Stay up to date on the digital shelf.



    We'll keep you up to date!


    Attribution is Dead. Long Live Statistical Modeling, with Meghan Corroon, Founder and CEO at Clerdata

    The data sources that the industry has used to measure the performance of media are disintegrating around us. Basically, attribution is dead. And the slowness, expense, and limitations of media mix modeling is not up to this moment of needing to drive both top line and bottom line growth with media investments. What will replace it? Meghan Corroon, Founder and CEO Clerdata, along with her mighty team of statistical modeling and data science brains, have a very compelling new SaaS-based, real-time answer to that most difficult question, and she joined the podcast to share it.  


    Our transcripts are generated by AI. Please excuse any typos and if you have any specific questions please email

    Peter Crosby (00:00):

    Welcome to unpacking the digital shelf where we explore brand manufacturing in the digital age.

    Peter Crosby (00:16):

    Hey everyone. Peter Crosby here from the Digital Shelf Institute. The data sources that the industry has used to measure the performance of media are disintegrating around us. Basically, attribution is dead, and the slowness expense and limitations of media mix modeling is not up to this moment of an needing to drive both top line and bottom line growth with your media investments. What we'll replace it, Meghan Corroon, founder and CEO at ClerDATA, along with her mighty team of statistical modeling and data science brains have a very compelling new SaaS-based real-time answer to that most difficult question. And she joined Lauren Livak Gilbert and me to share it. Meghan, welcome to the podcast and thank you so much for being here with us today. We're really grateful.

    Meghan Corroon (01:05):

    Thank you. Thanks so much for having me. It's nice to be here.

    Peter Crosby (01:09):

    So basically your career is that of a massive data geek, right? Is that fair to say?

    Meghan Corroon (01:14):

    It's a good term to use, yeah. I embrace that. I

    Peter Crosby (01:18):

    Identify you have many wonderful qualities, but that has been at the core, every step along the way, you've made data central to driving change and impact, and that's whether helping the Nigerian government change the way they operate, which would be a completely separate podcast, but is super exciting. And now you've turned your wonderful brain to helping CPGs use data to maximize the impact of retail media. I know that's quite a leap from global impact to, well, global Impact actually, if you think about it. So your strategic thinking and you have a really unique way of thinking about ROI for that, that has helped a lot of brands shape their strategy. So lay it out for us. What is it that you've been working on and what are you seeing out there for the people that you're working with?

    Meghan Corroon (02:04):

    Yeah, that's great. Yeah. So yeah, could be a totally different podcast and all the crazy times in Nigeria, Senegal, Bangladesh, all sorts of places where I've worked over the years. But yeah, I think the through line is probably that folks need to have those insights in their hands at the moment. They need to make a decision and they need to be able to trust the ROI or the incremental ROI they're given. And so high level of precision and timeliness. Those are essentially the two characteristics I'd say that we most highly value and have built into what we've built with clear data and the work that we did prior with the company. And so I think the main thing is really to approach it from the murky real world experience of a consumer rather than sort where data tends to live in these silos and this platform says it's this ROI and this platform that it gives you another ROI and you're like, which of the ROIs should I trust and should I feel is good to make my business decision upon? And so that's where we enter is standardizing and measuring across this, but from a consumer perspective, which is a lot more complicated to disentangle than just looking at individual siloed numbers. And so that's what we do.

    Peter Crosby (03:32):

    Yeah, it's an interesting thing. You mentioned incremental ROI, and we've been hearing a lot about the once formerly semi reliable data you could count on falling apart across the consumer's journey. How our listeners have been measuring and making decisions has sort of started to disintegrate with the loss of cookies. It's a whole bunch of things and I would love to zero in on incremental ROI for a minute because I feel like that seems to be where a lot of people are going to say keep doing this because of that almost seems to be maybe the arrival at a more reliable way of thinking. Is that true? Like a stat that is really becoming where people are starting to center on? It's always been important, but it feels like maybe more reliable in this period that we're in Now tell me if I'm crazy.

