Fractional/Contract Product Leader | Fintech, Lending, LOS/LMS, NBFC Integrations | ex-Khatabook, Progcap | IITB

Saket Bhardwaj  • Peerlist

Role: Senior Product Manager, Demand Generation &amp; Purchase — Ola Electric, Mar 2022–Oct 2022. Scope: 100% digital sales — ₹160 Cr+ monthly GMV, 1M+ monthly visits.The problem: Expensive, high-intent buyers arrived from marketing but leaked out at every stage of a fully digital purchase journey — from a landing page that didn't answer their real questions to a checkout that quietly blocked them at payment. The job: find and close the drop-off at each step, without diluting intent quality.The framework — two fronts: close the information gap at the top, strip friction through the funnel.Front 1 — Top of funnel: make the site answer the buyer's actual questions. Sessions were short and buyers bounced. Digging in revealed information asymmetry — a gap between what buyers wanted to know and what the site showed. Three gaps, each fixed:<ul><li>Unrelatable brand language: the hero was a woman in racing gear — aspirational, but irrelevant to someone buying a scooter for the office commute or city errands. Reworked the brand language to speak to everyday, practical use.</li><li>- Irrelevant information: the first scroll was dense technical specs buyers couldn't parse. Simplified terminology and translated specs into human terms (e.g. trunk space shown as the volume of groceries it holds) and cut specs that didn't aid the decision.</li><li>- Missing information: buyers wanted running costs, maintenance, and savings vs a petrol (ICE) scooter — surfaced from support calls and queries. Built an interactive ICE-vs-Ola running-cost comparison driven by average daily km, so buyers could see their own savings.</li><li>Led the full landing-page redesign to close all three gaps, driving +150% session time.</li></ul>SEO: despite being India's #1 EV two-wheeler seller then, Ola ranked on Google's 3rd page. Fixed technical SEO — indexing, pagination, on-page best practices — and the site moved to page 1 almost immediately.Front 2 — Down the funnel: instrument, find drop-offs, remove them.<ul><li>Instrumented the journey: orchestrated clickstream event tracking across the full purchase flow to pinpoint where buyers fell out.</li><li>- Color-selection drop-off: the page showed every color at once. Showed the 4 best-sellers with a "see more," cutting cognitive load.</li><li>- Registration drop-off: name/address/details were demanded before payment. Moved that collection to the post-purchase journey, since it isn't needed to take payment.</li><li>- Payment drop-off (the big one): buyers were failing at credit. Two root causes, each solved:</li><li> a. Cumbersome loan journey — buyers filled details separately for each lender just to check eligibility. Partnered with banks to build a phone-number-based pre-approval check, so buyers saw pre-approved offers the moment they reached the payment page.</li><li> b. Low credit coverage — many buyers were loan-ineligible. Introduced credit cards as a payment method, sharply widening credit access.</li></ul>Results:<ul><li>+25% online purchase conversion</li><li>- +150% average session duration on the landing experience</li><li>- Google ranking: 3rd page to 1st page after technical SEO fixes</li><li>- Pre-approved, phone-based credit offers at checkout + card payments, materially lifting payment-stage conversion</li><li>- Managed 2 PMs (purchase funnel, test rides) + 1 designer + 2 analysts</li></ul>

Lifting Purchase Conversion 25% at Ola

Role: Senior Product Manager, Supply-Chain Financing — Progcap, Jul 2021–Mar 2022.The problem: A ₹700 Cr+ AUM supply-chain lending portfolio serving 300,000+ retailers and 5,000+ distributors, bottlenecked by a 15-day retailer onboarding cycle. Slow onboarding throttled AUM growth and hurt distributor relationships — but speeding it up naively would let risk through the gate.The framework — remove manual gates without lowering the bar:<ol><li>Automate the gate: Replaced manual retailer verification with automated KYB verification, so the credit decision no longer waited on human back-and-forth.</li><li>Orchestrate the flow: Built approval-workflow orchestration and lifecycle management so each retailer moved through onboarding, approval, and disbursal without stalling between hand-offs.</li><li>Incentivise the right behaviour: Layered on a rewards-linked repayment incentive — the platform's first behavioural-incentive layer — to improve repayment and de-risk the book while coverage expanded.</li></ol>Results:<ul><li>-80% onboarding TAT (15 days to 3 days)</li><li>- ₹700 Cr+ AUM portfolio owned; 300,000+ retailers and 5,000+ distributors</li><li>- +12% lending coverage from the repayment-incentive product, while de-risking the AUM book</li><li>- Led 2 PMs and 1 designer across the full retailer lending lifecycle</li></ul>

Cutting Onboarding TAT 80% at Progcap

Role: Senior Product Manager, SMB Lending — Khatabook (10M+ MAU merchant app), Aug 2023–Mar 2025.The problem: Grow monthly loan disbursals on a 10M+ MAU merchant app without loosening risk or breaking under the weight of new lending partners. The three obvious levers fight each other — push growth and NPA rises, tighten risk and conversion drops, add partners and the system turns brittle. All three had to move at once.The framework — attack growth, risk, and infrastructure as one system:<ol><li>Funnel: Analysed where borrowers dropped in the credit-eligibility journey, paired drop-off data with borrower and ops feedback, and rebuilt the eligibility funnel, offer presentation, and onboarding flow.</li><li>Underwriting (negative-filtering): Instead of blindly relaxing rules, I studied how each underwriting parameter actually affected NPA, found several that filtered out creditworthy borrowers without protecting the book, hypothesised relaxing them, and validated through controlled experiments — each a ~500-loan cohort tracked over 3 months — rolling into policy only where delinquency stayed in range.</li><li>Infrastructure: Architected a partner-agnostic lending platform, owning both the LOS (partner API integrations, KYC, a configurable underwriting rule engine, autopay/e-mandate) and LMS (collections, reconciliation across user, payment gateway, and banking partner) — with validation checks and full audit logs, so a new partner's underwriting policy onboards as configuration, not code.</li></ol>Results:<ul><li>+71% monthly disbursals (₹70 Cr → ₹120 Cr) in 14 months</li><li>- 90% of loans disbursed within 3 days of KYC completion</li><li>- -4% borrower drop-off and +16% average ticket size from the funnel rebuild</li><li>- 4 NBFC/bank partners onboarded onto reusable infrastructure</li><li>- 60% adoption of a co-launched embedded credit-protection product (new revenue line)</li><li>- Led a 10-person cross-functional pod plus Risk, Legal, Compliance, and Partnerships stakeholders</li></ul>