Company Size Cohort Analysis 2025: SMB vs M...

Based on our analysis of hundreds of SaaS companies, company size is arguably the most important segmentation dimension for SaaS cohort analysis—yet many companies analyze blended metrics that mask critical differences between SMB, mid-market, and enterprise customers. Research consistently shows that enterprise customers retain at 90-97% annually while SMB customers retain at 70-85%, a gap of 15-25 percentage points that fundamentally changes unit economics, customer success strategies, and growth planning. A company showing 85% blended retention might actually have 95% enterprise retention subsidizing 75% SMB retention—a healthy pattern if intentional, but a warning sign if the SMB segment is a core growth focus. Company size cohort analysis reveals these segment-specific patterns, enabling tailored strategies that optimize each segment rather than one-size-fits-all approaches that underserve everyone. Beyond retention, company size segments show dramatically different expansion rates, sales cycles, support requirements, and lifetime values. Understanding these differences enables appropriate resource allocation: high-touch success for enterprise customers who justify it, efficient tech-touch for SMB customers who need scalable support, and balanced approaches for mid-market. This comprehensive guide covers how to segment cohorts by company size, interpret segment-specific retention patterns, design segment-appropriate strategies, and use size-based cohort data to inform go-to-market decisions and resource allocation.

Why Company Size Segmentation Matters

Company size creates fundamentally different customer dynamics that require distinct analytical approaches and operational strategies.

The Retention Gap Reality

Retention rates vary dramatically by company size, and failing to segment masks these differences. Typical retention benchmarks by size: Enterprise (1000+ employees): 92-97% annual gross retention, 110-130% NRR. Mid-market (100-999 employees): 85-92% annual gross retention, 105-118% NRR. SMB (10-99 employees): 78-88% annual gross retention, 98-110% NRR. Micro/Startup (<10 employees): 65-80% annual gross retention, 95-105% NRR. A company with equal customers across segments showing 85% blended retention might have: 95% enterprise + 88% mid-market + 82% SMB + 72% micro = 84% blended. The blended number hides that enterprise is best-in-class while micro needs serious attention. Segment-specific analysis reveals where to focus improvement efforts.

Different Economics by Segment

Company size determines unit economics fundamentals. Enterprise customers: High ACV ($50K-$500K+) justifies high CAC and dedicated CSM investment. Long sales cycles (3-12 months) require patience but produce durable relationships. Expansion potential is significant (land-and-expand strategies work). Support expectations are high but cost is amortized across large contracts. SMB customers: Lower ACV ($1K-$10K) requires efficient acquisition and low-touch success. Short sales cycles (days to weeks) enable rapid volume growth. Expansion is limited by company size—customers may max out quickly. Support must be scalable—dedicated CSMs don't make economic sense. Analyzing cohorts without size segmentation produces misleading LTV calculations. A $5K SMB customer retaining at 80% has different LTV than a $100K enterprise customer retaining at 95%, even if both segments contribute equally to current revenue.

Segment Mix Evolution

As SaaS companies grow, segment mix typically shifts—and this shift affects aggregate metrics even without any underlying change. Common evolution pattern: Seed/Series A: Mostly SMB and micro customers (easier to acquire, faster feedback loops). Series B/C: Increasing mid-market focus (higher ACV, better unit economics). Series D+/IPO: Enterprise becomes significant (validates large company fit, drives ARR growth). If aggregate retention appears to improve during this evolution, it might reflect mix shift (more enterprise in the denominator) rather than actual improvement in any segment. Conversely, a company expanding downmarket might see aggregate retention decline even while each segment improves. Only segment-level cohort analysis reveals true trends.

Strategic Implications

Company size cohort data informs fundamental strategic questions. Which segments should we prioritize? Segments with best unit economics and retention deserve focus. Which segments need investment? Poor retention in a strategic segment signals product or success gaps. Should we enter/exit segments? Data may show certain segments aren't viable for your product. How should we allocate resources? CS, sales, support resources should align with segment economics. Without segment-specific cohort data, these decisions become gut-feel rather than data-driven. Companies that master segment analysis make better strategic choices and allocate resources more effectively.

Segment Visibility

If you can't immediately answer "What's our enterprise retention? SMB retention?" you're flying blind on one of the most important segmentation dimensions. Implement size-based cohort analysis before making major go-to-market decisions.

