Interview: As The Cookie Crumbles, with Nishant Desai, Senior Director of Tech and Ad Ops at Xaxis, and Stu Richards, Lead Programmatic Strategist at Catalyst

Peter:

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

Peter:

Peter Crosby here, executive director of the digital shelf Institute, 2022, the year the cookie dies. What's a brand leader to do Rob and I grilled Nishant Desai, senior director of tech and ad ops at Xaxis and Stu Richard's lead programmatic strategists at Catalyst to detail. The actions brands can take this year to thrive in a cookieless world. So Nish and Stu, thank you so much for joining us on unpacking the digital shelf. We, we really appreciate it and, uh, cause I love cookies. I want to dive right in to the death of the cookie, uh, niece with you, uh, you know, you're the expert on all things, data technology and ad operations. So just get our audience started with an update of, of, you know, what I'm calling the death of the cookie. Uh, what, what does it mean and why should marketers,

Nishant:

Uh, thanks for having us? So a cookie is a foundational piece of web technology, um, that at this point is about 25 years old. Um, and cookies were originally introduced to store, um, little bits of data as a user was navigating across the web. So, uh, the web doesn't inherently have a state or memory. And so these cookies allowed you to store data in sessions. So a good example is an e-commerce site. Um, if you added things to your cart, um, without, uh, cookies in the early days as navigated from page to page, either things would not persist in your cart or if you, uh, left the website, uh, your session would end and you would lose all of the items in your cart. Um, so from a user experience standpoint, that wasn't great. And so cookies filled that gap to give the web a bit of memory.

Nishant:

Um, over the years the cookie has been used. Uh, some people would say abused, um, in, in a way to enable a bunch of different things that it was never really, that they were never really intended to do. Um, and the main thing that, uh, concerns advertising is that cookies were used to store a user identifiers. So anonymous alphanumeric strings that would tell a ad tech platform, um, or a publisher or an advertiser, um, that user was in an anonymized way. Um, this allowed a number of different things to happen. Um, this includes measurement, um, across a variety of sites. It also allows for personalization, um, frequency capping a lot of different things along those lines. Um, so the depth of the third party cookie, um, is really because it's going to folk it force, uh, ad tech, uh, and advertisers in general to think about how they reach users in a different way.

Rob:

The cookies are stored on the user's computer or phone right there. They're part of the browser's memory.

Nishant:

Yep, absolutely. So if you go onto your desktop or onto your phone, somewhere in your local hard drive settings, there's an older, um, it's different for every browser. Uh, so there's a folder somewhere that stores little text files, uh, that are actually the, these cookies.

Rob:

And so let's, let's define then for people, the difference between the third party cookies, which provide the, um, you know, the, when people will say they follow their follow you around the web, like what, what does that mean? And what is it, third party cookie versus the first party cookies. Cause the first party cookies are still okay. And I think are essential for website operations.

Nishant:

Yep. So a cookie in and of itself doesn't have a notion of whether it's first or third party is more so how the cookie is being read. So, um, when you're on a website, let's say, uh, you're on salsify.com. Um, and you're viewing the page. Um, any cookies or resources that get loaded on that page that are owned essentially by, by Salsify would be first party assets, right? That would be any cookies that were dropped by salsa buyer are being read there. Um, what you have in this case is also third parties, right? So a lot of, uh, websites today use a third party tools to load content or to manage their shopping cart, things like that, right? So you might use a CDN, a content delivery network to store images. Those images are coming from a different domain. So a good way to think about first-party and third-party is the first party is if you look in the URL bar on your browser, if it says site.com, that's the first party. If there are other things being loaded on that site that are coming from, you know, site two.com St three.com, those are third party assets. And that's where the difference between first and third party comes in. So third-party cookies are cookies that are owned by someone other than the owner of the page are actually gone. And this is what allows you to do cross site tracking.

Peter:

And so Nish, the, the, the reason why it's going away is, is, you know, it's, it's, Hey, not a perfect system, but B there's just, there's the concerns of privacy, et cetera, um, that, you know, what has happened in, in 2020, that's sort of catch us up on current state. Like what's, what's, what's making all of this sort of become urgent right now for brands.

