Every layer of revenue intelligence mapped to India-specific providers. MTA, MMM, and incrementality broken out as individual components. Each description is taken from the provider's own site, checked October 2026.
“Which touchpoints in this customer's journey drove the conversion?”
Requires CDP-grade identity resolution. Works at individual user level. Real-time.
“How should I allocate total budget across channels?”
Statistical model on aggregate data. Doesn't need user-level tracking. Survives iOS signal loss. Weekly/monthly refresh.
“Would this conversion have happened anyway?”
Holdout experiments, geo-lift, causal inference. Used to estimate how much of a result ads caused compared with organic demand.
Most Indian D2C brands are still on platform-reported last-click attribution. The brands that layer MTA + MMM + incrementality will reallocate 15–30% of spend to higher-ROI channels.
As described on our product pages: the ad spend decision layer. Evidence reconciled against actual orders, ranked recommendations from AdBuyer, human approval, and an outcome review. AdBuyer has no write access to ad accounts.
AI-first ad analysis, category-level benchmarking and actionable spend intelligence.
An advertiser CDP for data harmonization, profile unification, audience segmentation, customer journey mapping and omnichannel activation.
Connects causal MMM, geo experiments and incrementality-adjusted attribution with planning workflows and AI agents.
First-party data activation across audiences, events, analytics and agents.
See how evidence becomes a recommendation, how your team stays in control and how outcomes are reviewed.