    Meghan Corroon (04:38):

    Yeah, so there's sort of two ways to go about it and then there's how you get to incremental ROI. So the first two ways to get the number is what marketers, particularly in digital folks particularly had done in the past, which is some kind of attribution or multitouch as many of your viewers have purchased. A lot of these know that this is the way it has been done. And like you referred to not to get too data geeky, but as cookies and basically the ability to track a person across the internet has been vastly deprecated. And I'm saying both those words because I think people kind of know like, oh, okay, this isn't super reliable anymore. We have a lot of customers who say, yeah, I use it, but I don't really believe it anymore. But it has been significantly or vastly deprecated. So that reliability of that old school way of doing it, attribution approach, an ROI based on that kind of tracking mechanism is basically gone.


    So anyone who uses an iPhone not in your sample. So it is a very clear, you don't have to know all the technical behind cookies to know that, wait, that's a lot of people that might buy my product. And so that's not really a good enough number for a business decision anymore. And so people are all sort of slowly or quickly moving away from that approach. The other option that you have in the world is statistical models. And so those do not track individuals every touch through the web. They're using advanced algebra effectively of some kind, which is what we use to say, Hey, I pulled these levers in the world, I paid for these ads, and then here is the behavioral outcome. And so you're using fancy statistics to bridge the gap and replace this deprecated tracking system that's not working anymore. And so that would be what a lot of models are based on and what our product is based on at a base level is sort of the cookies are done, attribution approaches are really done for the most part at this point. In terms of reliability,

    Lauren Livak Gilbert (06:53):

    What do you find the reaction of the brands to be when you talk about statistical modeling versus what they were used to, which was, Hey, I do this, I get this. These are the results that I see. Here's the data that I see whether it's reliable or not, it's been the way that they've been doing it for a very long time. So is their reaction, do they know what questions to ask? Are they a little bit hesitant? I'm just curious what you've seen in this big shift or if they're like, wow, this is amazing and I'm totally in.

    Meghan Corroon (07:22):

    Yeah, so I think we work a lot with CPG companies obviously. And many of them are selling not just online. The majority of many of their revenue sources is in a store in a physical location. And so attribution solutions in the past never accomplished that leap. They could never track into, oh, what did you go buy at Walmart after you saw an Amazon ad that was unanswered territory. So because statistical models can do that as well as replace the general attribution approach, we have a lot of education that we do with our customers, but honestly, this gap hasn't been filled for them for a long time. And they're all pretty skeptical at this point of attribution solutions. They're smart people. They've worked in the industry a long time. They may not be statistics gurus, but they know the old way is not working. And so they basically need to have enough confidence and enough knowledge to say, this is better than what I have and this is better than what I've done in the past.


    And usually we leave it there. Sometimes with big enterprises, they have a whole data science division. And so then we relish that because then we really get into the details of the method and sort of the differentiator points. But with mid-market customers and even smaller enterprises that don't have data scientists, it's really about differentiation. And hey, they know the old thing's not working and they need a new approach. And these are really important questions they have to answer every day for their business. So they need to sort of trust at some point and understand, yeah, I think this is the way of the future. So that's how it usually goes.

    Lauren Livak Gilbert (09:02):

    And what are some of the results you've seen from working with brands or some of the aha moments that you're like, wow, I didn't know I was going to find this when I came into it after working with all these brands in their data.

    Meghan Corroon (09:16):

    So I assumed not being from CPG that you all had, it sort of buttoned up and we were going to come in and be better and at the margin certainly contribute value. But I have to say it's been really surprising just how much people have not had the answers to these questions and how much significant value we've been able to contribute with customers. I mean, the baseline is usually 20% marketing efficiency gains. Once they have these insights, it goes up to 35% with some customers, depends on what they were doing before, how much they were spending. But this question has not been measured well, I like to tell people we're super awesome. We're contributing all this value. And I'll say, and this is because of my academic background, yes, we're awesome, our team's awesome, but also this has been really poorly measured. And so when people have a lot of opportunity is usually in these questions that haven't been answered before very well for them.


