Customer Success

AI, Customer Success

6 AI for Customer Success Use Cases

6 AI for Customer Success Use Cases CSOps 12 min Updated: August 3, 2026 Efficiency has been the dominant theme in tech stack decisions for several years running: if a tool doesn’t clearly improve ROI, it gets cut. That pressure lands hardest on customer success, where the difference between a renewed account and a churned one increasingly comes down to one thing: a genuinely meaningful relationship with the customer. The numbers back up why that relationship matters so much. Acquiring a new customer costs five to ten times more than retaining an existing one, and existing customers are consistently easier to sell to than new prospects, since they’ve already validated your product and built trust with your team. With that much upside on the table, AI has become the obvious lever for customer success teams to reach for, the question is where it actually earns its keep. AI can meaningfully improve net retention rate (NRR) and the customer success processes underneath it. Yet adoption has historically lagged the opportunity: the Customer Success Collective’s State of Customer Success 2023 report found 66% of customer success professionals weren’t using AI in their role at all, a real gap at the time, and one worth checking against more current research given how much AI tooling has advanced since. This guide covers why AI matters for customer success specifically, and six concrete use cases to build around. Get our latest insights into your inbox How Has Customer Success Changed? Customer behavior keeps shifting. Preferences, pain points, and priorities evolve continuously, which means CS has to be agile and quick to adapt rather than running a fixed playbook indefinitely. Remote and hybrid CS is the norm. More customer success roles operate remotely than ever, and the tools and processes supporting them have to work as well outside an office as inside one. Digital-led operations are standard. Onboarding is increasingly automated, and generative AI in chatbots resolves customer questions using real account context rather than a generic script. The growth outlook has shifted from acquisition to resilience. Businesses have moved from “growth at all costs” toward driving more value from existing customers and existing tools, which raises retention’s importance relative to new-logo growth. Churn prediction has become a genuine discipline. Monitoring customer health to catch potential churn early, so a CSM can intervene before sentiment turns, is now a core CS function rather than a reactive afterthought. Why You Need AI for Customer Success Now Retention matters more than ever, and continuously proving value to customers, efficiently, is the harder half of that job. The challenge is scale: the sheer volume of customer behavioral data generated daily makes it genuinely difficult for a CSM to find the signal that actually matters, and even when the data is found, translating it into an unforgettable customer experience takes more than access, it takes the right tooling to act on it. Left unaddressed, that gap shows up as customers who feel forgotten, unclear on how to get value from your product, and quietly disengaging. The upside of closing it is real: even a 5% increase in customer retention can increase revenue by 25-95%. A few more reasons AI earns its place in a CS stack specifically: It automates workflows without replacing the human relationship, cutting reliance on manual data entry so CSMs spend more time actually building relationships. It surfaces more accurate, complete data from a pile most humans can’t realistically comb through manually, cleaner data means sharper insight. It flags sentiment shifts, positive to neutral to negative, and suggests next-best steps to course-correct before the relationship actually sours. It highlights new ways for customers to get value from your product, driving additional value continuously rather than only at renewal time. It shifts CS from reactive to proactive, since a health-score change is visible before a support ticket or a cancellation notice ever arrives. 6 Use Cases of AI for Customer Success 1. Track and Evaluate CSM Activities Most CS leaders can tell you how many accounts a CSM owns. Far fewer can tell you, with any real precision, what that CSM actually did on those accounts last month, or whether it worked. Activity tracking in CS has historically meant a task log or a calendar count: how many calls, how many emails, how many meetings. That tells you volume. It doesn’t tell you whether the activity was any good. The distinction matters because CSM effectiveness varies enormously even at the same activity volume. Two CSMs can each log fifteen customer touchpoints in a month, one spending that time in surface-level check-ins with a low-influence contact, the other running structured business reviews with the actual economic buyer. A dashboard counting fifteen touchpoints treats both CSMs identically. A CS leader trying to coach, staff, or forecast off that dashboard is working from a genuinely misleading picture. This is where Nektar’s CSOps capability is built to do more than log activity, it evaluates it. Every customer interaction is treated as a chance to understand its actual impact on revenue growth or churn mitigation, not just as a box checked on an activity report. Specifically, Nektar: Monitors whether activities are actually taking place, closing the gap between what a CSM reports doing and what the captured email, meeting, and call data actually shows happened, without requiring the CSM to self-report. Assesses effectiveness with detailed insight, not just a raw count. The same underlying data that shows an interaction happened also shows which persona was involved, when it happened relative to the renewal timeline, and what kind of meeting it actually was, a QBR, a support escalation, a casual check-in, each of which carries a different signal about deal or account health. Surfaces patterns a manager can actually coach against. Once activity is tied to persona, timing, and meeting type, a CS leader can see, account by account and CSM by CSM, whether engagement is concentrated in low-influence check-ins or genuinely reaching the stakeholders who matter, and use that as the basis

