SaaS에서 고객을 분류하는 방법: 단계별 가이드
게시일: 2025년 5월 1일
SaaS에서 고객을 세분화한다는 것은 공유 특성이나 행동을 기반으로 사용자를 그룹화하는 것을 의미합니다. 이 프로세스를 통해 SaaS 비즈니스는 사용자 기반을 더 잘 이해할 수 있습니다. 다양한 고객 그룹을 이해하면 제품 개발에 도움이 되고 더욱 개인화된 커뮤니케이션이 가능해져 유지를 지원할 수 있습니다. 이 가이드는 SaaS 고객 세분화를 구현하기 위한 실행 가능한 단계를 제공합니다. 이러한 단계를 따르면 고유한 사용자 그룹을 식별하고 전략을 그에 맞춰 조정하는 데 도움이 됩니다.
세분화 목표 정의
고객 세분화 SaaS 이니셔티브에 대한 구체적이고 측정 가능한 목표를 명확히 설명하십시오. "고객을 더 잘 이해하기"와 같은 모호한 목표는 충분하지 않습니다. 대신 고객 세분화 SaaS를 핵심 비즈니스 핵심 성과 지표(KPI)에 직접 연결하십시오.
방법론:
SMART(구체적, 측정 가능, 달성 가능, 관련성, 시간 제약) 또는 OKR(목표 및 핵심 결과)과 같은 프레임워크를 사용하여 목표를 구성하세요. 다음 자가 평가 질문을 고려해 보세요.
- 이 고객 세분화 SaaS는 어떤 구체적인 비즈니스 과제(예: 첫 90일 동안 높은 이탈률, 프리미엄 기능의 낮은 채택률, 비효율적인 리소스 할당)를 해결할 것인가?
- 이 고객 세분화 SaaS는 어떤 주요 KPI(예: 이탈률 X% 감소, 기능 채택률 Y% 증가, LTV Z% 향상)에 영향을 미치는 것을 목표로 하는가?
- 이러한 세그먼트를 기반으로 취한 조치의 효과를 어떻게 정확하게 측정할 것인가? (예: 코호트 유지 곡선 추적, 세그먼트별 기능 사용 메트릭 모니터링, 세그먼트 LTV 변화 계산)
- 이러한 세그먼트를 효과적으로 생성하고 추적하는 데 필요한 데이터를 사용할 수 있거나 수집할 수 있는가?
SMART 목표의 예시: “신규 중소기업(SMB) 고객(1-50명)의 이탈률을 6개월 이내에 15%에서 10%로 줄입니다. 처음 30일 이내에 위험 행동을 파악하고 목표를 설정한 온보딩 개입을 실행합니다.”
비즈니스가 발전하고 새로운 문제나 기회가 발생함에 따라 분기별로 고객 세분화 SaaS 목표를 재검토하고 개선합니다.
무료 SaaS 고객 세분화 템플릿
이 간단한 템플릿을 사용하여 주요 SaaS 사용자 그룹을 정의하고 목표를 설정한 작업을 계획합니다.
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주요 변수(플랜, 사용량, 가치, 규모) 식별
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고유한 세그먼트 프로필 정의를 위한 구조
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세그먼트 요구 사항, 위험 및 목표 개요
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목표를 설정한 참여 활동 아이디어 브레인스토밍
세분화 변수 선택 및 강조
정의된 목표(1단계) 및 데이터 가용성을 기반으로 잠재적 변수를 평가합니다. 여러 변수 유형을 결합하면 단일 차원에 의존하는 것보다 더 전체적인 관점을 제공합니다.