    And so I think we're going to see more and more granular tweaking and optimizing of marketing strategies, but right now they're shifting major pockets of money around because there's a lot of stuff that has been assumed to have been working that's frankly not working very well. And then the reverse, some stuff that's really surprisingly knocking it out of the park that wasn't considered to be a driver of sales, like earned and owned media or organic social media depending on the term that you prefer, that's driving conversions and sometimes a lot for certain types of brands. And so that hadn't been measured much before.

    Peter Crosby (10:58):

    So it's kind of like brands know they're wasting money, they just don't know which money, and now they do.

    Meghan Corroon (11:04):

    Yeah, the old adage, I think literally yesterday on a sales call with a very large enterprise CPG customer who were in talks with, he said, so basically you're telling me I can get rid of spent half my spend on marketing is not working and you can help me solve that. And that's from a massive organization with really smart people who all know how to do marketing really well, but we are in a really dynamic economy and we're in a really dynamic consumer environment. So it's not about how good your team is, it's often just trying to keep up with where the balls are flying ahead of you. And so that's been, I think the big change is just how dynamic things are with consumers right now

    Lauren Livak Gilbert (11:49):

    And with profitability as a main focus of brands, this is the ultimate silver bullet because there isn't extra money, budgets are not increasing, and you need to go back to the fundamentals and the basics of, hey, these are the products that we're selling, these are the consumers that like these products, how do we get in front of them and how do we build more value with those customers? So I think this is just really exciting in the time that we're living in now for brands to really be able to make the right strategic choices instead of peanut butter spreading, which I think has just been the approach for many years.

    Meghan Corroon (12:25):

    Yeah, the peanut butter spreading. Exactly. We signed a customer this week who had just gone through some really tough rounds of reorg, and that is happening in the CPG industry right now. And so what they're looking to do is they have to be smarter. They can't just hire more people and throw more money at the problem. They have to get really wise and quick and agile with their decision. And that requires having a science hat on rather than sort of a territorial hat or this has worked in the past kind of hat. You just have to be now thinking like a scientist and just test hypotheses. If it doesn't work, we try a new one and we move on. And so I see a lot more companies, which is exciting, move to this more evidence-based approach to how they are investing in trade retail media and marketing across the three really.

    Peter Crosby (13:22):

    I mean, so if I can net it out, it is kind of attribution is dead science or statistical modeling will replace it. I mean if I were going to be hyperbolic about it, that's sort of what you're saying and that what you've been working on is building those statistical models and then being able to apply it to your client's data to be able give them to drive towards incremental ROI and better performance across their

    Meghan Corroon (13:51):

    Yeah, and to basically harness that within a software product so that it can be delivered to them on a very timely basis. So monthly reset of their whole, it's for big CPGs. They purchase mixed marketing analysis from consultants usually, and that will take them a year to two years to receive that fancy statistical model. And it's very expensive. Instead of doing attribution, which as you said is dead, I'm sure I'm going to have some complaints in the comment section.

    Peter Crosby (14:21):

    You don't need to say that.

    Meghan Corroon (14:22):

    Yeah, you said that, right? But that's where it's at. But on top of that, there have been statistical models. They haven't been as rigorous as I think can be done, of course done by a lot of groups, but they're on a one two year cadence and they're super expensive. And so mid-market companies can't pay for those. And even big enterprises only have them once or twice a year, maybe max. And so we're really disrupting this and saying, data science is blowing through the roof right now across a lot of levels, and so we can do better business models of those companies are what they are, but we're agile, we're disruptive, and we're saying, no, it can be better, more rigorous and it can be in much more real time so that your business can make better decisions really. So that's what we're essentially doing

    Peter Crosby (15:14):

    And it's really in response to their changing business models that this becomes so vital to happen in semi real time because that's the speed of the consumer journey now is real time, not over a period of adjustments over months.

