AMA: Gainsight Senior Director of Customer Success, Meenal Shukla on Customer Success KPIs

Let me walk you through a systematic approach for developing customer success metrics: 1. Start with core business objectives: * Customer retention/churn rate * Expansion revenue/upsells * Customer lifetime value (CLV) * Logo retention (especially for enterprise) 2. Break those down into leading indicators * Product adoption rates * ROI delivered across the customer base * QBRs completed on schedule * Customer response times: AI can help identify this very easily (Across emails and support) * Customer engagement levels (support tickets, check-ins, training sessions) - AI can help look at the strength of relationships and how deeply embedded we are in a customer org * Sentiment: Customer sentiment across emails, support tickets and phone calls (AI can help here) * I don't believe that NPS is the right metric to measure CSMs on, but it is the right metric to track (low response rates). NPS can be related to overall experience with product, onboarding, and CS; therefore, one team can not be held accountable for it. So, while tracking NPS as a company metric is essential, it is not the right metric to track at a CSM level. The key is finding the right balance between: * Lagging indicators (like churn) that show results * Leading indicators that help predict and prevent issues * Activity metrics that drive desired outcomes * Quality metrics that ensure sustainable growth

For self-serve products, the KPIs shift to focus more heavily on product-led indicators and automated engagement metrics. Here's how I'd adjust them: Priority KPIs for Self-Serve: 1. Product Adoption Metrics * Time to first key action * Feature activation rates * Daily/weekly active users * Core feature usage patterns * User progression through key milestones 2. Automated Customer Health * Product usage frequency * Drop-off points in user journey * In-app help article usage * Self-service resolution rates * Account activity patterns 3. Scalable Growth Indicators * Viral coefficient/referral rates * Free-to-paid conversion rate * Time to upgrade * Expansion MRR from self-serve upgrades * User-initiated upsell rates 4. Automation Effectiveness * In-app onboarding completion rates * Help center search success rates * Automated email engagement rates * Chat bot resolution rates * Product tour completion rates The key differences from traditional CS metrics: * Focus shifts from human touchpoints to product engagement * Greater emphasis on self-service success rates * More granular usage analytics * Automated intervention triggers * Product-led growth indicators

Here's my framework for developing and cascading CS OKRs: 1. Start with Annual Company Goals * Revenue targets * Growth objectives * Strategic initiatives * Market positioning 2. Define Annual CS Department OKRs * What must CS achieve to support company goals? * Set 3-4 major objectives with measurable results * Example: * Objective: "Drive predictable growth through customer expansion" * OKRs: NRR target, expansion revenue, multi-product adoption rate 3. Break Into Quarterly Focus Areas * Which quarterly milestones lead to annual goals? * What seasonal factors affect prioritization? * Example Q1 focus: * Onboarding optimization for new year customers * Annual renewal preparation * New feature adoption campaigns 4. Map Projects to OKRs Example cascade: Annual OKR: Increase NRR to 120% ↓ Q1 OKR: Improve product adoption rates by 25% ↓ Projects: - Launch in-app onboarding automation - Create customer maturity model - Build product usage dashboards 5. Individual OKRs & Projects * Each CSM gets metrics aligned to team goals * Projects assigned based on: (Team leads and OPs team will own these) * Individual strengths * Account portfolio needs * Bandwidth/capacity * Development goals 6. Regular Check-ins * Weekly: Project status updates * Monthly: Metric reviews * Quarterly: OKR scoring & adjustments * Annual: Strategic planning

As the first CS hire at a startup, here's how I'd prioritize KPI ownership: KPIs You Should Own: 1. Direct Customer Outcomes * Onboarding completion rates * Time to first value * Basic product adoption metrics * Meeting/QBR completion rates * Customer engagement rates 2. Early Warning Metrics (Track these but your compensation should not be based on these metrics) * At-risk customer identification * Basic usage trends * NPS KPIs You Should NOT Own (Yet): 1. Revenue Metrics: These typically need more infrastructure and team support * Net revenue retention * Churn rate * Expansion revenue 2. Advanced Product Analytics: Let product/data teams lead these initially * Detailed feature adoption * Complex usage patterns * Custom health scoring 3. Scale Operations: Wait until you have more team members * Response time SLAs * Coverage ratios * Team efficiency metrics Key Considerations: * Focus on establishing baseline metrics first * Build manual processes before automation * Keep metrics simple and actionable * Document what you learn for future hires * Partner with product/sales on shared metrics

