The Cookie Collapse: How Are Savvy Marketers Adjusting?
The cookie collapse has shaken up the world of digital advertising, forcing marketers to come up with new ways to gather data and reach their target audience. In this article, we take a closer look at how savvy marketers are adapting to the changes in privacy laws and finding innovative solutions to overcome the challenges posed by the cookie collapse. From alternative data sources to cutting-edge technology, we explore the ways in which marketers are ensuring they can continue to provide a personalized and relevant experience for their users while respecting privacy standards.
The “cookie apocalypse” will mean the end of an era for digital marketing as we know it. We’ll discuss with digital analytics expert & CEO of Provalytics Jeff Greenfield how savvy marketers are adjusting and what strategies and marketing technologies these organizations are implementing right now to thrive in the new normal.
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Hugh Macken:
And good afternoon everyone. My name is Hugh Macken. I’m with VMR Communications and I’m joined by my co-host Danielle Milliken.
Danielle Milliken:
Hello.
Hugh Macken:
And we are here with a very special guest who I’ll introduce momentarily. And our topic today is the Future of Digital Advertising in a Cookie-Less World. And we’ll be looking at what the future is going to look like and how digital marketers can and should adjust to what is really a rapidly changing landscape for digital advertising. As I said, we do have a special guest. His name is Jeff Greenfield.
Jeff is an expert in all things digital measurement and he is an acclaimed expert. He’s been featured in many, many mainstream news outlets for his expertise. I recently watched an interview, you did Jeff, on Bloomberg and was just really impressed. We’ve known each other for several years. Jeff is the CEO of Provalytics. And Jeff, if you wouldn’t mind, I mean your resume is so long, I think you’d be better at summarizing your background. Would you, first of all welcome, and please tell us more about yourself and your background in digital measurement and analytics?
Jeff Greenfield:
Thank you so much Hugh, and Danielle as well. In terms of my background that’s applicable for the audience today is I started on the brand side specifically doing branded entertainment, these large scale programs for brands that really get the word out. But that was back in the early 2000s and back then there was no way to measure anything. The measurement was really lost. People started moving towards digital because you could actually measure clicks and that was really cool. But then digital advertising got so complicated and it was really tough to figure out what was working and what wasn’t working. So in 2008 I started a company called C3 Metrics. C3 was the leading multi-touch attribution company for enterprise clients. So we’re talking folks like JP Morgan, US Bank, a ton of folks in the pharma and the financial services space who spend a substantial sum each year on marketing.
And for them it’s very difficult to figure out what was working and what wasn’t. And it was an interesting time because we were able to get the entire digital trail back then we had tags that were on Facebook, even on Amazon for a period of time, even YouTube, we could collect all of this data and it was really incredible. So we had a full deterministic methodology of figuring out where you should spend your next marketing dollar to get the biggest bang for your buck, if you will. And then all of a sudden things, the world changed and Facebook stopped allowing tags, YouTube stopped allowing tags and that wasn’t a big deal. But now we’re living in a world where there’s so many different channels that don’t allow you to measure, not clicks, but the impression, which is that when somebody gets exposed or sees an ad. And so you don’t have independent ways of measuring that. So I exited C3 Metrics at the end of 2019 and started looking at the landscape to see what was going on out there. And that’s when I started Provalytics. And Provalytics is the next generation of attribution that works incredibly well and was designed for this new privacy centric cookie-less future that we have. And that’s part of the reason why cookie-less is coming up is it all revolves around privacy and stuff like that. So it’s coming… 2023 and 2024 are going to be very interesting years with all of the changes that are coming up. And I’ll leave it right there with that, Hugh.
Hugh Macken:
No, sounds good. So Jeff, if you wouldn’t mind just elaborate a little bit on at a very high level what some of those privacy changes are from a technical standpoint. And I want to look at the browser level at the OS level. Apple for example, with its privacy policies. So cookies, third party cookies being deprecated, identity solutions being proposed by the likes of the trade desk and being adopted, unified ID 2.0. So tell us a little bit more about what changes are going to be happening on the technical side. And then I’d love to talk about of ways that marketers can adjust on the technical side, but also ways that marketers can adjust on the strategic side. So how might marketers align more closely with media? But let’s just start off with what are all of the technical changes that are happening with, just give us a bird’s eye view.