Nishant:

So I would say this really started probably about five years ago. Um, when, uh, in the EU, uh, the GDPR privacy regulations first started emerging, um, before they were officially adopted, um, you know, pre draft were released. And this, this raised a lot of awareness about, um, what vendors were doing, um, with data that was stored, right? So, uh, a ad tech vendor has very, has a number of profiles across all of the users that they interact with. And so they started sitting on these massive troves of data. And the question became, what is this data really being used for? Um, we also, in the us, we had the CCPA regulations that, um, follow a similar different approach to how that data is handled and managed. Um, and then also at the same time, we had a number of data breaches where large, uh, holders, uh, data, uh, had, uh, network breaches and that data ended up, uh, personal data anonymized data, um, ended up on the open web and was utilized for, um, unscrupulous, uh, uh, means, uh, by not great actors, right?

Nishant:

So the notion of privacy for users became much more front of mind. Um, and as a result of that, the ad tech for the browser vendors really stepped in, right? So Apple in their Safari browser has been doing things to limit third party cookies, and third-party tracking for a number of years, um, Firefox last year. Um, I'm sorry, in 2019, um, actually released some, some tracking prevention in their browser as well. Um, and then Google early last year, 2020, um, and now it's, they would be ending support for the third party cookie in 2022. So, um, while privacy became the major driver of this, what really brought this to the attention of advertisers in the marketing landscape was that Google which holds the largest market share of the browsers, um, effectively said they were going to end this mechanism. So that's why there's a lot of, um, a lot of, uh, uproar about this as to how this is going to, uh, impact advertising moving forward.

Peter:

Google's deadline is, is when, when did they say it's?

Nishant:

So they, in the announcement in January, they said early 20, 22, obviously the pandemic has probably thrown a bit of a wrench in that. So, uh, they haven't shifted away from that early 20, 22, uh, date, but they haven't given an exact, uh, uh, date when they will cut it off or a version in which it, or a Chrome version in which it will be discontinued.

Peter:

That was a great summary. And there's a, it's interesting about Google's doing it on the Chrome browser is that the cynical person would say that this is not going to harm Google's advertising, but

Peter:

It kneecaps a lot of other competing advertising businesses. Rob I'm shocked, shocked,

Nishant:

Uh, definitely not. That's one you to take. Uh, I don't know if that's necessarily correct. Right. So Google walks a very fine line between their, their ads group and your browser group. They are independent, um, teams, right? So it's not that that one is necessarily driving what the other is doing. Obviously I assume there there's pretty close collaboration there. Um, but I think right, the real focus is, is, is that there, you know, aside from what may or may not benefit depending in the long run, I think the real motivator here is, is privacy.

Peter:

So Stu you, you have all of these client heads turning towards you with this reality coming out then like a freight train, you know, as, as the leading expert on programmatic strategy at catalyst, what are you telling them?

Stu:

Yeah, uh, I, I think really what it boils down to is that, you know, digital advertising in general is not about cookies, even though, you know, over time attribution models that are reliant on cookies have become really the lowest hanging fruit and the easiest form of implementation for a measurement solution of digital ads. And so really we've become hyper-focused on this, uh, this crunching of numbers and race to the ballroom in terms of cost pose across our digital media and measuring that on a last touch basis primarily, but really what the drew advertising about it is about is understanding the data that you have available analyzing it and getting insight from those analyses, uh, actioning those insights, uh, with your investment strategy and testing and learning over time. And so really on our end, when we're having these types of conversations with clients kind of hold it down to about six actionable steps that, uh, our clients can take so that they can be prepared for these changes, the nature and for, um, so firstly, uh, one of the biggest points, and this is something that seems obvious, but, uh, you know, at least in my experience has been so under, under utilized or under explored in the past, and that's gathering information about what has worked and what hasn't worked.

Stu:

Uh, there's always been this strive on the client side to get actionable insights, but we have tended to look at things from a very siloed perspective. Um, and when I say we, um, I mean the industry we've looked at channel specific insights, brought them together across a, you know, a lot summary deck, but we often don't see that all of those points tie together. So, you know, starting to build actionable insights really starts with measurement. Um, and measurement is definitely going to need to be redefined and adjusted in a more of an omni-channel model. And so what we're expecting to see is because of the issues with attribution, as it pertains to the changes of cookies, uh, we will likely see a return or a heavier shift, a bit investment in analytics toward a media mix modeling and, um, multitouch attribution that it's not cookie based.

