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TargetX : More response. Less spam. Smarter growth.

@sanjayuppalby Sanjay Uppal · FinbotsAI

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Convert more customers while dramatically reducing campaign waste. Traditional propensity models can take 3–6 months to develop, so institutions typically build a limited number of broad models around product, segment or campaign categories and reuse them across campaigns – producing coarse targeting, low response rates and excessive outreach. TargetX changes the economics of propensity modelling. Models can be developed and refreshed in days, making it practical to create campaign-level models tailored to each offer, customer population and objective. Instead of asking broadly "who is likely to buy this product?", institutions can continuously model "who is most likely to respond to this offer, through this campaign, now?" The result: tighter targeting, fewer irrelevant contacts, higher conversion and faster optimization – all explainable, traceable and controlled, delivering 3–8× conversion lift, 3×+ ROI and ~60% lower waste. Our partners: NEC, Worldline, Oracle and IBM.

Built withPythonJavaC++JavaScript/TypeScriptML AlgorithmsFastAPIFlaskSpring BootCustomer AcquisitionCustomer RetentionCross SellCustomer Lifetime ValueRevenue GrowthNext Best Action

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