Recommending a Product
Product Recommendation Mastery - Complete Prompting Guide
Overview: From Product Pusher to Trusted Advisor
The best sales reps don't sell products—they solve problems. This guide teaches you how to use Colby AI to match the perfect solution to each customer's unique situation, increasing deal sizes by 40% and reducing buyer's remorse to near zero.
What You'll Learn:
How to uncover true customer needs beyond surface requests
Techniques for solution mapping that maximize value
Advanced methods for competitive positioning
Strategies for upsell and expansion identification
The Science of Perfect Product-Fit
Research shows:
67% of lost deals stem from product-fit misalignment
Proper solution mapping increases deal size by 40%
Customer lifetime value triples with right initial fit
Churn drops 75% when expectations match reality
Understanding Colby's Recommendation Engine
Colby analyzes multiple dimensions to recommend solutions:
Needs Analysis: Maps stated and unstated requirements
Use Case Matching: Compares to successful similar customers
Capability Alignment: Ensures technical and functional fit
Value Optimization: Balances customer value with revenue
Risk Assessment: Identifies potential mismatch areas
Growth Planning: Considers future expansion needs
The Master Recommendation Framework
"Recommend solutions for [Company]:
Customer Context:
- Industry: [Specific vertical and sub-segment]
- Size: [Employees, revenue, locations]
- Maturity: [Current state sophistication]
- Growth: [Trajectory and plans]
Stated Needs:
- Primary pain: [What they say hurts]
- Desired outcomes: [What they want to achieve]
- Success metrics: [How they measure]
- Timeline: [Urgency and phases]
Discovered Needs:
- Underlying challenges: [Root causes]
- Unspoken requirements: [Political, technical]
- Future considerations: [Growth, market]
- Risk factors: [What could derail]
Constraints:
- Budget: [Range and flexibility]
- Resources: [Implementation capacity]
- Technical: [Integration requirements]
- Political: [Stakeholder concerns]
Output Requirements:
- Recommended package: [Core solution]
- Options: [Good, better, best]
- Justification: [Why this fits]
- ROI model: [Value demonstration]
- Implementation approach: [Success path]"
Recommendation Examples: From Generic to Genius
❌ Poor Recommendation Prompt:
"What should they buy?"
Why It Fails:
No context
No analysis
Random suggestion
No value story
✅ Basic Recommendation Prompt:
"Recommend product for a mid-size retail company"
Better Because:
Some context
Industry noted
Still too vague
✅✅ Good Recommendation Prompt:
"Recommend solution for 50-store retail chain wanting better inventory management. Budget around $100K."
Much Better:
Specific use case
Size context
Budget parameter
Clear need
✅✅✅ Exceptional Recommendation Prompt:
"Recommend optimal solution for Fashion Forward Retail:
Company Profile:
- 50 locations across 3 states
- $100M annual revenue
- 500 employees
- Growing 20% yearly
- Planning 20 new stores in 2 years
Current State:
- Using Excel for inventory
- QuickBooks for accounting
- No integrated systems
- 3-person IT team
- Manual processes everywhere
Pain Points (from discovery):
- Inventory accuracy: 70% (industry avg 95%)
- Stock-outs costing $2M/year
- Can't see cross-store inventory
- Seasonal planning is guesswork
- No real-time visibility
Desired Outcomes:
- 95%+ inventory accuracy
- Reduce stock-outs 50%
- Enable inter-store transfers
- Improve seasonal buying
- Real-time dashboards
Hidden Needs (uncovered):
- CFO wants predictive analytics
- Store managers need mobile access
- Expanding to e-commerce next year
- Considering franchise model
- Need multi-currency (Canada expansion)
Constraints:
- Budget: $100-150K year one
- Implementation: Must go live before holiday season
- IT capacity: Limited, need managed service
- Change management: Store staff not tech-savvy
- Integration: Must connect to existing POS
Competition:
- Looking at CompetitorX (cheaper but limited)
- CompetitorY quoted $200K
- Liked our UI better
Provide:
1. Core recommendation with rationale
2. Phased approach option
3. Premium option if budget flexes
4. ROI calculation with their numbers
5. Risk mitigation for concerns
6. Success stories from similar retailers"
Why This Is Exceptional:
Complete context
Stated and unstated needs
Competitive intelligence
Constraints acknowledged
Clear output requirements
Strategic thinking
Mastering Different Recommendation Scenarios
1. New Customer Recommendations
Purpose: Match first solution perfectly Focus: Foundation for growth
Master Prompt:
"First-time buyer recommendation for [Company]:
Buying Triggers:
- What broke/changed
- Why buying now
- Failed alternatives
Evaluation Criteria:
- Must-haves vs nice-to-haves
- Deal breakers
- Success definition
Stakeholder Map:
- Who's driving (and why)
- Who's blocking (and why)
- Who signs checks
- Who uses daily
Maturity Assessment:
- Current tool sophistication
- Change capacity
- IT resources
- Training needs
Recommendation Strategy:
- Start simple or go advanced?
- Quick win focus or transformation?
- Point solution or platform?
- Standard or custom config?
