has broader impact and, and measures kind of a higher level than, than what's happening on an individual brand basis and individual customer basis.
[00:13:16] Taylor: I love it. So where no commerce has traditionally sort of been a one-to-one. Set of questions to a set of customers. This can be a one to many where, where we can take one set of questions and distribute it to many different customers and they're, the, the speed at which they're able to deploy this across their network is what made it so intriguing.
It's like, yeah, we could set this up. We can, we, we did a webinar for a set of brands. We got them to opt into participating and boom, it was there and we were collecting data. It was incredible how their sort of, Enablement, their technology that they had set up allowed us to do this so quickly. And so that's what we have.
We have a post-purchase survey that gets deployed out to a set of, across a set of customers that have all opted their brand into participating in this survey that then gets aggregated and analyzed by Steve to create an indication or score about the sentiment that customers have. So, Steve, tell us what the questions are that we're asking.
And how you're using that response to create this score on behalf of the D T C C I. Yeah, so
[00:14:16] Steve: there were three original questions I came up with. How much did you spend in the last 30 days? How much do you intend on spending in the next three months? And how, what do you think about the economy in the future?
And those were the original three. And Jeremiah, Through in a bonus fourth that they were already asking. That was kind of an awesome question. Do you see yourself as a spender or saver? And it's nice to kind of correlate those together so that you understand whether it's a spender or saver that's responding in a particular way.
And then from our side, it's actually a, a joining of the data that really is useful because we have the status data from. You know, over 250 brands, close to 300 where we're pulling in m e r data, that marketing efficiency ratio, like for the aggregate. That way we can kind of see h what people are saying relative to what we saw in the marketplace.
And that, that's extremely relevant to tie it back to a physical metric
[00:15:20] Taylor: that you can actually measure. And this is, this is where Steve really would go after the existing consumer confidence index, which is awesome cuz it's existed for 60 years and it kind of gives you a history of a, the American consumer in a really interesting way.
But the limitation is, is it's not anchored or obligated to actually connect to any financial measure. It doesn't connect to gdp, it doesn't connect to the stock market. There's no indication that it actually represents anything about people's financial FU reality. And so we wanted to know, would understanding sentiment actually give us an insight into future behavior?
Cuz that's really the key here, is that if we can start to tie those things together, now we have something really, really powerful. So Steve, talk a little bit about how we're using and what you're doing with the data to not just historically analyze, or even in more real time, give an indication of what has happened, but how we're using it to make predictions about the future.
[00:16:15] Steve: we wind up taking the, the data that we have from STAs and those, the responses to the question. Obviously it's a percentage of the responses that respond a certain way that are, are relevant. And, and then taking that and creating what I would like to think of as variables for our modeling based upon their relationship to MER in the current week.
And that allowed me to actually create. The consumer confidence index number that we publish that we're gonna be publishing, it's gonna be tied back to m e r. So that is gonna be like our validation is that this week's consumer confidence should indicate what we expect from in terms of marketing efficiency ratio in aggregate for the next week.
[00:17:00] Taylor: Amazing. So what I'm, I'm looking right now at the chart. So it's live on DT cci.co and you can see that we had the month of March, we have, we have data going back to about March 10th is when we turned this on and started collecting data. So we're about March, April, April, may, about three months in now to collecting data.
And you can see that the last. Month starting really about May 8th has really been an in a significant increase in consumer confidence according to our chart. And that should, in theory, Steve, correlate to a increase in m e r. So is that what we've seen? Have we seen the last 30 days be better than the previous 30 days across our dataset that we're looking at?
Yeah, definitely. We've seen
[00:17:41] Steve: that in MER and we had based on the results I was publishing, it looked like the consumer confidence index had like a point nine six correlation to our mer Yeah. That, that was the idea decline. I hold myself to this, to a metric that we could physically measure that it's not just some made up number and.
You know, you point out the the issue in validation, but there's also an issue in time lag. And I remember just as we started collecting a lot of these Silicon Valley
[00:18:10] Jeremiah: Bank yes. Yes. We, we should have started two weeks earlier. Yeah,
[00:18:14] Steve: exactly. That would've been amazing to capture that data point. But we began basically the Monday after that happened.