Jeff Greenfield:
Sure. I’m going to give a little history with this and go back to something that was called GDPR, which was the start of all of this. And GDPR is the European version of privacy controls. And the way the whole internet has always worked is that the internet was free and the reason it was free was because of advertising and everyone was automatically opted in. And GDPR came out and said, “Guess what?” People are not automatically opted in. And if you have a list of people that have said, “Hey, I want to get your newsletter,” you have to assume that none of them are on your list and you have to email them and ask them for permission. So that caused a ripple effect because there were a lot of companies that had built businesses on identity stitching, meaning connecting someone who was at their work computer with their iPad at home and their mobile device.
And they had stitched together all of these different identities. So when GDPR came out, it ripple effect occurred where a bunch of companies that were operating internationally shut down their European operation because it was so stringent it would be starting brand new again even though they had been operating for a half a dozen years. So the next legislative thing that came about was in California, the CCPA. Now if you notice and if you have a website, you should already have this, there’s a button or a link down at the bottom that says, “Do not sell my personal information.” So California came out with this law that says you have to have a link at the bottom of your website for California residents where they can access their information, they can ask you to amend it or they can ask you to delete it.
And the regulation states that you have to have a form that people can fill out and also allow for an 800 number. So when this first came out, a lot of people thought it wasn’t that big of a deal, but California just recently fined a very large retailer for this. So they’re getting very serious about that. But shortly after that, Nevada came out with another law and Maryland came out with another one. So from an internet regulation standpoint of view, there’s pending legislation in at least 40 states for how you deal with people’s information. And that ties right in to how the internet has always operated. Now it and the operations have to do with cookies. Now if you notice when you go to Amazon, you log right into Amazon if you’re not using your phone and you go there, they know you, they know all your stuff.
That’s called a first party cookie. A first party cookie is the type of cookie that the site you’re on, the one that shows up in that URL bar where you type in whatever site that is, a first party cookie is written by them. So if you go to the New York Times and you log in, New York Times can read and write their own cookies. But the internet and all of advertising was built on third party cookies. And what that means is that when you’re on the New York Times, there’s a bunch of other vendors and ads that are there that are dropping cookies that are from different domains. And the reason they’re doing that is if I’m an advertising company and you’re on the New York Times and then you go to the Wall Street Journal, I want to know that you’re also at the Wall Street Journal.
If I show one of my ads so I can control things like reach and frequency. And that’s how the whole internet has always functioned. Now, Apple has always operated in their own ecosystem and privacy has been a very important component to the entire company. So in their Safari browser, they’ve shut off third party cookies for a very long time. They’ve been gone for a very long time. And to marketers, most marketers just said, “That’s okay,” because there’s more Android people than there are Safari, so it was no big deal. And that occurred both on the browser and on your cell phone as well too. Firefox, which is a small percentage of the browser community, they also shut off third party cookies. And then the big announcement is Chrome, was supposed to do it next year, but it’s being pushed to 2024, Chrome has about 65-70% of the market.
So what that means is that the way advertising has worked, the way you have targeted, the way you’ve measured is going to be completely different in the next year to year and a half. And we always talk about, and Hugh and I have talked about this for years, the concept of digital transformation. And most companies have gone through a digital transformation. Well guess what? I hate to say this. There’s another one coming and it involves how you target, but also how you measure. So the other bad news that goes along with this is that the number one metrics that everyone uses is Google Analytics. And everyone has years and years of data in there. And it’s great how you can go in and see your historical data. Well next July 1st, July 1st, 2023, Google has said that platform will stop taking in data. It will no longer accept new data and you have to use their new product, which is GA4.
Now, I don’t understand why a company the size of Google can’t just change the code and have it all secured in there, but they are unable to do that. So what that means is that you have to change a code on your website, which is no big deal for some people. But the other big issue is that the reports in GA4 look completely different. You don’t know where your conversion report is and all of these things. And so now there’s a learning curve. So if you have a small company or even a medium size or large company, not only do you have to pay to get the code change, but you also have to go through a whole new training. So the next year to year and a half is going to be a major transformation that’s going to have to go forward.
