❓ Problem

Every business that spends money on ads is trying to answer one deceptively simple question: which of these actually made me money? For a decade the honest answer was "the pixel will tell us." Then the pixel stopped telling us. Apple's App Tracking Transparency turned tracking into an opt-in that almost nobody opts into, Google spent years phasing third-party cookies out of Chrome, and the neat little user-level trail from "saw the ad" to "bought the thing" quietly went dark.

The numbers on how badly this is going are grim. 78% of marketing leaders say their attribution data no longer matches their revenue reports (Forrester, 2025), and 64% of attribution implementations fail to reflect reality at all (Gartner, 2025). People are paying for dashboards that confidently report a number, and the number is fiction.

Here is the real kicker. This is not just a small-business problem solved by buying better software. Cal AI, doing a $50M ARR run-rate before MyFitnessPal bought it, measured its influencer deals by eyeballing revenue spikes, slapping made-up multiples on promo codes, and manually switching channels off to see what happened. Founder Zach Yadegari, who sold the company at 18, one of the sharpest paid-acquisition operators around, described his own method for a $500k YouTube sponsorship as counting link clicks, guessing multiples, and watching for spikes. In his words, "it's just more subjective than anything." When the person best in the world at this is doing it on a napkin, the tools have not just gotten worse, they have stopped existing for the channels that matter most.

✅ Solution

A productised attribution engine that automates the gritty manual detective work every growth team already does by hand, and does it across the channels the dashboards genuinely cannot see.

• Connect everything once. Plug in your ad accounts, your sales and revenue data, your promo codes, your influencer and podcast campaigns. No pixel required, because the pixel is the thing that broke.
• It runs the workflow a growth analyst runs, at machine speed. It spots revenue spikes and ties them to what went live that week, orchestrates on/off channel tests so you are not doing it in your head, and cross-checks every signal (spend, clicks, promo redemptions, spike timing, geo) until the estimates converge on an answer it can defend.
• It measures the dark channels on purpose. Influencer posts, podcast reads, UGC, that $500k creator integration. The stuff that never shows up in an ad platform dashboard but very much moves revenue. If it moved money, it gets measured.
• It hands you the answer in plain English, weekly. "Your influencer spend returned roughly 2.1x, your TikTok prospecting lost money this week, move about £8k from the second into the first." Not a 40-tab model. A recommendation you can act on before Monday's standup.

The wedge is narrow on purpose: nail influencer and creator attribution, the single ugliest measurement gap, for the growth-stage consumer companies feeling it most. The engine underneath (multi-signal triangulation plus automated incrementality testing) then expands to every paid channel a business runs.

📊 Key Numbers

Market size

• Global digital ad spend is set to cross $1 trillion in 2026, and every dollar of it is spent by someone who wants to know if it worked. That is the ultimate TAM: the whole point of the spend is the question this tool answers.
• The channel nobody can currently measure is also the fastest-growing one. Creator and influencer advertising hit $37bn in 2025 and is projected at roughly $44bn in 2026, growing faster than the broader ad market. This is the dark-channel wedge, tens of billions of spend with no native attribution layer.
• The direct comparable, the marketing attribution software market, sits at around $5.4bn in 2026 and is forecast to roughly double to $10bn+ by 2030 at a ~13.6% CAGR. Crucially, multi-source attribution is already the largest segment at ~48%, and SMEs are the fastest-growing buyers at ~14.5% CAGR, which is exactly the "not an enterprise, still spending millions" customer this targets.

ARR potential

• Top-down: capturing just 1% of the $5.4bn attribution software market is a ~$54M revenue business. You do not need to displace Google, you need a sliver of the growing multi-source slice.
• Bottom-up: the sweet spot is consumer, DTC and app companies spending £1M to £50M a year on ads and running real creator budgets. Price it as SaaS scaled to spend, call it £500 to £5,000 a month. 3,000 customers at an average ~£1,500/mo is ~£54M ARR. Land 5,000 and you are knocking on £90M.
• Realistic path to £15-30M ARR within a few years as a focused creator-attribution product, with a nine-figure ceiling if it becomes the default measurement rail for the dark channels.