MarTech360 Interview with Brian Quinn, President & GM at AppsFlyer

“If you're solving a meaningful business problem and earning that trust, you have a much stronger foundation for growth.”

Brian, you’ve moved from strategic accounts at AT&T to scaling businesses at Kenshoo and App Annie, and now leading AppsFlyer’s North American operation. What early lesson still shapes how you think about building durable growth?

Start with the customer's business. It sounds straightforward, but when you're working in a fast-moving technology market, it's easy to get excited about what a product can do and lose sight of why someone would pay for it. The question I come back to is: what will this help the customer do better?

That's how I think about durable growth. You need to deliver value that holds up after the initial sale, and you need a team that understands what success looks like for the customer. At AppsFlyer, that means helping marketers make decisions they can trust. Channels and technologies will keep changing. If you're solving a meaningful business problem and earning that trust, you have a much stronger foundation for growth.

At Kenshoo, you helped scale a “startup within a startup” from $500K to $22M while expanding across multiple international markets. What did that experience teach you about knowing when a growth model is ready to scale?

The test is whether the value is repeatable. An early customer win can tell you that you've found an opportunity. It doesn't automatically tell you that you have a model you can take into another market or hand to a larger team.

I'd look at whether customers are getting consistent results, whether they stay and expand, and whether the team can explain why we're winning. International expansion adds another question: which parts of the model travel, and which need to change locally? You want to preserve what makes the business work while listening to the market you're entering. My advice is to establish repeatability before accelerating investment. Adding people and markets will expose the gaps in a model very quickly.

You’ve spent years navigating shifts from social and mobile advertising to privacy-driven measurement and now AI. What has changed most fundamentally in how marketers think about growth, and what principle has remained constant?

What's changed is how much marketers have to connect. Discovery, engagement, and purchase can happen across platforms, and both privacy and platform-level changes have forced the industry to rethink how it measures those journeys. AI is now accelerating both marketers' decisions and consumers' experiences.

What remains constant is the need to understand whether your marketing investment is producing valuable customers. An install or a click is useful information, but you also need to know what happens afterward. Does that customer transact, subscribe, or come back? What does their lifetime value look like, and how often are they engaging with your brand?

Marketers need a consistent view of those outcomes to decide where to spend the next dollar. The technology has become much more sophisticated, but the business question remains practical.

AppsFlyer’s 2026 data shows 1,914 companies connected to its MCP and 1,543 configuring AI agents. As software moves from helping marketers interpret data to acting on it, what changes about the standard for trustworthy measurement?

The standard has to rise because measurement is becoming an input to action. If someone sees a questionable number in a report, they can stop and investigate. An agent can use that same number to make decisions repeatedly, before anyone recognizes the problem.

So I'd want to understand where the signal came from, how current it is, whether conversions have been counted consistently, and what uncertainty remains. We need those checks before we give an agent more responsibility. You also need clear limits on what it can change and a way to review those changes. There's real value in using AI to handle configuration and data-quality work first. Getting the foundation right enables more ambitious automation.

AppsFlyer has expanded from mobile attribution into web, CTV, PC, console, incrementality, and data collaboration. As customer journeys become increasingly fragmented, what does modern measurement need to understand that traditional attribution was never built to capture?

It needs to understand how the parts of the journey contribute to the business. As consumers, we don’t often see one ad and buy a product or download an app. It takes a number of touchpoints across a number of channels.

A customer might discover a brand on CTV, research it online, and ultimately transact in the app. If each team measures only its own environment, you can end up with conflicting reports and investment decisions that miss the broader opportunity.

Marketing attribution needs to take this into account. Attribution remains important for understanding the touchpoints associated with a conversion. Incrementality helps answer the next question: how much of that outcome did the marketing actually cause? You need both approaches, along with cost, revenue, and customer value. Bringing web and mobile into a consistent measurement framework is a meaningful step toward helping teams make investment decisions across the whole journey.

You’ve spoken about privacy and measurement long before generative AI became mainstream. As AI increases the value and speed of data-driven decisions, how should marketers rethink the balance between privacy, usable signals, and confidence in their data?