Hugh Macken:
And so Jeff, what I hear you talking about really are two different aspects in a way of digital marketing. On the one hand measurement. So how do we do measurement? The way we do measurement going forward is going to change. And then the issue of audience targeting. And really there’s the identity resolution issue that relates to both of those issues. So on the one hand, we want to be able to measure the effectiveness of our advertising and the ability to do identity resolution effectively has aided us in doing that. At the same time, audience targeting cookies, third party cookies have enabled that. So that too will be impacted. Is that fair to say that it’s really those two areas primarily that will be impacted? And really the challenge before us is putting forth technology infrastructure that will allow us to adjust to the changing environment and then also strategies that will help us to adjust as well.
I’m just struck by how many articles I read about how to solve the problem of audience targeting and reaching audiences. Do you think it makes sense in terms of adjusting to the changes from a strategy standpoint for organizations to be maybe asking a fundamentally different question, which is what are different ways in which, what are different models we can use to do more effective advertising? Because at the end of the day, what matters for the CMO say at an institution, say a higher ed institution or an e-commerce provider, isn’t the ability ultimately to target audiences? Ultimately it’s the effectiveness of their advertising that really matters. So are there other ways of going about that? And we’ve spoken quite a bit about the idea of aligning with media. What do you see as the future from a strategy standpoint and a modeling standpoint to adjust?
Jeff Greenfield:
Well, and that’s part of the big adjustment that’s going to go on Hugh, which is that most marketers today in the digital realm are addicted to the granularity of data that they’ve been brought up on. A great example is my daughter. My daughter ran marketing for a large auto dealer group. And I remember her telling me a couple years ago, “It’s pretty amazing. I can go into Facebook, I can target Ford F-150 leaseholders whose lease is going to expire in six months.” I’m like, “That’s pretty granular.” Well that’s all gone. And so for a lot of digital natives who have never done planning and large scale, both in traditional and digital media, a lot of them are feeling like they’re being choked a bit because it’s like, “How am I supposed to work with this broadness?” But the reality is that the research has shown that this level of granularity that we’ve gotten so addicted to, it’s like we’re in the middle of the forest and we can’t see the trees, that old analogy. We’re in too deep and we’re too addicted as marketers to this granularity of data.
Now remember, third party cookies are going away, first party are not. So what that means is, in the example I gave of the New York Times and the Wall Street Journal, and there’s all sorts of other specialty publications out there, they have paywalls up that require people to sign up or people will sign up on the email. And what they’re doing is that they are mining that data and building up really great segments internally. So I think one of the things that’s going to happen is that when we had this explosion of ad tech, we had a move towards exchanges like the trade desk, but some of the earlier ones were all of the buys were done on these exchanges where if I’m the New York Times, I don’t have any people selling ads. What I’ve got is I’ve got ads that are all going through an exchange, but now we’re moving into a world where doing direct buys have a huge benefit because of the level of data that they have.
So for example, if you’re like a college or university and you’re focused on applications and enrollments, you can go and do a buy on Google and Facebook, but you start to dig into your data and you start to look at, you may have 20 different degree programs and there may be one on nursing that’s very, very popular. Well, there’s a lot of specialty publications out there that you can go to and do direct buys on that specialize in nursing and nursing careers. That would be great to flow into this strategy. So I think that’s part of it from the targeting aspect of where folks are going to have to start thinking because that granularity of data is, it’s not even available now and it’s going to be even less over the next year or so.
Danielle Milliken:
Yeah. So Jeff, you made a comment about direct buys and how that’s going to become the future of digital marketing. So can you speak a little bit more to that in the sense that how companies can maybe adjust what they’re doing? Like their team. How they might need to adjust their teams and their roles moving forward to make sure that they’re ready for this landscape change, whatever that might be.
Jeff Greenfield:
Well, I think one of the best things that a company can do is the difference between direct buys and the exchange buys all has to do when you’re negotiating. So sending your head buyer or the person who’s handling it out for a course on negotiation and researching online, what are the types of things that you can get with a direct buy? Because with a direct buy, you can negotiate a package where you’ve got ads on the site, they push out a couple of newsletters, maybe they do an advertorial for you as well, customized content.