(제안: 잠재적 변수를 구성하는 표 만들기)
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Variable Category |
Specific Examples |
Potential Data Source(s) |
Relevance Example (Goal: Reduce Churn) |
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Firmographic |
Industry, Company Size (Employees/Revenue), Geography |
CRM, Sign-up Form, Data Enrichment Tools |
Identify if specific industries/sizes churn more |
|
행동적 |
Login Frequency, Core Feature Adoption Rate, Key Workflow Completion, Session Duration, Specific Feature Usage |
Product Analytics (e.g., Mixpanel, Amplitude) |
Low login frequency/feature use are churn flags |
|
Value-Based |
MRR/ARR, LTV (e.g., Avg. MRR / Churn Rate), Subscription Tier, Upsell/Cross-sell History |
Billing System, CRM, Financial Analytics |
Focus retention efforts on high-LTV segments |
|
Needs-Based |
Stated Goals (from onboarding), Job-to-be-Done, Support Ticket Themes |
Surveys, Interviews, Support System |
Address unmet needs driving dissatisfaction |
|
Technographic |
Key Integrations Used (e.g., Salesforce, Slack), Tech Stack, Platform (Web/Mobile) |
Product Analytics, Surveys, API connections |
Ensure critical integrations work, optimize UX |
|
Psychographic |
Attitudes towards Technology, Risk Aversion, Motivation (obtained via surveys/interviews) |
Surveys, Customer Interviews |
Tailor messaging to align with user values |
Begin with hypotheses that are linked to your goal. For example, “Hypothesis: Users that sync with Salesforce within 7 days are less likely to churn.” Then choose variables needed to test this. Focus on the strong differentiators that are measurable, accessible, and relevant to your goal. Choose the RFM (Recency, Frequency, Monetary) model as specific behavioral and value methodology: score customers based on: how recently they engaged (R), how often (F), and their monetary contribution (M). This will allow you to identify segments like ‘Champions’, ‘At-Risk High Value’, or ‘Hibernating’.
A SaaS offering project management tools might separate users into: “High ARR Enterprise accounts using sophisticated reporting features daily” vs. “SMB accounts using the basic plan with task management features weekly”. This means combining Firmographic (ARR, size), Value-based (plan), and Behavioral (feature usage, frequency) data.
Prevent “analysis paralysis” by initially avoiding too many variables. Begin with 2-4 that are strongly related to your goal and iterate.
Start with variables already available in your existing systems (CRM, billing, basic product analytics) before investing in collecting new data types.
무료 SaaS 고객 세분화 템플릿
이 간단한 템플릿을 사용하여 주요 SaaS 사용자 그룹을 정의하고 목표를 설정한 작업을 계획합니다.
-
주요 변수(플랜, 사용량, 가치, 규모) 식별
-
고유한 세그먼트 프로필 정의를 위한 구조
-
세그먼트 요구 사항, 위험 및 목표 개요
-
목표를 설정한 참여 활동 아이디어 브레인스토밍
Implement Data Collection Mechanisms
Systematically gather data across customer touchpoints, ensuring accuracy, consistency, and integration.
- Product Analytics: Configure tools (e.g., Mixpanel, Amplitude, Heap, Userpilot) to track key events: user logins, specific button clicks, feature activation, workflow completions, and errors encountered. Ensure event naming conventions are consistent.
- In-App Surveys/Forms: Use tools (e.g., Hotjar, Userpilot, Typeform integration) for targeted data collection. Implement welcome screens asking for role or use case (as Miro does hypothetically) for immediate personalization. Trigger short, contextual surveys after specific interactions (e.g., “How easy was it to complete [X task]?”).
- CRM & Sales Data: Ensure that your team consistently populates fields for firmographics (industry, size), lead source, and key interactions (e.g., Salesforce, HubSpot).
- Billing System: Integrate subscription data (plan tier, MRR/ARR, billing status) with other payment systems (e.g., PayPro Global).
- Support System: Tag support tickets (e.g., Zendesk, Intercom) with product areas or issue types to quantify pain points per segment.
Conduct a data audit. Map your selected variables (Step 2) to existing data sources. Identify gaps and plan collection methods. Consider data integration platforms or CDPs (Customer Data Platforms) like Segment to consolidate data from various sources into a unified customer profile.
A B2B SaaS could track an event like feature_X_enabled in their product analytics. By combining this with company_size from their CRM and mrr from their billing system, they can analyze adoption rates of Feature X across different company sizes and revenue tiers. Statistics show that personalized onboarding based on initially collected role/goal data can improve activation rates significantly, sometimes by 10-20% or more, depending on the industry and baseline.
