Core Customer Metrics
Customer Acquisition
- Customer Acquisition Cost (CAC): Total marketing spend ÷ Number of new customers
- Conversion Rate: (Customers acquired ÷ Total leads) × 100
- Cost Per Lead (CPL): Marketing spend ÷ Number of leads generated
- Lead-to-Customer Rate: (New customers ÷ Total leads) × 100
Customer Retention & Loyalty
- Customer Retention Rate: ((Customers at end – New customers) ÷ Customers at start) × 100
- Churn Rate: (Customers lost ÷ Total customers at start) × 100
- Customer Lifetime Value (CLV): Average purchase value × Purchase frequency × Customer lifespan
- Repeat Purchase Rate: (Customers who bought again ÷ Total customers) × 100
Customer Value
- Average Order Value (AOV): Total revenue ÷ Number of orders
- Customer Lifetime Value to CAC Ratio: CLV ÷ CAC (target: 3:1 or higher)
- Net Promoter Score (NPS): % Promoters – % Detractors
- Customer Satisfaction Score (CSAT): (Satisfied customers ÷ Total responses) × 100
Customer Segmentation Models
Demographic Segmentation
- Age Groups: Gen Z, Millennials, Gen X, Baby Boomers
- Income Levels: Low, Middle, High income brackets
- Geographic: Country, region, urban/rural, climate
- Life Stage: Single, married, families, empty nesters
Behavioral Segmentation
- Purchase Behavior: Frequency, timing, occasion
- Usage Patterns: Heavy, medium, light users
- Brand Loyalty: Loyal, switchers, price-sensitive
- Benefits Sought: Quality, convenience, price, status
Psychographic Segmentation
- Lifestyle: Activities, interests, opinions
- Values: Environmental, social, personal priorities
- Personality Traits: Adventurous, conservative, social
- Motivations: Achievement, security, self-expression
RFM Analysis
Recency: How recently did they purchase? Frequency: How often do they purchase? Monetary: How much do they spend?
| Segment | Description | Action |
|---|---|---|
| Champions | High RFM scores | Reward, upsell premium products |
| Loyal Customers | High frequency, recent | Recommend products, early access |
| Potential Loyalists | Recent, low frequency | Onboarding, engagement campaigns |
| New Customers | Recent, low frequency/monetary | Welcome series, product education |
| At Risk | High value, but haven’t purchased recently | Win-back campaigns, surveys |
| Can’t Lose Them | High monetary, low recency | Intensive retention efforts |
| Hibernating | Low recency, moderate scores | Reactivation campaigns |
| Lost | Lowest RFM scores | Ignore or minimal spend |
Customer Journey Mapping
Key Stages
- Awareness: Customer becomes aware of need/problem
- Consideration: Researching solutions and options
- Purchase: Making the buying decision
- Onboarding: First experiences with product/service
- Usage: Regular interaction and value realization
- Advocacy: Recommending to others
- Retention/Renewal: Continuing relationship
Touchpoint Analysis
- Digital: Website, app, social media, email, ads
- Physical: Store, events, packaging, phone calls
- Human: Sales, support, account management
- Third-party: Reviews, partners, influencers
Data Collection Methods
Quantitative Data
- Web Analytics: Google Analytics, heatmaps, click tracking
- Transaction Data: Purchase history, payment methods
- Surveys: Structured questionnaires with rating scales
- A/B Testing: Comparing different approaches
- Social Media Analytics: Engagement, reach, sentiment
Qualitative Data
- Customer Interviews: In-depth one-on-one conversations
- Focus Groups: Facilitated group discussions
- User Testing: Observing product/service usage
- Open-ended Survey Questions: Detailed feedback
- Customer Support Logs: Common issues and requests
Analysis Frameworks
Jobs-to-be-Done (JTBD)
Framework: When I _____, I want to _____, so I can _____.
- Functional Jobs: Practical tasks to accomplish
- Emotional Jobs: How customers want to feel
- Social Jobs: How customers want to be perceived
Customer Persona Template
Demographics: Age, gender, income, location, education Goals: What they’re trying to achieve Pain Points: Challenges and frustrations Behaviors: How they shop, research, communicate Motivations: What drives their decisions Preferred Channels: Where they spend time and buy
Value Proposition Canvas
Customer Profile:
- Jobs: Tasks customers are trying to get done
- Pains: Bad outcomes, obstacles, risks
- Gains: Outcomes and benefits customers want
Value Map:
- Products & Services: What you offer
- Pain Relievers: How you address customer pains
- Gain Creators: How you create customer gains
Key Performance Indicators (KPIs)
Acquisition KPIs
- Cost per acquisition by channel
- Conversion rates by traffic source
- Time from first touch to conversion
- Marketing qualified leads (MQLs)
Engagement KPIs
- Website session duration
- Pages per session
- Email open and click rates
- Social media engagement rate
Retention KPIs
- Monthly/Annual churn rate
- Product adoption rates
- Support ticket volume
- Time to value realization
Revenue KPIs
- Monthly recurring revenue (MRR)
- Annual recurring revenue (ARR)
- Revenue per customer
- Upsell/cross-sell rates
Analysis Tools & Techniques
Statistical Analysis
- Correlation Analysis: Relationships between variables
- Regression Analysis: Predicting outcomes
- Cluster Analysis: Identifying customer groups
- Cohort Analysis: Tracking groups over time
Visualization Tools
- Dashboards: Real-time metric tracking
- Heat Maps: User behavior patterns
- Journey Maps: Visual customer experience flows
- Segmentation Charts: Customer group comparisons
Action Planning Template
1. Define Objectives
- What business question are you trying to answer?
- What decisions will this analysis inform?
- What success metrics will you track?
2. Data Collection Plan
- What data sources will you use?
- How will you ensure data quality?
- What’s your sample size and methodology?
3. Analysis Approach
- Which frameworks and tools will you apply?
- How will you segment your customers?
- What hypotheses will you test?
4. Insights & Recommendations
- What patterns did you discover?
- Which customer segments are most valuable?
- What actions should you take based on findings?
5. Implementation & Monitoring
- How will you implement recommendations?
- What metrics will you track to measure success?
- When will you review and update your analysis?
Common Pitfalls to Avoid
- Analysis Paralysis: Don’t let perfect be the enemy of good
- Confirmation Bias: Look for disconfirming evidence
- Small Sample Sizes: Ensure statistical significance
- Ignoring Context: Consider external factors affecting behavior
- Static Analysis: Customer behavior changes over time
- Overcomplication: Start simple, add complexity as needed
- Data Quality Issues: Validate your data sources
Remember: Customer analysis is an ongoing process, not a one-time activity. Regular analysis and updates ensure your understanding stays current and actionable.