And Hugh and I have talked before, and he’s even showed me examples of some of the work that you guys have done with really, really cool email capture that goes right into the client’s CRM, which is amazing and these types of deals are all out there. But I think the key is trying to figure out what’s available and then also negotiating the best deal because it’s a whole different world versus just buying ads and spending money. This you have to plan. That’s the other thing, is the time that’s involved too. And there’s a lot of work that goes into planning these types of campaigns.
Danielle Milliken:
For sure.
Hugh Macken:
For sure. Yeah, no, that makes sense. So Jeff, if you would speak a little bit about the key components of a marketing technology stack. So again, imagine you’re speaking to a CMO at a university say, and they’re trying to figure out, “Okay, what are the key components that we need marketing?” So I’m thinking like CDP, DMP, data warehouse, like Snowflake. What are a platform like Provalytics or there’s, I believe Oracle has a product in relation to measurement and analytics as well. So what are the key components that you would recommend to help marketers adjust to this new landscape?
Jeff Greenfield:
So the first thing is that I would set with the CMO, the understanding that Google Analytics, which 99% of marketers use, is an awesome web analytics platform. Meaning it does a really good job of once people end up at your site, you can see what they do on your site and you can see where they came from. Now anyone who’s dug into GA or even GA four, you will notice that 80% of the people come in, it’s called organic, they just show up. And the problem is from a marketer’s standpoint of view is that if you’re using Google Analytics to plan your media buys you’re doing the wrong thing. There’s no ifs, ands, or buts about it. Most marketers know that you have to fill the top of your funnel. There’s that AIDA, awareness, interest, desire, and action. And in order to fill that funnel, that awareness, you have to put messages out there, they can be targeted, but the broader, the better, as long as it’s within your target or your geo that you want to be at.
And what you really want to know is when you look in Google Analytics, what is driving, how did those people find out about my site to come directly in? Another way to look at it is that most larger brands will spend money in Google and what they call brand search so that when you type in the name of the company or the university, you’ll see them listed there first. Well how are people coming through brand search? Because Google Analytics will show you, “Hey, brand search is converting really well. Wow, you’re getting a lot of leads from brand search.” But it’s because brand search people already know about you in order to search. So you have to ask yourself, I really want to know who’s driving, what is driving brand search? What is behind that? And now for smaller marketers, one of the best ways to do it, so I’ll give a solution for smaller marketers first.
For smaller marketers, you want to do a little research into something called regression analysis. And regression is all about trying to figure out what’s the causation behind these things. And what you would do is you would take all of your channels that you’re spending on and you would look at how many impressions you’re producing and how many clicks each hour. And then you would run regression analysis between that and your brand search click. So you can see who’s actually driving brand search because that’s your top of the funnel drivers and that’s where you want to spend. Now for larger folks and larger players like a university, you need a platform that can automatically grab that impression data, not only analyze it, but have the proof behind it that it actually works. And you see this is a huge distinction. One of the biggest problems with most attribution and all of these platforms is they do a really good job of reporting about what happened last week, yesterday or last month.
But they don’t do a really good job of planning, which is what we’ve been talking about a lot. And planning is all about forecasting and saying, if you do this, you’re going to get this much money in return. And what’s really important with that is to be able to predict what the return will be on investment and then putting your money where your mouth is and showing confidence scores for that, which is what we do at Provalytics. And very few analytics platforms, if any, actually do that. And that’s important because remember at the end of the day you want to know how are people finding out about me?
That’s where you want to spend your dollars. It’s very easy to spend close to the bottom of the funnel. Google search is easy, Facebook retargeting is a great place to be. And then of course, as we talked about specialty publications that you can go to, these are great, but how do you fill that top of the funnel? In order to do that, you have to have a platform that can do it or you have to do a hand analysis to figure out what is driving your brand search, what is driving your organic traffic.
Hugh Macken:
Yeah, no makes sense. And I mean you’ve mentioned branding quite a bit and I wonder, are we going to see a pivot here on the digital side to where digital marketers start actually focusing on branding as opposed to just constantly focusing on direct response? I just read an interesting article recently on Airbnb and how they invested in branding and it really paid off with respect to direct response. So what’s your thought on that? Are we going to see more of a shift toward branding on the digital side?
