Rahul Kashyap

The role of product managers is evolving fast. I sat down with Saurabh Agarwal (VP Product Management - JioHotstar, ex-Google and LinkedIn) to decode what’s changing and what will not in the field of product management in the next few years. It was exciting to interact about his decade plus learnings on building products for 500 million+ Indians that most of us use daily along with uncovering how they came up with the ideation of the much revered Max-view feature in Hot-star, that went from idea to launch just in 6 weeks, as first ever vertical content consumption for live sports globally! Not just that, we covered the following elements in the conversation ✅ Engineer → PM → org-builder: how the scope and stakes grow ✅ Judgment vs data: when to trust instincts over dashboards ✅ The MaxView story: from insight → prototype → launch in ~6 weeks ✅ Product trade-offs at true billion+ internet scale ✅ Designing for India’s Next Half Billion (NHB) users ✅ Where AI is actually reshaping PM & design ✅ Why simplicity beats cleverness in execution He also dropped a bunch of timeless nuggets on velocity, taste, and building for constraints. Thanks to Rati and team at Product x People for the invite. Link to the full conversation in comments. #productmanagement

138

6 Comments

Blink and you’ll miss two things: a new AI model and a new Vertical Qcom marketplace. Ecom led to a wave of vertical marketplaces -  Myntra, FirstCry, Nykaa, Purplle, Urban Ladder, Lenskart, Swiggy, Zomato. Repeating trend with Qcom -  Swish, Slikk, First Club, Tyro, Cavi, Knot, Apollo/1Mg 10mins. One thing that’s surely different - VC money isn’t flowing like the Ecom era. Survivors will have to figure out how to scale profitably while maintaining CX metrics! The costs for Last Mile are quite high for Qcom than Ecom. And the competition is intense - Swiggy, Zomato, Blinkit, Instamart, Zepto, FK Minutes, everyone is competing for the same fleet. Sharing a few key learnings for marketplaces just starting up, from our journey of scaling Qcom. Had to split this into 3 parts! (Supply, Product, CM) Part 1/3 & the most important - Supply/Your Fleet 3 core things that matter to them - 1️⃣ 𝗙𝗿𝗲𝗲𝗱𝗼𝗺 - 𝗗𝗼𝗻’𝘁 𝗹𝗲𝘁 𝘆𝗼𝘂𝗿 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗲𝗲𝗹 𝗿𝗲𝘀𝘁𝗿𝗶𝗰𝘁𝗶𝘃𝗲.  • Enforce right behaviour while keeping the platform non-restrictive.  • Freedom of working hours - Accommodate 2Hr/ 4Hr/ 6Hr/ 8Hr shifts. Helps get multiple personas to your platform.  • Freedom of earning model - Build flexibility over Fixed Earnings / Per Order Earnings and around Weekly/Monthly payout cycles. 2️⃣ 𝗘𝗮𝗿𝗻𝗶𝗻𝗴𝘀 - 𝗪𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝘀 𝗘𝗣𝗛  • EPH (Earnings Per Hour), what do I earn if I put “X” hours with a company? >80% work at 2+ companies, which company at which hour depends on EPH. 3️⃣ 𝗛𝗮𝗽𝗽𝗶𝗻𝗲𝘀𝘀 𝗜𝗻𝗱𝗲𝘅 (𝗜𝘁’𝘀 𝗻𝗼𝘁 𝗮𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀)  • Avenues to Grow - Make sure there is a path of career progression. Rarely does someone want to just deliver orders for long.  • Timely Payouts - Pay them on schedule as you would your employees. One delayed payout churns ~10% of your fleet.  • RnRs - Find ways to connect better. Deliver "X" orders & earn ₹"Y" is very transactional. Small gestures like monthly RnR, leadership connects go a long way.  • Insurance - Help them get life, health, and accident insurance. We aren’t perfect on many parameters; these are core things that are being fixed as P0s. Zomato currently sets an industry benchmark on most, 10X better at least providing citywide resting shelters, medical insurance, riders becoming part of product teams, and even tax filing support! Btw, there is a much easier way to launch a Qcom vertical marketplace.  Just use Blitz Qcom rails and launch in 10 cities on Day 1. You source, we deliver!

55

2 Comments

💼 Product Management Across Indian Cities — An Unfiltered Observation 😄 After 10+ years of being a Product Manager across Bangalore, Hyderabad, Gurgaon, and now Mumbai, I’ve realized one thing — the role stays the same, but the vibe doesn’t. Here’s my totally unbiased take 👇 🚀 Bangalore: Everyone’s building a startup — even your Uber driver has an MVP in stealth mode. Meetings start with “Let’s brainstorm” and end with a Figma link. 💻 Hyderabad: Super structured. Jira boards are cleaner than my kitchen. And yes, people actually read PRDs. Absolute legends. 🏢 Gurgaon: Slides > Product. If it’s not in a deck, it doesn’t exist. Also, 90% of “quick syncs” end in buffet lunch plans. 🌊 Mumbai: Hustle + Humidity = true product-market fit test. People ship features faster than the local train schedule. After seeing it all, one truth remains — Good PMs adapt like chameleons. Great PMs laugh while doing it. Now I’m curious — 👉 Which city do you think has the most unique work culture? 👇 Drop your experience (funny ones welcome). Let’s make this a thread worth bookmarking! #ProductManagement #TechCulture #WorkLife #India #PMHumor #Leadership

179

32 Comments

At Zomato, the Home page and Search are powered by in-house, self-managed Solr clusters. Over time, the team acquired the expertise needed to handle the nuances of self-management. Despite that, we still experienced some unexpected downtimes — a few of which were relatively unknown to the broader community, probably because we were using Solr at a significantly high scale and had modelled our use cases quite differently. I’ve discussed one such downtime in detail in this blog: https://lnkd.in/gDJuBU_P While we kept improving the availability of the systems, we also built fallback disaster recovery mechanisms to handle downtimes. I had written about one of those in a previous post — it talked about serving the Zomato Home page using DynamoDB when the main flow was down. But that was only a partial recovery mechanism. Needless to say, customers rely heavily on Search to find their favourite dishes and restaurants. So we had to build a fallback mechanism for Search as well — since DynamoDB doesn’t support full-text search. Customers typically search either for a Dish/Cuisine or a Restaurant. While restaurant search is hyperlocal, Dish/Cuisine search is more universal. Interestingly, the majority of searches are for dishes — and even more interestingly, ~99% of those dish searches are confined to fewer than 1000 dishes globally. We looked at data for the top ~1000 dishes and discovered popular keywords customers use to search for them — e.g., sezwan for Schezwan, panir for Paneer, etc. We created an in-memory Trie of these strings, populated at container boot time. So, if the Solr index is unavailable, we can use this Trie to return matching dishes for the input keyword. This automatically handles phonetic matches and spelling mistakes for most user inputs. We could’ve gone further and implemented 1–2 edit distance matching, but the impact would’ve been negligible — the keyword data was already sufficient to serve the majority of real customer queries. All in all, this was a sub-10MB, almost no-cost in-memory data structure, capable of serving Dish/Cuisine searches with sub-millisecond latency on the server side. The result ranking was based on the universal popularity of dishes. While the results aren’t personalised and don’t include restaurant data, they still allow customers to search for their favourite dishes during downtimes — and in such cases, availability takes precedence over consistency. #datastructure #solr #trie #search

430

20 Comments