Last-click attribution is a relic. We deploy probabilistic, multi-touch models that reveal what your TV and Audio dollars are actually doing to your bottom line.
Request an Attribution AuditAttribution asks which ad touched a customer before they bought. Incrementality asks whether the sale would have happened without the ad at all. Attribution assigns credit after the fact; incrementality modeling measures cause. The distinction matters because a campaign can "win" attribution — touching thousands of buyers — while producing almost no incremental sales, quietly taxing customers you already owned.
Our entire measurement practice is built on incrementality: Marketing Mix Modeling calibrated to your actual sales data, isolating what each channel adds beyond your organic baseline. And when you're ready to choose a measurement method, our comparison guide covers exactly that: MMM vs. Incrementality Testing: What's the Difference?
Every serious approach to ad measurement falls into one of three frameworks — and knowing which question each one answers is half the battle.
Uses pixels and cookies to track users across touchpoints. Cheap and fast for daily lower-funnel optimizations — but vulnerable to privacy updates, blind to upper-funnel channels like CTV, and biased toward bottom-funnel touchpoints.
Scientific A/B testing that holds out ads in matched markets to measure explicit causal lift. Definitive, but resource-intensive — it requires pausing campaigns in holdout regions and answers one question per test.
Privacy-safe statistical modeling linking historical aggregate spend to revenue. Built for strategic budget allocation across every channel — including the ones that never produce a click.
Here's the math that exposes it: Meta claims 1,000 sales. Google claims 800. Your CRM shows 1,200 total. The platforms are collectively claiming 50% more conversions than actually exist, because each one takes full credit for any sale it touched — including customers who were already on their way to you.
Last-click makes this worse by handing 100% of the credit to the final touchpoint — usually a branded search — while assigning zero value to the CTV, TV, and audio exposure that created the demand in the first place. The result is systematic: budgets drain away from the channels that cause sales and pile into the channels that collect them. Your ad platforms aren't lying, exactly — they're just grading their own homework.
Incrementality modeling (sometimes called incremental attribution) is the practice of measuring how many sales your advertising caused that would not have happened otherwise. Instead of asking "which ad touched this buyer?", it establishes your organic baseline — what you would have sold with no ads at all — and measures each channel's lift above it.
In practice that means Marketing Mix Modeling calibrated to 12–24 months of your sales data, validated where possible with geo-lift experiments, and refreshed daily so budget decisions keep pace with your campaigns. It's HIPAA-compliant and cookie-proof by design, because it never touches user-level data — which is why regulated categories like telehealth rely on it.
TV and audio campaigns don't generate clicks — they generate awareness, trust, and demand that converts days or weeks later, often attributed by your reporting tools to a paid search ad. The Halo Effect is the invisible multiplier your current attribution stack is missing.
Our model learns a baseline from your historical sales data — stripping out seasonality, organic trends, and external noise. It then layers in the expected incremental effects of each paid media channel to isolate the true causal impact on your Amazon and retail sales, independent of the last ad clicked.
The model ingests 12–24 months of sales history to build a statistically robust organic baseline, separating genuine media lift from trend and seasonality.
Each channel's ad spend is modeled with adstock decay curves — capturing the carry-over effect of brand exposure across days and weeks after a campaign flight ends.
We measure incremental unit velocity, organic keyword rank lifts, and search share gains on Amazon as direct downstream effects of your broadcast investment.
Understand how TV primes paid social and search. Reveal the compounding multiplier effect where one dollar of TV spend generates $0.40–$0.80 of incremental digital lift.
Our three-stage Prescient AI pipeline transforms raw spend data into a forward-looking decision engine that tells you where to put tomorrow's budget — today.
Sales history ingested. Organic trend, seasonality, and external variables isolated. Clean signal established.
TV, audio, social, and search spend mapped with channel-specific adstock decay and saturation curves.
The model outputs daily ROAS forecasts, budget allocation recommendations, and channel saturation alerts.
Prescient AI's decision engine achieves 92% prediction accuracy in forecasting next-period revenue outcomes based on proposed spend allocations — validated against held-out actuals across hundreds of brand campaigns.
Brands that optimize spend based on model recommendations see an average 20–30% improvement in ROAS and CAC within the first 90 days — not by spending more, but by reallocating existing budget away from saturated channels and into under-invested ones.
Every attribution engagement we run is built on three foundational capabilities that turn measurement from a reporting exercise into a revenue growth lever.
TV and audio don't generate last-click conversions — but they generate demand. We unify these signals into a single attribution model that credits every channel for its true contribution to retail and Amazon sales velocity.
We move you from "wait-and-see" quarterly planning to daily optimization loops. With 92% prediction accuracy, the model tells you today where to shift tomorrow's budget before wasted spend compounds into a missed quarter.
The model outputs a clear saturation curve for every channel. Know exactly which channels still have room to grow — and which have hit diminishing returns — so you can reallocate budget with confidence and a financial model behind every decision.
MMM is a privacy-safe statistical method that links your historical aggregate ad spend to actual revenue, channel by channel. It requires no pixels, cookies, or user-level tracking — which makes it robust against iOS privacy changes and compliant for regulated categories like healthcare.
Last-click hands 100% of the credit to the final touchpoint — usually branded search — and assigns zero value to the TV, audio, and OOH exposure that created the demand. MMM measures each channel's true incremental contribution, including the conversions that never involved a click.
The Halo Effect is the measurable lift that upper-funnel channels create downstream: more branded searches, higher direct traffic, stronger Amazon and retail sales — all occurring after TV or audio exposure without a click. Our methodology isolates this signal from your organic baseline.
Traditional MMM was a quarterly consulting exercise. The platform we use, powered by Prescient AI, refreshes daily — so budget reallocation decisions happen at the speed of your campaigns, not your fiscal calendar.
Request a complimentary attribution audit. We'll run a diagnostic on your current measurement setup and show you exactly where the gaps are costing you growth.
Request an Attribution Audit Explore All ServicesTell us about your brand and current measurement setup. We'll run a complimentary diagnostic and show you exactly where your attribution model is leaving money on the table.