Jeff Greenfield:
I sure hope so. And I’ve mentioned this book before, I’ll just hold it up right here. The book is called Lemon, It’s by Orlando Wood from the IPA in the UK. It’s available on Amazon, full color. It’s amazing. And what the author has done is he has researched the effectiveness of ads and what they’ve seen is they’ve seen an increase since 2006 in what they call short-termism. And short-termism is spending closer to where the transaction is. So it started, because in 2005, most brands, all they had was a brand. There was no performance divisions or anything like that. But when digital started coming out, they started testing and then they built up teams. And now there’s for large brands, they spent a lot on branding, but they spend even more on performance marketing. And look at like CPG, how close to the sale can you get?
Well now not only can I be on Amazon, but I can also be on Walmart. And if I’m tied when someone puts a competitive product in their basket, I can push out an ad that will compel them to buy mine as well or to switch it out for a discount. That’s a lot of money being spent almost at the point of purchase. And that’s what you call a rise in short-termism. And it’s directly correlated to a decrease in ad effectiveness since 2006. Brands definitely need to invest more money, especially now as they say the R word, the recession. And at the ANA masters a couple weeks ago down in Florida, the big thing that everyone was talking about is that marketers and CMOs are going to have more pressure than ever on their marketing budgets to prove to their CFO that if I do this, I’m going to get this. And to be able to plan effectively and forecast and then of course actually hit those numbers. That’s the other problem is that if you don’t execute, you’re definitely not going to hit your forecasts.
Hugh Macken:
For sure. Yeah. So you’ve talked about this model, Jeff, in terms of demonstrating the effectiveness of the advertising, this new model that is a hybrid between media mix modeling and multi-touch attribution. Would you mind just explaining that in simple terms in terms of, well first of all, what is multi-touch attribution? What’s media mix modeling or marketing mix modeling and what’s this hybrid that you’ve been talking that you’ve been talking with me about?
Jeff Greenfield:
Yeah, absolutely. So we’ll start with marketing mix modeling, MMM, because it’s been around since the sixties and seventies and M is typically uses regression modeling that I talked about earlier. And it looks at all of your channels. Now remember this was developed before digital. So companies would do an annual marketing mix modeling where they would look at their TV, radio, print, direct mail, out of home and they would do an analysis and regression towards sales numbers. And then the output of that MMA model was to say, here’s how much budget you should allocate to each of these channels for your sales to continue to move up. And that was great. Now of course what happened is that companies didn’t do them every year. They would skip a couple years and they would just focus on the budget allocations from years prior. And then digital marketing came along and when they integrated digital marketing as a channel into it, since they didn’t understand digital marketing, the output would say spend more on brand search and on affiliate.
Now of course we all know that as we talked about before, brand search is only from people who are already knowing about you. You can’t spend more on brand search to get more and you can’t spend more on affiliate. They all happen as a result of all the other activity. So multi-touch attribution started around 2007 because digital marketing had become, at that time somewhat complicated because you had display, you had search, you had search across multiple platforms and how do you figure things out? And Multitouch attribution said, we’re going to collect all of the data, every bit of granular touchpoints, whereas MMM only took in aggregated data, sometimes monthly, sometimes daily, but primarily large scale privacy centric data, whereas MTA was all user level data, it all model it instead of regression use machine learning. Because if you do regression with that amount of data, it will take forever, forever to get it done.
But machine learning, you can teach the machines and we’ve got great technology. One of them has been around for hundreds of years called Bayesian, which is just incredible because it thinks, and it works the same way that human beings think because we are constantly updating the way we think. So when you think about self-driving cars that aren’t here yet, self-driving cars are always getting smarter, the more obstacles that they face. And that’s the same way with machine learning for marketing attribution. So MTA would take all of that data, put it into a Bayesian model, and it would constantly be updated every single day, sometimes throughout the day with the most updated result of what was going on right at the moment and historical. And that worked great. And the greatest thing about MTA versus MMA is that you weren’t just limited to sales. You could have multiple KPIs at C3 metrics.
