Aashish Kolagotla

99% of PMs are stuck in inside out thinking. They are looking at data all day but missing insights. ********************* Going down the wormhole of data feels exciting. Everything feels like an insight. But the problem is that most of them will be non actionable. It only tells you what is happening, you will not know what to do with it. Particularly true when the low hanging fruits are already gone. Lost? Let me explain - Let's say you worked very hard on your e-commerce booking funnel & the current conversion is say 10% from search results page to order confirmation page. Now, Let's say the booking funnel itself is a 5 step process - Step 1 - Search Results page (SR page) Step 2 - Product details page (PD page) Step 3 - Add to cart (AC page) Step 4 - Checkout (CO page) Step 5 - Order confirmation page (OC page) and step conversions are SR - PD (50%) PD - AC (70%) AC - CO (60%) CO - OC (50%) making it about 10% (0.5*0.7*0.6*0.5) Now, you look at the same step conversions by different device types, geographies & you find step conversions are worse on low end handsets or Tier 2 geographies leading to lower overall conversion but again this is what you expected. So you have looked at the data, sliced and diced it but you are still far away from a true consumer insight. You can go more granular. You decide let me zoom in on checkout to order confirmation as it has one of worst conversions. You look at conversion by pay modes & you find that net banking has lower payment success rate but it still is 20% of all payment attempts. Can you take it off? You probably can't, it still contributes significant attempt %age. At best you will stack rank it lower on payment page giving it less visibility hoping some users will shift. You get 10 bps (0.1%) improvement in step conversion you feel you have added great value. Now, let's say instead you did some customer calling, you profiled your user. You find the avg demographic profile is - the user draws a monthly salary of about 50k Rs/month. Let's say the median ticket size of your product category is 3.5k. That means buying your product is equal to parting with 7% of his monthly salary. Paying 7% of salary online in a self serve manner is hard, what if things go wrong. You try a simple experiment you take 10% of your traffic and experiment with call to buy. You get great results - 70% of users who call convert. Just calling calms them. Now you have materially changed the metrics. If you don't want to invest in a call centre, other options part pay, pay some token amount & confirm. So, speak to the user. Data is an imp tool, don't make it the only tool. Follow me at https://lnkd.in/dVGHnkRB AND https://lnkd.in/g7J_sDyN While you are at it grab my product assignments playbook, 19 real product cases, all successful submissions, real job offers, ~258 pages of content. ***Grab it now**** - https://lnkd.in/gHzyN-Xc #analytics #productmanagement #data

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I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.

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New pod alert - Naveen Mohan leads product and monetisation at Otter, bringing deep experience from Adobe and Loom. In this conversation, Naveen and I discuss: -What are the leading indicators that your pricing is falling behind product value? -How do you structure soft versus hard paywalls at Otter to balance conversion with retention? -When does monetisation need to live in product versus finance versus growth? (Link in comments) Enjoy! Sponsored by Flexprice

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GPU power is advancing so fast -- and companies are willing to spend so aggressively that they'll only rent the newest model GPUs -- that previous-gen GPUs are effectively worthless. That means data center economics don't work, because you can't recoup the cost of installing GPUs before they become un-rentable. https://lnkd.in/gagATgG2

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We chose a good subject for our march Autotech Council meeting, and after this year's CES, it seems even more obvious. Our meeting on the AI Defined Vehicle will show how it's a natural next step after the Software Defined Vehicle phase that we've been in Tesla launched the Model S in 2012. Now, most carmakers have SDVs. It's time to layer on the AI in user-facing roles, and in invisible functions managing the vehicle.

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Sharing my latest post on 🚀 Product Discovery: The Art of Grounding Your Team in Customer Voice Too often, teams build products based on gut feel and quickly realize it’s not sustainable. The real magic happens when discovery is structured, customer-driven, and anchored in a clear product strategy. In this post, I break down: ✅ Opportunity Discovery — How we validate the real customer problem, market size, and willingness to pay before investing. ✅ Solution Discovery — How we explore and test solutions that balance customer needs and business goals. ✅ Go / No-Go Decision — Why this critical pause saves teams from wasting time and money on ideas that don’t deliver value. ✅ Planning — How we align the entire team to build with confidence. ✅ AI’s role — How tools like Dovetail, M365 Copilot, and ChatGPT are transforming discovery, making it faster and smarter. 📌 Check out the full post and let’s discuss how you’re embedding discovery in your teams! I’d love your feedback or questions. https://lnkd.in/dvtf7u_e #ProductManagement #ProductDiscovery #CustomerVoice #ProductStrategy #AI #ProductDesign #BuildTheRightThing

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A useful discussion of the differences between co-pilots, agents and AI workflows. From Jack Mu at Paragon. Clarity leads to better design, and to better pricing. https://lnkd.in/g_Xkf5AT? #AI #agents #copilots #workflows

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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