MarTech Interview with Matt McGillicuddy, VP of Marketing at Infinity

Discover how Matt McGillicuddy, VP of Marketing at Infinity, explains the power of call data in AI-driven marketing to improve attribution, optimise campaigns, and boost ROI.

Welcome to MarTech Cube, Matt. We’re delighted to have you. To kick things off, could you share your professional journey and what led you to your role as VP of Marketing at Infinity?
My journey has always been about finding ways to use data and technology to drive real commercial impact. I’ve worked across both B2B and B2C sectors, leading integrated campaigns that bring strategy and execution closer together.

At Infinity, I saw the opportunity to help marketers unlock one of the most valuable – yet often overlooked data sources: phone calls. Customer conversations are rich with intent and outcome signals that can fuel smarter marketing decisions. Today, as VP of Marketing at Infinity, my focus is on helping businesses harness this offline data to optimise digital campaigns, prove ROI, and ultimately make more good calls – both in their marketing and in their boardroom decisions.

Why do you think call data is becoming such a valuable resource for marketers in today’s AI-powered ad tech landscape?
AI has created huge opportunities, but it’s only as powerful as the data you feed it. Too many marketers are still feeding tools thin information or relying on proxies for valuable interactions like clicks, impressions, or form fills. These are useful, but they don’t tell you much about the intent or outcomes of interactions. Calls, on the other hand, do.

In a world of tighter budgets and rising expectations, call data fills a critical gap. It gives marketers the ability to prove attribution, fuel bidding algorithms with high-quality signals, and uncover customer insights that can shape strategy. For example, Fred Olsen not only plugged their attribution blind spot with call data, but also used advanced categorisation to improve the quality of signals being used to fuel paid search campaign optimisation – resulting in a 16% improvement in return on Google Ads spend and a 37% year-on-year increase in revenue. That’s the kind of edge you need when AI is powering the competitive landscape.

How can analyzing call data help brands better understand customer intent and improve campaign performance?
When someone picks up the phone, it’s usually a high-intent moment. In fact, research from BIA Kelsey shows that call leads convert 10–15 times better than web leads. But it’s not just about whether a call happened – it’s about what was said and what the outcome was.

Beyond conversion tracking and AI-powered bidding fuel, conversation analytics gives marketers the unfiltered truth. Surveys or reviews are time-consuming, and the results quickly become outdated, whereas call intelligence instantly surfaces insights into exactly what customers want, the barriers stopping them from buying, and opportunities to adapt messaging. Marketers can and should use this real-time data to act quickly and capitalise on new opportunities. An example is Specsavers who used Infinity to identify overlooked needs and quickly refined their targeting and launched new campaigns. The result? Bookings improved by 25%. That’s the power of structured call data in action.

In your experience, what makes call data a differentiator compared to other digital data sources marketers rely on?
Most digital data points are binary – a click, an impression, a form fill. They show you what happened but not why. Call data gives you context. You’re not just seeing that someone engaged; you’re able to understand catalysts, intent, and outcomes.
That context is what makes it so powerful for AI-powered optimisation. It allows you to categorise interactions in real time and feed algorithms the quality signals they need.

In short, call data transforms optimisation from quantity-driven to quality-driven. That’s a differentiator that no clickstream alone can offer.

How do you see call data fitting into the larger trend of personalization and customer experience in digital marketing?

Personalisation today isn’t just about addressing someone by name in an email. It’s about anticipating needs, removing friction, and meeting customers where they are. Call data is a goldmine for this.

On a macro level, it helps you spot trends – emerging product wants, shifting preferences, or new objections – so you can impact strategy, form new compelling offers, and tailor messaging and campaigns. On a micro level, it empowers frontline teams. For example, if a visitor browses holiday extras online before calling to book a safari adventure, the data call intelligence platforms like Infinity capture and serve up enables the agent handling the enquiry to see that journey and use it to upsell during the conversation. That kind of joined-up experience is only possible when you stitch call data into the wider customer journey.

Using call data to conduct root cause analysis for specific challenges, understand complex customer journeys, and uncover friction points in the buyer journey is invaluable when working to improve experiences across digital marketing, sales, and customer service. Pendragon used this approach to reduce purchase friction by 66% and boost Trustpilot scores, as well as cut CPA by 63.8%. This is a great example of why marketers should be using call data and conversation insight to do more than optimise paid media tactics.

