Marketing Experimentation Framework

Marketing Experimentation Framework for Bloom Skincare

1. Core Framework Structure

Experimentation Philosophy
The Bloom Skincare experimentation framework operates on three core principles:

• Move from opinion-based to evidence-based decision making
• Embrace “fast failure” as a pathway to innovation
• Scale what works, learn from what doesn’t

Experiment Design Process

1. Hypothesis Formation
   • Each experiment begins with a clear hypothesis in the format: “If we [implement change], then [expected outcome] will occur because [underlying rationale].”
   • Example: “If we implement user-generated content on product pages, then conversion rates will increase by 15% because authentic social proof increases purchase confidence.”
2. Variable Isolation
   • Control variables: Product pricing, page layout structure, overall brand messaging
   • Test variable: Presence of user-generated content module
   • Measurement: Product page conversion rate, time on page, scroll depth
3. Control Group Establishment
   • Implement 50/50 traffic split A/B testing for high-traffic products
   • Use geographical isolation testing for lower-traffic items to ensure sufficient sample size
4. Statistical Significance Planning
   • Required minimum sample size: 3,000 visitors per variation
   • Minimum test duration: 2 weeks (to account for day-of-week effects)
   • Statistical confidence threshold: 95% for major changes, 90% for minor optimizations

2. Prioritization Framework

Impact-Effort-Confidence Model All potential experiments will be scored on three dimensions (1-10 scale):

• Impact: Potential revenue or conversion lift if successful
• Effort: Resources required for implementation
• Confidence: Probability of success based on existing data and prior tests

Priority Score = (Impact × Confidence) ÷ Effort

Prioritized Experiment Queue

1. Instagram Stories vs. TikTok creator collaboration (Score: 8.5)
2. Loyalty program restructure test (Score: 7.2)
3. Product bundling algorithm optimization (Score: 6.8)
4. Email subject line personalization test (Score: 5.4)

3. Specific Experiment Concepts

Experiment 1: Influencer Authenticity Test

• Challenge Addressed: Rising customer acquisition costs
• Hypothesis: Micro-influencers (10K-50K followers) with higher engagement rates will generate lower CAC than mega-influencers (1M+ followers)
• Design: Split $30,000 influencer budget equally between 5 micro-influencers and 1 mega-influencer, using unique tracking links
• Measurement: CAC, conversion rate, average order value, retention rate after 30 days

Experiment 2: Social Proof Optimization

• Challenge Addressed: Declining conversion rates on product pages
• Hypothesis: Dynamic social proof showing “X people purchased this item in the last 24 hours” will increase conversion rates by creating urgency
• Design: A/B test showing dynamic social proof vs. static reviews
• Measurement: Conversion rate, add-to-cart rate, product page bounce rate

Experiment 3: Email Retention Sequence

• Challenge Addressed: Low repeat purchase rates
• Hypothesis: A personalized post-purchase email sequence featuring complementary products will increase 60-day repurchase rates
• Design: Test group receives 5-part educational sequence with personalized recommendations; control receives standard transactional emails
• Measurement: 60-day repurchase rate, email engagement metrics, customer lifetime value

4. Knowledge Management System

Experiment Documentation Template

• Hypothesis and rationale
• Experimental design and variables
• Results (quantitative and qualitative)
• Team insights and observations
• Recommendations for implementation or further testing

Learning Repository Structure

• Searchable database organized by marketing channel, customer journey stage, and metric impacted
• Monthly experimentation review meetings to synthesize insights
• Quarterly “Experiment Showcase” highlighting key learnings (successful or not)

5. Organizational & Technology Requirements

Team Structure Recommendations

• Designate an Experimentation Lead (20% of Marketing Director’s time)
• Train all marketing team members on basic experimental design principles
• Create cross-functional “experiment squads” for major initiatives

Technology Stack Requirements

• A/B testing platform: Recommend Optimizely or VWO for website optimization
• Analytics integration: Enhanced Google Analytics 4 implementation with custom events
• Experimentation dashboard: Custom Looker dashboard for real-time experiment tracking
• Knowledge repository: Notion database with standardized templates

6. Implementation Roadmap

Month 1: Foundation

• Complete team training on experimentation methodology
• Implement technology stack
• Design first round of experiments

Months 2-3: Initial Execution

• Launch first three experiments
• Document processes and establish cadence
• Create knowledge management system

Months 4-6: Refinement

• Scale successful tests
• Adjust methodology based on learnings
• Begin to develop experimentation culture through regular reviews

Months 7-12: Expansion

• Increase experiment velocity (targeting 2-3 concurrent experiments)
• Establish experimentation as core part of marketing operations
• Begin to connect experiments across channels for holistic customer journey optimization