Every £ Twice: How much are brands wasting on duplicated reach?

Partner content with AudienceProject

At The Media Leader’s Future of Brands 2026 event, Bruno Furnari, CPTO at AudienceProject, will explore what overlapping reach is costing media budgets – and how to fix it.  The Media Leader spoke with him ahead of his session to learn more. 

Your session is titled “Every £ Twice” — can you give us a sense of the scale of the problem? How much budget are brands typically wasting on duplicated reach?

It varies by campaign, but the waste is usually significant and invisible. Channels planned and measured in isolation hide overlap. The same person gets reached across linear TV, online video, social and streaming. Unless you measure that duplication, it looks like productive reach when it’s repeated exposure. Stating the obvious: this is a structural problem, not a planning failure.

Once you measure reach and frequency across channels, you can separate useful frequency from accidental waste.

Measurement has long been siloed by channel. Why has the industry been so slow to fix this, and what has changed in 2026 that makes solving it more urgent?

I’d push back on the premise. Siloed measurement isn’t new. What’s changed is the cost of not solving it. Media plans are genuinely cross-screen now. Audiences move between linear, BVOD, YouTube, social, retail media and streaming. Budgets are under more scrutiny. AI is compressing activation cycles. That combination raises the stakes.

If buying decisions move faster but measurement stays siloed, you optimise efficiently in the wrong direction. In 2026, cross-media measurement isn’t an insight layer; it’s more akin to infrastructure for defensible decisions.

You outline four key approaches to cross-media measurement. Without giving too much away, where do most brands currently sit, and what is the most common mistake you see?

Most brands have a partial view. They’ll have platform reporting, or channel-level reports from a single streamer or supply source. Those pieces rarely come together into a single view of reach, frequency, and overlap. The common mistake is trusting platform-reported numbers as if they’re already deduplicated.

Each platform reports the reach it delivered; they can’t see into what they can’t measure. Add them up, and you overcount. Pick one, and you undercount. Either way, the number you’re optimising against doesn’t reflect reality. Cross-media measurement starts when you stop treating any single source as the answer.

You advocate for a hybrid model combining panel data, big data and AI. What does each bring to the table, and why is none of them sufficient on its own?

Each solves a different part of the problem. Panels provide ground truth. They’re representative and controlled, which makes them essential for calibration, audience profiling and validation. Big data brings scale, granularity and speed. It captures exposure signals across platforms and environments, often close to real time. AI is useful operationally: harmonising messy datasets, standardising schemas, flagging anomalies, and speeding up QA. That’s different from asking a model to invent an answer.

None of these works alone. Panels without scale miss too much delivery. Big data without calibration drifts from reality. AI without good inputs accelerates bad decisions. The value is in combining them so the system is scalable and accountable.

The concept of the “next pound spent” is central to your session. How should a brand be thinking about marginal reach, and at what point does additional spend stop delivering anything meaningful?

Brands should ask what the next pound actually buys. Early in a campaign, additional spending buys a lot of incremental reach. As the campaign builds, each extra pound does less work. Still buying impressions, but increasingly against people already reached elsewhere in the plan. That’s where the question changes. It’s no longer “is this channel performing?” (most channels perform in the right context).

The better question: is the next pound buying incremental reach, or adding frequency and duplication? Cross-media reach curves show this across the whole portfolio, not channel by channel. They show where additional investment creates new reach, and where reallocation beats adding more weight.

Real-world examples tend to land harder than theory. Can you share one finding from your work that genuinely surprised you, or that changed how a client approached their media planning?

One example I’ll talk about is how cross-media measurement makes marketing mix modelling more actionable. MMM is useful for broad ROI, but it doesn’t answer a practical planning question: which channels are reaching unique audiences, and which are duplicating people already reached elsewhere?

Adding deduplicated reach and frequency shifts you from performance reporting to planning. In one case, the client used it to support meaningful shifts in platform budgets because the data showed where video investment was incremental and where it overlapped.

That’s the point: cross-media measurement doesn’t just produce a cleaner report. It shifts the question from “what performed last quarter?” to “where should the next pound go?”

What is the one thing you want someone to walk away from your session ready to do differently?

Don’t wait for a perfect cross-media view before you act. Pick one high-spend campaign. Define the business question. Measure reach, frequency and overlap across channels. Find where spend is adding new reach and where it’s duplicating audiences. Adjust the current campaign where you can, or make the next one smarter.

You don’t need a perfect view of every exposure, but you need enough evidence to act.

You can see Bruno’s session at The Media Leader’s The Future of Brands London 2026. 

Bruno Furnari is CPTO at AudienceProject