We had some clients with 25 or 30 different conversion events because there were different teams that were interested in different things. Some teams were interested in traffic to a particular site that was their KPI, other folks were leads, other folks were actual enrollments when talking about EDU clients here. So that was really great and also it did really well with digital. But since MTA was built on being a deterministic model, MTA fell flat with TV and radio and then podcasts as they started to explode, it was like there’s nothing for anyone to click on. How do you measure this?
Hugh Macken:
Right, right.
Jeff Greenfield:
Yeah. And that was the big problem. The other big issue with multi-touch attribution is that there was no incrementality, so there was no measurement of, it would tell you where to spend more, but it didn’t tell you how much more business you would get.
And marketers always had this question of, “Well, what happened if I didn’t do that? Wouldn’t I get those sales anyway?” And that’s the basis of incrementality. Luckily marketing mix modeling is all based about the contribution that each tactic, but an MMM word, it’s all about the channel. So there is incrementality in MMM, but it’s at a channel level. And so what I wanted to do is I wanted to merge these two together. I wanted to take something that was always on, used some of the MMM background so that you could incorporate not just digital media, but also TV, radio, CTV, podcast, print, direct mail, bring it all together, and not just to sales but to multiple KPIs. And so now what Provalytics is the Venn diagram, if you will, of MTA and MMM. The basis is MMM, but it’s a whole new way of modeling that uses machine learning at scale.
We’re able to take in aggregated daily data from Facebook and from the platforms down to a creative level. And we’re not limited to just pushing out channel level recommendations, but we’re pushing out granular recommendations so that channel managers will know exactly how to allocate based upon their goals. And the forecasting we’re doing is incredible. If I had a team of 500 statisticians running through, because our models, when clients will give us what are their goals for a forecast, how far out do you want to go? Do you want to go 30, 60, 90, 180 days out? What is your risk quotient? How much are you actually willing to increase or decrease a tactic? And what do you want your budget to be? Do you want it to be the same? Do you want to go up 10%, 25% or do you want to go down? And then what our platform will do is go through and run hundreds of thousands of simulations and we’ll export out the optimum plan, meaning, “Hey, this is the amount of money you’re spending, here’s the ultimate, ultimate, this is going to get your biggest bang for your buck.”
Now, in all the years of me doing this, I have never seen any marketer take on all recommendations. They never, ever do. So this forecast is based on a perfect scenario. And so what ends up happening is that clients will take one or two of them, but you don’t actually know what day they put it in at and what the spend is every day. So what Provalytics does, and right now we’re starting to get a lot of the October data from our clients. Most clients send us data monthly. We have a few that send it to us weekly. But what we’ll do is we’ll borrow from MMM and we’ll do a technique called a holdout. So we will hold out and remove all of the sales data and all of the KPI numbers for each day for the last 30 days. And all we will give to our model, to Provalytics is the impressions and the clicks and the spend.
That’s all we’ll give. And then what marketers are able to look at is see how well the model predicted the outcome. And all models I should say are wrong, some are useful. And what clients are seeing is it’s for certain KPIs, the model is spot on. For others it’s not because it’s not perfect. But what happens over time is that since it’s machine learning, it gets smarter and smarter over time. And that’s another thing I’ll mention is that traditionally marketing mix modeling, you need to have three years worth of data in order to build out a model. And this is one of the other problems with MMM is that it becomes a whole project. It’s a lot of work to put this whole thing together. And so that’s why folks don’t do it more than once a year and sometimes skip years.
With Provalytics since it was built on the basis of MMM, but we borrow from the world of MTA, we can start with as little as the last three months. The more data we have, the better. So for customers of ours right now that we’re moving forward, what are we? Eight or nine business days away from when the Black Friday sales start. When we have the historical data from last year, we’ve been able to give them plans for how to execute to get the biggest bang for their buck for this upcoming Black Friday. So the more data we have, the better, but we don’t need three years worth of data. That can be overkill for our models.
Hugh Macken:
Interesting. Wow. And just in the last couple of minutes that we have, Jeff, how would you say a CDP would tie into all of this? A CDP customer data platform, obviously very useful from the standpoint of audience targeting, but with respect to measurement, is there a role that a CDP can play? Does a platform like Provalytics integrate with CDPs? How does that factor into all this?