    Meghan Corroon (15:33):

    It's now. And I think also the proliferation of a wild number of ways to spend your money that has really, I mean those are kind of the two pieces of volatile economy pressure on top line sales from leadership and then just, oh my gosh, these poor marketers and CPG companies, they could spend so much more than their normal budget just on every retail media emerging and putting pressure on them and performance media and every kind of marketing vendor you can imagine hitting them up for money. It's too much. So no human can process all those choices. No matter how smart you are, nobody can figure that out just by eyeballing it and flying by the seat of your pants. So this gives you a rigorous framework of evidence to just look one source of truth, look at this, what's working? That's where we put money. If we can't measure it, then we can't put a lot of money to it. We might test something that we can't measure, but we're not going to put major money

    Peter Crosby (16:36):

    Behind it. And that's why I love is that it also gives our listeners ammunition in the conversations with their retailers that want them to spend because they can bring data to the flight.

    Meghan Corroon (16:48):

    Yes, sir.

    Peter Crosby (16:50):

    And then you can have a conversation based on that data. So anyway, why don't we dig into, let's bring this to life. We would love some examples that if there's some that you can share, and particularly what you said in the beginning when you talked about it's a real model based on how consumers experience things. So I'd love that if you can share some examples with us of where this has been working and what that means that sort of how consumers experience things. How does that come to life in the data that your clients see?

    Meghan Corroon (17:24):

    Yeah, so I think there's the basic measurement of comparatively apples to apples all in one place is meta performing as well as Kroger 84 51 as well as Google ads as well as and on as well as my discounting in store and on and on and on. And so that's all in one place, but then there's really interesting stuff that's happening because the consumer and we are all these people are using things like Amazon is the most classic example that's clearest I think to everyone is it's a search engine. It serves you ads, it's a search engine. My eight and 11-year-old kids use it as a toy search engine right now very heavily to get ready for the holidays. And so what's going to happen, and what happens every day in consumers' lives is somebody's using it as a search engine. They might buy one thing on Amazon, but it's not that toy that my eight-year-old because she just puts it in the cart for me to look at later.


    And then I live very close to a Target and a Walmart. And so what I'm going to do is I'm going to have that list sitting and they will keep advertising to me for those toys on Amazon. And this weekend I will probably go to Walmart, which is slightly cheaper prices, sorry, I'm not working with either Walmart target directly and pick up the toy in store. And so what happens then is that's just one simple example that I think everyone can relate to, but that is happening across a ton of different evolving shopper marketing platforms and other things that are pushing ads in the digital space on your phone to you while you're standing in Target and I bought hits me up about something at Walmart. So you can start to just begin to think about the complexity of where your money might go if you're a CPG brand and how it's actually fully driving incremental sales or not in a full manner across your sales channels.


    So that's the kind of thing we're measuring is these halo effects. So if you put a dollar into Amazon, what is actually the return on that across revenue in the company or in Kroger sales for the Kroger sales team to be able to speak to that point. It's not just what they're selling on Amazon, it's also affecting consumers' behavioral choices in other settings where they might buy something. So those are the kinds of really interesting things that we're seeing evolve with the data and with how consumers are using and consuming these platforms in these ads or not. So sometimes

    Lauren Livak Gilbert (19:58):

    Does that include social too? So you had mentioned social previously, so let's say that there's an Instagram targeted ad. Can you also bring that into the picture when you're looking at Amazon or in-store or Kroger as well as the Instagram ad that's been targeted to the consumer?

