FAST INC CASE EXAMPLE
How One “Faster Product” Was Misunderstood — And What Changed Everything
Quick Summary
Company: Fast Inc
Product: XYZ2000 (fastest widget processor on the market)
The Problem
Fast Inc believed customers bought their product to save time.
Base on their supply side marketing program they identified the strength of their product.
They then began searching for companies that could benefit from the speed of their product.
But despite:
- Strong performance
- Happy customers
- Clear differentiation
➡️ Growth was slow.
What Compra Revealed
Customers were not buying to save time as the company had believed
They were buying to:
- Increase throughput
- Handle growing demand
- Manage predictable workflows
Companies had seen that with shorter cycle times they could do more.
This desire to do more was the main focus because of their context.
What Changed
Using:
- Compra Interviews (Context + Narrative)
- DDM Value Scoring
- Group Decision Tradeoff Crystals
- Organizational Design Dashboard
Fast Inc discovered:
Their product wasn’t a “faster machine”
It was a throughput and predictability system.
Resulting Shift
FROM:
“Save time with faster processing”
TO:
“Increase output without increasing complexity”
Business Impact
- Better targeting
- Stronger messaging
- Reduced reliance on discounts
- Clear understanding of when customers leave—and return
This information not only impacted marketing strategy but was also applied to
- Product roadmap
- Pricing strategy
- Retention fixes
- Segment prioritization
🔍 Deep Dive: What Actually Happened
1. The Starting Point (Before Compra)
Fast Inc built the XYZ2000, a product with:
- 2x faster cycle time than competitors
- Premium pricing
- Higher but predictable error rate
Marketing Strategy:
Focused on:
- Speed
- Time savings
- Efficiency
Assumption:
“Customers buy because we are faster than our competitors.”
“We may have more errors, but we are worth it.”
“Customers will pay a premium because of the time it saves them.”
2. The Hidden Problem
Fast Inc did not know:
- Why customers actually bought
- Why they stayed
- Why they left
Key Supply Side Assumptions:
- When a company needs to get lean, they hire us
- Customers leave because of price because we are premium
- Speed is the primary driver of value
3. Compra Findings (Context-Level Insight)
Sample:
1 group of 10 new or recent customers interviewed to determine: Why they bought.
1 group of 8 current customers: Why they stayed
1 group of 5 past customers to determine: Why they left.
Shared Buying Context
Customers bought when:
- They had high, consistent workload
- They expected continued growth
- They needed to increase capacity without adding complexity
What Customers Said
“We didn’t need it to be faster—we needed to get more done.”
“The speed helped us process more, not save time.”
4. DDM Quantification (Why They Bought)
TAPS Score (Decision Readiness)
82 / 100 → High Readiness at the time of the purchase
⚖️ Value Structure
| Value Factor | Weight | Score |
|---|---|---|
| Throughput Capacity | 45% | +2 |
| Predictability | 20% | +2 |
| Ease of Integration | 15% | +1 |
| Speed | 10% | +1 |
| Cost | 10% | -1 |
Total Score: +1.10
5. Tradeoff Crystal Insight
Using Group Decision Tradeoff Crystals, Fast Inc could clearly see:
🚨 Needed to Be Improved at Time of Purchase
- Throughput capacity
- Predictability
🛡 Needed to Be Protected at Time of Purchase
- Ease of use
- Workflow stability
⚠️ Critical Insight
Speed was not the value driver
It was only valuable because it increased output
6. Why Customers Stay
🧠 Context:
- Stable workflows
- Continued high demand
⚖️ Value Stability
- Predictability → Strong
- Integration → Strong
- Ease of use → Strong
💬 Customer Insight
“It fits into what we already do, it is easy to onboard new staff since we are growing.”
7. Why Customers Leave
❌ Assumption:
Customers leave due to price because it is premium.
✅ Reality:
Customers leave due to context change, they no longer have the workload level and can use other products and processes to handle low workloads
🧠 Leaving Context:
- Lower workload
- Reduced demand
- Less need for high throughput
⚖️ Value Breakdown
| Value Factor | Score |
|---|---|
| Throughput | -2 |
| Fit to Need | -2 |
| Cost | -1 |
💬 Customer Insight
“It was too much machine for what we needed. But it works great.”
8. Why Customers Come Back
Customers who left:
- Still trust the product
- Still recommend it
🔁 Return Trigger:
- Increased workload
- Renewed need for throughput
📊 Required Conditions:
- TAPS > 75
- High volume returns
9. Organizational Design Impact
Using the Organizational Design Dashboard, Fast Inc aligned internally:
🔄 Messaging Transformation
FROM:
Faster processing
TO:
Higher output with predictable workflows
🎯 Targeting Shift
FROM:
Inefficient companies
TO:
High-volume, growth-stage companies
💡 Product Repositioning
FROM:
Speed advantage
TO:
Predictable throughput engine
10. Strategic Outcome
Fast Inc can now:
- Target customers in the right context
- Explain value in customer language
- Avoid unnecessary discounting
- Predict customer exit and return behavior
They are also well positioned to continue their own growth through:
- Product development to serve new markets
- Pricing strategy that does not include discounts
- Retention fixes through ongoing communication after a company leaves
- Segment prioritization to growth companies and away from inefficient time stressed companies
Final Insight
Fast Inc didn’t have a product problem.
They had a decision visibility problem.