Dollar in, dollars out: The over-simplification of ROI

The first time I had an espresso martini I felt like a poser.

Not because I was surrounded by advertising types far cooler than me. Though I was. But because it was at a Twitter Christmas party which I’d been invited to as a client from a high growth startup, which had gone bust a month earlier. The good folks at Twitter didn’t pull the invites, but it made the name badge situation more awkward. 

Twitter at that time, like Google and Facebook, felt like magic to me. Put this many dollars in, get this many results out, and scale as much as possible. Brilliant. My colleagues and I would stress over CPAs and LTVs and CACs and there’d be long arguments about attribution models, brand vs performance budgets, and ROI. You could, with a straight face, say things like “If we look at last click data PoP, the ROI based on current CPAs looks down because our CAC is rising, but LTV is actually up…” and people would nod their heads gravely. Good times.

The promise and simplicity of spend more on a channel, get this result was very compelling. Largely because it gave marketing hard numbers to send back to finance to justify cost, with return, and forecast more scale for more budget. Never mind that those compelling promises were from platforms that simultaneously sold the ads, the inventory and the black-box measurement touting their own efficacy…

The apparent data transparency from digital has created a lasting expectation that ROI in modern marketing is easy to figure out because everything is measurable. Most marketers I speak to, or whose work I follow, understand the real-world complexity. They get that return on this quarter, or this year’s investment will not be fully ‘returned’ this quarter or this year, or even next year, that different channels come with different measures, that much of marketing is probabilistic rather than deterministic, as Rory Sutherland reminds us.

Subsequently, a lot of folks a lot smarter than me are fed up with basic ROI as a metric. I get it. But does your Finance Business Partner, or CFO, or CEO? Or have they come up through the last-click, demand-harvesting, PPC era where dollar in = dollars out?

AI creates yet more promise, because in theory it has the capacity to move everyone closer to the data, get questions answered faster, with more specific context. But even if your AI platform can access and make sense of your marketing data (…that’s a whole other post), if you ask it the wrong question it will still answer using the framework you give it, and you may well then pay that answer forward.

If you're chasing a closed loop concept of money spent - measurable data touch points - money returned, you’ll always be chasing. Marketing is a growth engine for the enterprise. Where you're forecasting market share growth, product sales growth, revenue growth, those are all outcomes where marketing is having an influence. And for each there will be measurement concepts that everyone at the table needs to have an understanding of in order to properly articulate marketing's value creation.

Some of that includes direct return from advertising, but some of it will be the result of years of good work creating fame which compounds over time, and creates more opportunity for potential upside as it compounds. Using the language of finance alone without the deeper work of a shared understanding of how value is being created over time leaves you at risk of feeling much like I did, in East London at a tech platform party drinking espresso martinis… like a poser.

If you want to get specific on how you’re measuring and demonstrating your marketing impact, get in touch, I’d love to help.

Simon Jared is a Co-Founder of Latent and directly manages all Build Operations engagements.

The legacy vendor AI repositioning problem

There is a particular kind of pressure that doesn't get talked about in deep tech marketing conversations, because the companies feeling it most acutely are the ones least likely to admit it publicly. 

They've been in market for twenty, thirty years. They have real customers, real revenue, and a reputation earned the hard way across generations of product cycles. And somewhere in the last two years, the conversation shifted. The buyers they've been selling to are now asking a different set of questions. The competitors they've spent years outmaneuvering have repositioned as AI companies. The analysts who used to write about them in predictable ways are starting to frame the category differently. 

The problem isn't that these companies don't have an AI story. Most of them do. But like everyone else who stamped AI on their product, it sounds like everyone else's.

There's a version of AI repositioning that almost every legacy vendor defaults to. They take the existing product description, add the words 'AI-powered' or 'AI-enabled', update the website headline, and brief the PR team to find an AI angle for the next product launch. It ticks the box. It does not move the market. I’ll admit I’ve been asked ‘Can you make this more AI-washed?’ and the best answer in my experience tends to be ‘no’. 

The reason it doesn't work is that technically sophisticated buyers; the CTOs and architects and procurement leads at the OEMs and hyperscalers and enterprise customers that these companies are trying to reach have seen this exact skulduggery from fifteen different vendors in the past eighteen months. They know what it looks like, they have a filter for it and when your repositioning looks like wallpaper, you don't just fail to gain credibility — you actively lose it, because the attempt signals that you don't have a story worth telling. 

What Arm went through is a useful reference point. The business had built its position over three decades on a clear, defensible model: IP licensing to chip designers who built the hardware that everyone else then built on top of. That model was well understood. The relationships built around it were real and deep. And then the AI infrastructure buildout created a moment where the architecture question — what compute is the world's most demanding AI workloads going to run on — was genuinely up for grabs. 

The challenge wasn't technical. Arm's position in AI infrastructure was already strong. NVIDIA runs on Arm. AWS, Google, Microsoft, and Meta were all building custom silicon on Arm Neoverse. The trajectory was there, but the challenge was commercial: how do you reposition as a company at the center of the AI infrastructure story without disrupting the licensing relationships that built the business, and without making claims that your ecosystem of partners will immediately fact-check and find wanting? 

The answer wasn't a campaign. The proof points that were specific, verifiable and credible to a technical audience. The broader narrative came second, built around those proof points rather than built in advance of them. And the message was calibrated differently for each audience: for the investor, the story was about where AI compute was concentrating and why Arm's architecture was structurally advantaged; for the OEM and the chip designer, the story was about what specifically had changed in the tools, the ecosystem, and the programme support available to them. 

That sequencing, with proof before narrative, and different narrative for each seat is what most legacy vendor AI repositioning gets wrong. The instinct is to announce the position before the proof is assembled, because the competitive pressure feels urgent and the board wants to see something in market. The result is a claim without substance reaching the exact audience most skilled at identifying claims without substance. 

The other failure mode is narrowing too early. Legacy vendors often try to claim AI relevance in the specific product category they already occupy, because that's the safest ground. What they miss is that the more interesting and defensible AI story is often structural — about where they sit in the supply chain, what they enable that nothing else can, and why the AI transition makes that position more valuable rather than less. That's a harder argument to make, but it's the one that lands with the buyers and investors who are trying to understand what the next five years look like. 

The companies getting this right are the ones who've accepted that AI repositioning is not a marketing exercise. It's a commercial strategy question that marketing then has to express. The sequence is: understand where you actually sit in the AI value chain, build the proof that substantiates that position, then build the narrative. In that order. Reversed, it produces exactly the kind of repositioning the market has learned to ignore. 

Dale Kaszycki is a Co-Founder of Latent