An AI-driven attribution and audience engine that feeds the ad networks a cleaner, customer-aware signal — so their machine-learning bidding optimises for high-value trade acquisition rather than raw conversion volume.
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Every customer is classified by purchase behaviour and account type — trade purchase, retail purchase, trade sample, retail sample, plus lapsed segments for each. The segmentation logic learns from live order data daily and re-scores customers as their behaviour shifts.
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Each purchase fires a conversion to the ad networks with the customer type and true order value attached. A high-value trade order arrives tagged as exactly that, not an undifferentiated 'Purchase' — giving the platforms' bidding AI the right thing to chase.
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Each segment seeds the ad networks' lookalike AI so prospects resemble the client's actual trade buyers, not their retail base. Existing trade and retail account holders are suppressed from acquisition campaigns so budget goes to net-new buyers rather than people already on the books.
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Abandoned baskets and sample orders are retargeted by funnel stage, lapsed trade buyers see add-on product ads timed to their predicted buying window, email audiences are suppressed during send windows so the client never pays twice, and trade account sign-ups are tracked as a secondary conversion goal the AI can optimise against.
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