Here are the most problematic KPIs to commit to, especially as CS leader: 1. Pure Satisfaction Metrics * Raw NPS/CSAT targets * "Customer happiness" scores Why: Too easy to game, often doesn't correlate with business outcomes, and heavily influenced by factors outside CS control 2. Zero-Tolerance Metrics * "0% churn rate" * "100% renewal rate" * "No escalations" Why: Creates a culture of hiding problems versus addressing them. 3. Activity-Based Metrics Without Outcomes * "X QBRs per quarter" * "Y customer touches per month" * "Z training sessions delivered" Why: Drives busywork instead of value; teams hit numbers without impact. Without specific outcomes, check-in meetings have zero value 4. Metrics Without Context * "Increase product adoption" * "Improve time to value" * "Reduce support tickets" Why: Too vague to be actionable or measurable 5. Metrics You Can't Influence * Product development timelines * Sales cycle length * Engineering bug fix rates Why: Sets you up for failure when you lack control over outcomes 6. Lagging-Only Indicators * Annual churn rate without leading indicators * Lifetime value without progress metrics Why: By the time you miss these, it's too late to course-correct Better Approach: * Choose metrics with clear line of sight to influence * Include both leading and lagging indicators * Set realistic ranges rather than absolute targets * Establish baseline for each metric that you are trying to improve. If you don't know where these metrics stand now, how will you know if you have improved it. It is common sense but you will be surprised at how often this happens. * Focus on trends over time versus point-in-time goals

Effective Shared KPIs: 1. Deal Quality Metrics * % of deals meeting minimum readiness criteria * Technical fit score accuracy * Implementation scope accuracy vs reality * Time from close to successful onboarding 2. Revenue Collaboration * Account expansion rate from CS-identified opportunities * Renewal rates by sales rep/deal type * Joint upsell win rates * Reference customer creation rate Commonly Missed Opportunities: 1. Pre-Sale Engagement Impact * Win rates with CS involvement vs without * POC success rates with CS guidance * Risk assessment accuracy * Technical validation completion rates 2. Early Warning Signals * Implementation milestone variances * Usage vs committed volumes 3. Deal Quality Feedback Loop * % of deals requiring unplanned resources * Customer effort scores in first 90 days * Feature usage alignment with sales use cases * Actual vs forecasted support needs Key Success Factors: * Share visibility into metrics both ways * Define clear handoff criteria * Create regular feedback mechanisms * Align compensation on shared outcomes * Build joint account planning process

When bandwidth is extremely tight, focus on high-leverage KPIs that align with essential business outcomes and don't create unnecessary admin overhead: 1. Must-Have Metrics * Critical account retention rates * Revenue at risk identification * Core product adoption milestones * Basic customer health scoring (simple red/yellow/green) - AI generated and not CSM-owned (NO MANUAL SCORING!) * Minimum touch frequency for key accounts 2. Efficient Activity Metrics * % of proactive vs reactive work * High-risk account coverage * Essential QBR completion (top accounts only) * Response time to critical issues * Key milestone completion rates Crucial Adjustments: * Segment customers ruthlessly - focus metrics on top/strategic accounts * Simplify reporting - weekly numbers only for what's truly critical * Automate what you can (basic health scores, usage alerts) * Cut nice-to-have activities entirely * Accept "good enough" on non-critical accounts What to Skip: * Detailed activity tracking * Complex scoring systems * Non-essential customer touchpoints * Granular feature adoption metrics * Extensive documentation requirements The key is choosing 3-5 metrics maximum that: * Have clear business impact * Are simple to track * Don't require manual data entry * Focus team on highest-value work