AI, Customer Success

Transforming Customer Success in the Age of AI

Rebuilding Customer Success for the AI Era: Lessons from a VP of Customer Success A conversation with Chad Gorman. Executive Summary This article examines how customer success leaders should rethink AI adoption, using insights from an in-depth conversation with Chad Gorman, VP of Customer Solutions and Success (North America) at LivePerson, on the Revenue Lounge Podcast hosted by Randy Likas. Rather than focusing on automation or AI features, Gorman argues that AI-ready customer success is fundamentally about visibility, data discipline, and relationship intelligence. Readers will learn: Why most AI initiatives in customer success fail before deployment  How unifying fragmented CS data is a prerequisite for AI or automation What an effective early warning system looks like  Why engagement and relationship depth are stronger leading indicators than product usage alone How AI can expose relationship “white space” across complex buying committees Where buy vs build decisions actually differ across enterprise and mid-market segments How to embed AI into CSM workflows over-relying on automation Which metrics matter when measuring AI’s impact on retention, risk, and productivity Why the real promise of AI in customer success is reclaimed time for strategic customer work Facebook Twitter Youtube From Call Centers to Customer Outcomes Gorman’s perspective is shaped by an unusual career arc. He started in contact center operations, moved into IT at DirecTV, and then crossed over to the vendor side after a colleague recruited him to Splunk. “I didn’t even know what a CSM was,” he admits. “But once I saw how customer success could be built as a scalable engine, I was hooked.” — Chad Gorman From Splunk, he went on to lead global cloud customer success at VMware, before joining LivePerson, where he now oversees customer success and professional services across North America. That mix of operator, builder, and enterprise leader shows up in how he thinks about AI. Practical. Outcome-driven. Skeptical of hype. ​​AI Adoption Fails Before Deployment Most AI initiatives stumble long before a model is ever deployed. According to Gorman, the real friction points show up earlier in the buying and approval cycle. The Hidden Gates to AI Adoption Governance reviews and AI councils Legal, compliance, and security documentation Industry-specific scrutiny, especially in financial services Undefined success metrics “You can sell software all day long. But if you are not there to shepherd customers through governance, compliance, and approval gates, adoption will stall.” — Chad Gorman Customer Success Has Become Revenue Insurance In volatile markets, customer success is no longer a post-sale support function. It is a revenue protection layer. That shift forces CS leaders to answer harder questions: Where is risk building right now? Which accounts look healthy but are quietly disengaging? Where is expansion hiding in plain sight? The answer, Gorman says, is an early warning system built on stitched data. https://youtu.be/sDdV747jBJA?si=fVE8O2bqTcf2yTeN The Anatomy of an Early Warning System Gorman is blunt about the prerequisite. “Data is non-negotiable. Full stop.” — Chad Gorman Before AI enters the picture, organizations must understand what their book of business actually looks like. Engagement Is the Most Underrated Risk Signal Product usage is table stakes. Engagement is the differentiator. Gorman emphasizes that many churn events are preceded not by usage decline, but by relationship decay. “If engagement drops and you do not notice, you end up ghosted and surprised later.” — Chad Gorman What Engagement Actually Means Engagement is not email volume or meeting counts alone. It is relationship depth across the buying group. Who shows up to meetings? Who stopped showing up? Which roles are missing entirely? Who influences decisions but never engages directly? This is where Gorman believes AI has its most immediate impact. Relationship Intelligence: Where Art Meets Science Gorman describes relationship intelligence as the intersection of human judgment and system-derived insight. “We think we know our accounts. AI shows us the white space we missed.” — Chad Gorman AI-Assisted Relationship Mapping AI can analyze: Calendar data and meeting attendance Email and collaboration patterns Role changes and stakeholder turnover Sentiment from meeting notes and transcripts At LivePerson, Gorman’s team increasingly relies on workspace-level intelligence using Google Gemini to surface patterns across meetings, documents, and communications. You can literally ask, ‘Who used to attend and no longer does?’ and get an answer.” — Chad Gorman Buy vs Build Is No Longer Binary Enterprise customers increasingly want flexibility. Some bring their own LLMs. Others rely on vendor-provided AI. Most land somewhere in between. Enterprise: Build and bring your own models Upper mid-market: Hybrid Down-market: Out-of-the-box AI The common denominator remains the same: clean, structured, accessible data. Embedding AI Into CSM Workflows Even the best insights fail if CSMs do not trust them. Gorman stresses three adoption levers: Data transparency Always link insights back to source systems. Prescriptive guidance Do not just flag risk. Recommend next steps. Respect experienceAI should augment gut instinct, not override it. Measuring AI Impact in Customer Success AI success is not measured by novelty. It is measured by outcomes. What’s Next: Agentic AI and Time Reclaimed The next wave, according to Gorman, is not better summaries. It is execution. The thing CSMs hate most is admin. AI agents that actually do the work change everything.” — Chad Gorman Examples include: Auto-generated QBRs with live data Scheduled reporting without manual pulls Automated follow-ups and task execution The payoff is not speed. It is reclaimed time for strategic customer work. Leadership Lessons From the Field When asked what advice he would give his younger self, Gorman’s answer is simple. “Know your book. Be curious. Admit what you do not know.”” — Chad Gorman Growth mindset Curiosity within and beyond the “box” Meticulous organization Executive presence Tight partnership with the AE  Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