Stu:

Um, as I was saying before, you know, last touch attribution has, has really kind of hindered the growth of the Omni channel measurement strategy. Uh, but I, I definitely see that there is going to be more focused on traditional models and regression analyses and all the fun stuff that, uh, is what data science, so marketing science is for. Uh, I think you're also going to see, uh, additional, uh, understanding of what's working, what isn't through, uh, traditional geo holdout tests, finding black markets, let's call it Los Angeles and San Francisco activating one piece of media in Los Angeles, not activating it in San Francisco and understanding what the different has differences being. So a lot more traditional methods. Um, but

Peter:

You, um, let me just, before you go on to the, to the next actionable steps that when you get back to the gather, the Intel, you, you made a great point about omni-channel and you tire started talking about mixed market studies. So, uh, those have been, and are pretty pricey investments for our brand to make on a, like, do you see anything changing industry that's going to sort of make that data potentially more readily available or, or, or easier to gather frankly, less expensive to gather, to be able to do some of this

Stu:

In terms of the, the muddling insights or in terms of that solutions in place?

Peter:

I think, I think both, I mean, I, you know, I'm not familiar enough with it to exactly know what it does. Other than that, I've heard that these measurement models can be, can be quite onerous and expensive,

Stu:

Very, very pricey. And Dave, and I mean, it definitely depends, but I, in my personal opinion, expect to see more players coming into that space that are able to offer lower cost solutions where they're implementing regression models that are out of the box rather than something that is, uh, you know, specifically tailored for each client, which typical measurement vendors do that they go in, they look at the data across each of these channels and they apply a rigorous model test and loan over time. And then, uh, the clients then utilize that for their investment strategies. I think what you might find is a pod is coming into the space that have a general understanding of the impact of TV. And what you know is often misrepresented there versus what's happening in digital direct five us retargeting strategy, understanding the general waste, and then being able to lay a model layer model on top of that, you might see some of these law costs more out of the box solutions, come into play at a low price point, but you may also see coming through on the clients side where the focus of the data analysts no longer becomes pure reporting pulling, but more becomes creating these models themselves.

Rob:

This is a one way to react to that is that this is bleak because you know that you're going, you're going from a digital data collection model where, I mean, attribution, isn't perfect with these third-party cookie attribution models, right? It's not perfect, but the causal lines are a lot clearer. And the statistical evidence is a lot easier to get. And, and, you know, the, the, the traditional marketing studies that you're talking about are really fraught with all kinds of problems. Um, I was just listening to a couple of very recent Freakonomics podcast episodes on does advertising work is advertising effective, and it's, there's a lot of debate as to whether or not TV advertising is even worth doing at all. And on the one hand, you've got market mixed models that show that there's significant value. On the other hand, you've got academic studies that showed that it's not in.

Rob:

And so the, the there's an art to it as much as anything else. Whereas when you've got the cookies and you've got the data, there's less of an art, it's a lot more just like pure data, you know, in the extreme example is if, you know, if you're just doing everything on a first party, um, Amazon, for example, and you've got the advertisement all the way to the purchase, and you could see that, that trend line, and there there's no ambiguity in between it's, you know, the data is clear as day. And so I'm this continuum, the market mixed model studies and the regression models are on some, on an end of the continuum that is not inspiring, or is, am I, am I, am I overreacting here? Is that too negative?

Stu:

No, no. I don't think of reacting. I think how are the, there has been this biased towards, uh, solutions that aren't perfect in the digital space. Um, uh, and more, maybe less on the solution side of things, um, because you know, large brand marketers that have money to invest in very large scale and rigorous modeling, uh, you know, uh, aside from them, many of the other advertisers that can't afford that investment up to date, uh, they they've been relying on last click or last touch attribution. And, you know, I kind of expected see that fade out about four years ago. It's still very much prevalent to this day. So, uh, in the out of the box packages that something like a typical ad server will offer, uh, the, the attribution's solutions set by default, that of can become this industry standard, uh, still very floated in their own rights.

Stu:

So, um, you know, going back to that TB comment as well, I think with the cookie based attribution solutions we've had in play, uh, because we've only been able to get that, you know, strong, uh, one-to-one data on some environments, we've just anticipated that the, you know, the other more traditional formats kind of just don't work because we're not seeing it in the same data set or the same way. And I still think that there's a severe lack of investment in brand, both performance marketing, big com, so focused on performance marketing, so focused on, you know, crunching every penny to make the most out of a add to basket that we've just lost sight of the fact that brands need to build over time to have long-term success rather than just show up. Um, so, uh, you know, in general, I think you can look at it two ways. I don't think it's necessarily too bleak the outlook for it. I think it's, uh, it's going to become a noisiest space to look at when I, you know, when we're talking about measurement, but I don't think all hope is lost.

