BEH: Behaviors
Document Type
Code: BEH
Name: Behaviors
Domain: Customer & Value
Abstract
This specification defines the Behaviors document type within the BSpec 1.0 Universal Business Specification Standard. It establishes normative requirements, structured templates, and implementation guidance for organizations documenting behaviors within the customer-value domain. This specification enables systematic, machine-readable documentation that supports strategic planning, operational execution, and organizational alignment.
Purpose and Scope
The Behaviors document analyzes customer usage patterns, behavioral data, and interaction analytics to understand how customers actually use products and services, revealing gaps between stated preferences and actual behavior.
Document Metadata Schema
yaml
---
id: BEH-{behavior-analysis-identifier}
title: "Customer Behavior Analysis: [Behavior Focus]"
type: BEH
status: Draft|Review|Accepted|Deprecated
version: 1.0.0
owner: Product-Analytics|User-Research
stakeholders: [product-team, design-team, marketing-team]
domain: customer
priority: High|Medium
scope: behavioral-study
horizon: current
visibility: internal
depends_on: [PER-*, USE-*]
enables: [REQ-*, UXD-*, GAI-*]
analysis_type: Usage|Navigation|Engagement|Conversion
data_sources: [Analytics platforms, tools, and systems]
analysis_period: [Date range for behavior analysis]
customer_sample: [Size and characteristics of analyzed users]
success_criteria:
- "Behavior patterns are clearly identified and validated"
- "Insights reveal actionable optimization opportunities"
- "Findings inform product and experience decisions"
- "Analysis drives measurable improvements"
assumptions:
- "Behavioral data represents typical usage patterns"
- "Data quality is sufficient for reliable analysis"
- "Observed behaviors indicate customer preferences"
data_sources:
- "Web/app analytics platforms"
- "User session recordings"
- "A/B testing platforms"
- "Customer support interactions"
sample_size: X # Number of users/sessions analyzed
analysis_confidence: 95%
statistical_significance: p<0.05
review_cycle: monthly
---Content Structure Template
markdown
# Customer Behavior Analysis: [Behavior Focus]
## Overview
*Summary of behavioral analysis purpose, scope, and key findings*
## Analysis Framework
### Research Objectives
**Primary Behavioral Questions**
- [Question 1]: [What behavioral pattern we want to understand]
- [Question 2]: [What behavioral pattern we want to understand]
- [Question 3]: [What behavioral pattern we want to understand]
**Hypotheses to Test**
- [Hypothesis 1]: [Predicted behavior pattern]
- [Hypothesis 2]: [Predicted behavior pattern]
- [Hypothesis 3]: [Predicted behavior pattern]
**Business Questions**
- How do customers actually use our product?
- Where do customers struggle or drop off?
- What drives engagement and success?
- How do behaviors vary by segment?
### Data Collection Framework
**Behavioral Data Sources**
- Web analytics: [Platform and metrics tracked]
- Mobile analytics: [Platform and metrics tracked]
- Product analytics: [Platform and metrics tracked]
- Session recordings: [Tool and sample size]
- Heatmap analysis: [Tool and coverage]
- A/B testing data: [Platform and experiments]
**Data Quality Assessment**
- Data completeness: X% coverage
- Data accuracy validation
- Sample representativeness
- Tracking reliability assessment
**Analysis Period**
- Start date: [Date]
- End date: [Date]
- Total period: X days/weeks/months
- Seasonal considerations
- External factor impacts
### Customer Sample
**Sample Characteristics**
- Total users analyzed: X
- Active users: X (X%)
- New vs. returning: X% new, X% returning
- Geographic distribution
- Device/platform distribution
**Segmentation**
- Customer segment breakdown
- Persona representation
- Experience level distribution
- Usage frequency categories
## Usage Behavior Analysis
### Overall Usage Patterns
**Activity Metrics**
- Total sessions: X
- Average session duration: X minutes
- Pages/screens per session: X
- Bounce rate: X%
- Return visit frequency: X days
**Engagement Metrics**
- Daily active users (DAU): X
- Monthly active users (MAU): X
- DAU/MAU ratio: X%
- Session frequency distribution
- Engagement depth scores
**Feature Usage**
- Most used features: [List with usage %]
- Least used features: [List with usage %]
- Feature adoption rates
- Feature usage correlation
- Power user feature patterns
### Navigation Behavior
**User Flow Analysis**
- Most common user paths
- Entry point analysis
- Exit point identification
- Navigation patterns by user type