Customer Success

Proactive Strategies for Growth & Engagement in Customer Success

Proactive Strategies for Growth & Engagement in Customer Success A conversation with Daniel Silverstein, VP of Customer Success & Head of Business at Carta. Customer Success has long carried a reputation as the team that steps in when something goes wrong. For many organizations, CS is positioned as a problem-solver, a renewals manager, or, worse, a support escalation point. But in today’s reality, where retention and expansion are the real engines of growth, that view of Customer Success is not just outdated, it’s dangerous. We spoke about this to Daniel Silverstein, VP of Customer Success and Head of Business at Carta. Over nearly six years, Daniel has helped turn a small, reactive post-sales team into a proactive, lifecycle-driven engine that now supports nearly 30,000 customers. His philosophy is simple yet powerful: revenue should not be forced; it should flow naturally as the byproduct of deep engagement, education, and timing. “Revenue is the result of the engagement. It’s not the purpose of it.”— Daniel Silverstein, VP of Customer Success at Carta This blog unpacks Daniel’s playbook: how to operationalize “moments that matter,” build scalable engagement models, and use data creatively when traditional adoption metrics don’t apply. Along the way, you’ll find infographics, templates, and checklists you can use to design a CS motion that doesn’t just retain customers. It accelerates their growth, and yours. Facebook Twitter Youtube Carta’s Starting Point: From Firefighting to Strategy When Daniel joined Carta in early 2019, the post-sales organization was a skeleton crew of five people. Their job was to wait for the phone to ring — firefighting when customers had issues, occasionally upselling without much repeatability, and otherwise remaining largely reactive. Carta itself was already a critical part of the private company ecosystem. Known as the cap-table management platform, Carta became the single source of truth for equity ownership. Whether you were a founder issuing stock options, a shareholder tracking your holdings, or a CFO managing dilution, Carta sat at the center of the equity lifecycle. The problem? Customers didn’t interact with the product daily. Unlike collaboration or productivity tools, cap-table management tends to be episodic. It spikes at key lifecycle moments — fundraising, audits, new share classes, compensation planning — and then recedes. This made traditional product adoption metrics useless as a barometer of customer health. Daniel’s mandate was clear: build an end-to-end post-sales strategy that could scale across tens of thousands of customers, drive revenue through expansion and retention, and, most importantly, deliver value at the right time. Why Daily Usage Metrics Don’t Work. And What to Track Instead In many SaaS companies, CS leaders live and die by usage data: logins, daily active users, feature adoption rates. At Carta, those metrics simply didn’t make sense. Customers didn’t need to log in every day — but they did need Carta to be correct, compliant, and ready for high-stakes events. This forced Daniel and his team to think differently. Instead of measuring frequency of use, they began tracking lifecycle and compliance signals. These signals became leading indicators of customer engagement, satisfaction, and future expansion. Key signals included: Whether a customer had an up-to-date 409A valuation. Gaps between issuances sent and issuances accepted. The health of HRIS integrations. Changes in company admins or CFOs. Whether the customer cleared the top 15 “health checks” that predict transaction readiness. Together, these signals painted a far richer picture than simple login counts. If adoption lagged, it might mean a churn risk. If a 409A was missing, it could mean a compliance problem. If new share classes appeared, it likely meant a fundraising round was imminent — a perfect time to engage. https://www.youtube.com/watch?v=KK436mBkToI&t=536s The Heart of Carta’s Strategy: “Moments That Matter” Instead of chasing customers with generic check-ins, Daniel built the CS motion around “moments that matter.” These are inflection points in a company’s lifecycle where Carta can provide outsized value — and where thoughtful engagement builds trust and, eventually, revenue. Consider just a few examples: New share class created → A likely fundraising or structural change. Carta CSMs reach out with planning guides and compliance checklists. Company admin changes → A new persona joins the account. Carta triggers a tailored onboarding flow, with education based on whether the new admin is in finance or HR. 409A out of date → A compliance risk. CSMs advise on timelines, audit defensibility, and why an updated 409A matters. Large hiring round (e.g., post-Series A) → HR workflows get complex. Carta introduces its total compensation tool. “If we’re doing it right, we leave the customer with something they didn’t know — and a plan for what’s next.”— Daniel Silverstein Scaling Engagement: High Touch, Medium Touch, and Tech Touch Supporting 30,000 customers with a lean CS team meant Daniel needed to segment ruthlessly. Carta developed a three-tiered approach: High Touch: Growing accounts with strong valuations, shareholder expansion, or new fundraising. These accounts got proactive EBRs, white-glove guidance, and strategic planning. Medium Touch: Accounts showing some, but not all, growth markers. CSMs engaged regularly but scaled their effort through playbooks and templates. Tech Touch: Accounts with limited growth signals or maxed-out product adoption. Engagement here leaned heavily on Carta’s digital library of 45-second explainer videos, community forums, and automated emails triggered by lifecycle signals. This segmentation ensured that every customer received value, but CSM bandwidth was directed where it mattered most. Turning Engagement into Expansion One of the most powerful insights from Daniel’s philosophy is that expansion is not the starting point. It’s the outcome. When Carta educates customers at the right moment, expansion follows naturally. For example, when a customer approaches an audit window, Carta doesn’t start with a sales pitch. Instead, they provide a detailed briefing on what the audit will require, what compliance risks exist, and how companies at a similar stage prepare. The conversation naturally leads to Carta’s stock-based compensation module. “Get there a couple of clicks ahead of whatever is going to happen next. The revenue comes back in when it needs to.”— Daniel Silverstein The Playbook Engine

