Mozilla’s Privacy-Friendly Ad Targeting

Summary

MarTech Podcast™: Mozilla’s Privacy-Friendly Ad Targeting

Release Date: July 7, 2025
Host: Benjamin Shapiro
Guest: Graham Mudd, SVP of Product at Anonym (acquired by Mozilla)

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Introduction

In the July 7, 2025 episode of the MarTech Podcast™, host Benjamin Shapiro engages in an insightful conversation with Graham Mudd, the Senior Vice President of Product at Anonym, a privacy-centric advertising company recently acquired by Mozilla. The episode delves into the evolving landscape of digital advertising, emphasizing the critical balance between effective ad targeting and consumer privacy.

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The Challenge of Modern Ad Targeting

Benjamin Shapiro opens the discussion by highlighting pressing statistics from Deloitte's 2024 Connected Consumer Survey:

• 85% of US consumers have taken steps to protect their online privacy.
• 75% feel powerless in safeguarding their personal data.
• 25% believe that both companies and hackers can access their data regardless of their actions.

These figures underscore the growing consumer apprehension towards data privacy and the consequent challenges marketers face as traditional cookie-based tracking becomes obsolete.

Quote:

"These are not random numbers. These are your customers."
— Benjamin Shapiro [01:15]

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Privacy-Preserving Targeting: A Coexistence Paradigm

Graham Mudd posits that effective ad targeting can indeed coexist with consumer privacy. He emphasizes the importance of leveraging first-party data, which refers to the data collected directly from consumers through interactions such as purchases or sign-ups. By utilizing advanced, privacy-safe machine learning techniques, marketers can identify "lookalike" audiences that mirror their existing customer base without sharing actual customer data with ad platforms.

Key Points:

• First-Party Data Utilization: Focus on data collected directly from customer interactions.
• Privacy-Safe Machine Learning: Employ algorithms that process data within secure environments, ensuring data privacy.
• Lookalike Modeling: Identify and target audiences similar to existing customers without direct data sharing.

Quote:

"We can use it in a way that doesn't require you to share those customers' data with any of the ad platforms you want to work with."
— Graham Mudd [02:31]

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Consumer-Friendly Privacy Explained

Shapiro seeks to simplify the concept by comparing traditional tracking to dating dynamics, underscoring the shift from invasive tracking to respectful, privacy-conscious targeting.

Analogy:
Consumers are akin to individuals who, after an initial positive interaction (a "first date"), prefer not to be relentlessly pursued but are open to being introduced to similar, like-minded individuals.

Quote:

"Consumers are basically saying, thanks for the drinks but I don't want a second date."
— Benjamin Shapiro [05:32]

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Comparative Analysis: Anonym vs. Google’s Privacy Sandbox

The conversation transitions to how Anonym’s solution stands apart from Google's Privacy Sandbox:

• Privacy Sandbox: Primarily browser-based (focused on Chrome), relying on on-device computations and lacking cross-device capabilities.
• Anonym’s Approach: Utilizes marketers' own first-party data, operating in a platform-agnostic manner without depending on browser data, thus offering broader applicability across devices.

Key Differentiators:

• Data Source: First-party data vs. browser-collected data.
• Device Compatibility: Platform-agnostic vs. limited to browser environments.
• Data Handling: Confidential computing environments ensuring no direct data sharing.

Quote:

"Ours is kind of platform agnostic. It doesn't really matter where the data was collected by the retailer, as long as they have it, we can use it."
— Graham Mudd [08:45]

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Effectiveness of Privacy-Preserving Targeting

Addressing skeptics, Mudd presents evidence supporting the efficacy of Anonym’s methodology:

• Performance Metric: Approximately 30% increase in the efficiency of identifying converters compared to traditional broad targeting, especially in niche markets where precise targeting is crucial.
• Use Case Sensitivity: While broad products (e.g., laundry detergent) may not see significant gains, specialized offerings benefit substantially from privacy-centric targeting.

Quote:

"On average, what that's boiled down to is roughly a 30% increase in the efficiency of finding converters using our method relative to sort of a baseline."
— Graham Mudd [10:10]

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Advanced Measurement Techniques

The discussion delves into Anonym’s dual measurement methodologies:

1. Attribution Modeling:

   • Purpose: Determine if an ad led to a conversion based on predefined business rules (e.g., last touch within seven days).
   • Use Case: Tactical adjustments during campaigns, such as optimizing creative content or targeting strategies.

