I’ve wanted my product analytics to follow me into every conversation, doc, and code review. Now they do—and it changes how quickly I can move from question to insight to decision.
Pendo is now available as an MCP (Model Context Protocol) server, easily accessible in Claude, ChatGPT, and Cursor.
Practically, this means my core product analytics, segments, and qualitative feedback can be surfaced right where I plan sprints, refine opportunity solution trees, and write specs. Fewer context switches, tighter feedback loops, and faster product decisions.
Here are five ways I put Pendo MCP to work across my day-to-day workflows—grounded in product management leadership habits and built for speed and clarity.
1) Daily triage and decision support: In ChatGPT or Claude, I quickly query product analytics to spot anomalies, usage spikes, or drop-offs by segment. Prompts like “Highlight top features by week-over-week growth and flag statistically notable anomalies” help me focus standups on what matters, tightening the loop between observability and action.
2) Continuous discovery prep: Before customer interviews, I pull recent NPS verbatims, feature adoption by persona, and journey mapping signals. In seconds, I have a concise brief that blends behavioral analytics with customer interviews, so I can ask sharper questions and validate assumptions faster—without leaving my AI workspace.
3) Evidence-based prioritization: When shaping the roadmap, I bring in retention analysis, user activation metrics, and cohort views to weigh impact vs. effort. Using Pendo MCP inside Claude or ChatGPT, I translate insights into driver trees and a clear product strategy narrative that aligns stakeholders around outcomes, not output.
4) Product-led growth and onboarding: I review onboarding funnels, identify friction in first-run experiences, and draft in-app guides and tooltip copy that meets users at the exact drop-off points. With Pendo MCP, the context for product tours and in-app guides is right where I’m writing, so iteration cycles stay tight and data-informed.
5) Customer success and QBR prep: For account health and QBRs vs OKRs alignment, I generate succinct summaries of feature adoption, sentiment, and value realization—ready to paste into email, decks, or a CRM integration. This keeps sales-led and product-led growth motions unified, with a single source of truth visible in ChatGPT, Claude, or when I’m coding in Cursor.
The net effect: higher-quality decisions, faster. By bringing product analytics into my AI workflows, I reduce context switching, improve context window management, and keep my team anchored to real user behavior. Wherever I’m working—ideating in Claude, drafting in ChatGPT, or reviewing code in Cursor—my Pendo context is right there with me.
If you’re leading empowered product teams, this is a pragmatic way to operationalize continuous discovery, speed up alignment, and turn insights into outcomes. It’s a simple shift with outsized leverage.
Customer experience is where strategy, data, and execution converge—and where AI can deliver compounding value when thoughtfully designed. In my work, I’ve seen how the right CX vision becomes a growth engine when it’s operationalized through clear measures, robust analytics, and disciplined product practices.
"Amanda Sime is the Customer Experience Strategy Lead at Amplitude. She shapes CX strategy and partners across orgs to design and scale AI-powered solutions." That concise description captures a model I deeply respect: start with a strong CX strategy, then partner across the organization to make AI real in the day-to-day. It’s not just about new technology; it’s about aligning teams, systems, and incentives to deliver consistent customer value.
Translating that approach into practice requires a rigorous AI Strategy, anchored in measurable outcomes and informed by behavioral analytics. I prioritize journey mapping to expose friction, then connect those insights to AI workflows that enhance customer success and in-product guidance. When cross-functional partners—from solutions engineering to support—operate from a shared driver tree, the roadmap balances speed with sustainability.
Data is the backbone. A unified analytics platform—often centered on Amplitude analytics—helps teams move beyond vanity metrics to track user activation, feature adoption, and retention analysis with precision. With that foundation, we can test responsibly, iterate quickly, and validate impact with product-led growth motions that scale across segments without sacrificing quality.