ai in customer success
Customer Success

Empowering Customer Success Through Data & AI

Empowering Customer Success Through Data & AI A conversation with Aditya Vasudevan, former VP of Customer Success at Cohesity. Customer success has grown from a reactive support checkpoint into a deliberate, strategic engine for growth. But in an ocean of customer data, how can organizations extract meaningful insight, respond in real time, and nurture long-term loyalty—especially when budgets are always tight? We spoke with Aditya Vasudevan, former VP of Customer Success at Cohesity, a visionary who has transformed raw telemetry into timely triggers, dashboards into human-centric nudges, and silos into insights, all powered by data and AI. Facebook Twitter Youtube From Engineer to Customer Champion Aditya’s genesis story starts like many technologists’: he began life as an engineer but quietly discovered his true calling was with people—especially customers. Over 22 years, he journeyed through roles at Capgemini, VMware, Hitachi, and even ran his own Kubernetes-focused startup. That hands-on run-up, solving real customer problems in code, steadily shifted his path—not away from tech, but toward how technology meets human need. “I enjoyed finding where customers derive value out of a product… iterating the product… pre-sales, post-sales, and success.” Cohesity, with its mission to protect enterprise data from modern threats like ransomware, became his canvas—first leading solution architects to win Fortune 10 accounts, then steering the customer success ship itself. In that role, he faced head-on the growing pains of using data and AI to meet the evolving expectations of enterprise-scale customer success. When Running Lean Demands Smarter Playbooks In the world of customer success, budget isn’t elastic. “When is the last time your CFO gave you enormous budget to build customer success teams? Probably never. That’s why data and AI matter. They help you do more with less.” Thanks to this financial reality, Aditya and his team embraced a philosophy: scale through precision, not people. Data and AI didn’t replace the team—they upped the game of every individual. The AI Advantage in CS Imagine a platform that: Spots at-risk accounts earlier than a human might. Detects expansion opportunities without guesswork. Sends timely nudges along a digital journey. Prioritizes the right action—at the right time. That’s the power of a data-driven strategy in CS. It pays off in both retention and impact. https://www.youtube.com/watch?v=ent5fDPwls8&t=409s The Hidden Hurdles: Data Isn’t Always Your Friend Behind every shiny AI dashboard lies a set of sobering hurdles: Data Availability – Without product telemetry, you’re flying blind. Traditional companies often lack insights on whether customers are even using the product. Data Sprawl – Usage metrics, CRM entries, support cases… all scattered across systems. Aditya’s answer: consolidate into a data warehouse like Snowflake. Data Accuracy – Garbage in, garbage out. Trust must be earned via spot-checks and validation. Only once these are solved can you start asking sharper questions and building reliable automation and AI layers. The CS Data Maturity Model Stage Description 1. Manual Tracking High-touch, intuition-led, human to human 2. Data Consolidation Central data warehouse (Snowflake) 3. Insight Visualization Dashboards, renewal risks, adoption tracking 4. Automation Digital nudges, renewal