Defining Company Size Segments

Effective size-based segmentation requires clear definitions, accurate data collection, and segment boundaries that match your business context.

Common Segmentation Frameworks

Several frameworks exist for company size segmentation. Employee count (most common): Micro: 1-9 employees. SMB: 10-99 employees. Mid-market: 100-999 employees. Enterprise: 1,000+ employees. Strategic/Global: 10,000+ employees. Revenue-based: Often correlates with employee count but captures economic capacity more directly. Industry-specific: Some industries have different size dynamics (a 50-person law firm operates differently than a 50-person startup). Choose a framework that: Aligns with how your sales team thinks about customers, Has data you can reliably collect, Creates segments with meaningfully different retention patterns.

Data Collection for Size Segmentation

Accurate size data requires intentional collection. Primary sources: Self-reported during signup (ask directly for employee count or company size range). Sales-captured during qualification (verified through conversation). Enrichment data from providers (Clearbit, ZoomInfo, etc.). Secondary signals: Email domain type (gmail vs corporate domain). Payment method (credit card vs invoice suggests size). Initial contract value (larger deals suggest larger companies). Data quality matters: Stale data degrades over time as companies grow or shrink. Implement refresh processes at renewal or periodically. Flag accounts where size signals conflict.

Handling Segment Boundaries

Customers near segment boundaries require thoughtful handling. Edge cases: A 95-person company may behave more like mid-market than SMB. A 1,100-person company may still have SMB-like buying behavior. Fast-growing companies may cross boundaries during their customer lifecycle. Approaches: Use ranges that overlap slightly and assign based on multiple signals. Create "emerging" categories for companies growing rapidly. Re-segment at major milestones (renewal, expansion). Assign once and track separately if customers cross segments during lifecycle. The goal is meaningful segmentation that predicts behavior, not arbitrary cutoffs that create false precision.

Segment Definitions by Product Type

Size thresholds vary by what you sell. Horizontal SaaS (broadly applicable): Standard SMB/mid-market/enterprise definitions usually apply. Vertical SaaS (industry-specific): Industry norms may differ—a "small" hospital or law firm might be 500+ employees. Developer tools: Team size may matter more than company size. Consumer-ish B2B: Individual user economics may dominate company size effects. Customize your segmentation to match how your customers think about themselves and how your product delivers value. A segment definition that doesn't predict retention differences isn't useful—find the boundaries where behavior actually changes.

Segment Validation

Test your segment definitions by calculating retention for each segment. If two segments show similar retention (within 3-5 points), they might not be meaningfully different for your product—consider combining them or redrawing boundaries.

Analyzing Retention by Company Size

With segments defined, analyze how retention patterns differ across company sizes to reveal optimization opportunities.

Segment-Specific Retention Curves

Plot retention curves separately for each segment to visualize differences. Typical patterns: Enterprise: Steep initial curve (long implementation means slow start), then very flat (once implemented, rarely leave). Curve shape: High month-1 retention (95%+), gradual decline to stable floor (90%+ annual). Mid-market: Moderate curve with steady decline. More price-sensitive than enterprise, less volatile than SMB. Curve shape: Month-1 around 93%, settling to 85-90% annual. SMB: Steeper initial decline that stabilizes after 90 days. Early churn from poor-fit customers, then retained customers become stable. Curve shape: Month-1 around 88%, significant drop to month-3, then stabilization around 80% annual. Overlay these curves on a single chart to visualize the gap and identify where intervention makes the biggest difference.

Early Warning Indicators by Segment

Churn signals differ by company size. Enterprise warning signs: Declining usage among key stakeholders, Executive sponsor departure, Reduced engagement in QBRs, Competitor evaluation signals. Mid-market warning signs: Support ticket volume increase, Usage decline across team, Late payment or billing disputes, Reduced feature adoption. SMB warning signs: Login frequency drop, Core feature abandonment, Credit card failures, Support ticket then silence. Build segment-specific health scores using appropriate indicators. A "healthy" enterprise account looks different from a "healthy" SMB account—universal health scores miss these differences.

