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 approval, approval rate, pricing acceptance rate (where pricing is given at application stage), NPL / charge-off 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 NPL / charge-off 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, NPL / charge-off 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.