Peter:

Uh, I'm sure Rob feels much better. Um, so I wanted to cause we, we interrupted your flow a bit. So, so you had said, uh, you know, in terms of your verse six steps, I think you gathered this Intel, you know, and just get it getting the best Intel that you can across omni-channel.

Stu:

Yeah. Yeah. And I'll just quickly expand on that a little bit more, you know, w we, you have a lot of the data from the previous marketing activities that you've done. You've got a lot of first party data as well, but there's a ton of data available online for free that you can utilize to understand what's working and what's not, I mean, some of this data could include things like census data, uh, you know, uh, platforms like Statista or other market research tools that allow you to subscribe to get data about your specific industry. Um, as well as the likes of the more data science focused platforms such as Kaggle, uh, you know, each of these platforms has really rich data sets that you can take in and you can analyze your match up against, you know, your performance that you're seeing on your end and get some really, really smart insights.

Stu:

Like we, we actually measured the, uh, visitation rate of a finder retailer page for one of our brands against the humidity in that primary, uh, cities, uh, day over day. And we found a strong correlation. So we started implementing that within our being a bit high up when we saying high levels of humidity can remember from which humidity of temperature, but one of the two, but you see where I'm going there. So a lot of value that, um, and you know, that leads into the next point is, you know, geo-targeting strategies looking at the pockets of success in the, uh, uh, dimensions that you have available in your marketing platforms. You know, maybe it's geo maybe it's device, maybe it's time of day understanding the performance there and taking those learnings into the future with you

Rob:

A new way of it's sort of, uh, the way of AB testing sort of in this new era.

Stu:

Yeah. Yep, exactly. And I mean, I don't think the advertisers models will change too drastically, so to gain those learnings now and transfer them across, I think will be a big step up. Um, so, you know, my, my recommendation there would be to start looking into this now, the, the next thing is because we are not going to have the ability to third party, uh, target using third party cookies, contextual strategies are a must to stop building out. So understanding, you know, the, the types of keyword datasets that you can utilize, uh, with real time contextual bidding, um, understanding the different types of publishes or topics that are leading to performance, uh, for any given advertise, uh, uh, as well as the publishes themselves, understanding that the best contextual alignment, Beth, and even utilizing some of your first party data in conjunction with those publishers to test out how audiences work on a publisher by publisher basis. And an extension of that

Rob:

On that one, I know, uh, Molly Schoenthal on the digital shelf Institute likes to talk about, um, behavioral marketing, which it's basically, you look at what somebody does as opposed to somebody is, and you market to them based on what they do. And that, that contextual angle there is exactly that you're basically on whatever, whatever digital property or where, however, you were looking at the way that they behave, you're crafting the advertisement based on the behavior. So you don't need a user ID. You don't need pass. You don't need the user's past purchase behavior. You're just, and you don't even need to know anything about them. It's instead of people like you are interested in things like this, it's people who do things like you are interested in, right? Yeah, exactly. Interesting take on it. Yeah.

Stu:

Yeah. And I, I think, you know, a lot of the focus previously has been finding a user that sits within an audience wherever they are. The shift is now going to the funding, a user in that audience where they have the intent to be consuming content about that behavioral segment. I think the signal stronger. However, I do think the scale will be smaller and, um, you know, leads to a lot of, uh, economic conversations there and how that will play out in the market. But, um, I, I think it's a, uh, you know, a very strong strategy to start testing and learning rather than, you know, having to put it in place of loan come 2022. Uh, and, you know, finding what note on that one is to stop looking at cleaning up your supply path. If you're buying from publishes that, uh, you know, if you're buying from different sellers that are reselling or, you know, finding the most optimal way to buy from the publishers that you are buying from is something to look at because, you know, if, if you can provide value through the most efficient buying, uh, and utilize that to your advantage, why not?

Stu:

Uh, and the last thing that I'll note is, uh, first party data strategies. So, um, you know, across various business units within your organization, look at the data that they have available bid and Google analytics be it, and something like Mixpanel bid in Salesforce, the amount of small chunks of insight that have led to really effective strategies in media buying that have come from these data sets. Uh, it is remarkable. And really one of the greatest signals to use full clear.