And we were pointing out at the time that like the economy could have burned down by the time they actually published the number for March. Right? Like,
[00:18:31] Taylor: this is going to
[00:18:32] Steve: be published on like April 10th or 12th. Like there could be, we could be absolute chaos by then. And that's part of the lagging problem that you particularly didn't like Taylor?
Yeah. About the old consumer confidence index.
[00:18:44] Taylor: Yeah. I've just seen things move so quickly like that and, and it's a very common dialogue to see people turn to Twitter and be like, I'm experiencing this negative thing, are you? Or like, Hey, is the s is the Silicone Valley Bank thing affecting your guys' performance?
And we sort of trade these anecdotes and then someone throws out a data point and someone else throws a data point and it's like, It's my nightmare really, is because I end up having to respond to a customer who said that somebody else's ad account wasn't performing. And it's like the worst sort of trade of anecdote around.
And so it's like, how could we grab something that reflects the sentiment outside of our own individual bubbles, our own little Twitter bubble in a little bit broader way? And that's what we're after. So. So Jeremiah, how many respondents are we at right now and what is the goal that we're after in terms of how many we want to get to?
[00:19:30] Jeremiah: Yeah, so today we're sitting around 200 a day. Give or take a little bit. And so we are looking to get to 5,000 a day ultimately. So what what we're doing on our end is we are giving people the ability to opt in to participate in this then the data gets shared back into the network. It's fully anonymized.
There's. There's no sensitive information shared. I mean, unless there's one person living in your zip code and it's you, then, then you're, you're good. Otherwise there's no way to track it back to a specific person. So, so we're, we're like really careful about things like that, but. So all of that data is, is being collected.
And then also I just, one thing I wanted to make clear too, if you are using our platform for other surveying capabilities, the way that this works is it's basically a secondary survey. So if somebody's already answered the questions you want them to answer, then we can throw this on there. And so that's part of the reason for the lower amount of data that we're collecting.
And that's really the, the goal as well. We want more. We want a variety of customers across an entire ecosystem. And, and I'll say we're, we're early in this, right? Like that, that's why you want to get to that 5,000 a day. I mean, we're, we're basically, we're pretty getting pretty close to that 5,000 a month number.
I think we're actually over it. Given. Given this, so we're still like, you know, similar sample size on a monthly basis as the, the actual consumer confidence index. But ideally what we have is a handful of people from a hundred different brands every day answering this question. And that gives us a really good view of, of what it looks like across the entire scope of the economy.
[00:20:59] Taylor: That's right. So we're not, as a no commerce customer, you're not being asked to substitute this in place of your other survey. This is in addition to it's totally opt-in basis. You have access to the data regardless. But one of the things that we do want to do is that as we build on top of this, there's sort of a secondary layer where we're gonna give out the D T C C I for free for everybody.
We think it's an important measure that should be publicly available in track. But as an additional layer, what we're gonna be releasing here in the next few months is the ability to one, see your individual customer sentiment score. So track your subset of data customers over time, and then the data on top of how the D T C C I correlates to M E R and the ability to build a predictive model for your own.
Customer base and performance are all things that are gonna be opportunities to put this data to use on your behalf. So our goal is to be at 5,000 a day by the end of the year. We want to continue to add in as many different brands as we can and get to that representative sandal where every single day we are reporting on a macroeconomic indication of the perception of the e-commerce customer across the country.
And, and we're gonna. Limited to the United States for now. We could expand this into other countries in the future. Again, the thing that no commerce enables is that this is just, it's a, you know, the, the, the flick of a switch, so to speak, if you will, to deploy across what is an international set of stores.
Right. Jeremiah, you have stores all over the globe. Yep. So we can continue to expand this. And turn it on from here. So Steve, I'm curious, what, what else will you be doing with the data? What else can this enable for brands in terms of their ability to put this, to use on their behalf, other than going like, oh, people are happy, or people are unhappy.
How, how can we use this? Maybe like to contribute to spending CAC models or thinking about forecasting. How else can this become a different data point to put to use?
[00:22:44] Steve: Yeah, I, I think it's particularly useful in a strategic one at least immediately on a week by week basis, how you can expect things to kind of be go going for your business.