2. Incrementality Testing:

   • Purpose: Assess the genuine impact of ad campaigns by comparing conversion rates between test and control groups.
   • Use Case: Strategic decisions on budget allocation and platform effectiveness.

Quote:

"Attribution is often the tactical evaluation, because you need that real-time signal to tweak the knobs during the campaign."
— Graham Mudd [17:50]

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Navigating the Regulatory Landscape

Mudd provides a forward-looking perspective on privacy regulations:

• Groundwave Regulation: A multitude of state-level privacy laws in the US, with 19 states already having comprehensive legislation, expected to rise to 25-30 in the near future.
• GDPR Evolution: In Europe, ongoing discussions aim to update GDPR and the E Privacy Directive to accommodate modern privacy-enhancing technologies.
• Regulatory Impact: Regulatory fragmentation necessitates adaptive, privacy-preserving marketing solutions that can comply across various jurisdictions without exhaustive manual adjustments.

Quote:

"Your focus should be real good marketing in the form of great creative, great products, all the fundamentals and building relationships and gathering first-party data."
— Graham Mudd [21:26]

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Recommendations for Marketers

Mudd advises marketers to proactively adopt privacy-preserving technologies to future-proof their advertising strategies. Key recommendations include:

• Embrace First-Party Data: Prioritize data directly obtained from customer interactions.
• Leverage Expert Technologies: Utilize specialized privacy technologies to handle data securely and compliantly.
• Focus on Core Marketing Fundamentals: Invest in creative content, product quality, and customer relationships rather than solely on tracking and data acquisition.

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Conclusion

The episode culminates with Graham Mudd emphasizing the inevitability of increased privacy regulations and the necessity for marketers to adapt by integrating privacy-friendly technologies. By doing so, businesses can maintain effective ad targeting while respecting and protecting consumer privacy, thereby fostering trust and achieving sustainable growth.

Final Quote:

"Let the privacy folks out there that really understand how to deploy tech to protect that data and those users, let them be the experts in that."
— Graham Mudd [21:26]

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Connect with Graham Mudd

For further insights or to connect with Graham Mudd, listeners can refer to his LinkedIn profile or visit Anonym's website.

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This summary encapsulates the key discussions, insights, and expert opinions shared in the "Mozilla’s Privacy-Friendly Ad Targeting" episode, providing a comprehensive overview for both listeners and those exploring the podcast content for the first time.

Transcript

[Benjamin Shapiro]

The Martech Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iheareverything.com.

From advertising to software.

[Graham Mudd]

As a service to data across all of our programs and clients, we've seen a 55 to 65% open rate. Getting brands authentically integrated into content performs better than TV advertising.

[Benjamin Shapiro]

Typical lifespan of an article is about 24 to 36 hours.

[Graham Mudd]

If we're reaching out to the right person with the right message and a clear call to action, then it's just a matter of timing.

[Benjamin Shapiro]

Welcome to the Martech Podcast, a member of the I Hear Everything Podcast network. In this podcast you'll hear the stories of world class marketers that you technology to drive business results and achieve career success. Here's the host of the Martech podcast, Benjamin Shapiro.

85% of US consumers have already taken steps to protect their online privacy. According to Deloitte's 2024 Connected Consumer Survey, 75% of consumers feel powerless when trying to protect their personal data. And 25% believe that both companies and hackers can access their data no matter what they do. These are not random numbers. These are your customers. And yes, targeting is getting harder and cookies are disappearing and US marketers are scrambling for solutions. So how can you maintain ad performance without fulfilling your customers fears about the use of their personal data? I'm Benjamin Shapiro and joining me today is Graham Mudd, the SVP of ProductNonum, which was recently acquired by Mozilla. And today Graham is going to explain how privacy preserving technologies can actually improve your targeting results. Graham, welcome to the Martech Podcast.

[Graham Mudd]

Thanks for having me. Looking forward to it.

[Benjamin Shapiro]

Excited to have you here. Congratulations. Your privacy centric advertising company was acquired by Mozilla, an industry leader, which goes without saying. And I'm excited to have you on the podcast talk a little bit about a new way of advertising. Let's start off at the top. Can effective targeting coexist with consumer privacy?

[Graham Mudd]

I think it can. You knew I'd say that, right?

[Benjamin Shapiro]

Of course, Absolutely.

