Latest insights and best practices
Upcoming events and on-demand content
Celebrate customer-led growth leaders
Review plans, watch recap, and engage with speakers
Comprehensive guide to customer marketing
Templates from the EverAfter Success Stack library
Key terms and definitions
Framework and guide to customer-led growth
The 2026 Top 100 CLG Awards honored the leaders who turn onboarding and adoption into a revenue function. Four winners exemplify the discipline. Nate Alcorn, Manager of CX Operations at ZoomInfo, designed autonomous, self-paced onboarding journeys for downmarket segments using behavioral signals and targeted human intervention. Mariah Urueta, Scaled Customer Success Manager at Asana, turned recurring customer questions into infrastructure (virtual Office Hours, repeatable educational resources, cross-functional health-signal alignment) at thousands-of-customer scale. Gina Frazee, Senior Director of Customer Experience & Adoption at UserTesting, ran a connected digital journey that produced a 14:1 self-service-to-support ratio and 61.5% of new creators launching a test within 30 days. And Radhika Narayanan, Customer Marketing Lead at Freshworks, ran a dual-layer AI adoption motion that delivered 74.7% cohort engagement, 11× configuration lift, and a 3.5-point NDR delta in six weeks.
Three forces are reshaping the activation moment in 2026, and most companies are still trying to address them as separate problems.
The first is the cost of slow onboarding. The CSM that spent two hours on a kickoff call could have spent fifteen minutes on a recorded walkthrough. The product team shipped features the customer never finds. The lifecycle marketing platform sends generic education to a customer who is already three milestones past it. The CSM platform shows "healthy" because the customer logged in twice, not because they activated the workflow that will produce ROI. Time-to-first-value stretches from days into weeks into a renewal conversation where the customer is not sure what they have actually gotten yet. The CFO question that follows is the one that defines the next budget cycle. Why is NRR not higher when we are paying for all of these CS tools?
The second is the volume of customer signal that goes unused. Every customer generates a stream of behavioral data (in-app events, support tickets, community posts, NPS responses, certification progress) that should be routing to the next-best action. Most companies still send the same email to the same segment on the same schedule, regardless of what the customer is actually doing. AI is supposed to fix this. Most implementations of AI do not.
The third is the rise of AI as the activation layer. The 2026 winners below are using AI as the substrate that routes the right intervention to the right customer at the right moment. Nate uses behavioral signals to deploy human intervention precisely. Mariah uses health signals to align cross-functional engagement. Gina uses chatbot resolution to compress support load. Radhika uses dual-layer cohort logic to convert AI adoption into NDR.
That is the shift. Onboarding stops being a service. It becomes an instrumented system that produces time-to-value as a leading indicator of NRR.
Base AI is the AI engagement OS that runs onboarding, adoption, lifecycle communications, success plans, and CSM motions on one connected operating layer. Customer signal (in-app events, support sentiment, certification progress, NPS responses, community participation) flows into one place. AI agents inside the platform decide the next-best action in real time. A stalled milestone triggers an automated nudge with the right resource. A strong adoption pattern triggers a CSM expansion conversation. A low-confidence chatbot resolution escalates to a human. The CS team measures the function on time-to-first-value, feature activation, retention, and expansion lift on one dashboard.
The four winners below are doing pieces of this thesis at their own companies. Nate built ZoomInfo's onboarding as a systems-design problem. Mariah turned recurring customer questions into permanent infrastructure at Asana. Gina built UserTesting's connected digital journey as a 14:1 self-service-to-support engine. Radhika ran the lifecycle marketing motion that converted Freshworks' AI Copilot into measurable NDR lift.
Onboarding is the most expensive moment in the customer relationship and the moment most companies still get wrong. The CSM spends two hours on a kickoff call that could have been a 15-minute self-serve walkthrough. The product team ships features the customer never finds. The lifecycle marketing platform sends generic education to a customer who is already three milestones past it. The CSM platform shows "healthy" because the customer logged in twice, not because they activated the workflow that will produce ROI.