You know, if we have a particularly good consumer confidence index, you can kind of anticipate that next week you're going to be able to push sales a little bit more. You're going to be able to, to spend more and get more back in return. Likewise the other way around, if you, you see a lower consumer confidence index, you're obviously gonna be facing more headwind in getting sales out there and perhaps have a lower MER than you might like for that week.
In terms of the individuals, I mean, we also have additional questions we haven't even tied in yet on whether they intend on spending on the website or, or buy more on Amazon. Yeah, we, yeah. So there's a lot of different factors that they can take in for their brand and analyze to see how they want to use that data.
Yep. That's right. Go ahead
[00:23:34] Jeremiah: Jeremy. Oh, one one quick note on that too. So we obviously have the core questions we're asking and, and that's what Steve's using in the modeling, but we are trying to experiment here and, and just learn more. So one of the things that we're doing on our side is no commerce is, this is not.
If you want to go in and enable this, you don't just have to ask those questions. We have a, a set of a broader set of questions that we're, we're looking at across an ecosystem. On in addition to these. So we're, we're constantly looking at are there other questions in this that we can pull in that are gonna have more impact, that will provide more meaning?
And so we'll definitely keep people posted on that as that that goes. But yeah, this is not just a, we we're not looking at this saying, we figured this out in three months. This is very much a, a research
[00:24:16] Steve: research project. Yeah.
[00:24:17] Jeremiah: It's a research project. Work in progress. And, and I, we, we obviously, I mean, just looking at Steve, what was the The modeling from last week, it was super close, right?
The, the modeled mer from this
[00:24:28] Steve: yeah, yeah. I, so I had predicted a 6.06 for the MER for last week, and then we came in at 5.96. Yeah. So, which is
[00:24:39] Jeremiah: 0.1 off. Yeah. Yeah. Which is insane. I mean, you're talking about like, that's less than less than 1%. Right. So, yeah. Yeah. Very, very close.
[00:24:49] Taylor: And if you look out like, so let's take this and you go, okay, let's put ourselves on the hook.
So CCI this week comes in at 1 0 4 0.7, so it's sort of normalized to a hundred scale. So this is the second highest data point that we've recorded since we started. Mm-hmm. So with that, Steve predicts an m e r next week of 6.39. And so what I think is really. Amazing about the potential here is that imagine if you knew the environment that you were going to be buying in in the future.
Right. The, I wrote this article in May of 2020 and the headline was e-commerce store owner Now's the Time to Swing, or something like that. It was like, it had become clear to me that were, there were such significant tailwinds that this covid thing that we were all afraid of was actually gonna be a huge moment of arbitrage for the, the, for our community and.
That was based on a series of experiences, right? It was a combination of our data, the performances, the brands, but I didn't have any indication like this that showed me that this specific subset of customers were actually really bullish on, you know, their future in some way. And so now, like the idea that next week's m e r is going to be better based on the feeling of people today.
Is a really interesting idea that in theory, if I could begin to press into that more effective environment, now you've got something that could be really, really compelling. So we're not there yet. We're not moving all our ad budget on the basis of Steve's model just yet, and we've got a lot more data to collect, but it begins to be A really interesting potential signal that whether you are, you know, buying Shopify stock or trying to scale your Facebook ad spend becomes, you know, an interesting indication that I, I'll go out on a limb and I'll say, if we get to the volume of data that we want and we continue to work on the numbers, I don't know that there's a better macro economic indicator that exists in the world because there's nothing that's this real time in terms of people's perception of their own behavior.
[00:26:38] Jeremiah: Yep. And one, one quick thing too that I, we were talking about this earlier today. Something I've seen recently is you can tell that macro economic trends have a massive impact on the effectiveness of ad spend. And, and looking at things like running Evergreen campaigns during holidays, you are basically just taking something that, you know, has worked in the past.
You keep it running, and basically what's, what's all that's happening is it's just more effective at a time when people are more likely to buy at the end of the day. Like that's, Basically it. And so you can see the impact of that happening. I think there's, like, we have a lot of work to do still in, in terms of this is mostly Steve's work, but in terms of understanding like impact of holidays and things like that on this whole equation.
