Episode 129: Early-Stage Product Management, Product-Led Revenue, and Startup Monologues with Diana Hsieh

Timestamps(01:41) Diana shared her upbringing in Orlando and her undergraduate experience studying Economics at MIT.(03:27) Diana reflected on valuable lessons from her internships during college.(05:58) Diana brought up her learning during the year working as an investment banking analyst in the technology group at Morgan Stanley.(09:10) Diana recalled her transition from investment banking into venture capital at Norwest Venture Partners.(11:52) Diana discussed the takeaways from her time as a venture associate meeting entrepreneurs on a regular cadence.(14:23) Diana recalled her decision to leave venture capital and become the first hire at the product organization at Cockroach Labs.(19:00) Diana went over the challenges and learning curves as a non-technical first product hire at Cockroach.(21:26) Diana extrapolated on the idea of determining the best-fit product strategy rather than blindly following frameworks.(23:55) Diana described her experience as the first hire into the product organization at TimescaleDB.(26:40) Diana highlighted the challenges of open-source GTM.(27:56) Diana reflected on her 3-part blog series on building a product for the most dissatisfied customers first, the majority next, and the full need in the long run.(30:40) Diana shared 2 tactical lessons to cultivate focus as a product manager.(32:44) Diana share the founding story of Correlated alongside her co-founders, Tim Geisenheimer and John Pena.(35:38) Diana briefly touched on her 2 entrepreneurial attempts during COVID-19.(38:30) Diana unpacked the notion of Product-Led Revenue and described how Correlated works at a high level.(40:49) Diana highlighted the role of integrations within Correlated's product strategy.(43:04) Diana mentioned Correlated's product-led playbooks to help users manage their product-led strategy from start to finish.(45:40) Diana explained how she leveraged customer feedback to ship the feature called PQL Scoring that leverages machine learning to identify the best leads.(48:51) Diana shared the consistent principles that have remained the same for successful communication in Product Management.(52:30) Diana discussed her learnings on customer discovery at early-stage startups.(56:08) Diana reflected on the early signs of product-market fit that carry through all of her startups.(58:58) ConclusionDiana's Contact InfoLinkedInTwitterMediumSubstackCorrelated's ResourcesWebsite | LinkedIn | TwitterProduct Overview | How Correlated WorksBlog | Podcast | DocsPLG Playbook LibraryCorrelated Launches to Bring Product-Led Revenue to Market with $8.3M in FundingWhat Is Product-Led Revenue?Correlated launches PQL Scoring to accelerate your product-led strategyMentioned ContentBlog Posts"The Standard Due Diligence Process" (Jan 2016)"Mistakes to Avoid when Pitching to a VC" (Jan 2016)"My Startup Litmus Test" (Feb 2016)"Why I left VC to join Cockroach Labs" (April 2017)"My First 90 Days as the First Product Hire" (May 2017)"Coding != Technical: What It Means to be Technical as a PM" (Aug 2017)"How learning to sell makes for a better product manager" (Nov 2017)"Roadmap Planning: Users First, Features Second" (March 2018)"Build something people will use more than once" (May 2019)"Focus on the unhappiest, most dissatisfied customers first" (May 2019)"Build for the majority" (May 2019)"Why user interviews can fail you when starting a startup" (Sep 2021)"Tackling the challenges of communicating effectively in product management" (Jan 2022)"Some Learnings on Customer Discovery at Early-Stage Startups" (May 2022)"4 early signs of product-market fit" (Sep 2022)"Give customers what they want, but not what they ask for" (Sep 2022)PeopleLenny RachitskyJulie ZhuoNate StewartJeff SposettiBook"Crossing The Chasm" (by Geoffrey Moore)About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

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Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.