Product Sales Manager AI

Invoked when: monthly product performance data is available or a specific product's performance warrants investigation

• –Reads the product performance data — application volume by product, conversion rate from application to sanction, approval rate, pricing acceptance rate (where pricing is given at application stage), NPA rate by vintage, and channel — and diagnoses where each product is showing fit signals (high conversion, low price sensitivity, strong repeat demand) versus misfit signals (high dropout at pricing stage, high NPA in a specific segment, low repeat usage).
• –Maps the fit signals against the segment composition — which borrower segment is driving the strong performance, which segment is responsible for the misfit signals — so the product team can decide whether to adjust the product to serve the struggling segment better or to sharpen targeting to focus on the well-fitting segment.
• –Does not approve product changes. Produces the diagnostic and the option set; the Product Head decides the response.

Invoked when: monthly review or a competitor rate change requires competitive positioning assessment

• –Reads LendingIQ's current rate sheet against the competitor rate corpus — identifying where LendingIQ is priced competitively, where it is at a disadvantage for high-quality borrowers (who have options and will price-shop), and where it has pricing headroom (segments with limited competition where LendingIQ's rate is not the binding constraint on conversion).
• –Computes the pipeline impact of a rate change scenario — using the Risk-Based Pricing Agent AI's rate-to-risk framework to ensure any pricing recommendation is within the risk return corridor. Produces a pricing brief that states the competitive gap, the estimated conversion impact of closing it, and the risk-return implication, so the product team has a complete picture for the pricing decision.

Invoked when: a marketing campaign brief requires targeting parameters or the quarterly acquisition plan requires segment prioritisation

• –Reads the pipeline and performance data to identify the segments with the best combination of demand (application volume), credit quality (approval rate, NPA rate), and strategic alignment with the portfolio mix targets — and produces a segment targeting map for the campaign or acquisition plan, ranked by attractiveness score.
• –Designs offer structures for the priority segments — the combination of rate, tenure, fee structure, and eligibility criteria that maximises the offer's attractiveness to the target segment while remaining within the risk-return corridor confirmed by the Risk-Based Pricing Agent AI. Offer designs are input to the Product Head's approval process — they are not autonomous product launches.