But it's the same sort of thing. If you see a massive. Spike in positively in consumer confidence, you should expect that it's, it, things are just going to work better from a an ad performance standpoint. And if it drops a lot, it's just going to work worse. It's the same concept as a, as a holiday for instance.
Where, where people are just gonna be more or less likely to spend money.
[00:27:38] Taylor: That's right. So here's what we want from all of you listening. We want you to participate in this journey to contribute to the research to give us ideas and feedback. And that starts, there's a few ways for you to do that.
One, go to dtcci.co right now. Check out the score, sign up. And in that newsletter or that email sign up, we're gonna be providing additional research. We're gonna be providing updates of new ways to engage with D T C C I, about how to get your own brand involved. Lots of stuff there. So that's, that's the first action.
Second, if you want to contribute to the, to the data set, which would be fantastic you do that through participating with no Commerce. You go to, is it no commerce.com Jeremiah? Am I, am I gonna say that right? And you sign up? Yep. KNO commerce.com. Yep. And you sign up and they're Jeremiah that they, they don't have to just there's different ways to engage with D T C C I, right?
They could do just that. They could get the whole survey product. There's all sorts of ways that they can engage. Is that
[00:28:32] Jeremiah: correct? Yeah. And to be clear, right now, it's a manual process in terms of coming in and, and we'll onboard you to the experience. It's fast. It's, it's kind of a couple of clicks for us to turn it on, but we, we just need you to say that you want it.
And that's mostly to make sure that we're, we're one collecting the data for you to. Putting it in the right flow and, and positioning all this. We'll have all that automated later this year. So that's, that's again, kind of manual right now. So just message us if you, if you wanna, you can send us a message on Twitter or LinkedIn email us firstname.lastname@example.org.
Reach out to me too. I am going on vacation here shortly though. So. If I don't get back to you for a couple of days. And I'm also just like generally speaking, it sometimes takes me a few days, so, just reach out to the team and, and somebody will help you out.
[00:29:15] Taylor: Great. And then Steve, if they wanna read about the correlation to m e r, the predictions to follow along with the scores, impact on media performance, where can they do that?
[00:29:24] Steve: I mean obviously we will have that somewhat on the DT cci.co, but also I'm gonna have that in a D two C index, which is what I'm pulling the MER from. And a lot of other data, we pull at least 26 different metrics that we look at through six different verticals. So it gives you kind of more detail into how that relates.
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[00:29:49] Taylor: So that's right. So that's on common thread code.com. You can go to our site and sign up for the DDC index, and we'll be bringing you some of that research in that newsletter. And then we're gonna continue to build out DT cci.com. So sign up for no sign up for the DDC index and follow us on that journey, share it, track it.
And we hope that this is becomes another. Other than what I'll say in terms of utilization is I just got back from the Meta Performance Summit and they. They have this sort of methodology that they call Measurement 360. It's this idea that there is no single data point that can tell you about the health or performance of your brain.
So they're talking about using MM m and attribution and incrementality, and these are all useful indications about the performance. But what's missing in that, and Jeremiah hit on it, is that we'd all like to, and sometimes we have this hubris as an industry, that we are moving markets, that we are creating demand all the time.
But if there's anything I've learned the last. Three years is that we are really harnessing and it's more of putting up a sale than it is really driving the motor. And what's happening in the broader macro economic environment is usually the strongest indication about the future of our businesses. And so adding this data point, adding a macro signal into your consideration alongside all of your editor data, not in replacement of it, not in substitution of it, but alongside it, I think is another powerful way to begin to harness the wave appropriately.
So check us out. We're excited to continue to do this project. Jeremiah, I appreciate you for believing in the research and contributing the tooling. Yeah. And Steve, we appreciate your mind, man. Keep building. I, yes. Hey, exciting Steve's this, the Steve team is growing. We got a new data scientist starting this this coming week.
So you're, hopefully you'll get to meet Ra Tesh and some, some of the team that's going on there. And we appreciate you both. For stopping by and chatting on the
[00:31:27] Jeremiah: podcast. Yeah. This is all Steve. So thanks Steve for, for all your work on this.
[00:31:33] Steve: Thank you guys for all the health and support and the data you know, and providing the questions.
[00:31:39] Taylor: Hey, have a great week everybody. You too. Okay, you too.