Rob:

Yeah. It feels like an area that's ripe for AI investment because you're looking for us instead of like the big demographics, like women that are about to be mothers for the first time, which is, you know, you're painting with a pretty broad brush. They're the types of behavioral signals that you're talking about, tend to be a lot more nuanced and specific. And so using statistical models or AI to pull the signal out of the noise, there feels like it would be a good application and almost necessary to do it. Right.

Stu:

Definitely. And I mean, like, let's take an example that say you're a large retailer and you have loyalty data from your customers and as stock of an item that you need to get, you know, off the shelves, uh, maybe utilizing AI or ML to look through your loyalty data set, understanding who has previously, you know, kind of interacted with these deals off the being a long-term purchase, uh, and then remarketing to them through your first party data publisher relationships.

Rob:

Yeah. Um, today we had a conversation actually with the executive forum where, where kind of this topic was, was coming up in that, um, one, the, one

Peter:

Of the guests on the, on the conversation kind of said that DTC, and this was in the context of CPG company was saying like deep, deep direct to consumer is optional, was what she said. And CA back came kind of a torrent of disagreement, which I thought was fascinating because it's a lot for a brand to take on, right. It it's huge investments across everything, but everyone brought up the value of the data as the reason why they're doing it and the ability to, to test small and then take those learnings out across the omni-channel environment, which I thought was super interesting. And it goes to your point about the importance of first party data. One, does that, does that resonate with you? Like, do you feel like DC is, will not be optional simply in, in, in some ways, because of this phenomenon that you're talking about here, which is you need to know your consumer better if you're gonna, if you're going to pull this off.

Stu:

Yeah. I, I, it's tough one. I definitely think that it's an extremely valuable asset. It's something that's being taught a ton about a recent years and something that is a, to be, you know, insights, uh, able to be garnered from it and then taken in an omni-channel approach is really not where we want to end up looking. The thing is that, uh, as you said, it's, it's a large investment. It is, uh, something that not all brands can do. Uh, but I think there, you know, that there's a lot of ways that you can do it in more of a cost center in more of a cost effective manner. Um, but in general, I think really whatever data signals that you can get some better on the same, you consume as the better, uh, the, the first party data is at the core of that.

Stu:

But if, if maybe a first party data is something that you don't have access to, you don't have the resources to build out that there's potential is looking at second party data relationships. You know, if, if you're a movie studio, look at some of the theater level data set providers, or if you're an auto other than, um, older, uh, data providers that, so that there is the potential to kind of augment the minimal data that you have with someone else's data, to be able to better inform what's actually happening on the backend and help that, uh, help you use that data to inform your strategy. Um, but you know, these things can be as basic as looking at, uh, loyalty programs, looking at content strategies, looking at the actual material that, uh, people are resonating with. So let's say for example, you're in the B2B space and you three different white papers with different topics that you are wanting to uses to download. If you're looking at a pure, um, site visitation to download ratio across each of those three white papers, you probably going to get a general sense between topic one, two, and three what's resonating mode based off that simple, uh, metrics. So I think there's a lot of wide learnings that can be generated outside of having an email address, first name, last name off of the consumer, but it is going to help significantly in the future tap up.

Peter:

Yeah, it seems like there's a real opportunity in the post-purchase stage to build those relationships, uh, even, you know, even wherever the customer comes from, if you're creating programs that draw them to you post-purchase, um, that you can get the data that without actually needing to transact commerce directly with you.

Stu:

Exactly, exactly. And you know, that may be promotions. It may be warranties. It may be a ton of different things that are native to either the purchase or the product itself that you can start collecting that data, which is pretty low hanging fruit.

Peter:

So when you think about this challenge, you know, uh, of, of measurement, you know, we talked a little bit about it before, but if, uh, you know, you're at the bar standard thing, there's always a drink involved, usually you're at a bar with a client and they really are struggling with this measurement. Well, assuming you could be in a bar, I dunno, you know, do one of those zoom cocktail things right. Someday, and the cookies are dead and you're in a post-apocalyptic bar in that post-apocalyptic world. If, um, if a brand executive is begging you for advice on, on how to up their measurement game, uh, where, where might you send them?

Stu:

It's a great question. Um, I think the first and foremost thing is to get them to understand that data, uh, uh, you know, I think I said this before, but so many people that I've spoken with historically, hadn't no idea that they have the access to this set of data or that set of veteran another platform, uh, that can be really, really valuable in, on the standing, what consumers behaviors are, uh, which can then be utilized in your media investment and planning strategy. So that is by far, first and foremost, just getting the pieces together. Then it's kind of linking the pieces saying how they need to relate whether the one dataset is based off timestamp. One is based off of, you know, the, the actual first party name, email address, kind of getting that all and merging it in a way that's a political.