- Path completion rates
**Page/Screen Performance**
- Most visited pages/screens
- Time spent per page/screen
- Interaction rates per page/screen
- Scroll depth and engagement
- Content effectiveness
**Search Behavior** (if applicable)
- Search usage rates
- Common search terms
- Search success rates
- Search refinement patterns
- Zero-result search analysis
### Task Completion Analysis
**Goal Achievement**
- Primary goal completion rates
- Secondary goal completion rates
- Task abandonment points
- Success factor identification
- Failure pattern analysis
**Conversion Funnel Analysis**
- Funnel stage conversion rates
- Drop-off point identification
- Conversion optimization opportunities
- Segment-specific conversion patterns
- Time-to-conversion analysis
## Behavioral Patterns by Segment
### Segment-Specific Behaviors
**Segment 1: [Customer Segment]**
- Unique usage patterns
- Engagement characteristics
- Feature preference differences
- Success rate variations
- Behavioral trends over time
**Segment 2: [Customer Segment]**
- Unique usage patterns
- Engagement characteristics
- Feature preference differences
- Success rate variations
- Behavioral trends over time
### Persona Behavior Validation
**Persona 1: [Persona Name]**
- Predicted vs. actual behavior
- Behavior pattern confirmation
- Unexpected behavioral insights
- Persona refinement needs
**Persona 2: [Persona Name]**
- Predicted vs. actual behavior
- Behavior pattern confirmation
- Unexpected behavioral insights
- Persona refinement needs
### New vs. Experienced User Patterns
**New User Behaviors**
- Onboarding completion rates
- Time to first value
- Feature discovery patterns
- Early usage characteristics
- Retention prediction indicators
**Experienced User Behaviors**
- Advanced feature adoption
- Efficiency improvements over time
- Usage optimization patterns
- Power user characteristics
- Expansion behavior indicators
## Engagement and Retention Analysis
### Engagement Patterns
**Engagement Depth**
- Shallow engagement indicators
- Deep engagement characteristics
- Engagement progression patterns
- Engagement maintenance factors
- Re-engagement triggers
**Engagement Frequency**
- Usage frequency distribution
- Frequency change patterns
- Seasonal engagement variations
- Event-driven engagement spikes
- Engagement decline indicators
**Engagement Quality**
- Quality vs. quantity metrics
- Meaningful interaction identification
- Value realization indicators
- Satisfaction correlation
- Advocacy behavior connection
### Retention Behavior
**Retention Cohort Analysis**
- Day 1, 7, 30, 90 retention rates
- Retention curve patterns
- Cohort performance differences
- Retention improvement trends
- Churn prediction indicators
**Resurrection Patterns**
- Dormant user reactivation
- Win-back campaign effectiveness
- Reactivation trigger identification
- Second chance success rates
- Long-term resurrection value
### Churn Behavior Analysis
**Churn Indicators**
- Early warning behavioral signals
- Declining engagement patterns
- Feature usage decline patterns
- Support interaction correlations
- Subscription/usage changes
**Churn Pattern Analysis**
- Immediate vs. gradual churn
- Seasonal churn patterns
- Segment-specific churn behaviors
- Preventable vs. inevitable churn
- Churn recovery possibilities
## Pain Point Identification
### Friction Analysis
**High-Friction Areas**
- High abandonment rate pages/screens
- Long task completion times
- High error rate interactions
- Repeated action patterns
- enable-seeking behavior hotspots
**User Struggle Indicators**
- Excessive clicking/tapping
- Back-and-forth navigation
- Form abandonment patterns
- Search refinement loops
- Support ticket correlations
**Error and Failure Patterns**
- Common error occurrences
- Error recovery success rates
- Error impact on user experience
- System error vs. user error
- Error prevention opportunities
### Efficiency Analysis
**Task Efficiency Metrics**
- Time to complete key tasks
- Number of steps in workflows
- Error rates in critical paths
- Success rate by approach
- Efficiency improvement over time
**Workflow Optimization**
- Streamlining opportunities
- Unnecessary step identification
- Shortcut discovery and usage
- Process improvement potential
- Automation opportunity identification
## Success Pattern Analysis
### High-Performance Behaviors
**Success Indicators**
- Behaviors correlating with success
- High-value user characteristics
- Optimal usage patterns
- Feature combination success
- Timing pattern optimization