alerts, playbook triggers 5. AI-Driven Insights Sentiment from case logs, pattern deviation alerts Building Toward Intelligence: The Layered Strategy At Cohesity, the progression looked like this: Data Warehouse – We combined telemetry, support, and CRM data into a central hub. BI Layer (Tableau) – Clean, contextual dashboards visualizing adoption, risk, and opportunity. Automation – Renewal risk lists auto-generated for CS teams to act on. Digital Journey Mapping – Identifying deviations from healthy product usage, sending nudges, and escalating where needed. AI Anchors – Sentiment analysis and LLM signals feeding into dashboards as “red/yellow/green” risk markers. “Every customer has a journey with your product. If they’re not following the right pattern, you nudge—digitally or by a call.” Customer Digital Journey Playbook: A Template Stage Signal to Track Healthy Behavior Nudge if Missing Owner Onboarding Deployment logs Full setup achieved Trigger email guide CSM Adoption License usage ≥80% of seats active Proactive check-in CSM Expansion Feature adoption 3+ features utilized QBR upsell recommendation CSM/AE Renewal Preparation Support cases + NPS Positive sentiment CS leader escalation CS Leader   Where AI Adds Real Punch Let’s be clear: much of CS data like usage stats and renewal dates is best handled through analytics, not AI. However, unstructured data, especially from support tickets or emails, is fertile ground. AI can detect: Negative sentiment Competitor mentions Subtle engagement shifts Suddenly, dashboards become smarter—and CS teams get sharper signals. “AI is best with unstructured data. Sentiment analysis in cases and emails augments statistical dashboards and surfaces risk earlier.” The Results: Tangible Impact at Scale Taking telemetry, dashboards, automation, and AI together produced striking results: 98% CSAT in the last quarter Highest retention rate in company history Increased adoption—making customers more secure Peace of mind for CS teams—‘one-stop’ visibility, fewer frustrations “Efficiency gains meant our team could cover more accounts with less frustration. Customers benefited with higher adoption and stronger security.” Next Level Strategy: Expansion Intelligence Data also became a beacon for new revenue—not just retention: Usage Gaps: When peers in the same vertical back up more asset types, the gap becomes an upsell opportunity. Compliance Patterns: Financial customers usually maintain 3 backup copies. Falling short surfaces cross-sell potential. Smarter QBRs: Instead of “nice to haves,” CSMs deliver pointed insights—“…you’re missing your second and third backup copy; here’s how to complete your security posture.” Expansion Opportunity Framework Signal Context Example CS Action Outcome Usage Gap Only 2 of 5 assets backed up Propose securing additional assets Upsell Compliance Gap Single backup copy only Recommend adding secure copies Expansion Plateau in Adoption Feature under-used Suggest supplemental training Higher adoption First Steps for CS Teams: Start Simple, Scale Smart Aditya’s advice for leaders just getting started: Nail the manual process first, define value, and own metrics. Build telemetry early, even basic logging helps. Ensure data quality before layering automation. Launch with dashboards, identify risk clusters. Pilot AI projects, like sentiment detection or journey mapping. “If you don’t have the manual process figured out, going digital is harder.