[Graham Mudd]

I think it can. First party data is really the mantra these days. In the days where it's getting harder and harder to find consumers, it's really important to use the data that you have and base your targeting on your existing users who tell you a lot about the types of people who are going to buy your product in the future. So in our world, what we really focus on is leveraging that first party data using advanced and privacy safe machine learning techniques to find people who look like your existing customers, but doing it in a way that doesn't require you to share those customers data with any of the ad platforms that you want to work with. That is a tried and true method. This idea of a lookalike model, it's been around for a long time. What we've done is tried to find ways to leverage that exact same technology, but do it in a privacy safe way in a trusted execution environment. We can talk more about the tech later, but the important thing is find a way to use that data without sharing it with platforms who you may not be able to or want to.

[Benjamin Shapiro]

One of my favorite GPT prompts is explain it to me like I'm an 8th grader. Generally I understand how 8th graders think apparently, and I want to get into a little bit about consumer friendly privacy, but it's also targeting and those two things seem to be at odds. My layman's term of how I think about privacy is you go online, you conduct an activity, something gets dropped in your browser or there's some sort of tracking, the company gets some data and then they can use that hopefully on their own sites, but potentially other places as well. How can you do that without knowing who the person is?

[Graham Mudd]

I think you're absolutely right. The way that you described the way it's been done for a long time in the background, that was a key. I think you use those terms in the background. You're collecting information about consumers and it's that collection of data without them even understanding it that can disturb and disappoint your customers. Instead of doing that, what we've encouraged our customers to do is use the data that you're collecting from the interactions that you have with consumers. In other words, if you're a retailer and someone's buying a product from you, they're of course giving you their email address, their phone number and so forth. If they give you permission at that moment to reach out to them directly, deterministically, I want to hear from you again about sales or new products or whatever. Great. Use email. Use all of the custom audience techniques at major ad platforms. Reach them directly. But a lot of people that you have as customers likely won't give you that permission. They will say, I'm happy that you sold me the product, but I would prefer you not share my data with MarketingPlus platforms. Okay, great. Well then let's figure out how we still use that data. But do so in a way that doesn't require you to share their data and violate the promise you just made to them. So that's where we move from sort of a deterministic approach one to one. Ben gave me his email address. I am now going to reach Ben using that email address to a predictive approach where we use the data that we have to find people who look like Ben but aren't Ben. And that's I think, a really powerful way to use data that doesn't require you to give it out to marketing platforms and again violate that promise that you've made to consumers when they tell you they don't want you to share their data.

[Benjamin Shapiro]

I've got a bad dating metaphor that I'm gonna try to roll out offending anybody. What I'm hearing from you is consumers are basically saying, thanks for the drinks but I don't want a second date. They don't wanna share or for you to continue to reach out to them. And the privacy friendly way of advertising is not saying, well, I'm going to track you around everywhere you go and hopefully show up in front of you again and maybe you'll change your mind. It is, you know what, you liked going on dates with this person and they said yes to at least one. Here's where other people like them are going.

[Graham Mudd]

That's exactly what dating apps do. I bet you swiped right. These two connected. It didn't work. We're going to find people that maybe are like that person, long, tall, skinny.

[Benjamin Shapiro]

Brunette, you know, whatever it is. But you're using the other variables and identifiers, essentially creating profiles. So let's talk a little bit about the competitive landscape. I get the general, like how consumer friendly privacy works where you don't track individual people, but you're targeting essentially lookalike audiences, people with the same profiles, hopefully the same ROI or dating habits depending on what metaphor we're using. It sounds very similar to Google's privacy sandbox. So how does the technology differ between Mozilla and Anonymous solution and what Google is doing?

[Graham Mudd]