Jeff Greenfield:
Yeah, and you stated it, One of the best advantages of a CDP is that it allows you to leverage that first party data you have, your first party cookie data. So similar to Google Analytics, which looks at how people traverse your site, a CDP is taking that and putting it on steroids, and it allows you to create unique segments within them. Now the cool thing about segments is that obviously you have a segment of people who have purchased, you have people that are multiple purchasers. If you’re in retail and then you have people that show up, maybe fill out a popup, but don’t go all the way through, let’s say in the case of EDU, the lead process, they don’t want to just subscribe to a newsletter or you just have a ton of people who just show up and then leave.
And so one of the advantages with the CDP is you can create unique customer segments, and then what you can do is you can link those segments to your media buys so that you’re buying the multi shoppers, you’re buying the existing customers to bring them back for retargeting. You’re buying the folks who showed up but didn’t actually fill out a form or anything. And then you can do it on the conversion side to see how many of those people who showed up but didn’t fill out a form, who then were exposed to later advertising ended up purchasing. And what we do with those segments is that becomes a dimension within our model. So now when you think about multiple KPIs, you can look at, let’s say those folks who show up and don’t do anything, but let’s say the segment is they have to spend at least three and a half minutes on the site and you’ve tracked how they’ve converted.
Well, with Provalytics, we can track which media is driving them and what levers to push if you want to increase their conversion rate. So those segments are really important. The other type of dimension that’s also available in Provalytics is geo. So a lot of folks just like to look at the entire, let’s say the continental US, but there’s a lot of brands that target differently. The best one that comes to mind is Southwest Airlines. They are regionally based. All of their ads are regionally purchased. Even their television, they do not buy national TV. For them, they would want to look at things in a region by region basis, and Provalytics is built from the ground up to include that.
Hugh Macken:
Interesting. Wow. So lots to consider for those of us who are trying to make sense of all of this. And Jeff, your perspective definitely is invaluable. I’d love to have you back on again soon to talk.
Jeff Greenfield:
Oh, it’d be my pleasure. Hugh. Absolutely. Anytime.
Hugh Macken:
Yeah, for sure. Just to talk more about this new model that you still haven’t come up with a name for it. I’ve been going back and forth with different ideas.
Jeff Greenfield:
Well, the name that we’re using is, it’s Agile, so it’s not locked in like MMM. And I like the word impact versus incrementality and it’s modeling. So we’re going forward with the concept of AIM, “Agile Impact Modeling”. How do you like that?
Hugh Macken:
Agile Impact Modeling. All right, there you go.
Jeff Greenfield:
You like that, Hugh?
Hugh Macken:
Yeah.
Jeff Greenfield:
Good.
Hugh Macken:
I like it. Because honestly, I was going with something that was going to be MMM.
Jeff Greenfield:
It’s ad tech. We have to have an acronym, so we’ve got it now, AIM. AIM is the acronym.
Hugh Macken:
There you go. There you go. Oh my gosh, Jeff, it was just such a pleasure. Thank you so much for joining us and definitely thank you Danielle as well for joining us. And I want to thank Elijah Medcore, our producer who’s in behind the scenes. Thank you Elijah. And thanks be to God that we were able to make this happen. This is great.
Danielle Milliken:
Yeah.
Jeff Greenfield:
Absolutely. Thanks so much for having me.
Hugh Macken:
Hopefully we’ll be able to do this again. Yeah, thanks so much. And so just final thoughts would be for those who are interested in learning more about you, Jeff or Provalytics, so your website and the website for Provalytics?
Jeff Greenfield:
Yeah. So best website to go to is Provalytics, that’s P-R-O-V-A, prova, which is proof in Italian, lytics. So P-R-O-V-A-L-Y-T-I-C-S.com. And you can go there, fill out some information and get in contact with us. And if you want to chat with me, just go to jeffgreenfield.com is my personal website, jeffgreenfield.com.
Danielle Milliken:
Awesome.
Hugh Macken:
All right. Awesome. Great. All right, well thank you. Thank you so much again, Jeff.
Danielle Milliken:
Thanks Jeff.
Hugh Macken:
And have a great every week. Thanks everyone for joining us. Thank you.
Jeff Greenfield:
Thank you so much. Have a great day.
Danielle Milliken:
Bye.
Hugh Macken:
You too.
Jeff Greenfield:
Bye.