**Power User Analysis**
- Power user identification criteria
- Unique behavioral characteristics
- Advanced feature utilization
- Efficiency achievement methods
- Success replication potential
**Viral Behavior Analysis**
- Sharing and referral behaviors
- Content creation patterns
- Community participation
- Advocacy behavior indicators
- Network effect contributions
### Value Realization Behaviors
**First Value Achievement**
- Time to first value behaviors
- Actions leading to quick wins
- Onboarding success patterns
- Early engagement predictors
- Value recognition indicators
**Sustained Value Behaviors**
- Long-term engagement patterns
- Value optimization behaviors
- Expansion usage indicators
- Mastery development patterns
- Continuous value extraction
## Behavioral Change Analysis
### Behavior Evolution
**Learning Curves**
- Skill development patterns
- Efficiency improvement rates
- Feature adoption progression
- Expertise development stages
- Mastery achievement timelines
**Adaptation Patterns**
- Response to product changes
- Feature update adoption
- Workflow adaptation speed
- Change resistance indicators
- Positive change embrace
### Seasonal and Temporal Patterns
**Time-Based Variations**
- Daily usage patterns
- Weekly activity cycles
- Monthly variation trends
- Seasonal behavior changes
- Holiday/event impact analysis
**Lifecycle Behavior Changes**
- Behavior changes over customer lifetime
- Maturity impact on usage
- Business growth correlation
- Contract renewal behavior
- Expansion behavior timing
## A/B Testing and Experimental Insights
### Experiment Results
**Test 1: [Experiment Name]**
- Hypothesis tested
- Behavioral changes observed
- Statistical significance: p=X
- Effect size: X% improvement
- Segment-specific results
**Test 2: [Experiment Name]**
- Hypothesis tested
- Behavioral changes observed
- Statistical significance: p=X
- Effect size: X% improvement
- Segment-specific results
### Behavioral Response Patterns
**Feature Introduction Impact**
- Adoption rate of new features
- Usage pattern changes
- Engagement impact measurement
- User satisfaction correlation
- Long-term behavior modification
**Design Change Impact**
- Navigation behavior changes
- Task completion improvements
- Error rate modifications
- User satisfaction correlation
- Unintended consequence identification
## Mobile vs. Desktop Behavior
### Platform-Specific Patterns
**Mobile Behavior Characteristics**
- Session duration differences
- Feature usage variations
- Navigation pattern differences
- Task completion variations
- Engagement quality differences
**Desktop Behavior Characteristics**
- Session depth and duration
- Multi-tasking behavior patterns
- Advanced feature utilization
- Productivity workflow usage
- Complex task completion
**Cross-Platform Behavior**
- Multi-device usage patterns
- Context switching behaviors
- Continuity expectations
- Platform preference indicators
- Seamless experience requirements
## Competitive Behavior Intelligence
### Market Behavior Comparison
**Industry Benchmark Comparison**
- Engagement rate benchmarks
- Conversion rate comparisons
- User experience benchmarks
- Feature adoption standards
- Retention rate comparisons
**Switching Behavior Analysis**
- Customer acquisition sources
- Competitive switching patterns
- Feature comparison behaviors
- Trial usage characteristics
- Decision-making behavior patterns
## Actionable Insights and Recommendations
### Immediate Optimizations (0-30 days)
**Priority 1: [High-Impact Quick Win]**
- Behavioral insight: [Specific finding]
- Optimization opportunity: [What to change]
- Expected impact: [Projected improvement]
- Implementation complexity: [Resource requirement]
- Success measurement: [Metrics to track]
**Priority 2: [Friction Reduction]**
- Behavioral insight: [Specific finding]
- Optimization opportunity: [What to change]
- Expected impact: [Projected improvement]
- Implementation complexity: [Resource requirement]
- Success measurement: [Metrics to track]
### Product Enhancement Opportunities (1-3 months)
**Enhancement 1: [Feature/Experience Improvement]**
- Behavioral evidence: [Supporting data]
- Customer value: [Expected benefit]
- Business impact: [Revenue/retention/satisfaction]
- Development requirements: [Technical scope]
- Success criteria: [Measurement approach]
**Enhancement 2: [Workflow Optimization]**
- Behavioral evidence: [Supporting data]
- Customer value: [Expected benefit]
- Business impact: [Revenue/retention/satisfaction]
- Development requirements: [Technical scope]
- Success criteria: [Measurement approach]