The result is the experience every executive recognizes from the buyer side. The buying process was sharp, the contract was signed quickly, and then everything slowed down. Time-to-first-value stretches from days into weeks into a renewal conversation where the customer is not sure what they have actually gotten yet. The CFO question that follows is the one that defines the next budget cycle. Why is NRR not higher when we are paying for all of these CS tools?
The 2026 Top 100 winners below have stopped trying to fix this by hiring more CSMs or buying another adoption platform. They have started building the onboarding-and-adoption layer as an instrumented system. Milestone-driven journeys, behavior-triggered intervention, AI-driven lifecycle cohorts, instrumented learning programs that compresses time-to-value and turns adoption into a leading indicator of NRR. Here is what that looks like across four very different operating models.
Nate does not think like a customer marketer or a CSM. He thinks like a systems designer. "I lead CX Operations at ZoomInfo," he explains, "where my focus is modernizing how Customer Experience scales."
His starting premise is sharp. "I believe Customer-Led Growth does not begin with campaigns." It begins with how customers are onboarded, activated, and empowered to succeed independently. Over the past year, he led the modernization of ZoomInfo's Digital CX motion. Rather than defaulting to manual, high-touch onboarding for all customers (the model most companies use because it is the path of least operational design) Nate's team built autonomous, self-paced, personalized onboarding journeys for downmarket segments. "We built milestone-driven digital journeys," Nate explains, "that combine guided workflows, behavioral signals, and targeted human intervention at key inflection points." The goal was not to reduce support. It was to deploy support intentionally, where the human touch creates disproportionate value.
The philosophy that animates the work is the part every onboarding leader should steal. "My philosophy is simple. Simple first. Durable always. Mature over time." Nate is not chasing the most elegant onboarding map. He is reducing variance, eliminating heroics, and building durable operating systems that let onboarding scale without losing intention. The metrics reflect the system. Time-to-first-value, milestone completion within self-paced journeys, weekly active usage lift among downmarket customers, 30 to 60 day core feature activation, and post-activation expansion-ready account volume. The deepest indicator is reduction in variance between similar customer segments. The metric that proves the system, not the operator, is producing the outcome.
Mariah's story is the cleanest illustration in the 2026 Top 100 of how a single Scaled CSM rebuilds onboarding and adoption at population scale without expanding the team. "My formula," Mariah says, "scrappy builder + systems thinker, is what makes me effective in scaled CS."
In her first month at Asana she noticed something most CSMs notice and most companies never act on. Customer calls surfaced the same handful of workflow, reporting, and validation questions again and again. The instinct in most CS functions is to answer the questions one customer at a time and let the volume become a headcount argument. Mariah asked a sharper question. How do we solve this once, well, at scale for thousands of customers?
The work that followed earned her Top 100 recognition. She launched virtual Office Hours that gave the same answer to hundreds of customers at once. She built repeatable educational resources (the durable infrastructure that replaces the next 200 1:1 calls). She partnered cross-functionally to align customer-health signals with engagement strategy so the team could see who needed intervention before the renewal conversation. The operating model underneath is the part to copy. Every initiative starts as a hypothesis, launches fast, measures real behavior, then formalizes what works into infrastructure. She measures success by impact-vs-effort, tracks customer touchpoint consolidation, and watches the shift from 1:1 reactive work to 1:many scaled engagement.
The deeper takeaway for any VP Scaled CS leading a team of CSMs and watching them drown in 1:1 calls. Every recurring question is a system gap, not a CSM gap. Solve it once, well, and the customer base gets faster without the team getting bigger.
Gina's framing is unusually direct. "I lead Customer Experience, Learning, and Adoption with one guiding principle," she says. "Digital Customer Success should drive revenue, not just engagement." At UserTesting, where she leads Digital Customer Experience, Learning, and Adoption, she has built a connected digital journey that turns learning, in-product guidance, self-service, advocacy, and customer insight into a scalable engine for adoption and growth. "My work," Gina explains, "focuses on transforming education, community, and advocacy from support functions into scalable growth engines."
The infrastructure is layered with intent. Structured certification programs and persona-based learning paths meet customers where they are in their journey. Milestone-based customer anniversary programs reinforce value at the moments that matter for renewal. A stronger customer voice platform feeds insight back into the system. The expansion of self-service (through a comprehensive Knowledge Base and chatbot) produces the leverage that lets the team scale.