    Meghan Corroon (20:15):

    Absolutely. So there's what we talk about it as a hypothesis versus then what it actually goes and does in the world to a consumer with brand marketers and with sales teams too. We look at it and say, okay, you think it was a targeted ad to your pancake mix? I just got off a call with one of our customers who sells lots of things, but also pancake mix. So we talked to them, oh, well no, this was targeted to pancake mix, okay, but me, I'm a busy mom of two, I'm just using me as the example and I'm looking through and I need gluten-free flour for my daughter. And so yes, you advertised to me a pancake mix, but I'm not buying it. But the same ad will drive me to buy a different skew because I remembered you and I just happened to be standing at Kroger or whatever at the shelf in terms of my grocery shopping. And so these are the things where we back it way up and say, Hey, you hypothesize this to be targeted. Let's actually look at how it plays out with a consumer with observable data rather than, and then we're just test your hypothesis. It might do better than you think or it might not perform as expected. And then you have to go back and say, where did this break down? How can we change and shift investment strategies around

    Peter Crosby (21:31):

    Are you able to, is it almost like you can run the test of their hypothesis against the data without them even having tested it yet? Is that true? So you're sort of running the test for them in a certain way to kind of predict how that campaign would perform. Or sorry if I'm not exactly.

    Meghan Corroon (21:52):

    That's okay. So we do not predict out into the future our modeling can do that. Peter and I have done a lot of that. He's our CTO of the company in other settings, predicting into the future requires stable priors or a stable data environment to ever be. Right. Okay, not so we do not. Correct. Right. You're laughing already, which is what, and honestly, as soon as someone, a customer says, well, what about prediction? Our model is a predictive model. It could predict all sorts of things. Will we be wrong? Probably because things are so bananas. And so instead we've structured the whole software and everything, put all of our emphasis on real time. So we're basically looking month over month with teams and saying, let's catch it before it goes downhill. Let's catch it as it goes up. And so that's our value that we hold, which is we don't predict into the future when we know we're going to be wrong because Peter and I have done a lot of that in the past with academics.


    So it's not that, but what it is is looking back in time, scraping all your historic data and then month over month as close to real time as we can get it with the sales data access that we have and saying, let's watch the curve of returns. Is this doing well? Is this not? And companies are now switching and becoming more agile and more able to pull levers back and forth and they have to Lauren's point, there's a lot of pressure on top line revenue right now and growth. There are a lot of companies that have to do more with a flat budget. We hear that more and more, even with very big ones that have big budgets, it's still a lot of pressure right now to win that growth game and that category lead or keep it depending on who it is. And so

    Peter Crosby (23:41):

    That's now increasing profitability.

    Meghan Corroon (23:43):

    So we base it on your actual company performance as close to real time as possible. We've made a decision that we don't project forward even though we could do so, but it's kind of a fool's errand basically, I think.

    Peter Crosby (23:55):

    And so I think for another example, I think you had mentioned something about maybe around Instacart or something. Could you walk us through that example?

    Meghan Corroon (24:05):

    Yeah, so Instacart is another place where this idea of you have a hypothesis and you want to test it comes into play. So in Instacart you can obviously target ads to certain retail accounts. I can give, again, I was thinking just on the phone with this company, so they're selling flour, different kind of flour products, pancake mixes, all these things. And so they're saying, well no, I targeted it to Walmart. Let's say I did a special thing targeted at these set of SKUs. We sell them in Walmart, this is how it goes. Well, in reality, consumers are making shopping baskets that just live in a digital space sometimes all week. I've done it before myself where I'll just kind of pop things in there throughout the week as we run out of groceries in the house. And then at the end of the week I decide what am I going to go and get in person and what's kind of a pain?


    I don't feel like going to Costco this weekend. It's too much too crazy with Christmas. I'm going to Instacart to my house, all the Costco items, which are only the things I buy in bulk, and then I'm going to run over to Target or Walmart and get a couple of things that are just some produce at the co-op and this over here. And so you're basically saying the marketer thinks they targeted an ad to a Walmart sale, and so they look at their Walmart sales and they're like, that didn't work. Conversion doesn't look what it should. Well, in reality, if you're measuring it as a full hypothesis with potential halo effects, they were advertising to me all week and I just went and bought it at Kroger or at Costco or ordered it through Costco. So the sale may not go through Instacart and it may happen elsewhere in real life. And so you want to understand from the consumer perspective, what is the halo effect. I do this in the world to drive a sale, but did it drive it there or did it actually result in a sale someplace else? And so that's what we're measuring.