consumption based selling
Customer Success

Transforming Customer Success into a Revenue Engine with Consumption-Based Selling

Transforming Customer Success into a Revenue Engine with Consumption-Based Selling A conversation with Santosh Sahoo, Global Leader – Consumption Selling at Mulesoft. The enterprise software world is undergoing a seismic shift. Traditional seat-based licensing models are giving way to consumption-based pricing, where customers pay only for what they use. Popularized by infrastructure leaders like AWS and Snowflake, and now supercharged by AI-driven applications, this approach is reshaping how companies think about value, revenue, and customer success. For Customer Success leaders, this isn’t just an operational change. It’s an opportunity. Customer Success is no longer a “cost center” focused on retention—it’s becoming a monetizable growth lever. Through consumption-based selling, every interaction, use case, and success plan can translate into revenue while ensuring customers get immediate, measurable value. In this blog, based on an insightful conversation with Santosh Sahoo of MuleSoft, we’ll unpack: Why the industry is moving to consumption-based selling The challenges and opportunities this model creates How Customer Success leaders can monetize their efforts Best practices and frameworks to operationalize the shift Facebook Twitter Youtube Why Consumption-Based Selling Is the Future SaaS began with subscription models (think Salesforce’s seat-based pricing). Then came infrastructure providers like AWS, which introduced pay-as-you-go for compute and storage. The **third wave—AI-powered applications—**is now pushing consumption to the forefront. “Every new AI product is inherently consumption-oriented. Customers want to pay per transaction, per query, per outcome—not per seat. It’s a true utility-based model.” – Santosh Sahoo Unlike seat licenses where customers pay upfront for promised value, consumption aligns cost with actual usage. This eliminates shelfware, increases transparency, and ensures value delivery happens side-by-side with spending. Evolution of Pricing Models Stage 1: Seat-Based SaaS Stage 2: Infrastructure-as-a-Service (IaaS) Stage 3: AI & Transaction-Based Pricing Internal Shifts: Rethinking Sales, CS, and GTM Alignment Moving to consumption-based selling isn’t as simple as changing pricing. It requires a redesign of the entire go-to-market model: Product Design – Ensure your product can be consumed in granular units (transactions, queries, credits). Pricing Strategy – Move from promises to usage-based models. Sales Incentives – Comp plans must reward continuous expansion, not just large upfront deals. Customer Success Enablement – CSMs must move from “relationship managers” to domain experts driving backlog consumption. Transparency Tools – Customers must see real-time dashboards showing usage, costs, and ROI. “Think of it like moving from waterfall to agile. Instead of one big upfront purchase, it’s frequent small purchases tied to value delivery.” – Santosh Sahoo https://www.youtube.com/watch?v=SmT2lGHx6Yw&t=2s Customer Success in a Consumption World Customer Success is the linchpin in this new model. Instead of quarterly check-ins or post-implementation support, CSMs must now: Continuously drive backlog consumption (help customers take use cases live faster). Co-own expansion with sales by identifying new use cases. Quantify value delivered per transaction, making ROI visible in real time. Evolve into domain experts (deep industry/functional knowledge builds trust and unlocks access to customer roadmaps). Consumption Success Planning Sheet Category Traditional SaaS CS Consumption