Predictive Analytics for Product Managers in 2025 and Beyond

Welcome and Introductions

Dean Peters | 00:00–01:12
So welcome to Predictive Analytics. For all you product managers here in 2025 and beyond, we’re going to talk about going from reactive guesswork to proactive strategy here. And we’re glad you joined us. As Kenny said, I’m Dean Peters, principal consultant and trainer with Productside. I’ve been here for about three years; before that, 20 years of product management, doing a lot with analytics and AI in a variety of fields.
I’m located right outside of Raleigh, North Carolina in the township known as Apex, North Carolina. Kenny, why don’t you introduce yourself?

Kenny Kranseler | 01:12–01:37
Yeah, I’m also a consultant and trainer here at Productside for a little longer than Dean. And before that, I’ve been a product manager for about 25 or 30 years, maybe. Believe it or not, I started my product management career at Kentucky Fried Chicken. I’ve since worked for places like Amazon and Microsoft. I’m located just outside Seattle in Bellevue, Washington, and happy to be here with you today.

About Productside and Webinar Logistics

Dean Peters | 01:37–02:54
Yeah, so Productside is the sponsor for this webinar. We both work there. Productside knows product managers have a difficult job—delivering products that people love, aligning stakeholders, and managing endless backlogs. That’s why we’re here: to be your outcome-driven product partner. We provide tailored solutions, from assessments to coaching and transformation programs. We’ve got a team of invested experts who act as an extension of your team.

Kenny Kranseler | 02:54–03:15
And yes, we answer questions, so please use the Q&A. The first question I always get is—can I watch this recording later? Absolutely yes. You’ll get a link to share with your colleagues afterward.

Dean Peters | 03:15–03:35
And don’t stop there! There’s a chat feature where you can connect with others here today—ask questions, share your experiences, or just cheer each other on.

Engagement, Resources, and Staying Connected

Kenny Kranseler | 03:35–04:24
We’ve got more ways to connect with Productside. You can scan the LinkedIn QR code on screen to follow us, get notified of new webinars, and network with other product pros. Or subscribe to our weekly newsletter—it’s a five-minute, no-clickbait roundup of insights, funding trends, and templates to keep you ahead.

Agenda and Overview: Predictive Analytics in Context

Dean Peters | 04:24–05:55
Alrighty, we’ve got a lot to talk about today. We’ll start with some stories, dive into predictive outcomes, do a few demos, and finish with key takeaways and Q&A. Let’s talk about what’s hiding in plain sight—predictive analytics represents around $82 billion in opportunity. While generative AI dominates headlines, predictive analytics has been quietly delivering impact for decades.

Predictive Analytics vs. Generative AI

Dean Peters | 05:55–07:28
Predictive analytics evolved from deep learning and statistical modeling. Generative AI adds creativity, but predictive analytics remains essential for accurate, deterministic forecasting. When we need precision—weather models, demand forecasting—we want predictive systems, not creative ones. Generative AI is great for ideas, but predictive models ensure accuracy.

Historical Evolution of Predictive Analytics

Dean Peters | 07:28–09:50
Predictive analytics dates back to WWII when early computers forecasted weather patterns. Over time, statistical models evolved into machine learning and deep learning. Preventative maintenance, fraud detection, supply chain predictions, and A/B testing—all of these are predictive techniques under different names. Determinism means reliable outputs—2+2 always equals 4—not 7, like in hallucinating generative models.

Determinism, Accuracy, and AI Myths

Dean Peters | 09:50–10:19
People say predictive analytics is too complex or slow. That’s nonsense. It turns hunches into measurable outcomes and helps us work proactively. It’s not just reporting—it’s foresight.

Real-World Predictive Analytics Stories

Kenny Kranseler | 10:19–12:15
I’ve been working with predictive analytics for over 20 years. In 2005, I helped build real-time traffic prediction for roadways using vehicle data—before Google Maps did it. We analyzed patterns, predicted slowdowns, and found anomalies like stalled vehicles. Predictive models allowed us to simulate rush-hour traffic and even factor in local events like basketball games or state fairs.

Dean Peters | 12:15–16:40
I’ve used predictive analytics for seismic sensors, wind turbines, and frequent traveler systems. From border detection to energy efficiency, predictive models helped forecast and prevent problems. Even in 1990s airline terminals, predictive modeling helped us plan capacity and scale. These applications show predictive analytics isn’t new—it’s mature and powerful.

Poll: How You’re Using Predictive Analytics

Dean Peters | 16:40–20:45
We ran a poll: most attendees are using predictive analytics for proactive forecasting, anomaly detection, and churn reduction. A smaller portion isn’t using it yet—showing a big opportunity for growth in predictive adoption.

Applications and Problem Solving with Predictive Analytics

Kenny Kranseler | 20:45–24:43
Churn prediction, spend optimization, and anomaly detection are key use cases. Predictive analytics improves stability, prioritization, and proactive problem-solving. Collecting the right data is critical—you can’t predict without understanding why customers stay or leave.

Real-World Case Studies: Netflix to Rolls-Royce

Dean Peters | 24:43–31:09
Netflix uses predictive models to drive 80% of its viewing. PayPal uses them for fraud and churn detection. Rolls-Royce reduces jet engine downtime with predictive maintenance. Walmart prevents stockouts. ABB and Microsoft use proactive telemetry to prevent failures and service interruptions. Predictive analytics delivers value across industries.

Future Outlook: Predictive Analytics in 2026

Dean Peters | 31:09–33:06
In 2026, predictive guardrails will define safe AI systems. As agentic AI grows, we’ll need dynamic governance and prediction-based safety mechanisms. Predictive analytics will complement generative systems to ensure reliability.

Data, Trust, and Product Management in the AI Era

Dean Peters | 33:06–37:34
Data completeness and trust are central. PMs must build relationships with data science, understand governance, and ensure transparency. Predictive analytics gives PMs accountability and foresight—critical for responsible AI product management.

Becoming Data-Driven: Skills and Tools for PMs

Kenny Kranseler | 37:34–39:29
If your company isn’t collecting enough data, start small—prioritize high-impact metrics. Learn tools like Power BI, Tableau, and Kibana. PMs don’t need to be data scientists, but they must be fluent in predictive thinking and approach.

Demo 1: Exploring Predictive Analytics Tools and Time Series Data

Dean Peters | 39:29–43:40
Dean demonstrates open-source tools: Observability for time-series analysis, Streamlit for synthetic data, and Grafana for visualization. Predictive analytics starts with time series data—capturing trends and forecasting from signals.

Demo 2: Synthetic Data and Generative + Predictive Hybrid Models

Dean Peters | 43:40–50:09
Dean shows how to combine generative AI with predictive analytics using ChatGPT and Gemini to simulate IoT datasets, inject anomalies, and visualize dashboards. Predictive forecasting meets generative creativity—an emerging hybrid capability.

Poll: What Will You Do Differently? + Upcoming Events

Kenny Kranseler | 50:09–53:17
Audience poll: most plan to explore Slack anomaly alerts, analyze product time series data, or connect with data scientists. Kenny promotes upcoming Productside trainings, AI courses, and webinars with Roger Snyder and Tom Evans.

Q&A and Closing Takeaways

Dean Peters | 53:17–56:42
In a world of AI, when should you use predictive versus generative? Use predictive for deterministic, data-driven accuracy; generative for ideation. Product managers should collaborate with data science and use predictive analytics as a foundation for trustworthy AI. Don’t forget—predictive analytics isn’t new. It’s been driving impact for decades, and it’s time PMs lead the charge in using it.