    Lauren Livak Gilbert (25:58):

    So the halo effect is obviously real from a consumer standpoint. We live this every day. We understand it, we feel it now, let's go inside an organization. The way that they're structured, the way that teams are structured are not really conducive to the halo effect. You have the Kroger sales team, the Walmart sales team, the Amazon sales team, maybe there's an e-commerce, COE, how have you seen the brands that you're working with be able to collaborate internally to make this happen? Because there's a lot of natural silos in large organizations that have started to break down, but we're definitely not there yet. So have you seen anything work that's been really successful or any techniques or strategies to help bring this to life so that you're working with the right people?

    Meghan Corroon (26:50):

    Yes. And I listened to actually your podcast with Aaron Conant on Digital Deep Dive, and you talked about the structure of these organizations because a lot more about it than I do. I mean, I know through my customers and sales calls and through bumbling through this process of learning, but you're an expert on it. So that was very helpful to me. Oh, thanks. Yeah. So number one, I think you already said this, they're not there yet, right? Everybody's still siloed. I think there's two things, and this is from working in my previous life in very complex government institutions, which I can promise you are more siloed and more territorial than any CPG company I've yet worked with and not around profit. That's not what they're driving towards. So I think what happens is you have carrots and sticks, and so you have a situation where we work a lot with a champion inside the company and we have had champions that are CFOs.


    We've had champions that are CMOs, head of marketing, e-comm leads increasingly are becoming people that were bringing us in to the company quite a lot right now actually. And we've had VPs or senior sales national accounts directors who are like, Hey, we need you and this marketing person over here, I want her to drive more or him to drive more sales. So come on in and can you measure this better so I can hit my numbers better? So what we do is because we've worked through a variety of mechanisms, that's how we enter. But once we get in there, it's really about getting a few key people and decision makers in the boat and understanding what they get from it. So each of those individuals gets something concrete out of using evidence to drive their investment decisions. Absent of that, right now, honestly, the leadership and boards of all of these companies are really pushing very hard on these senior leaders that they have to reduce budgets or keep budgets the same and they best be driving more growth.


    And so honestly, that has accelerated any kind of territorial, the silos have broken down a little quicker for us. I've seen the last year. I think that's really accelerated because they're all in the same boat together, the sales head, the marketing head, everyone's concerned about losing headcount and they want to do better, and they're not complacent. We're not encountering very many complacent leaders in CPG right now. People are feeling the pressure, and so when they feel the pressure, they all quickly get in the boat and start rowing like, okay, we all need to get to here. Let's get in together and go. And so that's been actually very helpful for us, even if it's a stressful time for the industry to move people more quickly to use evidence, drive their decisions together in an ideal fashion. Right.

    Lauren Livak Gilbert (29:46):

    Yeah, I love to hear that you're getting interest from multiple different functions. CMOs, obviously they hold the budget, so they're probably the one losing their hair the most with all of this, right? But the marketing side, the sales side and e-commerce, I think our audience specifically brand manufacturers mostly in the e-commerce kind of space, they're the ones feeling a lot of the pressure around my brand budgets aren't changing. I am trying to figure out how to connect digital to retail media. I need to have a better connection across the team. So it makes a lot of sense that they're bringing people like you in to the conversation. But I'm glad that it's more broadly recognized across the different functions because that naturally will help break down the silos. To your point, everybody's marching towards the same point. So for brand manufacturers listening, this is a great idea, this is a great concept. Meghan's a great person to bring into the conversation to help bridge some of those gaps.