I think in spirit you're right. That sandbox is trying to accomplish a lot of the same types of use cases that we are. And I'd say the big two use cases are measurement and there are a handful of forms of measurement. Did my marketing work and then how do I make it work better? And targeting is a big piece of that. You just call that ad delivery. But I want to find the right people. Now. How are we different? You asked? Well, I think the primary Way that we're different is what Sandbox is largely based on is the browser is Chrome and it is finding ways to collect information about the user through the browser, but then not sharing user level data from the browser, instead doing some on device work, doing it in confidential computing environments like we use, but it's based on browser data collection for the most part, whereas our solution doesn't use browser based data collection and instead uses the first party data of the marketer themselves. So let's just go back to the retailer example. You're a retailer, you've got a, you would think a pretty rich CRM of all of your customer data. That's the data source for all of our work, whether it be measurement or audiences developing audiences targeting. On the other hand, what Chrome would do would be to say I'm going to try to observe your customers converting after seeing an ad in the browser and I'm going to take that data and do interesting things with it. So very different. The benefit, just to be super clear of a browser based approach is browsers are, if you're lucky to be in Chrome's position, you have fantastic market share and that's a rich source of data if you can get brands and companies and so forth to opt into it. One of the downsides of it is it is browser based and therefore it is not cross device. It's hard to do anything that tries to either measure or activate users when they move from browser on their iPad, to their phone, to their laptop to whatever. Ours is kind of platform agnostic. It doesn't really matter where the data was collected by the retailer, as long as they have it, we can use it.

[Benjamin Shapiro]

Let's talk about how it works. What evidence do you have or what evidence shows that privacy preserving targeting, the anonym methodology works as well as the ad tech solutions that kind of disregard consumer privacy. Basically your cookie based stuff.

[Graham Mudd]

The work that we've done has largely compared what happens when you use our approach to private machine learning, which is everything we just talked about. Compared to if you did broad targeting on an ad platform and sort of let the algorithm alone find the users, I can tell you that what we see is hugely variable and you can imagine why that might be. If you're for instance, marketing laundry detergent, everyone does laundry, broad targeting, you can imagine it works pretty darn well.

[Benjamin Shapiro]

I work for a laundry and dry cleaning delivery startup. Marketing laundry services is harder than you think it might be.

[Graham Mudd]

Okay, Coca Cola, we'll use a different one, or soda or whatever. But the point is if it's a very widely used product, high reach marketing and broad targeting can work really, really well. But if it's more of a niche offering, then you need precision. Right. If you don't want to waste your marketing dollars. Pretty straightforward notion. So you can imagine if we're making comparisons to broadly targeted products, we're not going to do quite as well. Whereas if it's a more niche offering, we tend to do really well. On average, what that's boiled down to is roughly a 30% increase in the efficiency of finding converters using our method relative to sort of a baseline.

[Benjamin Shapiro]

It seems like the underlying and we kind of get into this conversation with synthetic data too. You need a small amount of data to be able to create. What I'm inferring is kind of like a lookalike model. You've got some data, you've got some first party conversions, you're targeting lookalikes and trying to find other similar people as opposed to being direct looking for specific interactions. A 30% lift sounds phenomenal. So how is it possible that you're inferring that people will convert so much better and so much more efficiently than using people that have the exact same pattern of behavior? Why a 30% lift?

[Graham Mudd]

Okay, I want to make sure that I'm clear on the comparison here. The comparison isn't if I retarget users who I have seen directly putting detergent in their Amazon cart, for example, compared to our method of a lookalike model. I would suspect that in that case we wouldn't probably compare all that favorably. We really are focusing more on the prospecting use case. I want to find more new customers, people that I don't have a relationship with, that I don't have a cookie on. That's really what we're focused on.

[Benjamin Shapiro]

Am I oversimplifying by saying this is like a lookalike audience for open web? I think of like I do a Facebook campaign, I see my conversions, I take my conversions, I create a lookalike. Now I can expand my audience. It seems like a very similar process for open web.

[Graham Mudd]

It's a very similar process. Although it does work on walled gardens. It's not just for sort of the open web. But yes, the notion of it you got it is very similar to that idea. Except in our world you don't have to share your conversion data with the platform, whereas you do in the one you just mentioned.

[Benjamin Shapiro]

Can you share your conversion data? Like, does that actually provide more efficiency? It seems like that's good data to give to even if it's an anonymous platform of like, yes, this, it's not a pixel, but this type of user that you're looking for. I'll go back to my dating metaphor. Tall, skinny, brunette. Yes, that was a conversion. Something that's not tall, skinny and brunette was not. Now I know how to refine my lookalike audience. Doesn't the conversion matter?

[Graham Mudd]

I think what you had mentioned is the idea of using your conversion data as observed by meta to build a lookalike audience. This is different in the sense that you don't need meta to have your conversion data for this to work. We take that conversion data, we put it together with user data from ad platforms and we build that lookalike model, then we upload that lookalike model.

[Benjamin Shapiro]

I get it. Okay. There still is a full funnel conversion from ad delivery to a specific target down to conversion. It's just that you're getting the conversion data and you don't actually have the user identifier. So it's still private as opposed to feeding Meta the conversion. And now they're getting all of the PII for which person converted.