Operational excellence matters just as much as vision. I’ve learned to treat CX programs like enduring products: build reliable feedback loops, connect customer support AI strategy to clear service-level outcomes, and empower product management leadership to make evidence-based tradeoffs. When teams have clarity on the problem space and access to trustworthy insights, they deliver solutions that feel both intelligent and human.
The real win is cultural: empowering product trios and partner teams to co-own outcomes, not just outputs. That’s how AI moves from a promising experiment to a durable capability—by aligning strategy, analytics, and execution so customers experience value at every touchpoint.
Inspired by this post on Amplitude – Perspectives.
Leading the Support function for a company that builds a leading Agent and AI-forward customer service platform has been, for me, unique, exciting, and yes—daunting. It’s where product ambition meets operational reality, and where every decision I make is immediately tested by customers who expect excellence.
It’s unique because we use the same technology as our customers. We live in the product every day, which puts us in a privileged position to be the voice of the customer across the organization. That tight feedback loop has shaped how I prioritize, what I build next, and how I measure success.
It’s exciting because we get to try all of the new features and capabilities of Fin and the Intercom helpdesk. With a relentless focus on AI innovation, I’ve had access to remarkable tools that help us deliver an incredible customer experience—and I’ve seen firsthand how the right workflows and guardrails turn those tools into outcomes.
And it’s daunting because expectations for our own Customer Support (CS) team are sky high. If we can’t deliver incredible support using our own technology, we undermine its value proposition. That imperative has kept me honest, focused, and fast.
In our new research, “The 2026 Customer Service Transformation Report,” we’ve been sharing how forward-looking teams use AI to transform their support models. If you’d like to get straight to the report, download it here.
When Intercom changed its focus in late 2022 to prioritize the customer service use case, we undertook a critical review of the support experience we were delivering and committed to driving meaningful change under an AI-first framework. That was a turning point: I aligned product strategy and operations around a single north star—automate with quality, and elevate humans to higher-value work.
Three years on, Fin now resolves over 81% of all our customer support volume, delivering immediate and high-quality resolutions. We have absorbed a 300%+ increase in customer demand since 2022 without proportional headcount growth. Without Fin, we would have needed at least 100 additional CS team members to meet that demand and our improved service levels – a net saving to Intercom of between $7.5M–$9M annually.
Throughout this work, we drew on research from the 2026 Customer Service Transformation Report and applied the lessons directly to our own org design, knowledge management, and AI workflows. What follows is our story of transformation and how we achieved a mature deployment of Fin.
The problems we set out to solve
Back in 2022, our challenges looked familiar to any modern support organization, and I knew we needed a step-change—not incremental tweaks.
We faced increased support demand from new and existing customers: Intercom was launching major features and changes at speed, driving up overall customer conversation volume and requiring additional headcount for the CS team. I could see we were scaling people faster than processes—unsustainable without automation.
Our support policy (as defined by our service level objectives) was not based on a high bar: In most cases, we were only committed to “business hours” coverage for the majority of our customers, impacting first response times. Even with SLOs that were not considered best in class, we were struggling to meet our commitments. I wanted 24/7 coverage and faster first responses without sacrificing quality.
We wanted to do more: As we pivoted our strategy, we wanted to open new routes to our support team, such as providing support to website visitors with technical questions and to trial customers. That meant meeting customers earlier in their journey with accurate, on-brand responses—at scale.
What we did
We made a very conscious decision to become our own best reference customer. As Intercom embraced the opportunity that generative AI presented to transform customer service, we intentionally moved to an AI-first strategy for our Customer Support team. I set a simple operating principle: ship value quickly, measure relentlessly, and let evidence guide the next bet.
We started with the highest-volume, informational queries and saw our resolution rates climb quickly. With that foundation in place, we pushed Fin further, training it on deeper documentation and internal procedures, and eventually giving it the ability to take actions on behalf of customers. As Fin took on more complex work, our results started to compound—and trust in the system grew across the organization.
