Seven years in enterprise integration taught me to look there first.
The biggest product opportunities are usually buried inside manual processes, workarounds people have stopped noticing, and frustrations nobody has thought to report.
Enterprise Product Management isn't about shipping the most features. It's about making the right trade-offs.
Over the past seven years at IBM, I've worked across enterprise products in automotive, energy, and telecom, partnering with engineering teams and business stakeholders to build reliable, business-critical platforms.
This portfolio reflects how I think as a Product Manager. Through independent case studies, I explore customer problems, validate ideas, evaluate trade-offs, and document the decisions behind building products from 0→1.
Every case study in this portfolio follows the same structured approach.
Click any card to read the full case study, including diagrams and supporting documents.
End-to-end product case study built from scratch. 10 user interviews across 3 segments revealed the real pain was not finding a laundry vendor. It was the anxiety and zero visibility after handing clothes over. The entire product pivoted on that insight.
Analysing ONDC's unit economics, adoption barriers, and what it should focus on to win Tier 2 India rather than fight Amazon on its home turf.
Diagnosing why Nykaa's post-purchase experience is breaking down tracing the root cause from marketplace seller quality to last-mile delivery failures.
Redesigning Naukri's core discovery flow using JTBD to uncover what job seekers actually want versus what the platform currently offers.
Unpacking the RBI action, the structural compliance failure, and what the forced pivot means strategically for Paytm's business model.
A product built from 7 years of firsthand enterprise integration experience at IBM. Trading partner onboarding today takes 4 to 12 weeks of emails, PDFs, and manual configuration. This case study designs the self-serve alternative.
Each of these was used on a real product problem in the case studies above.
Used to find where users experience the most friction, both online and offline touchpoints.
When I am in X situation, I want to do Y, so I can achieve Z. Reframes features as user outcomes.
Maps outcomes to opportunities to solutions to experiments. Prevents jumping to solutions too fast.
Must have, Should have, Could have, Will not have for scoping. RICE for data-driven prioritisation.
Awareness, Acquisition, Activation, Retention, Revenue, Referral. One metric per stage, one North Star.
Confirm the drop is real. Slice by dimension. Hypothesise. Test. Validate the fix. Never assert, always ask.