Expansion Patterns by Size

Expansion rates and patterns vary dramatically by company size. Enterprise expansion: High expansion potential (30-50%+ NRR contribution). Often driven by multi-year expansion agreements or new department adoption. Longer cycles but larger expansion amounts. Mid-market expansion: Moderate expansion (10-25% NRR contribution). Seat expansion as teams grow, tier upgrades as usage increases. More price-sensitive than enterprise on expansion conversations. SMB expansion: Limited expansion (5-15% NRR contribution). Natural ceiling—small companies can only grow so much. More likely to upgrade tier than significantly expand seats. These patterns inform expansion strategy: Focus enterprise CSMs on expansion conversations; SMB success should prioritize retention over expansion.

Cohort Quality Trends by Segment

Track whether cohort quality is improving or degrading within each segment. Segment-specific trending: Compare 90-day retention of January enterprise cohort vs June enterprise cohort. Plot trends for each segment separately. Investigate divergence (one segment improving while another degrades). Common findings: Marketing channel changes affect segment mix and quality. Product changes may improve one segment while neutral to others. Economic conditions affect SMB more than enterprise. Segment-specific trending reveals whether your efforts are working within target segments, not just whether aggregate metrics are moving.

The 10-Point Gap

If any segment shows retention more than 10 points below your top-performing segment, investigate deeply. A 10+ point gap usually indicates product-market fit issues, inadequate success resources, or fundamental mismatch between product and segment needs.

Segment-Specific Success Strategies

Each company size segment requires tailored customer success approaches that match their economics and needs.

Enterprise Success Model

Enterprise customers justify high-touch investment. Dedicated resources: Named CSM (1:20-50 account ratio depending on ACV). Executive sponsor program with VP/C-level engagement. Technical account managers for complex implementations. Engagement cadence: Quarterly Business Reviews with stakeholder group. Monthly check-ins with day-to-day users. Annual strategic planning sessions. Proactive monitoring with immediate response to risk signals. Success metrics focus: Stakeholder satisfaction and relationship health. Use case expansion and value realization. Multi-year contract commitment. Reference-ability and advocacy.

Mid-Market Success Model

Mid-market requires efficient but personalized approaches. Pooled resources: Shared CSM (1:50-100 account ratio). Triggered engagement based on health signals. Dedicated support for expansion opportunities. Engagement cadence: Semi-annual or annual business reviews (video call). Automated check-ins with option for live conversation. Proactive outreach triggered by usage patterns. Success metrics focus: Product adoption depth. Expansion indicators. Support efficiency (self-service vs assisted). Renewal predictability.

SMB Success Model

SMB success must be highly scalable. Tech-touch resources: One-to-many CSM model (1:200-500+ accounts). Automated engagement sequences. Community support and peer learning. Self-service resources prioritized. Engagement model: Automated onboarding with milestone tracking. Email sequences triggered by behavior. In-app guidance and tooltips. Community forums for peer support. Live support reserved for critical issues. Success metrics focus: Activation and onboarding completion. Product adoption of key features. Self-service resolution rates. Cohort-level retention (individual tracking not economical).

Resource Allocation Framework

Allocate CS resources proportional to segment economics. Calculate: Revenue per segment, Expected LTV per segment (based on segment retention), CS cost capacity per segment (what can you afford at target margins?). Allocation guidelines: Enterprise: 15-25% of ACV can go to CS given high retention and expansion. Mid-market: 8-15% of ACV for CS, blending high-touch and tech-touch. SMB: 3-8% of ACV for CS, primarily tech-touch with pooled support. Use segment cohort data to validate allocation: Are you over-investing in segments with declining returns? Under-investing in segments that could improve with more resources?

Segment Mismatch Risk

The biggest CS mistake is applying the wrong model to the wrong segment—high-touch for SMB (economically unsustainable) or tech-touch for enterprise (relationship damage). Match your model to segment economics, and use cohort data to verify it's working.

Go-to-Market Decisions from Size Cohorts

Company size cohort data informs fundamental go-to-market strategy decisions beyond just customer success.

Segment Prioritization

Use cohort data to prioritize which segments deserve investment. Prioritization factors: Retention rates: Which segments retain at rates that support your growth model? Expansion potential: Which segments offer meaningful expansion upside? Unit economics: Which segments have positive CAC payback at achievable costs? Market size: Are there enough customers in high-performing segments to support growth goals? Strategic assessment: Double down: Segments with strong retention AND expansion AND market size. Optimize: Segments with potential but current gaps (invest in improvement). Maintain: Segments that work but offer limited upside (efficient service, don't over-invest). Reconsider: Segments with persistent poor performance despite investment.