Stu:

And then really, I mean, it's a little more complex, but you can do things as simple as, uh, you know, forms of correlation analysis. So maybe that that's a bit too difficult, but, um, if, if there are data people on your team or on your side that have some basic knowledge of how to analyze Vega, uh, and look at a specific conversion point and understand, uh, the, the relationship between different variables as it pertains to driving that conversion point, you can get some really good high-level insight, um, as to, you know, not just how a channel is working, but how channels are working together to drive success. Uh, but yeah, I, I, I think it really all starts with your data, understanding it, and typically the more you understand what you have available to the more, uh, strategic that you can get with your approach to analyzing it. So you're not just looking, you know, blindly at a spreadsheet with 30 tabs on it. Um, but you're, you're actually understanding how the data gets tied together and what can be driving success and what should be analyzed. Uh, so yeah, so it's a little tedious, but if you're into that thing, like I, um, uh, it's a fun exercise.

Peter:

I did just see. Well, while you were talking about 30 tabs in a spreadsheet, I did see niche start to laugh.

Stu:

I'm familiar with that, uh, that experience. Yeah, not just in the spreadsheet, in my browser as well in the tutorial overload.

Peter:

Yes. I've, I've killed my computer more than once with, uh, with my Chrome tabs. So let's, let's wrap this up. Gentlemen, this has been a really helpful survey of, of what's going on. Um, as they prepare for the, uh, the death, the final crumble of the cookie, uh, in 2022, what, what's your sort of partying, partying wish for them that they get on top of, uh, in the 12 months to come. Nice. Do, do you want to start?

Stu:

Uh, sure. Um, so Safari is a really good place to start, right? So when we talk about third-party cookies, um, Safari is a really good indicator of where the landscape is going to be, um, you know, 12 to 18 months from now, right. And supporting our ready blocks, most third party cookies in that context. So it's a really good test bed to see, uh, what the overall impact on your media will be. Uh, when third party cookies no longer exist as a whole. So you have a good amount of time right now to test and learn and figure out what the actual impact is going to be. Um, it's also a really good time to look at, um, what strategies we're using and where they really, uh, effective or driving a lift that you were hoping for. Right. Were you buying audience as a proxy for content, right? Where you, um, what was the audience signals that you were previously using where you were you doing that? Because you were looking to drive down media costs and buy more and long-tail, and you can get similar audiences by working with, uh, within pump with publishers in certain verticals. So look at what you can do in terms of, um, alternatives to those, those audience-based methods. There's a ton out there, and a lot of times they're cheaper or more effective than audience-based buy.

Peter:

That is great. Uh, stew you're up.

Stu:

Uh, cool. Uh, I mean, it's definitely low lots, lots to say about the issue, but, um, I think really, you know, don't panic this again, uh, digital media is not about just cookies. Uh, I think we need to be more strategic in our planning than ever. Um, I, I think that the system around last touch attribution that we've relied on, uh, with, with getting away from cookies, it's going to be a refreshing change to, uh, take a step back and look at things that, you know, more, more of a, uh, uh, an appropriate lens. Um, I think that really now is a time to look at what you're doing from a publisher relationship standpoint and not just looking at what works in terms of performance as it currently stands, but look at the content that you're supporting, look at the, uh, publishes the journalists that you're supporting and kind of really create brand affinity through your alignment with content. And, you know, the last point on that is keep investing in your brand. Uh, so much focus has been on performance marketing, uh, getting the most out of every dollar from the attribution solutions in place that, you know, it's great to kind of get your foot in the door, maybe as a startup, if you're not investing in your brand, you're not setting up setting yourself up for long-term success. Uh, I think just keep that top of mind. And I think with, with everything going on, that will become more top of mind organically.

Peter:

Sure. So to sum up don't panic and, and that there, there are concrete steps forward that, that you can take on, uh, on, on these, on these issues as we head through 2021. Gentlemen, thank you so much for bringing the, of the combined brainpower of catalyst and access to the, to the four here. We really appreciate you spending time with us and, uh, and filling us in. Thank

Stu:

You so much for the conversation.

Peter:

Thanks again, Nish and Stu for joining us, our new watch word for 2021 don't panic, and as always, thanks for being part of our community.