CS Cadence QBRs + Support Continuous, backlog-driven Value Tracking Promised ROI Real-time usage-value correlation CSM Persona Relationship Manager Domain Expert & Advisor Expansion Motion Post-implementation upsell Ongoing micro-expansions Overcoming Customer Concerns: Transparency and Risk The biggest hesitation from customers? Cost unpredictability. They want answers to: Will we end up spending more than before? Can we budget effectively? Do we know what value we’re getting per dollar? The solution lies in radical transparency: Self-serve dashboards showing daily/weekly/monthly usage Clear unit economics (e.g., $5 per transaction vs $5,000 per seat) Value assessments tied to each use case The Consumption Value Equation Define use case → 2. Track usage units → 3. Calculate cost → 4. Show realized business outcome Tiered Success Packages: Monetizing Customer Success Consumption models naturally pair with tiered Customer Success offerings. Instead of selling “support hours” or “better SLAs” alone, companies should bundle experiences across all touchpoints: Tier 1 (Standard) – Basic onboarding, standard SLAs, limited training. Tier 2 (Advanced) – Faster SLAs, personalized onboarding, domain-aligned CSM support. Tier 3 (Premium/Strategic) – White-glove onboarding, architecture advisory, dedicated CSM, priority support, advanced training. 📌 Template Idea: Success Package Blueprint Touchpoint Standard Advanced Premium Onboarding Standard Customized White-glove Training Self-serve Live workshops Domain consulting Support 24-48 hrs 12 hrs <4 hrs priority Architecture Advisory None Quarterly check-ins Dedicated architect Measuring Success: From Lagging to Leading Indicators Traditional CS metrics (renewals, churn) are lagging. Consumption introduces leading indicators that predict expansion and retention. Leading Indicators: Quarter-over-quarter consumption growth % of contract burned down mid-cycle (target 80% by halfway mark) Pipeline of use cases going live Lagging Indicators: Renewal rates Net revenue retention (NRR) Expansion ACV Executive Alignment: Who Needs to Buy In? Transitioning to consumption selling requires C-level alignment: CEO – Sets vision, aligns with customer-centric strategy CFO – Manages forecasting challenges, ensures revenue predictability CPO – Designs product units & telemetry dashboards CRO – Aligns sales incentives and GTM motions CCO/CS Leaders – Drive adoption, retention, expansion Best Practices & Lessons Learned From Santosh’s early journey at MuleSoft, here are actionable best practices: Design net-new roles – Don’t just repurpose CSMs. Build domain-centric, consultative CS roles. Prioritize data rigor – Treat use cases like a pipeline, tracked with sales-like discipline. Enable real-time transparency – Build dashboards customers can self-serve. Monetize success packages – Create tiered offerings tied to tangible business outcomes. Adopt agile mindset – Land small, expand fast. Value and revenue must scale in lockstep. The Road Ahead: What’s Next for Consumption Selling? In the next 12–18 months, expect: More SaaS companies moving to hybrid models (mix of seats + consumption). AI-driven agents priced per transaction, not per user. Greater demand for CS-led monetization, where every touchpoint is productized. CFOs rethinking forecasting models, balancing lumpy usage with committed minimums. “The future is clear—consumption will be everywhere. The challenge is building the muscle for forecasting, value transparency, and continuous expansion. But for companies that get it right, customer success becomes the ultimate growth engine.” – Santosh Sahoo Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to