Product Investment by Segment

Cohort data reveals which segments your product serves well. Product-market fit signals by segment: High retention + positive feedback = strong fit, invest in growth. Low retention + feature requests = fit gaps, invest in product improvement. Low retention + customer silence = fundamental mismatch, reconsider segment. Guide product roadmap: If enterprise cohorts retain well but SMB struggle, SMB features may be missing. If all segments show declining cohort quality, core product issues need attention. If specific features correlate with segment retention, prioritize those features. Avoid spreading product investment too thin—use cohort data to focus on segments where you can win.

Pricing Strategy by Segment

Segment retention data informs pricing decisions. Pricing insights from cohorts: Segments with high retention can support premium pricing. Segments with low retention may need lower prices (improving value/cost ratio) or may not be viable at any price. Expansion patterns suggest where usage-based or seat-based pricing works. Segment-specific pricing: Enterprise: Custom pricing with annual commitments typical. Mid-market: Standardized tiers with some flexibility. SMB: Self-serve pricing with clear, simple tiers. Test pricing changes carefully and monitor cohort impact—price increases that don't affect retention are value-aligned; those that spike churn need reconsideration.

Sales and Marketing Alignment

Align acquisition efforts with segment performance. Acquisition implications: If enterprise retains at 95% but SMB at 75%, enterprise-focused acquisition is more efficient. CAC targets should reflect segment-specific LTV. Marketing channels that attract higher-quality segments deserve more investment. Sales team structure: Consider specialized sales teams for different segments. Compensation should reflect segment economics (enterprise quota vs SMB volume). Qualification criteria should identify segment fit early. Use cohort data in marketing: Showcase segment-appropriate case studies. Target content to segment-specific pain points. Qualify leads for segment fit before heavy sales investment.

Strategic Clarity

The clearest go-to-market strategies come from accepting segment realities: "We're excellent for mid-market and good enough for SMB, but enterprise isn't our strength" is more actionable than "We serve all company sizes." Use cohort data to identify your sweet spot.

Building Size-Based Cohort Analytics

Implementing company size cohort analysis requires data infrastructure, consistent methodology, and operational integration.

Data Requirements and Collection

Build the data foundation for size-based analysis. Required data: Company size attribute (employee count, revenue, or segment label). Reliable collection method (signup field, enrichment, sales-captured). Regular refresh process to keep data current. Historical preservation (segment at signup, segment at each renewal). Data quality processes: Validate size data against multiple sources. Flag accounts with missing or conflicting size data. Build exception handling for edge cases. Monitor segment distribution for anomalies.

Analytical Framework

Structure analysis for actionable insights. Core views: Retention by segment: Monthly/annual retention rates for each size segment. Segment comparison: Side-by-side cohort triangles for visual comparison. Trend analysis: Cohort quality trends within each segment over time. Revenue composition: What percentage of revenue comes from each segment? Advanced views: Segment migration: Customers who grow from SMB to mid-market during lifecycle. Segment-channel matrix: Retention by segment AND acquisition channel. Segment-geography matrix: Retention by segment AND region. Automation: Build dashboards that auto-update with new cohort data. Create alerts for segment-specific retention anomalies.

Operational Integration

Make segment data actionable in daily operations. CRM integration: Size segment visible on every account record. Segment-specific health scores and alerts. Routing rules based on segment (enterprise to dedicated CSM, SMB to pooled). Success playbooks: Segment-specific onboarding paths. Intervention triggers calibrated to segment norms. Expansion playbooks tailored to segment potential. Reporting cadence: Weekly: Operational metrics by segment. Monthly: Cohort analysis by segment. Quarterly: Strategic review of segment performance and resource allocation.

