From Feature Factory to AI Growth Engine: Pendo’s Transformation Story
Trisha Price, Chief Product Officer @ Pendo
Trisha Price is the Chief Product Officer at Pendo.io where she leads product strategy and execution across product management, product marketing, product operations, and product design. She has over 20 years of experience in all aspects of the product and software development life cycle. Her previous roles include leadership positions at nCino, Primatics Financial, and Fannie Mae. Trisha is most passionate about building intuitive products that solve real customer problems.
In today’s interview, we cover:
Transforming Pendo’s product organization
Building the product team’s GTM muscle
Moving from delivering features to outcomes
Pendo’s Generative AI product strategy
Finding the right Generative AI operating model
What follows below is a condensed and lightly edited version of our interview.
Trisha, it is great to have you. Let's start at the top. You have been at Pendo for 2 years now. May I ask, what was your mandate when you joined Pendo?
Trisha Price: Mustafa thanks for having me.
When I first joined Pendo almost 2 years ago, I was given a three-part mandate. The first and most important focus was driving growth, specifically through increased ARR. At that point, Pendo was expanding into the enterprise market beyond our initial b2b SaaS customer base. So my role was to ensure we successfully moved upmarket.
The second part of my mandate was to launch new products. Pendo has always had an integrated suite of tools at the intersection of product analytics, digital adoption, etc. My role was to expand that suite.
Finally, I was tasked with investing in and developing the product team into a best-in-class organization that could serve as thought leaders for other companies utilizing our tools. I have a passion for growing other product leaders.
When you got there what did you see? What were some of the key challenges you had to overcome to meet/exceed your mandate?
TP: When I first joined Pendo, I saw a company that was rapidly scaling and growing under the strong product vision and leadership of our CEO Todd. We had an incredible group of talented product managers who could execute well on Todd's vision and broaden Pendo's capabilities.
However, what I felt was missing was more strategic guidance and leadership from the product team itself.
The key challenge I saw was a lack of a commercial mindset among the product management group. For example, the PMs were great at building and delivering features, but they weren't focused on how customers would use and buy those capabilities. There was a gap between our ability to build great products and our ability to get them to market successfully.
The second challenge I saw was that we needed to instill more strategic, customer-centric thinking across the product organization. We had to level up our aptitude for understanding what problems customers needed to solve and how our products translated into real business value.
For me, bridging that gap between excellent product execution and commercial orientation was critical. It required major changes to processes, metrics, and leadership thinking.
Instilling a more commercial mindset is always hard. How did you fix the GTM? What did you do?
TP: You are right. It was hard. Here is what we did.
First, we changed the organizational structure to embed commercial leaders within our product groups. We brought product marketing under my product organization instead of having it separate. This ensured tighter day-to-day integration between product managers and positioning/messaging.
Next, we created new GM roles responsible for our product lines. One of those GMs is a Pendo founder, and we brought others into the business to take these positions. They brought crucial go-to-market expertise into core product leadership.
And then beyond organizational changes, we overhauled processes to support more customer-driven prioritization and planning. We instituted quarterly product scorecards that track metrics like usage, adoption, and win rates and tie them directly to business outcomes. These provide the data to highlight what will drive growth and retention.
Finally, we focused heavily on monitoring competitive dynamics and win rates. We standardized tracking key competitors and made win rate a prominent metric on all product scorecards. This keeps the external market visible and helps prevent solely internal-looking prioritization.
This of course took time. But I think the steps were necessary. They have really helped the product team internalize an outside-in, go-to-market-oriented mindset.
You mentioned that you wanted to move to a more outcome-based model. And how do you use a scorecard to drive that change? Can you share more about that?
TP: Shifting to a more outcome-based model was a major priority because we wanted to align everyone around the business impact, not just features.
To drive this change, as I mentioned above, we instituted product scorecards with metrics tied directly to high-level goals like revenue growth, customer retention, account expansion, etc.
For example, we track metrics like product adoption rates, whether usage of certain features increases retention, win rates against key competitors, etc. These provide data to connect product KPIs to overarching business outcomes.
We also created a standard template but customized each product's scorecard based on maturity and objectives.
But as you know, creating an artifact is not enough. To really drive home the behavior change, we as a leadership team, review the product scorecards weekly. We also cascade the scorecards through the product organization. This helps with both alignment and team focus.
And the best part is that now, as an organization, there is more rigor now around what success looks like. It is just not about shipping products.
This is great. And you are right, creating a scorecard is easy. Using it to drive change is much harder. If it is ok, can you share examples of your scorecard?
TP: Yes of course. The scorecard is a template, with instructions for others to leverage. I also included an example of the Pendo dashboard used at the team level (it includes sample data!)
Sample Scorecard:
Download template.
Awesome. Extra points for bringing in visuals.
TP: Thanks (laughing). We try to do what we can.
If it is okay with you, let's shift focus to Generative AI. How are you planning to leverage this new technology? What type of products are you trying to build?
TP: Like everyone else, we see a lot of potential to leverage generative AI across our products, but we're taking a strategic, customer-driven approach. Broadly, there are three main opportunities we're pursuing:
The first is using AI to automate and personalize in-app messaging like user tips or walkthroughs. By tapping into our usage data and a customer’s own documentation, we can hyper-personalize messaging for each user's own experience.
The second is around product discovery - using AI to analyze large volumes of customer feedback and surface insights to inform roadmaps and positioning. This helps managers cut through the noise to build what users really want.
Third is assisting customers with achieving business outcomes, like improving growth or increasing retention. AI can recommend usage metrics to track, features to promote, and users to target based on the customer’s business goals.
We have pilots and experiments underway in all these areas to validate concepts and integrate AI thoughtfully into our existing products vs. just bolting it on. We want to apply it only where it unlocks a new value. We talk about all of these pilots and products at Pendo.io/ai
That is great. How are you planning to execute? This stuff is so new. Do you have a COE or distributed model?
TP: Good question. Right now we are taking both approaches.
On one hand, we have a centralized data science team with deep expertise in machine learning and AI models like generative AI. They act as a center of excellence, training others and assisting product teams. However, we are also trying to distribute ownership and execution of AI initiatives throughout our product organization.
For example, our feedback product team is best positioned to pilot generative AI for synthesizing insights from customer input. While our data science COE provides guidance, the product manager is figuring out how to best apply AI to enhance their product experience.
This model allows us to build institutional AI expertise but also embed it directly into the product groups for more customer-centric applications. The key is not about one model or the other. It is about striking the right balance.
One last question, are there any lessons learned that you would like to share with the audience?
TP: Yes, a few come to mind.
First is the importance of focusing on clear customer value versus using AI just for the sake of it. We had an "AI for AI's sake" phase where our data science team identified interesting patterns but we struggled to turn those into usable product features. We've refocused on applying AI to solve real pain points.
Second is distributing AI expertise across product teams rather than concentrating it in a specialized group. AI needs to be grounded in solving problems for each product area, not a separate initiative. I know I just talked about it above. But I cannot re-emphasize how important that is to us.
Third is taking an iterative, MVP approach and continually testing AI-enhanced features with customers. The tech is changing so rapidly that over-engineering the perfect AI solution upfront doesn't work. We get incremental versions out to learn and evolve.
Fourth, be wary of potential bias in training data and ML models. We involve our UX researchers early to help assess. AI should aid human decision-making, not replace it.
Trisha this is great. Thanks for sharing both your experience and insights. This has been a lot of fun.
TP: Same here Mustafa. This was fun. Thanks for having me.