What challenges do marketers face when it comes to collecting, analyzing, and activating call data—and how can they overcome them?
A few years ago, I’d have said scale and complexity were big barriers. But today, with platforms like Infinity, that’s simply not the case. Implementation is quick – a small script on your site – and you can start collecting data immediately.

AI then does the heavy lifting. Tools like Smart Outcomes automatically categorise calls by intent and outcome, while tools like Conversion Barriers structure unfiltered conversation data, highlights what stopped a customer from buying, and gives recommendations on how to fix it. That means marketers don’t need armies of analysts and can focus on actioning improvements.

The bigger challenge now is buy-in. In tough economic climates, new tech can be dismissed as “nice to have.” My advice is to start small: use benchmarking tools to highlight opportunities to drive improvement and ROI calculators to demonstrate the quantifiable upside. One client justified a 125% increase in ad budget based solely on Infinity’s attribution data. Wins like that make the case for wider adoption.

As AI continues to evolve, what new opportunities do you see for combining AI-driven insights with call data for more effective ad targeting?
One of the biggest opportunities is using call outcomes not just to optimise bids, but to proactively shape who sees your ads. Signals from calls give marketers control over audiences in a way that clicks never can.

For example, you can exclude low-value calls like service or support interactions. They don’t generate new revenue – and feeding them into campaigns wastes budget. With Infinity, intent and outcome signals can create exclusion audiences in Google Ads, cutting inefficiency at the source.

On the flip side, you can overlay audiences of proven high-value callers to accelerate value-based bidding strategies. Even if you’re already running a tROAS strategy, seeding the algorithm with “lookalikes” of your best leads gives it a head start. Instead of waiting weeks to learn who’s valuable, you’re telling it from day one: “Go find more people like this” based on caller intent and outcomes; massively compressing optimisation cycles which is invaluable for marketers working in industries where sales cycles are long and you can find yourself waiting 30+ days for the revenue values needed to fuel value-based bidding tactics.

When call outcomes are applied to both bidding and audience optimisation, campaigns don’t just get more efficient – they learn faster, scale smarter, and deliver stronger commercial returns.

On a personal level, what’s your go-to strategy for staying ahead in such a rapidly changing marketing and ad tech environment?
The temptation is to chase every shiny new tool. My approach is different: be strategic, not reactive. I evaluate every piece of tech through a simple lens – will it help us hit our commercial objectives faster, more efficiently, or more effectively? If not, it’s noise.

When adopting technology, integration is key. It has to plug into existing systems and processes, and the team needs to be trained to get value from it. Otherwise, you just create complexity in the stack.

Finally, I view AI as an enabler of efficiency, not a silver bullet. It won’t magically build pipeline or cut costs on its own. But when combined with the right data – especially rich, first-party sources like call data – it becomes transformative.

What advice would you give to marketers who are just beginning to explore call data as part of their strategy?
First, assess whether inbound calls are a meaningful sales touchpoint for you. If they are, then call data is a must-have. If not, it’s not going to add much value.

Second, start with a clear, measurable use case. Paid media is often the best entry point. Feeding call intent and outcome data into bidding algorithms delivers quick, tangible wins – lower CPL, higher ROAS, and more sales-ready leads. From there, you can expand into areas like conversion barrier analysis and sales enablement.

And finally, don’t try to do everything at once. Work with a partner who can help you prove ROI fast. That’s the key to getting internal buy in and driving business -wide adoption.

Finally, what closing thoughts would you like to share with our readers about the future role of call data in AI-powered marketing?
If there’s one message I want to leave marketers with, it’s this: if you want to realise the full potential of AI, data isn’t just nice to have – it’s critical.
With third-party cookies disappearing and privacy tightening, first-party data strategies are no longer optional. Call data is one of the richest first-party data sources you can leverage. It’s structured, high-intent, and outcome-driven. And thanks to AI, what used to be unstructured and inaccessible is now available in real time to optimise campaigns, personalise experiences, and improve conversion rates.

The future of marketing belongs to those who can fuel AI with the best data. For too long, calls have been the blind spot in attribution and optimisation. That’s changing fast. The marketers who embrace call data today will be the ones outperforming their competition tomorrow.

About Infinity: 

Since 2011, Infinity has been helping marketing, sales, and customer service teams make smarter business decisions and improve experiences with call analytics.

Its award-winning call tracking and speech analytics tools are used by enterprises across the world to optimise marketing campaigns, improve sales performance, streamline contact centre operations, and create better experiences for their customers.

For more information, please visit www.infinity.co.

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