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The 2026 Top 100 CLG Awards honored the leaders who turn post-sale into expansion in the Upsell, Cross-sell and Expansion Impact category. Four stories anchor the conversation. Majhon Phillips, Head of Services and Success, Americas at UpSlide (ranked #1 overall in the entire Top 100) reshaped Customer Success from a retention-led cost center into a growth engine that lifted NRR from 109% to 114%. Andressa Oliveira, Senior Customer Marketing at RD Station (#2 overall) turned a community platform into a B2B revenue engine driving 3× higher adoption and 50% higher expansion rates. Josh Brzeszkiewicz, Principal Customer Success Manager at Zendesk, embedded AI workflows so deeply into his CS practice that AI sits alongside him in every customer interaction. And Chad Horenfeldt, Head of CS at Avoca, ran an AI-driven expansion engine that improved GRR by 43% and pushed NRR past 110%.
Three forces are reshaping post-sale at the same time, and most companies are still trying to address each one in isolation.
The first is the renewal-month treadmill. Boards want NRR above 110%. Most companies measure expansion at one moment in the year, 30 days before contract end, when the CSM finally has time to look. The accounts that should have been expansion conversations six months earlier never were, and the accounts that should have been save plays at month four end up as churn at month nine. The function looks defensive because the operating model is defensive.
The second is the fragmentation of customer signal. Product usage lives in the data warehouse. Support sentiment lives in the ticket platform. Adoption metrics live in the CSM platform. Lifecycle engagement lives in the marketing automation platform. Community participation lives in the community platform. Expansion intent lives in CRM notes. The CSM is asked to synthesize all of it into a renewal forecast. The customer marketer is asked to run campaigns based on segment, not signal. The CFO is asked to believe that NRR will improve. None of them have the same picture of the same customer.
The third is the rise of AI as the connective tissue. The 2026 winners below are not using AI as a campaign trigger or a content generator. They are using AI as the layer that connects customer signal to the right action. Josh embedded AI into his daily practice so it sits alongside him in every customer interaction. Chad operationalized expansion signals with AI analysis of usage and support data. Majhon built CSM-sourced expansion plays that surface in the moment. Andressa instrumented a community engine that identifies high-engagement accounts as expansion candidates.
That is the shift. Post-sale stops being a defensive function measured at renewal month. It becomes a revenue-orchestrating function that produces expansion all year and lets renewal month be the result, not the event.
Base AI is the AI engagement OS that runs the post-sale function as one connected motion instead of a federation of tools. Onboarding, success plans, QBRs, lifecycle communications, expansion plays, advocacy, and renewal preparation all sit on one operating layer. The signals that already exist (product usage, support sentiment, advocacy intent, NPS, CSM notes, community engagement) flow into one place. AI agents inside the platform decide the next-best action across the lifecycle in real time. The customer experiences one continuous journey. The CS, CM, and product teams act on the same signal. The CFO measures the function on one dashboard.
The four winners below built pieces of this thesis at their own companies. Majhon connected CSM-sourced expansion plays, Scale CSM motions, Solutions Spotlight campaigns, and an executive community into a single connected journey. Andressa turned a community platform into a B2B revenue engine instrumented to identify expansion candidates. Josh embedded AI into the individual CSM practice so context produces targeted action. Chad rebuilt CS as a system, not a function of heroics.
After the deal closes, the customer enters a fragmented black hole. Onboarding is owned by one team in one tool. Success Plans are owned by another team in another tool. QBRs are run quarterly in slides built from scratch. Lifecycle marketing campaigns are sent from a separate marketing automation platform that does not know what the CSM said in the last meeting. Adoption signals live in the product analytics warehouse. Retention risk lives in the CS platform. Expansion conversations live in CRM notes.
The result is the experience every executive recognizes from the buyer side. The post-sale journey feels nothing like the buying journey. The customer inherited a stack of independent vendors, all employed by the same company, none of them talking to each other. The metric the board cares most about, net revenue retention, sits at the end of that fragmentation, paying the price.
The 2026 CLG winners in the Upsell, Cross-sell and Expansion Impact category have stopped trying to fix this by buying another tool. They have started rebuilding the post-sale layer as one instrumented motion (onboarding, adoption, expansion, advocacy) running as a single system with AI as the connective layer. Here is what that looks like in practice across four very different operating models.
When the judges scored the 2026 Top 100, Majhon landed at #1 overall. The work that earned it reframes what Customer Success is for. Most CS leaders defend revenue. Majhon orchestrates it.
She took UpSlide's Customer Success function and transformed it from a retention-led model into a Customer-Led Growth engine that systematically influences expansion revenue, executive engagement, and community impact. Crucially, she did not bolt expansion onto existing workflows. She designed an integrated lifecycle model (the pattern Base AI customers like Vidyard, ZoomInfo, and Okta run in production) that weaves CSM-sourced expansion plays, Scale CSM motions, Solutions Spotlight cross-sell campaigns, and an executive community program called "The Slide Show" into a single connected journey.
The impact was transformative. Expansion pipeline grew 585%, from $292K to $2M. NRR climbed from 109% to 114%. Churn was cut from 6% to 2.5%. Her team drove 75+ strategic product pitches through Solutions Spotlight campaigns, and "The Slide Show" averaged a 52% executive event attendance rate that added new senior stakeholders to expansion conversations every quarter.