Customer Success

Building a Data-Driven Customer Success Strategy

Building a Data-Driven Customer Success Strategy A conversation with Sam Slevin, Global SVP of Customer Success at Alphasense. In today’s enterprise SaaS landscape, retaining customers isn’t just about renewals—it’s about delivering continuous value from the first interaction to every milestone that follows. Sam Slevin, Global SVP of Customer Success at AlphaSense, breaks down how a truly data-driven customer success strategy is built—one that aligns people, processes, and platforms across the full customer lifecycle. In this blog, we dive deep into Sam’s frameworks for onboarding, team structuring, digital touchpoint execution, and renewal forecasting—plus how AlphaSense leverages data to power intelligent decision-making at scale. Facebook Twitter Youtube 🔑 Why Retention Starts at the First Touchpoint “There are so many things that happen over the course of a lifecycle that are not in your control. But the first interaction? That’s fully in your control.” – Sam Slevin Many organizations treat customer success as a reactive, post-sale process. Sam’s approach flips that thinking: renewal begins the moment a buyer engages with your sales team. That means every insight gathered by AEs and pre-sales needs to be transferred with context—not lost in CRM notes or email chains. 📌 Key Hand-off Elements from AE to AM: Proposal with stated customer goals Trial qualification criteria Buying committee context Success metrics in the customer’s own words These inputs directly shape the AM’s kickoff meeting, aligning expectations and building trust early on. Structuring CS Teams by “Jobs to Be Done” At AlphaSense, customer success isn’t confined to a narrow definition. The team is built to address specific jobs across the customer journey, from onboarding to support to expansion. Team Structure: Account Managers (AMs): Own renewal and collaborate with AEs on expansion. Bonus-tied to growth, not commission-heavy. Product Specialists: Technical experts aligned to product usage and value delivery. Support Ops: 24/5 global team handling tickets, internal account setup, and external queries. Pre-Sales Consultants: Integrated into CS to improve handoffs and accelerate early-stage value delivery. This model ensures that every customer gets the right expertise at the right moment—especially during trials and onboarding. https://www.youtube.com/watch?v=xdaeKaWXzzY&t=59s The Onboarding Experience: It’s Not a Meeting—It’s a Strategy Onboarding is often treated as a quick call or checklist. But for AlphaSense, it’s a strategic, data-backed process. “Onboarding isn’t a one-time call. It’s an experience—one that should be tied to usage data and milestones that indicate customer health and stickiness.” – Sam Slevin ✅ Onboarding Success Checklist: Confirm North Star Goals (from sales process) Map stakeholders to outcomes Track milestone adoption (e.g. feature usage, content access) Complete product configuration with Product Specialist Validate value realization in the customer’s language Inputs Drive Outcomes: Sam’s Performance Framework Rather than chase lagging indicators, AlphaSense tracks daily and weekly inputs that lead to renewal success. 🧮 Core Inputs: Number of high-value customer calls % of users touched per month/quarter SBRs conducted and aligned to success metrics Expansion referrals and sourced pipeline 📊 Core Outputs: Gross Renewal Rate Net Revenue Retention Forecast Accuracy “We believe if you do the right inputs consistently—like high-value calls and user engagement—the outcomes will follow.” – Sam Slevin 📌 QBR Operating Cadence: Monthly: Managers analyze rep-level data and submit summaries Quarterly: AMs present full retrospectives and forward-looking plans Discretionary compensation tied to key activity benchmarks Aligning AMs and AEs for Expansion Expansion isn’t a handoff—it’s a co-owned strategy. AMs focus on value delivery and pipeline sourcing, while AEs “hunt” into new divisions. “If a rep calls a user and they say, ‘I love AlphaSense, I was going to recommend it to you’—that’s when you know you’re doing it right.” – Sam Slevin 📌 Pro Tip: Ask happy users for intros to adjacent teams. Draft the email for them. Keep it low-lift. Digital CS: Air Cover at Scale Contrary to the belief that digital CS is only for SMBs, Sam views it as air cover for both large and small accounts. With thoughtful segmentation and trigger-based workflows, AlphaSense ensures digital motions augment—not replace—human touch. 🔍 Readiness Checklist for Digital CS: Contact hygiene validated Usage triggers mapped Strategic accounts white-labeled Segments reviewed quarterly by RevOps & CS Clean and trusted source systems (e.g. Catalyst, Salesforce) Data Hygiene: The Cornerstone of Digital Strategy “Before we move anyone to digital touch, there’s a manual scrub. We don’t just click a button and walk away.” – Sam Slevin Poor contact data leads to impersonal messages and a broken customer experience. AlphaSense blends automation with manual segmentation—then revisits it every quarter to ensure consistency. Future of CS: AI, Personalization & Smarter Data Despite being a native AI company, AlphaSense is cautious about AI overhype in CS. “AI is the right answer, but only if you have clean data and the infrastructure to support it.” – Sam Slevin Potential Use Cases: AI agents trained on call transcripts and sales collateral Enablement bots that reduce ramp time Smart segmentation based on usage and buying signals But the current state of enterprise data hygiene means these use cases are still aspirational for most CS teams. Cross-Functional Alignment & Change Management Sam attributes a huge part of AlphaSense’s success to trust and alignment across functions—from sales to marketing to product and RevOps. 🧩 Internal Collaboration Practices: Weekly one-on-ones with key functional leaders Structured kickoff plans for cross-team projects Aggregated asks to reduce “Slack fire drills” Empathy-driven partnerships: “I don’t want your job, and I know how hard yours is.” Final Advice from Sam: Start with the End in Mind “Assume your customer will cancel in 365 days. What are you doing today to change that outcome?” – Sam Slevin Sam’s frameworks are designed to help CS leaders drive accountability, adoption, and long-term value creation. His advice is clear: align on the North Star early, track your progress rigorously, and build systems that make renewal the natural outcome of ongoing success. Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! 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