[Graham Mudd]

We do have the PII from both the ad platform and the advertiser. It's in this confidential computing environment. That's where the ML happens and then the audience is shared with the ad platform. We don't share any PII that they don't already have. So what we're basically our ML model is saying users A, B, D, F, Y and Z are good customers for brand X, go target them. Right.

[Benjamin Shapiro]

It's not that the PII doesn't exist, it's that it's not shared with the marketer.

[Graham Mudd]

You got it?

[Benjamin Shapiro]

Okay, talk to me about who this is for. What companies should be pivoting towards privacy friendly targeting.

[Graham Mudd]

I think eventually this is going to be for everybody in the sense that I think the direction of travel on privacy as it relates to ads is very clear. So our bet is that over the course of the next handful of years, this is going to be increasingly the standard in terms of the approach. I think in Europe it's already very much. That's the case in a handful of states in the US were there. And it's kind of shifting over the course of the next couple of years. But right now we're seeing a handful of segments really benefiting from this. One is if you're in a sensitive vertical. Examples of those might be healthcare, financial services, education, credit, things that have a lot of regulation around them. Privacy tech is a good unlock for you Second, if you are an app advertiser, in other words, if you care a lot about app based conversions, especially iOS conversions, there's a lot of constraints on how you can use that data or even get access to that data as a result of iOS's ATT or app tracking transparency. That's another big vertical or big segment for us. And then a third I mentioned, which is geographically sensitive, you are in a regulated market or you want to be in a regulated market and you need to use this kind of data. So those are three. One last one would be, if you believe, and a lot of folks do, particularly bigger players, that their conversion data on their users is a real valuable asset for the company. And you don't want to share that, not necessarily to protect your consumers, although you may also care about that. It's more. I have really valuable information on consumers and their intent and when I put it into the black box of a major ad platform, I worry that it's going to benefit my competitors in ways that I don't really like. I would rather find ways to use that data that don't require me to share it directly with an ad platform. That's another near term value proposition for us. Forgetting completely about regulation and user privacy.

[Benjamin Shapiro]

Yeah, if I read that back, what I'm hearing is if you are in a very competitive market and you're the market leader and you don't want to put your conversions into Facebook so your competitors can then essentially market against who has converted to become your customer. Privacy centric Tracking and conversion works. Talk to me a little about not just the tracking piece, but the other end where you're doing the monetization and evaluation. How do you figure that out?

[Graham Mudd]

Finding the relevant audience is of course a really critical element of it. But then understanding how well that worked and learning from your previous campaign so that you can do better the next time is of course a brush practice. We've focused on building two measurement methodologies out that leverage the same sort of technology. One is the core, which is attribution. And what you're looking for, of course is did people convert and buy my product after having seen an ad? And you tend to use business rules or heuristics that say, look, it has to be the last touch, or it has to be a touch within seven days, or there's a whole bunch of different ways and it's all very configurable. But using those same two data sets, the impression data and the conversion data, you put them together, you can run an algorithm on that data and say, here's how many attributed conversions this campaign was responsible for. The other is closer to the scientific method. Right. Where you're doing experiments, test versus control, in ad parlance, incrementality. Here what you're doing is you're setting up an experiment, typically through an ad platform, where they hold out impressions from some of your audience. We get data from the ad platform about who saw the ad, who was in the test group and who was in the control group. And then we compare using the conversion data to see if the ads resulted in conversions that wouldn't have happened if you hadn't shown them an ad. And that is oftentimes called the gold standard in measurement. We think it's really important, we tend to see is that advertisers use attribution for tactical learning. This creative worked, this one didn't, or this targeting worked, or this one didn't. Whereas they use Lyft or incrementality measurement to understand allocation decisions. Look, this was really incremental and put more money into this platform or to this placement. This one wasn't very incremental. I'm going to start moving money elsewhere.

[Benjamin Shapiro]

When you're looking at Lyft and that sort of incrementality, I always think of those as being longer term evaluation periods. Are you able to get feedback to understand your ad campaign performance in real time or close to it? If you're trying to rely on things.