The deeper innovation is the move from program to operating model. Majhon's framework was piloted in the US and is now being rolled out globally as a scalable, repeatable Customer-Led Growth blueprint. For any VP Scaled CS being asked to defend the function as a revenue driver, Majhon's case is the cleanest answer in the awards corpus. Customer Success does not protect revenue. It orchestrates growth.
Andressa's work at RD Station (a TOTVS business unit) earned the #2 spot in the entire 2026 Top 100. "My success as a Community Manager," she explains, "is rooted in the ability to transform community initiatives into a core business driver rather than just a social space." Her story is the proof point every VP Customer Marketing should keep handy when defending community as a revenue function.
"I structured and launched Conecta, our flagship community and education program," Andressa says. The platform scaled to 10,000+ monthly visits in 2025. The strategy was deliberate: turn customers into co-creators rather than content consumers. "My profile," she explains, "combines data-driven community management with a high-touch experiential strategy that turns users into brand evangelists." The downstream commercial results validate the architecture. Engaged community users showed 3× higher adoption and 50% higher expansion rates than non-engaged users. That is the kind of metric that ends the perennial "is community a marketing or a CS function" debate. It is a revenue function.
Andressa extended the model upmarket by launching Prime Conecta, an exclusive community designed for marketing and sales leaders in Brazil. These high-touch events create strategic opportunities to deepen relationships with RD Station's most valuable business partners. They are the seam where community engagement becomes executive engagement, which is the seam where expansion conversations originate.
The lesson for any leader designing a post-sale motion. Community is not a content channel. It is the operating layer where customers self-organize around adoption, advocacy, and expansion. When it is built deliberately, the metrics show up downstream as multiplied adoption and expansion lift.
Most enterprise AI conversations in Customer Success focus on platform-level deployments. Josh operates at a sharper resolution. As Principal CSM at Zendesk, he has integrated AI workflows so deeply into his daily practice that AI sits alongside him in every customer interaction, surfacing small but high-leverage details from customer statements, pain points, concerns, opportunities customers see, strategies they are contemplating, and metrics they are trying to hit.
The output of that context layer is targeted action. "I use these insights," Josh explains, "to help customers adopt the exact product features needed for their goals, and also to direct our cross-functional teams on where larger expansion opportunities sit." The pattern is one most VP Scaled CS leaders should study. AI is not replacing the CSM. It is giving the CSM the contextual depth that used to be the difference between a great CSM and a good one, and standardizing it across the practice.
His north-star KPI is engagement. The more context he gathers, the more targeted the whole organization can be to each customer's strategy. Because his practice is built on cross-functional collaboration, expansion and retention move together in his accounts. He is accountable for retention, but the cross-functional flywheel he has built means expansion comes naturally and reinforces retention. His Top 100 recognition is the answer to a question every VP CS asks. What does world-class AI-augmented CSM practice actually look like at the account level? Josh's work is the case study.
Chad's framing of the Scaled CS problem is sharper than most. The reason expansion is unpredictable in mid-market and enterprise CS is that it usually relies on individual CSM performance instead of an operating system. He has spent his career proving the alternative, now as Head of CS at Avoca.
The case study that earned his Top 100 recognition is from a prior role. "We shifted from reactive account management to a structured, value-led growth engine," Chad explains. "We moved away from check-ins and built ROI-driven conversations." He re-engineered the CS function into a value-led growth engine with three moves. First, every customer interaction tied product usage to a defensible business outcome, and expansion became a natural next step instead of a separate motion. Second, "we used AI to analyze product usage, support data, and customer conversations to identify expansion opportunities early," so the team could prioritize accounts with real growth potential instead of relying on gut feel. Third, he built the system: segmentation (Strategic / Growth / SMB), clear ownership across CS, Support, and Services, and repeatable upsell and cross-sell plays.
The results reframe what predictable expansion looks like. GRR improved 43%. NRR climbed past 110% through multi-product adoption and deeper usage. A scalable expansion engine was built that did not rely on individual CSM heroics. The AI use went one step further. Chad uses it to surface advocates and customer stories across recorded calls and support tickets, turning unstructured conversation data into both retention intelligence and reference pipeline. The lesson for any VP Scaled CS chasing NRR: expansion is not earned through pressure. It is earned through value, and value is operationalized through a system.
Four very different roles (#1 and #2 award winners at the strategy level, an individual CSM practice, and a system-builder turned department head) and the same architecture.
If your post-sale function still operates as a federation of teams with separate tools, separate dashboards, and separate definitions of the customer, the 2026 Top 100 pattern translates directly. Unify the post-sale layer as one journey. Instrument it end-to-end. Layer AI where it compresses the seams. Measure the function on expansion orchestration, not just retention.
That is what Base AI operationalizes. Onboarding, success plans, QBRs, lifecycle communications, advocacy programs, references, and expansion plays all run on one operating layer. The signals that already exist (product usage, support sentiment, advocacy intent, NPS, CSM notes, community engagement) flow into one place. AI inside the platform routes the signal to the next-best action. The customer experiences one continuous journey. The CS, CM, and product teams act on the same picture of the same customer. The CFO sees NRR move on one dashboard.
The next blog in the series goes deep on the discipline that ties the entire CLG model together. Turning advocacy and community into a measurable growth engine.
Next in the series: How to Scale Customer Advocacy with AI in 2026 (4 Programs Driving Real Revenue) →
→ See how Base AI operationalizes this pattern: Expansion Solution · Retention Solution · Base AI Platform
See how Base helps you build advocacy programs that drive growth.
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