The metrics reflect what happens when adoption is instrumented as a revenue function. 24,281 Knowledge Base users in a single month. A 14:1 self-service-to-support ratio. 58% chatbot resolution without escalation. 61.5% of 378 new creators launching a test within 30 days after engaging with in-app tours, a time-to-first-value signal most companies struggle to produce. On the learning side: completion rates rose to 60%, CSAT reached 98%, returning learners more than quadrupled, and certifications increased as learners progressed deeper into the journey. The lesson for any leader running customer education and adoption. When the layers are connected and instrumented, adoption stops being a cost center and starts producing the metrics expansion teams already trust.
Freshworks sold Freddy AI Copilot strongly. Then post-purchase activation lagged. Large cohorts sat below 30% license configuration and 70% agent activation. Adoption plateaued, churn risk climbed in low-usage segments, and AI revenue was at risk of becoming AI write-off.
"I lead Lifecycle Adoption and Retention for Employee Experience (EX) products at Freshworks," Radhika says, "where my focus is on turning product purchases into sustained usage, expansion, and long-term retention." She was tasked with operationalizing AI adoption at scale, and she did it by treating lifecycle marketing as the activation infrastructure. She designed a dual-layer lifecycle framework. A 21-day Onboarding Journey drives time-to-first-value for newly purchased Copilot accounts. License assignment, configuration, first agent activation, before stagnation can set in. A 45-day Adoption Journey reactivates low-adopter cohorts, moving customers through Purchase → Configuration → Active milestones with defined entry and suppression rules, cool-off periods, re-entry triggers, and cohort governance cycles.
"I engineered a synchronized, milestone-driven engagement ecosystem," Radhika explains, "across digital, educational, and human touchpoints." The execution synchronized email nurture aligned to milestone triggers, in-app nudges at the moment of action, live Masterclass webinars, Freshworks University certification paths, peer community, and white-glove CSM touches for high-value accounts. Governance came from a cross-functional Adoption Steering Committee spanning Product, Analytics, Marketing Ops, CS, Pro Services, Brand, and Advocacy.
Within six weeks: 74.7% engagement across the targeted cohort, 11× configuration lift, 9× activation lift, 133 new AI Copilot licenses expanded, and 159 accounts progressed across the adoption matrix. The Net Dollar Retention reading told the board the real story. Engaged accounts hit 107.1% NDR vs 103.6% for non-engaged, a 3.5-point delta in a single quarter on a core AI portfolio. The lesson for VP CX and VP CS leaders. When AI is the product being adopted, lifecycle adoption marketing is the revenue motion.
Four leaders, four operating models, one architecture.
If your onboarding function still depends on individual CSM motion to walk every customer through every step, and your adoption signals only surface at renewal time, the 2026 Top 100 pattern translates directly. Design onboarding as a system. Instrument time-to-value as the leading indicator. Layer AI on the activation seams. Route the CSM hours that come back into strategic, high-impact customer work.
That is what Base AI operationalizes. (Customers like Vidyard, ZoomInfo, and Okta already run this pattern in production.) Onboarding, adoption, lifecycle communications, success plans, and CSM motions all run on one operating layer. The signals that already exist (in-app events, support sentiment, certification progress, NPS responses, community participation) flow into one place. AI agents inside the platform decide the next-best action in real time. A stalled milestone triggers an automated nudge. A strong adoption pattern triggers a CSM expansion conversation. A low-confidence chatbot resolution escalates to a human. The CS team measures the function on time-to-first-value, feature activation, retention, and expansion lift on one dashboard.
This blog closes the 6-part 2026 CLG Awards series. The operating playbook for Customer-Led Growth in the AI era, drawn directly from the leaders who built it.
→ See how Base AI operationalizes this pattern: Onboarding Solution · Base AI Platform · The CLG Playbook
Next in the series: Start the series: AI in Customer-Led Growth — 4 Lessons from the 2026 Top 100 Leaders →
See how Base helps you build advocacy programs that drive growth.
Book a demo