[Graham Mudd]

Like Lyft, it is definitely not as real time as attribution is. But again, with incrementality, what it all really comes down to is statistical power. If you're running a huge campaign and there's a lot of spend, a lot of people being reached, you can get reads of the incrementality within a week. If you're running a smaller campaign that sort of trickled out over a month, it's unlikely, unless the campaign is working incredibly well, that you're going to be able to see the delta between test and control very quickly. So that's why I said attribution is often the tactical evaluation, because you need that real time signal of let's twist the knobs during the campaign. Whereas a bigger decision is how much am I going to spend? And you don't make that typically day to day.

[Benjamin Shapiro]

Yeah, it seems like there is an optimization metric and then there is an evaluation metric.

[Graham Mudd]

That's right.

[Benjamin Shapiro]

Your lift really tells you whether the campaign was worth it or not. Last question I want to ask you before we get into our lightning round. I know you're on the product side. But you're also the CEO of the privacy friendly tech company. I want you to look into the crystal ball. I'm assuming you know more about the regulations in privacy than I do. So help marketers understand what's coming down the pike. As we think about what ad solutions we should be betting on over the next year or so. How is the landscape changing and where should we put our chips first?

[Graham Mudd]

Just to correct, I wasn't the CEO. My partner Brad Smallwood was. But I was a co founder with him.

[Benjamin Shapiro]

Sorry, Brad.

[Graham Mudd]

That's all right. I just, you know, I don't want to hear it from him, not be with you. To answer your question, where are things headed? I actually just spent a week in D.C. and then another week in Brussels talking to regulators, civil society folks and so forth. I think that the, as I said, direction of travel is clear. Regulators and legislators are clearly hearing from consumers. You got to do something about this. At the same time, like it or not, there is a sort of anti big tech zeitgeist right now.

[Benjamin Shapiro]

Unless it's AI tech, then everybody loves it.

[Graham Mudd]

Sure. Yeah. So you see that kind of influencing decision making and they're trying to find ways to constrain these companies and again, not issuing an opinion on that. But that's just reality. What we're basically seeing is a couple of key trends. One is it doesn't look like much is going to happen on the federal front. I don't want to discount completely that possibility, but it just seems unlikely. Instead, what seems likely in the next couple of years? We're already at 19 states with privacy, comprehensive privacy legislation and that will no doubt move up to 25, 30 over the course of the next couple of years. And once you get to that level, you're kind of in a de facto federal state because at some point it's not efficient to try to treat each state a little bit differently. You just go to the lowest common denominator. That's what's happening there. And then in Brussels, what they're looking at is how do we make changes to GDPR and the E Privacy Directive that just make it easier to operate. And one of the things that we were really advocating for is make it very clear in this regulation that if you use privacy enhancing technologies in such a way that you're not sharing your user's data with other companies, then you're doing the right thing and it's okay. Because right now that legislation was written so long ago these technologies didn't even exist. It's unclear what is and is not okay. I hope they update that. That is their plan. We'll see what it says.

[Benjamin Shapiro]

What I'm hearing from you is there is a ground wave of continued privacy regulation. It's not a large piece of legislation coming out from the EU or from the United States government. It's individual entities are all making their own rules, which means operationally you have to kind of go to the lowest common denominator, as you said. So privacy is going to get harder and harder to manage because there's different rules in different places.

[Graham Mudd]

That's right. And so our argument to businesses is, look, you can either try to parse all of these little things and stay abreast of all of it, or you can move to approaches that are a fundamentally more privacy preserving by their very nature and that are able to sort of adapt for you. So what your focus should be is really good marketing in the form of great creative, great products, all the fundamentals and building relationships and gathering first party data. Let the privacy folks out there that really understand how to deploy tech to protect that data and those users, let them be the experts in that. Unless you really want to go out and build all that stuff yourself.

[Benjamin Shapiro]

All right, that wraps up this episode of the Martech podcast. Thanks for listening to my conversation with Graham Mudd, the SVP of products at Anatom Part Mozilla. If you'd like to contact Graham, you could find a link to his LinkedIn profile in our show notes or on martechpod.com or you can visit his company's website, which is anonymco.com, which is a n o y m c o dot com. If you haven't subscribed yet and you want a daily stream of marketing and technology knowledge in your podcast feed, hit the subscribe button in your podcast app or on YouTube and we'll be back in your feed every week. All right, that's it for today, but until next time, my advice is to just focus on keeping your customers happy.

Thanks for listening to the Martech podcast and I hear everything. Production. Looking to launch or scale a podcast like this one for your brand? Then visit iheareverything. Com.