173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work

173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work

Released Tuesday, 8th July 2025
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173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work

173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work

173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work

173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work

Tuesday, 8th July 2025
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Todd Olson joins me to talk about making analytics worth paying for and relevant in the age of AI. The CEO of Pendo, an analytics SAAS company, Todd shares how the company evolved to support a wider audience by simplifying dashboards, removing user roadblocks, and leveraging AI to both generate and explain insights. We also talked about the roles of product management at Pendo. Todd views AI product management as a natural evolution for adaptable teams and explains how he thinks about hiring product roles in 2025. Todd also shares how he thinks about successful user adoption of his product around “time to value” and “stickiness” over vanity metrics like time spent. 

 

Highlights/ Skip to:

  • How Todd has addressed analytics apathy over the past decade at Pendo (1:17)
  • Getting back to basics and not barraging people with more data and power (4:02)
  • Pendo’s strategy for keeping the product experience simple without abandoning power users (6:44)
  • Whether Todd is considering using an LLM (prompt-based) answer-driven experience with Pendo's UI (8:51)
  • What Pendo looks for when hiring product managers right now, and why (14:58)
  • How Pendo evaluates AI product managers, specifically (19:14)
  • How Todd Olson views AI product management compared to traditional software product management (21:56)
  • Todd’s concerns about the probabilistic nature of AI-generated answers in the product UX (27:51)
  • What KPIs Todd uses to know whether Pendo is doing enough to reach its goals (32:49)  
  • Why being able to tell what answers are best will become more important as choice increases (40:05)

 

Quotes from Today’s Episode

  • “Let’s go back to classic Geoffrey Moore Crossing the Chasm, you’re selling to early adopters. And what you’re doing is you’re relying on the early adopters’ skill set and figuring out how to take this data and connect it to business problems. So, in the early days, we didn’t do anything because the market we were selling to was very, very savvy; they’re hungry people, they just like new things. They’re getting data, they’re feeling really, really smart, everything’s working great. As you get bigger and bigger and bigger, you start to try to sell to a bigger TAM, a bigger audience, you start trying to talk to the these early majorities, which are, they’re not early adopters, they’re more technology laggards in some degree, and they don’t understand how to use data to inform their job. They’ve never used data to inform their job. There, we’ve had to do a lot more work.” Todd (2:04 - 2:58)
  • “I think AI is amazing, and I don’t want to say AI is overhyped because AI in general is—yeah, it’s the revolution that we all have to pay attention to. Do I think that the skills necessary to be an AI product manager are so distinct that you need to hire differently? No, I don’t. That’s not what I’m seeing. If you have a really curious product manager who’s going all in, I think you’re going to be okay. Some of the most AI-forward work happening at Pendo is not just product management. Our design team is going crazy. And I think one of the things that we’re seeing is a blend between design and product, that they’re always adjacent and connected; there’s more sort of overlappiness now.” Todd (22:41 - 23:28)
  • “I think about things like stickiness, which may not be an aggregate time, but how often are people coming back and checking in? And if you had this companion or this agent that you just could not live without, and it caused you to come into the product almost every day just to check in, but it’s a fast check-in, like, a five-minute check-in, a ten-minute check-in, that’s pretty darn sticky. That’s a good metric. So, I like stickiness as a metric because it’s measuring [things like], “Are you thinking about this product a lot?” And if you’re thinking about it a lot, and like, you can’t kind of live without it, you’re going to go to it a lot, even if it’s only a few minutes a day. Social media is like that. Thankfully I’m not addicted to TikTok or Instagram or anything like that, but I probably check it nearly every day. That’s a pretty good metric. It gets part of my process of any products that you’re checking every day is pretty darn good. So yeah, but I think we need to reframe the conversation not just total time. Like, how are we measuring outcomes and value, and I think that’s what’s ultimately going to win here.” Todd (39:57)

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From The Podcast

Are you an enterprise data or product leader seeking to increase the user adoption and business value of your ML/AI and analytical data products?While it is easier than ever to create ML and analytics from a technology perspective, do you find that getting users to use, buyers to buy, and stakeholders to make informed decisions with data remains challenging?If you lead an enterprise data team, have you heard that a ”data product” approach can help—but you’re not sure what that means, or whether software product management and UX design principles can really change consumption of ML and analytics?My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting product designer’s perspective on why simply creating ML models and analytics dashboards aren’t sufficient to routinely produce outcomes for your users, customers, and stakeholders. My goal is to help you design more useful, usable, and delightful data products by better understanding your users, customers, and business sponsor’s needs. After all, you can’t produce business value with data if the humans in the loop can’t or won’t use your solutions.Every 2 weeks, I release solo episodes and interviews with chief data officers, data product management leaders, and top UX design and research professionals working at the intersection of ML/AI, analytics, design and product—and now, I’m inviting you to join the #ExperiencingData listenership. Transcripts, 1-page summaries and quotes available at: https://designingforanalytics.com/edABOUT THE HOSTBrian T. O’Neill is the Founder and Principal of Designing for Analytics, an independent consultancy helping technology leaders turn their data into valuable data products. He is also the founder of The Data Product Leadership Community. For over 25 years, he has worked with companies including DellEMC, Tripadvisor, Fidelity, NetApp, Roche, Abbvie, and several SAAS startups. He has spoken internationally, giving talks at O’Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College. Brian also hosts the highly-rated podcast Experiencing Data, advises students in MIT’s Sandbox Innovation Fund and has been published by O’Reilly Media. He is also a professional percussionist who has backed up artists like The Who and Donna Summer, and he’s graced the stages of Carnegie Hall and The Kennedy Center. Subscribe to Brian’s Insights mailing list at https://designingforanalytics.com/list.

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