Measure what your marketing actually caused.

Ad platforms grade their own homework. They count every conversion that so much as touched an ad, including the sales that would have happened anyway, and report it back as proof the ad worked. We measure incrementality instead: only the sales that genuinely would not have occurred without the ad. That gap is not small. It routinely runs 40-60% of platform-reported numbers, and it is exactly where wasted and misallocated budget hides. Two channels can show an identical reported ROAS while one is driving real growth and the other is quietly taking credit for demand you already had. The baseline is the counterfactual, what your sales would have been without the ad, and incrementality is simply the gap between what actually happened and that counterfactual. Once you can see the difference, you stop paying for credit your platforms take but did not earn.

You see two kinds of results on two different clocks. Within the first few weeks the always-on model reads your historical spend and sales and surfaces your first reallocation opportunities, the channels that look over or under-funded once incrementality is accounted for. The first experiment-proven savings land in roughly seven weeks, one full geo-lift cycle, because a clean causal read needs time to play out in-market rather than being asserted from a dashboard on day one. After that the system compounds: the model stays live and recalibrates with every new experiment, so the numbers only get sharper and the recommendations more confident over time. You are not buying a one-off audit, you are standing up a measurement capability that keeps paying back.

Less than most teams expect. The core is just your ad spend and your sales data. Read-only access to your ad accounts plus a regular sales export is usually enough to begin, and read-only means we can never touch or change your live campaigns. There are no tags to install, no SDKs to embed, and no engineering lift on your side, which is what lets brands start measuring in weeks rather than after a quarter-long implementation. From there our team handles experiment design, modelling and analysis end to end, so the burden stays on us and your people stay focused on growth rather than plumbing.

No, and this is the objection we hear most, so it is worth being precise. Every test is designed to keep business impact negligible. We vary spend on a small, carefully chosen slice of the business, often 10-15%, usually at a geo level, so the vast majority of your market is never touched and keeps running exactly as it does today. Before anything goes live we pre-test for statistical power, which means we confirm the design can actually produce a clean, readable result rather than discovering after the fact that it could not. The brief, contained trade-off during a test is consistently dwarfed by the budget you redirect once you know what is truly working. In practice a well-run experiment pays for itself many times over.

A tool hands you a dashboard and leaves you to interpret it, which turns your team into part-time measurement scientists. An agency hands you opinions, often without rigorous causal proof underneath them. TrueLift is deliberately built as advisory + platform, so you get both halves that are usually sold separately. The platform is an always-on model, calibrated by real geo-lift experiments rather than assumptions, so its outputs are anchored to ground truth. The advisory is senior, ex-Meta and Amazon measurement specialists who design the experiments, read the results and translate them into board-ready decisions, sitting with you rather than emailing a PDF. You get the rigour of a dedicated measurement-science team with the speed and consistency of software, and you are never left alone to figure out what a number means.

We price so that you see the value before you commit to it. You can start fully performance-based: zero upfront, and you pay a share of the savings we actually prove, with most of it due only after results land rather than in advance. That deliberately puts the risk on us, because we only win when you win. If you would rather run measurement in-house at scale, we also offer an annual platform license with dedicated advisory hours included, priced by data volume. Either path, there is no long procurement cycle to sit through before you learn anything, and the commercial model is designed to reward proven impact, not just activity.

Your data stays yours, and we ask for as little of it as possible. Access to your ad accounts is read-only, so we can measure but never alter or spend against your campaigns, and the core inputs are simply spend and sales rather than anything personally identifiable about your customers. On the annual license, measurement runs against your own data warehouse, so sensitive data does not have to leave your environment at all. We treat access as least-privilege by default: we take only what a given piece of analysis genuinely needs, and nothing more. If your security or legal team has specific requirements, our team works through them directly during onboarding rather than pointing you at a form.