### Strategic Initiatives (3-12 months)
**Initiative 1: [Major Behavioral Improvement]**
- Behavioral opportunity: [Data-driven insight]
- Strategic value: [Long-term impact]
- Competitive advantage: [Market differentiation]
- Investment requirement: [Resource allocation]
- Transformation plan: [Implementation approach]
### Continued Analysis Plan
**Ongoing Monitoring**
- Key behavioral metrics to track
- Alert thresholds for behavior changes
- Regular analysis cadence
- Segment monitoring priorities
- Trend identification processes
**Future Research Priorities**
- Behavioral questions requiring deeper analysis
- Experimental opportunities
- Cross-platform behavior studies
- Longitudinal behavior tracking
- Predictive behavior modeling
## Validation
*Evidence that behavioral analysis provides reliable insights and drives improvements*Quality Standards
Bronze Level (Minimum Viable)
- [ ] Analysis framework with research objectives and data collection approach
- [ ] Basic usage behavior analysis with activity and engagement metrics
- [ ] Pain point identification with friction analysis
- [ ] All required metadata fields completed
Silver Level (Investment Ready)
- [ ] Comprehensive behavioral patterns analysis by segment and persona validation
- [ ] Detailed engagement and retention analysis with churn behavior insights
- [ ] Success pattern analysis with high-performance behaviors and value realization
- [ ] Behavioral change analysis with evolution and temporal patterns
- [ ] A/B testing insights and experimental results
- [ ] Actionable recommendations with immediate and strategic initiatives
Gold Level (Operational Excellence)
- [ ] Dynamic behavior tracking with real-time analytics and predictive modeling
- [ ] Advanced behavioral segmentation with machine learning clustering
- [ ] Continuous behavioral optimization with automated A/B testing
- [ ] Behavior-driven product development and experience personalization
- [ ] Cross-platform behavioral intelligence and competitive analysis
- [ ] Automated behavioral insight generation and recommendation systems
Common Pitfalls
- Data quality issues: Poor tracking implementation leading to unreliable behavioral insights
- Correlation vs. causation: Misinterpreting behavioral correlations as causal relationships
- Sample bias: Analyzing only engaged users while missing struggling customer behaviors
- Analysis paralysis: Collecting behavioral data without translating insights into actions
Success Patterns
- Data-driven insights: Rigorous analytics revealing clear patterns and actionable opportunities
- Behavioral-outcome correlation: Strong links between observed behaviors and customer success
- Continuous optimization: Regular testing and refinement based on behavioral insights
- Cross-functional impact: Behavioral insights informing product, design, and strategy decisions
Relationship Guidelines
Typical Dependencies
- PER (Personas): Personas provide context for interpreting behavioral patterns
- USE (Use Cases): Use cases define the intended behaviors to analyze
Typical Enablements
- REQ (Requirements): Behavioral insights drive functional and experience requirements
- UXD (User Experience Design): Behavior analysis informs experience design decisions
- GAI (Gains): Behavioral patterns reveal opportunities for value creation
Document Relationships
This document type commonly relates to:
- Depends on: PER (Personas), USE (Use Cases)
- Enables: REQ (Requirements), UXD (User Experience Design), GAI (Gains)
- Informs: Product optimization, user experience improvement, customer success strategy
- Guides: Feature development, interaction design, engagement optimization
Validation Checklist
- [ ] Analysis framework with research objectives, data collection, and customer sample
- [ ] Usage behavior analysis with overall patterns, navigation, and task completion
- [ ] Behavioral patterns by segment with persona validation and user type analysis
- [ ] Engagement and retention analysis with patterns, cohorts, and churn behavior
- [ ] Pain point identification with friction analysis and efficiency assessment
- [ ] Success pattern analysis with high-performance behaviors and value realization
- [ ] Behavioral change analysis with evolution and temporal patterns
- [ ] A/B testing insights with experiment results and behavioral response patterns
- [ ] Platform-specific behavior analysis and competitive intelligence
- [ ] Actionable recommendations with immediate optimizations and strategic initiatives
- [ ] Validation evidence of analysis reliability and business impact