@sanjayuppal2
Sanjay UppalFounder▲5
Founder & CEO, FinbotsAI
1d ago · edited · New project · Agents · Singapore

Shipped: TargetX | More conversions. Fewer contacts. Smarter growth.

What if you could increase conversions 3–8× while contacting significantly fewer customers ?
Most institutions still rely on broad propensity models developed over months and reused across products, campaigns and customer segments.

The result ? Too many irrelevant offers, wasted marketing spend, declining customer engagement and missed revenue.
TargetX changes the economics of customer targeting.

It industrializes propensity modelling, enabling institutions to target every campaign with precision - improving conversion, reducing wasted outreach and protecting customer engagement.

Traditional propensity modelling takes months, limiting institutions to a handful of broad models. TargetX reduces this to days, making offer-specific, continuously refreshed customer intelligence practical at enterprise scale.

The impact goes beyond conversion:
✅ 3–8× higher conversion - more customers responding to relevant offers.

✅ ~60% less wasted outreach - fewer irrelevant contacts and better marketing efficiency.

✅ 3× ROI - stronger returns from marketing investments.

✅ Models in hours & days, not months - continuous optimization as customer behavior changes.

All with explainable, traceable and governed AI models.

TargetX is part of FinbotsAI's Decision Control Intelligence platform, enabling enterprises to industrialize predictive decisions at scale.

Strategic partners: NEC | Worldline | Oracle | IBM

targetX : TargetX : More response. Less spam. Smarter growth. logotargetX : TargetX : More response. Less spam. Smarter growth.@sanjayuppal/targetx · finbots.ai/targetxConvert 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.
Customer AcquisitionCustomer RetentionCross SellCustomer Lifetime ValueRevenue GrowthNext Best Action

Comments2

  • @ryanallis
    Ryan Allis
    Founder of AI Lounge and SaasRise, helping founders reach $100M+ exits
    1d ago

    Building iContact taught me that every irrelevant email costs more than the send, because people who keep getting offers that don't fit them stop opening and start unsubscribing. If TargetX can show institutions that the ~60% cut in wasted outreach also means fewer customers opting out, that could be a stronger pitch to your beta users than the conversion lift alone.

  • @sanjayuppal
    Sanjay Uppal
    Decision Control Intelligence™ : Industrializing Enterprise AI at a Fraction of Today's Time & Cost
    1h ago

    Thanks Ryan. Irrelevant outreach has a compounding cost - wasted spend today & declining customer engagement tomorrow. That's the challenge TargetX helps enterprises overcome: from broad, repetitive targeting to offer-specific intelligence that continuously identifies who to engage, with what, and when. The opportunity is lower spam & higher conversion - delivering more revenue from fewer customer interactions, while protecting long-term customer engagement.

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