Early adoption and building trust. When “AI Assist” features came to the Intercom Inbox, the CS team got early exposure to AI and were empowered to provide feedback directly to our product teams. This built awareness and trust across the team about what we were trying to achieve with AI, and helped shape the product roadmap. We were also the first beta customer for Fin, rolling it out to a subset of customers to watch sentiment and outcomes closely. With no adverse reaction and an initial resolution rate of over 25%, we deployed Fin to most customer segments within weeks. I’ll never forget the first week we put Fin in front of real customers—the silence of issues that never reached humans was the loudest signal of success.
Knowledge management as a product. We recognized quickly that time spent tuning our help center and knowledge assets for Fin would pay dividends. We transitioned our Help Center Manager into a “Knowledge Manager,” with a dedicated remit to optimize content for Fin. We embedded knowledge creation into our “New Product Introduction” (NPI) process, targeting that Fin would resolve at least 50% of customer issues at every new product and feature launch. Over time, we added new sources, including “Developer Documents,” enabling Fin to handle increasingly complex issues. We built a culture of continuous improvement—allocating “out of the inbox” time so every teammate could close content gaps and raise the bar.
Conversation design end-to-end. To ensure a consistent, high-quality customer experience, we created a new “Conversation Designer” role that owns the journey across automation and human handoffs. Using Intercom’s Workflows, we introduced “skills-based routing” so that when a customer asks for a human, the conversation reaches someone with the right expertise quickly. This is now handled by Fin directly using a feature called “Attributes.” The result: a seamless, on-brand experience regardless of channel or escalation path.
Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.
Organization changes that unlocked leverage. As we scaled Fin, we stood up a dedicated AI Support team under a senior CS leader to continuously optimize automation and define our AI adoption strategy across the journey. We restructured human roles into “Technical Support Specialist” and “Technical Support Engineer” to better align with the complexity of incoming work. We also expanded Support Operations to focus on optimization—using AI to uplevel Enablement, Workforce Management, QA, Process Management, and Data Insights. Just as important, we reset expectations about the balance between time spent supporting customers directly versus improving AI. That mindset shift created compounding returns.
Pushing Fin further with new capabilities. As capabilities matured, we were early adopters and saw measurable wins:
Fin Guidance: Multiple Guidance rules provide additional controls and a more personalized, targeted experience for customers.
Fin Tasks and Procedures: Enables Fin to carry out activities such as updating customers on incident status and deep troubleshooting for technical issues.
Insights: AI-driven dashboards provide deep insight into Fin’s performance and surface recommendations for further optimization. Insights also provides a Customer Experience (CX) Score for every customer interaction, enabling more targeted improvement efforts and opening up new ways to close the loop with customers who have had a poor experience.
What we achieved
What started as a focused effort to improve our customer support experience became the strongest proof point for what’s possible when you fully embrace AI. Fin now resolves over 81% of all our customer support volume and has allowed us to absorb a 300%+ increase in demand without proportional headcount growth. Over 90% of our customers now benefit from improved first response performance, 24/7 coverage, and outbound phone support.
What the numbers don’t fully capture is the shift in how our team operates. With volume absorbed by Fin, our CS teammates now deliver consultative support—guiding next best actions, deepening product adoption, and contributing directly to retention and expansion. Customers that receive these engagements adopt Fin at a much deeper level and achieve greater support success. What was once a reactive, volume-driven team is now a function that generates significant revenue.
What’s next
Customer expectations are always rising, so we’re building on our progress by embracing the Fin Flywheel—an actionable framework for ongoing improvement and optimization. This keeps us honest about the discipline required to sustain AI performance at scale.
Train: Teach Fin to resolve even the most complex queries with Procedures, knowledge, and policies.
Test: Run fully simulated customer conversations from start to finish to see exactly how Fin will behave before going live.
Deploy: Set Fin live across every channel – voice, email, chat, and social – for consistent support wherever customers reach out.