Common Implementation Pitfalls

Avoid common mistakes in size-based analysis. Pitfall: Stale segment data. Companies grow; yesterday's SMB may be today's mid-market. Update segment data at least annually or at major account events. Pitfall: Inconsistent definitions. If sales calls 50 employees "mid-market" but analytics calls it "SMB," insights are meaningless. Align on definitions across teams. Pitfall: Over-segmentation. Creating 10 size segments spreads data too thin. 3-5 segments usually provides enough granularity with sufficient sample sizes. Pitfall: Ignoring segment mix in aggregate metrics. Rising aggregate retention might reflect segment mix shift, not actual improvement. Always check segment-level trends alongside aggregates.

Implementation Priority

Start with simple segment definitions (SMB vs Enterprise) and two key metrics (retention rate, expansion rate). Add complexity only after proving value from basic segmentation. Perfect is the enemy of good in analytics implementation.

Frequently Asked Questions

What if I don't have accurate company size data for all customers?

Start with what you have and improve over time. Use proxy signals: contract value often correlates with company size, email domain type (gmail vs corporate) suggests size, number of users/seats indicates team size. For existing customers without size data, consider outreach campaigns to update profiles or use enrichment services. Calculate retention separately for "known size" and "unknown size" segments to understand data quality impact.

Should I use employee count or revenue for company size segmentation?

Employee count is most common because it's easier to collect (customers know their headcount, may not share revenue) and more publicly available for enrichment. Revenue is useful when your product's value correlates with customer revenue rather than employee count—e.g., fintech products where transaction volume matters more than headcount. Choose whichever better predicts retention differences in your data.

How do I handle customers who grow across segment boundaries?

Two approaches: (1) Keep customers in their original segment for cohort analysis consistency—this shows how "customers acquired as SMB" perform over time. (2) Re-segment customers and track movement—this shows current segment composition accurately. Many of the companies we work with do both: track cohorts by segment-at-acquisition for retention analysis, track current segment for resource allocation and forecasting. Document your approach clearly.

Our SMB retention is significantly lower than enterprise. Should we stop serving SMB?

Not necessarily. Lower retention can still produce positive unit economics if acquisition cost is proportionally lower. Calculate LTV/CAC by segment—SMB with 75% retention but $50 CAC might have better unit economics than enterprise with 95% retention but $10,000 CAC. Consider strategic value too: SMB can provide product feedback, market presence, and future enterprise customers as they grow. Only exit a segment if unit economics are negative AND strategic value is low.

How do I set retention targets by company size?

Start with industry benchmarks for your segment type (B2B SaaS, vertical SaaS, etc.), then adjust based on your historical performance. If benchmark enterprise retention is 92% and you're at 88%, target 90% (closing half the gap). For SMB, benchmark might be 80% and you're at 75%—target 77-78%. Avoid targets that require dramatic improvement in one quarter; retention improvements compound over multiple cohorts.

What's the minimum sample size needed for segment-level cohort analysis?

Aim for 50+ customers per segment per cohort period for directional insights, 100+ for confident conclusions. If monthly cohorts are too small, aggregate into quarterly cohorts. If a segment consistently has too few customers for analysis, consider whether that segment is actually strategic for your business—low volume suggests low prioritization or poor fit.

Disclaimer

This content is for informational purposes only and does not constitute financial, accounting, or legal advice. Consult with qualified professionals before making business decisions. Metrics and benchmarks may vary by industry and company size.

Key Takeaways

Company size cohort analysis reveals the segment-specific patterns that aggregate metrics hide—patterns that fundamentally shape strategy, resource allocation, and growth planning. Enterprise, mid-market, and SMB customers have different retention rates, expansion potential, support needs, and unit economics. Treating them identically underserves everyone and produces misleading metrics. Build size-based cohort analysis by defining clear segment boundaries based on employee count or revenue, ensuring accurate data collection and regular refresh, and implementing analytical views that compare segments side-by-side. Use segment insights to tailor customer success models: high-touch for enterprise customers who justify the investment, efficient tech-touch for SMB customers who need scalable support. Let segment performance data guide go-to-market decisions: which segments to prioritize, where to invest in product improvement, how to allocate sales and marketing resources. The companies that master company size cohort analysis make better strategic decisions because they understand their business at the level where meaningful differences exist. They allocate resources appropriately, set realistic expectations for each segment, and build success programs that match segment economics. Start with simple segmentation and basic metrics, then add sophistication as you prove value. The insights from even basic size-based analysis typically transform how teams think about customers and where they focus improvement efforts.

Ben Callahan