Marketing Intelligence & Optimization

MARKETING INTELLIGENCE

Find out which channels actually drive sales, what to bundle, and whether your test result is real.

Marketing budgets get allocated on last-click reports, agency decks and gut feel. This connects your spend data to your sales data and returns the answer in dollars: what each channel genuinely contributes, where the next dollar should go, and which results are signal rather than noise.

Runs on CSV or spreadsheet exports from your ad platforms, POS or e-commerce store. No data warehouse, no analytics implementation, no agency retainer.

WHAT IT ANSWERS

  Which channels actually work. Last-click gives all the credit to whoever spoke last, which is usually branded search — the channel that was going to convert anyway. This measures each channel's real incremental contribution to sales, accounting for the fact that spend keeps working for weeks after you spend it, and that every channel stops paying back once it's saturated.

Where the next dollar should go. Budget reallocation recommendations with the projected return attached. "Meta is over-invested past the point of diminishing returns. Move $24,000 to email for a projected 14% lift."

What sells together. Real purchase affinities from your transaction history, ranked by strength, so bundles and cross-sell come from evidence rather than merchandiser instinct.

Whether a test result is real. Significance, confidence interval and — the part most tools skip — statistical power. It will tell you a test is under-powered and that you're about to call a winner that isn't one.

How a new product will ramp. Adoption curves fitted from early sales, so launch planning and inventory get a shape rather than a guess.

FOR THE C-SUITE — CEO, CFO, CMO

The problem: marketing is the largest line item nobody can defend. When the CFO asks what the spend returned, the answer is platform-reported ROAS — numbers supplied by the same companies being paid, each claiming the same conversion.

You get one independent read on contribution by channel, built from your own sales data rather than platform self-reporting. Budget shifts arrive with a projected dollar return, so reallocation becomes a decision with a number attached instead of an argument between agency and team.

And the numbers are honest about their limits. Every figure ships with a confidence range, and channel contribution is framed as directional evidence rather than proof of causation. Anyone selling you certainty about marketing attribution is selling you something. A range you can trust twice is worth more than a point estimate you believe once.

FOR VPs — MARKETING, GROWTH, ECOMMERCE

The problem: you already know last-click is lying to you. You just have no affordable alternative, because proper attribution modelling has lived with agencies and enterprise analytics platforms priced far above mid-market. So budget gets defended with vanity metrics, tests get stopped the moment they look good, and bundles get chosen by whoever merchandises the category.

Attribution you own. Contribution and return by channel, recomputed as spend and sales evolve, with the trace behind every number — not a black box, not a quarterly agency deck.

Test discipline built in. Power calculated before you launch, guardrails against calling results early, and an explicit verdict on whether a difference is real. The most expensive mistake in growth marketing is shipping a losing variant because a test looked good on day three.

Bundles from evidence. Affinities computed across your real basket history, with weak or coincidental pairings filtered out rather than presented as insight.

Launch forecasting. Early sales fitted to an adoption curve so you can plan spend and stock against a projected ramp.

FOR AGENT BUILDERS — DEVELOPERS, ISVs, AGENCIES

The problem: attribution modelling is genuinely hard, and it fails silently. Correlated channels, carryover effects and diminishing returns all quietly wreck a naive approach, and the output still looks plausible. An LLM asked to do this returns a confident, wrong answer, and a different one if you ask again.

Seven deterministic MCP tools. Same input, same model version, same answer, every time — structured for an agent to consume rather than a human to interpret.

Production-hardened modelling. Instrumented against the specific ways this class of analysis degrades on thin, noisy or highly correlated data, and it refuses with a reason code rather than returning a confident number your client acts on and regrets.

Everything versioned and explained. Model version, training window and full reasoning trace on every response, so an attribution claim made this quarter is still defensible next year.

Confidence intervals as first-class output, plus explicit directional-not-causal framing, so an agent built on this doesn't overclaim on your behalf.

The seven tools cover channel contribution and return, budget reallocation, product affinity discovery, bundle recommendation, A/B evaluation with power, new-product adoption forecasting, and explaining any attribution result.

PRACTICALITIES

Works from CSV and spreadsheet exports first, with ad-platform, store and CRM connectors as they mature. Full analysis returns in under two minutes at any data size. SOC-compliant infrastructure, with consent capture, retention policy and delete-on-request built in for GDPR and DPDP-style requirements — relevant here because transaction data carries personal data, and basket analysis runs on anonymised inputs.

Not an analytics platform, not a tag manager, not an ad buyer. It doesn't track users, place pixels or spend your budget. It reads what you spent and what you sold, and tells you what that relationship is actually worth.

Free while in early access. Full product, no card, no feature gating, with advance notice before pricing arrives.