Ensure strict adherence to data privacy regulations like GDPR and CCPA. Clearly communicate data usage and obtain necessary consents. Maintain data hygiene through regular validation and cleanup.
Automate data collection wherever possible to ensure consistency and reduce manual effort. Use APIs and integrations to connect different tools.
무료 SaaS 고객 세분화 템플릿
이 간단한 템플릿을 사용하여 주요 SaaS 사용자 그룹을 정의하고 목표를 설정한 작업을 계획합니다.
-
주요 변수(플랜, 사용량, 가치, 규모) 식별
-
고유한 세그먼트 프로필 정의를 위한 구조
-
세그먼트 요구 사항, 위험 및 목표 개요
-
목표를 설정한 참여 활동 아이디어 브레인스토밍
Analyze Data and Refine Segments
Utilize analytical tools and techniques to identify meaningful patterns and group customers based on the collected data and chosen variables. Actionable analysis:
- Basic Analysis: Start with descriptive statistics for potential segments (e.g., average MRR, common behaviors, feature usage) using spreadsheets or BI tools (e.g., Tableau, Looker).
- 코호트 분석: Group users by join date or another shared characteristic and track their behavior (e.g., retention rate, feature adoption) over time. This reveals how different groups evolve. 예시: A cohort analysis might show that users acquired through a specific partnership channel have a 25% higher 6-month retention rate compared to those from paid ads, suggesting a higher quality source or better initial fit.
- Clustering Algorithms: For complex datasets, use statistical techniques (e.g., K-means clustering, available in some analytics platforms or via Python/R) to identify natural groupings based on multiple variables.
- Segment Validation: Evaluate potential segments: Are they distinct (significantly different behaviors/characteristics)? Measurable (can you track them)? Accessible (can you target them with actions)? Substantial (large enough to warrant specific efforts)? Actionable (can you tailor strategies for them)?
SaaS customer segmentation is an iterative process. Analyze initial results, identify potential segments, and then validate them against your goals and the criteria above. Refine variable definitions or groupings as needed. Ask: “Does Segment A exhibit significantly different churn behavior than Segment B?”, “Can our marketing/product tools effectively target Segment C based on its defining variables?”
Analysis reveals a segment of users with high login frequency but low usage of core features beyond basic tasks. They have average MRR but a higher-than-average churn rate after three months. This segment is defined as “Superficially Engaged – At Risk” and requires a strategy focused on demonstrating deeper product value.
Don’t expect perfect, non-overlapping segments immediately. Focus on identifying the most distinct and impactful groups first.
Visualize segment data using charts and graphs to make patterns more apparent. Compare key metrics (LTV, churn rate, feature adoption) across different segments.
무료 SaaS 고객 세분화 템플릿
Define key SaaS user groups and plan targeted actions using this customer segmentation SaaS template.
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주요 변수(플랜, 사용량, 가치, 규모) 식별
-
고유한 세그먼트 프로필 정의를 위한 구조
-
세그먼트 요구 사항, 위험 및 목표 개요
-
목표를 설정한 참여 활동 아이디어 브레인스토밍
Develop, Implement, and Measure Tailored Strategies
Translate segment insights into specific, targeted actions across the customer lifecycle. Continuously measure the impact of these actions against your initial goals.
Customer segmentation SaaS strategies:
- 맞춤형 온보딩: Design different 온보딩 checklists, tutorials, or welcome messages based on segment (e.g., role, use case, company size). 예시: Guide enterprise users towards admin settings and integrations, while guiding individual users towards core creation workflows.
- Targeted Communication: Send emails, in-app messages, or feature announcements relevant to a segment’s specific usage patterns, needs, or lifecycle stage. Example: Notify users heavily utilizing Feature A about an advanced update to Feature A, while ensuring it reaches users who have utilized it. HubSpot reported that segmented and targeted emails generated significantly higher open and click-through rates compared to generic blasts.