Package Options:
- Starter: Solve immediate pain
- Professional: Room to grow
- Enterprise: Full transformation
Implementation Reality:
- Phase 1 quick wins
- Phase 2 expansion
- Phase 3 optimization
- Success milestones"
2. Expansion/Upsell Recommendations
Purpose: Grow existing relationships Focus: Additional value delivery
Master Prompt:
"Identify expansion opportunities for [Customer]:
Current Usage Analysis:
- Products owned
- Adoption rates
- Value realized
- Satisfaction scores
Usage Patterns:
- Power users vs casual
- Feature utilization
- Workarounds spotted
- Support ticket themes
Growth Indicators:
- Company expansion
- New use cases
- Team growth
- Process maturity
Trigger Events:
- Leadership changes
- Funding rounds
- Acquisitions
- Market pressures
Expansion Options:
1. Deeper: More users/usage
2. Broader: New departments
3. Higher: Advanced features
4. Wider: Additional products
Value Story:
- Additional ROI available
- Risk of not expanding
- Competitive advantage
- Success examples
Approach:
- Champion identification
- Business case development
- Pilot proposal
- Executive presentation"
3. Competitive Displacement
Purpose: Win against incumbents Focus: Superior value proposition
Master Prompt:
"Build displacement strategy against [Competitor]:
Current Situation:
- What they have
- Contract details
- Satisfaction level
- Switching costs
Pain with Current:
- Functionality gaps
- Support issues
- Cost concerns
- Strategic misalignment
Our Advantages:
- Feature superiority
- Price/value
- Support quality
- Future roadmap
Migration Strategy:
- Data transfer plan
- Training approach
- Parallel run option
- Risk mitigation
ROI of Switching:
- Hard savings
- Soft benefits
- Opportunity costs
- Strategic value
Objection Handling:
- Switching costs
- Implementation risk
- Change management
- Relationship inertia
Proof Points:
- Similar switches
- Reference customers
- Pilot proposal
- Guarantee options"
4. Solution Architecture
Purpose: Design complex solutions Focus: Integration and scalability
Master Prompt:
"Architect enterprise solution for [Company]:
Enterprise Requirements:
- Scale needs (users, data, transactions)
- Geographic distribution
- Security/compliance
- Integration ecosystem
Technical Landscape:
- Current architecture
- Key systems
- Data flows
- Technical debt
Business Architecture:
- Process flows
- Department needs
- Reporting structure
- Decision flows
Solution Design:
- Core platform config
- Module selection
- Integration approach
- Customization needs
Phasing Strategy:
- MVP definition
- Rollout sequence
- Success metrics
- Growth path
Risk Analysis:
- Technical risks
- Adoption risks
- Integration risks
- Mitigation plans
Investment Model:
- Initial investment
- Ongoing costs
- ROI timeline
- Value milestones"
Advanced Recommendation Techniques
Technique 1: Value Engineering
"Optimize solution value:
Value Drivers Ranked:
1. Time savings: Worth $X/hour
2. Error reduction: Worth $Y/mistake
3. Revenue increase: Worth $Z/point
4. Cost reduction: Worth $A/unit
Package Components by Value:
- Must have: 80% of value
- Should have: 15% of value
- Nice to have: 5% of value
Price/Value Optimization:
- Minimum viable: $X for Y% value
- Recommended: $X+30% for Y+50% value
- Premium: $X+60% for Y+80% value"
Technique 2: Use Case Stacking
"Build comprehensive solution:
Primary Use Case:
- Core problem solved
- Immediate value
- Quick win
Adjacent Use Cases:
- Natural extensions
- Same user base
- Incremental effort
Future Use Cases:
- Growth enablers
- New departments
- Market expansion
Create Bundle:
- Start with primary
- Add adjacent for 20% more
- Include future for 30% more
- Show total value multiplication"
Technique 3: Risk-Adjusted Recommendations
"Account for implementation risk:
Risk Factors:
- Technical complexity: High/Medium/Low
- Change management: Easy/Moderate/Hard
- Resource availability: Full/Partial/Limited
- Timeline pressure: Relaxed/Normal/Urgent
Adjust Recommendation:
- High risk: Simpler solution, phased approach
- Medium risk: Standard solution, support heavy
- Low risk: Advanced solution, self-service
Success Probability:
- Option A: 90% success, 70% value
- Option B: 70% success, 100% value
- Option C: 50% success, 130% value"
Common Recommendation Mistakes
Mistake 1: Overselling
❌ Pushing most expensive option always ✅ "Right-size based on actual needs and capacity"
Mistake 2: Underselling
❌ Minimum viable to ensure close ✅ "Show full value potential with phased approach"
Mistake 3: Feature Focus
❌ List all capabilities ✅ "Map features to specific business outcomes"
Mistake 4: Ignoring Politics
❌ Perfect technical solution ✅ "Solution that key stakeholders will actually support"
Measuring Recommendation Success
Quality Metrics:
Close rate by package type
Average deal size
Time to value
Expansion rate
Churn rate
NPS scores
Recommendation Effectiveness:
Adoption rates post-sale
Support ticket volume
Feature utilization
Customer success stories
Reference-ability
Pro Tips for Recommendation Excellence
The Three-Option Rule
The Future-State Visualization
The Crawl-Walk-Run Strategy
The Reference Architecture
Practice Scenarios
Scenario 1: The Conservative Buyer
"CFO-led evaluation, burned by previous tech purchase, wants guarantees"
Scenario 2: The Innovative Leader
"CEO wants to leapfrog competition, willing to invest, needs board buy-in"
Scenario 3: The Complex Enterprise
"Global company, multiple divisions, competing priorities, political landmines"