Analyze: Use AI-powered Insights to analyze and improve Fin’s performance and deliver better customer experiences.
We are also investing in our support teammates so they can adjust to the new world of AI—taking on more complex work and being valued for the subject matter expertise, consultative engagement, and empathy they bring to the role. That human layer is where differentiation shines.
We will continue to develop and share best practices for deploying an Agent, based on our own experience with Fin and the lessons learned from our most forward-looking customers. These are captured and continually evolving in The Agent Blueprint.
Transformation takes commitment
The most successful teams aren’t bolting AI onto old processes; they’re rebuilding support around it—investing in knowledge and people alongside technology, and treating AI as a continuous discipline rather than a one-time deployment. That’s the real change required. For support teams willing to make it, there’s a rare opportunity to redefine what customer service can deliver—higher CSAT, faster resolution, and durable ROI.
Net Recurring Revenue (NRR) is the cleanest truth-teller in my operating system. When I review NRR, I’m not just looking at whether we renewed accounts—I’m assessing whether our product and customer success motions are compounding revenue from our existing customers. Put simply: good CS teams protect revenue; great CS teams grow it through adoption, expansion, and durable retention.
Here’s how I frame NRR with my teams: it reflects revenue from our current customers after expansion, downgrades, and churn. If it’s at or above 100%, the installed base is self-sustaining; if it’s materially above 100%, the base is funding growth without net-new sales. That’s the holy grail for product-led growth and the benchmark I use to separate good from great.
At HighLevel, I’ve learned that you can’t “wish” your way to high NRR. You operationalize it. We align incentives, dashboards, and rituals so everyone—from PMs to CSMs to Solutions Engineering—owns the same outcome. Our “QBRs vs OKRs” discussions anchor on NRR drivers: activation rates, time-to-value, feature adoption depth, and expansion readiness. Those leading indicators tell me where we’ll land on lagging revenue results.
The best Customer Success teams operate like product teams. They use behavioral analytics and retention analysis to segment customers by use case and maturity, then design journey mapping to move each segment from first value to habitual value. They proactively reduce risk while creating clear expansion paths—new seats, premium features, or higher-tier plans—based on real product usage, not guesswork.
Onboarding is where great NRR trajectories begin. I focus on compressing time-to-first-value and time-to-second-value because those moments create the habit loops that underpin renewal and expansion. In practice, that means targeted in-app guides, contextual product tours, and nudges that drive user activation across the “sticky” features that correlate most with long-term retention.
To make this scalable, we blend human and product-led touchpoints. CSMs run outcome-based playbooks, while the product experience handles education and reinforcement at scale. When usage signals an expansion opportunity—say, a team consistently bumps into plan limits—we generate a product-qualified expansion lead and equip the CSM with the exact value storyline and proof points to close it.
Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.
I’ve seen this playbook move the needle. After instrumenting our key workflows and deploying targeted in-app guidance, we watched adoption of our highest-retaining features climb, risk flags surface earlier, and expansion conversations become far more data-driven. We didn’t chase shiny objects; we built a reliable pipeline of retained and expanded revenue directly from product usage.
If you’re aiming to level up NRR, start with a crisp blueprint: define the critical events that predict renewal and expansion; set activation milestones per segment; deploy in-app guides and product tours to remove friction; give CSMs a single-pane view of risk and readiness; and review NRR weekly with the same seriousness you apply to new ARR. Consistency beats intensity here.
Finally, keep the narrative simple. Your leadership story isn’t “we shipped features,” it’s “we created customer outcomes.” Tie every CS and product initiative back to NRR drivers—and make the wins visible. When teams see the direct line from great onboarding and adoption to measurable expansion, they naturally operate like a unified, product-led growth engine.
NRR rewards rigor. Treat it as the top-line health metric for your installed base, make the software do more of the teaching, and empower CS to coach to outcomes. Do that well, and you won’t just separate the good from the great—you’ll build a compounding machine.