- Tiered Support & Success: Allocate Customer Success Manager (CSM) resources based on segment value (e.g., high-touch for high ARR, tech-touch/automated for lower ARR). Offer proactive check-ins for high-value segments showing signs of risk (e.g., decreased usage).
- Product Roadmap Prioritization: Use feedback and usage data from high-value or strategically important segments to inform feature development priorities.
- Pricing & Packaging: Analyze willingness-to-pay and feature usage across segments to potentially refine 가격 책정 티어를 수용합니다. or create add-ons targeting specific segment needs.
- A/B 테스트: Test different messages, offers, or interventions within specific segments to optimize effectiveness. 예시: A/B test two different email subject lines for a re-engagement campaign targeting an “Inactive Users” segment.
Map strategies to segments. For each key segment identified in Step 4, ask:
- What is the primary objective for this specific segment (e.g., increase adoption, reduce churn, drive upsell)?
- What specific action(s) (e.g., targeted email campaign, in-app guide, CSM outreach) will best achieve this objective for this group?
- What channel(s) (email, in-app, phone) are most appropriate for reaching this segment?
- How will we measure the success of this specific action (e.g., feature adoption rate increase post-campaign, churn reduction in targeted cohort)?
Prioritize implementing strategies for segments with the highest impact on your business goals.
Ensure a consistent experience. If you target a segment with a specific message in-app, make sure that support and sales teams are aware of the segment’s context in case they interact.
Start by implementing one or two high-impact strategies for your most critical segments. Measure the results before rolling out more complex, multi-segment initiatives.
결론
In summary, methodical SaaS customer segmentation requires defining clear goals, selecting appropriate variables, establishing robust data collection, performing insightful analysis, and implementing targeted, measurable actions. This structured approach enables SaaS businesses to move beyond generic interactions towards personalized experiences that drive engagement, retention, and growth. Remember that effective segmentation is an ongoing, iterative process that adapts to your evolving customer base and business objectives.
FAQ
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SaaS customer segmentation is the practice of categorizing your user base into groups based on shared characteristics like behavior within the app, company size (firmographics), subscription value, or their specific needs. This makes for a targeted understanding and engagement rather than a one-size-fits-all approach.
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Segmentation encourages personalized communication, prioritizes product development based on specific needs, and makes targeted retention efforts possible which reduces churn. It also creates efficient resource allocation and can significantly improve customer lifetime value (LTV).
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Common B2B approaches use firmographics (industry, company size, location), behavioral data (product usage, feature adoption), value-based metrics (ARR, LTV, plan tier), and needs-based criteria (job-to-be-done). Technographic data (tech stack used) is also important for B2B SaaS.
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Start by defining clear, measurable business goals (like reducing churn for new users). Then, select a couple of trackable variables (e.g., login frequency and core feature usage) using available data. Study this data to determine initial segments and implement simple, targeted actions before adding to them.
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Behavioral segmentation creates cohorts based on their interactions with your SaaS product, like often they log in, which features they use, the key workflows they choose, or the support tickets they open. This identifies engagement levels and helps locate power users or those at risk based on actual usage patterns.
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Absolutely. By grouping users, you will identify groups with behaviors and characteristics related to churn (like low product engagement or numerous support issues). Then you can target those at-risk segments with custom retention campaigns, personalized support, and educational content.
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일반적인 도구에는 행동 데이터를 위한 제품 분석 플랫폼(예: Mixpanel, Amplitude, Userpilot), 인구 통계/상호 작용 데이터를 위한 CRM(예: Salesforce, HubSpot), 가치 데이터를 위한 결제 시스템(예: PayPro Global) 및 설문 조사 도구가 포함됩니다. 경우에 따라 데이터 통합을 위해 CDP(Customer Data Platforms)가 사용됩니다.
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RFM stands for Recency (last active date), Frequency (how often active), and Monetary value (MRR/ARR); it is a method to segment users based on their engagement and value level, helping identify key groups like highly engaged high-value users (“Champions”) or infrequent low-value users.
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