I've long believed the industry needs to keep improving measurement while protecting the consumer. Talking about privacy might be less en vogue than it was in recent years, but it still must be a priority for marketers, and for tech platforms.

With more signal loss and data fragmentation, there’s less access to individual-level data. This makes good analytics, aggregate signals, and a clear understanding of what the data can support even more important.

AI makes that discipline more urgent. Simply, bad data in = bad data out.

Before a signal goes into an automated workflow, marketers should understand whether they're permitted to use it for that purpose, how reliable it is, and where its limitations are. More data doesn't automatically produce more confidence. Sometimes an aggregate analysis or a well-designed experiment gives you a better answer to the business question. The goal is to give marketers useful evidence while respecting the people behind it.

Also Read: MarTech360 Interview with Taylor Stewart, VP, Growth and Emerging Channels at TripleLift

AppsFlyer now measures ChatGPT Ads and AI-driven discovery across multiple AI engines. As consumers increasingly discover brands through AI rather than traditional search, what new signals of intent do you think marketers need to understand?

A recommendation request carries far more context than a search query, but advertisers can’t see that conversation. What we can measure is what happens after the click, and that’s where intent shows up.

It starts with the source. AppsFlyer now attributes organic referrals from eight AI engines, including ChatGPT, Claude, Perplexity and Gemini, alongside 13 traditional search engines. On the paid side, our ChatGPT Ads integration connects campaigns to installs, app opens, in-app orders and subscriptions, across apps and websites. There’s already a lot of momentum with major brands already using it.

Then comes the question of what the customer did. Was it a paid ad or an organic recommendation? A new customer or an existing one coming back? Did the visit turn into an order or a subscription?

Because ChatGPT Ads sits in the same dashboard as other platforms like Meta, TikTok, Snap, and Google, marketers can compare AI outcomes with every other channel, measured the same way. An AI referral might be a first-time buyer, a loyal customer, or a quick look that goes nowhere. Measurement tells you which, so budget follows evidence instead of assumptions.

Your role at AppsFlyer spans Sales, Marketing, Customer Success, Partnerships, and operations. How does seeing the business end to end change the way you decide where to invest for the next stage of growth?

It makes you look beyond the next deal. It’s more than just winning new customers. We must create durable value by onboarding the customer well, helping them succeed, and keep earning their business by driving value through our products. Seeing those functions together makes the dependencies much clearer.

When I think about where to invest, I'd start with the customer value constraint. Do we need to reach more of the right customers, make adoption easier, or help existing customers get more from the platform? Those are different problems, and they require different investments. I also want the teams aligned on how we'll judge the result. Revenue matters, but retention, adoption, and the cost of delivering that value help tell you whether you're building a healthy business.

AppsFlyer increasingly operates across a broad ecosystem of platforms and partners while maintaining its position around independent measurement. What does independence need to mean when effective measurement also depends on deeper integration with the platforms being measured?

Independence needs to show up in how measurement works. Marketers should be able to compare channels using consistent standards and trust that the results aren't shaped to favor a particular platform. Deep integrations are important because useful measurement depends on access to reliable signals. The responsibility is to preserve neutrality as those relationships develop.

Advertisers need that confidence to make informed decisions across the ecosystem.

You’ve now seen several generations of technology reshape how companies acquire and grow customers. As AI agents begin influencing those decisions themselves, what is one assumption about marketing or measurement you think leaders will need to rethink next?

One assumption I'd challenge is that measurement happens after the marketing buying decision. As agents take on more campaign work, how you define and measure success increasingly shapes the decisions being made in the first place. If you reward the wrong outcome, automation can pursue it very efficiently.

Leaders will need to spend more time deciding what they're optimizing for and how they'll know it's creating additional business value. AI is making it easier to build products and produce creative, but consumers still have limited attention. That puts more pressure on choosing where to invest and understanding which customer relationships are worth building. The opportunity is substantial. Realizing it requires leaders to stay accountable for the goals and evidence guiding the automation.

Thanks, Brian!