    Meghan Corroon (30:43):

    And the e-comm folks, leaders have really have a culture I found of being data-driven over the years. And actually I find them to be really frankly quite easy to work with in a lot of ways because they're super data-driven. They tend to be fairly candid and to the point, if I can be clear

    Lauren Livak Gilbert (31:05):

    Culturally, you have to be,

    Meghan Corroon (31:06):

    Yeah, have to be. Things move fast. They know what it means to take risk on a new channel. They're kind of at the forefront and they're open to new tech disruptive solutions that could make their lives better. They have lived and been part of that community their whole career. So actually we're having a lot of traction with your audience in particular right now, even within that sit within big, bigger enterprise CPG companies because they have a lot of characteristics that honestly are good fit usually to work with our team too. They're very curious usually, which is a great characteristic thing

    Peter Crosby (31:43):

    You've just described the reason why we founded the Digital Shelf Institute.

    Meghan Corroon (31:47):

    Oh, okay. Well, thank you for doing it

    Peter Crosby (31:50):

    Because these are the people that are at the forefront will probably be certainly the CMOs, maybe the future CEOs of these brands over time because they are used to working in a constantly changing environment and being the champions of these concepts and these opportunities within the rest of the organization. They're the coolest people to work with and help in the world. I'm glad you're finding that. I feel very happy for our community that you are. So I keep trying in my head partly because a marketer, but just this is sort of how I think trying to find a phrase that describes what you're doing. And you mentioned media mix modeling earlier. What would be wrong or insufficient about calling you sort of real-time media mix modeling. Is that insufficient to what you're doing or is that a good starting place to sort of center people who are accustomed to that model? But no, it doesn't work in today's environment.

    Meghan Corroon (32:57):

    Yeah, we usually do a yes and of like, yes, that's a good tagline, I think to start to anchor us somewhere conceptually for folks who are familiar with that and then is sort of like media mixed marketing is not often granular, not often trusted by the sales and trade teams and a company because it's not rigorous on that side of the model, usually slow and it doesn't measure halo effects in this real world new, and many of them aren't even doing retail media yet really. So I would say that it's sort of yes, and that's the place to start, plus a ton of other kind of high urgency measurement questions that are part of this.

    Peter Crosby (33:37):

    Well, Meghan, your data geekness worked for Nigeria. It will work for the CBG industry, hope and Peters as well.


    Hope. Yeah, no, that's super exciting. Thank you so much for joining us and walking us through kind this shift in thinking for how to achieve top line and bottom line growth with the spending that you're doing. And it's a moment where we're seeing it in a lot of places where things are shifting not because of, but alongside of the top line, bottom line growth challenge that people are seeing. And I think it is going to transform the industry to that next level of agility and accuracy and results in a way that I think is super exciting. And I'm grateful that your brain and Peter's brain and your company's brain are on this problem. I think it's important. So thank you so much for joining us here. I know people can reach out to you on LinkedIn. Let me just spell your name live so that, because Meghan is with M-E-G-H-A-N and Corroon, C-O-R-R-O-O-N. Meghan is on LinkedIn and I'm sure would love to provide some wisdom if you're looking for some in this area. Is that fair? Can I do that? That's fair.

    Meghan Corroon (34:58):

    Yep. Okay. And that sounds great. Thank you so much. This has been so fun. I feel like I could talk to you guys all day and I'd love to turn the table and pick your brains. I have so many questions just because you guys have such deep insight into this community and into this section of the industry especially. So thank you for having me. It's an honor to be able to speak to your people who are increasingly my people too.

    Peter Crosby (35:22):

    Thank you. Love it. Welcome to our people. Thank you. Thanks, Meghan.

    Meghan Corroon (35:28):

    Thank you.

    Peter Crosby (35:29):

    Thanks again to Meghan for joining us. You should join us at the Digital Shelf Summit in April in Nashville. For a day of the DSI Learn, connect, give back to the community with your brains and hearts. Find out more and register at Thanks for being part of our community.