ROI Case File No.272｜'BlueHarbor Retail's Empathy Design'

Chapter 1: Inventory Without Sales—Customers Invisible to Data

The week after resolving the NovaEdge Finance AARRR case, a consultation arrived from North America regarding a retail chain's sales decline. Case File 272 of Volume 22 "The Pursuit of Reproducibility" is a story about diving into customers' inner worlds to discover true needs.

"Detective, we have ample inventory, competitive pricing, and excellent locations. Yet customers don't buy. Even looking at data, we can't see why they're not buying."

Emily Chen, Chief Marketing Officer of BlueHarbor Retail from Boston, visited 221B Baker Street unable to hide her confusion. In her hands were massive purchasing data analysis reports contrasted sharply with stagnant sales graphs.

"We operate omnichannel clothing retail in the New England region. We've invested in data analysis. We know customer age, purchase history, browsing history—everything. Yet sales have been flat for three years."

BlueHarbor Retail's Inexplicable Stagnation: - Founded: 2012 (omnichannel retail) - Store count: 85 stores in North America - EC site: 450,000 monthly visitors - Annual sales: 28 billion yen (flat for 3 years) - Customer data: 1.2 million purchase histories - Data analysis investment: 80 million yen annually

Emily's expression held deep anxiety.

"The problem is data only tells us 'what they bought.' A 35-year-old woman bought a blouse. We know that fact. But why she chose that blouse, what problem she had, what she was seeking—data doesn't show us."

Truths Data Doesn't Tell: - Purchase history: Product name, price, datetime (tracked) - Browsing history: Products viewed, time spent (tracked) - Unknown: Purchase motivation, selection reasons, unmet needs - Unknown: Reasons for not buying, hesitation factors, expectations

"We're looking at numbers, but not at people."

Chapter 2: Empathy as Starting Point—Digging for Insights Through Observation and Interviews

"Emily, how is your current customer understanding conducted?"

To my question, Emily answered.

"Mainly through surveys and data analysis. We conduct customer satisfaction surveys twice yearly and analyze purchase data with machine learning. We've also introduced a recommendation engine saying 'people who bought this also bought that.'"

Current Customer Understanding (Quantitative): - Method: Surveys (5-point scale), purchase data analysis - Questions: "Are you satisfied?" "Is pricing appropriate?" - Responses: Numerically scored choices - Result: Only superficial evaluation

I explained the importance of qualitative understanding.

"Numbers tell us 'what happened,' but don't tell us 'why it happened.' Design thinking—it's the technology of empathizing with customers' inner worlds to discover true needs."

⬜️ ChatGPT｜Catalyst of Concepts

"Observe, listen, empathize. Answers lie not in numbers, but in stories"

🟧 Claude｜Alchemist of Narratives

"Customers don't buy products. They choose products to move their own story forward"

🟦 Gemini｜Compass of Reason

"Design thinking is a journey of discovery. Discover→Define→Create→Test—these iterations generate innovation"

The three members began analysis. Gemini deployed the "Retail-Specialized Design Thinking" framework on the whiteboard.

Design Thinking's 4 Stages: 1. Discover - Gain deep insights through observation and interviews 2. Define - Articulate the true problem 3. Develop - Create solution prototypes 4. Deliver - Experiment, learn, and improve

"Emily, let's discover BlueHarbor customers' 'stories.'"

Chapter 3: Observation as a Window—Truths Numbers Don't Tell

Phase 1: Deep Interviews (1 month)

Instead of data analysis, we began conversations with customers.

Interview Subjects: - Recent purchasers: 20 people - Browsed but didn't purchase: 30 people - Haven't purchased in over a year: 15 people

Conventional Questions (Superficial): "Are you satisfied with our product selection?"

New Questions (Deep): "Tell me in detail about the last time you bought clothes. What happened that made you need clothes?"

Interview Case 1: Sarah (38, office worker)

Conventional Understanding: - Data: Purchased 1 blouse 3 months ago (5,800 yen) - Classification: "Regular mid-price purchaser"

Design Thinking Interview:

"Why did you decide to buy a blouse that day?"

"Actually, I got promoted at work. I wanted something to wear on my first day in the new department. I looked in my closet but nothing felt 'just right.'"

"What kind of clothes were you looking for?"

"Something that looks 'professional' but not 'trying too hard.' Confident but approachable—clothes with that kind of impression."

"How did you search in the store?"

"Honestly, I struggled. Lots of clothes, but I couldn't tell which fit the 'first day of promotion' context. In the end, I just told the staff 'clothes for work' and bought what they recommended."

"Are you satisfied?"

"The clothes themselves are fine. But what I really wanted were clothes that would support 'my promoted self.' Whether I found that—honestly, I'm not sure."

Interview Case 2: Mike (42, IT company employee)

Data Behavior: - Weekend store visit, 30-minute stay, no purchase

Interview:

"Why did you come to the store?"

"Looking for a birthday present for my daughter. I wanted to buy clothes a 12-year-old girl would be happy with."

"Why didn't you buy?"

"I didn't know what my daughter would like. When I asked staff, they recommended 'this is popular now,' but I was anxious whether my daughter would be happy. In the end, I went with an Amazon gift card."

Phase 2: Behavioral Observation (2 weeks)

We observed customer behavior in stores.

Observation Case: Weekend afternoon female customers - Store time: Average 18 minutes - Items picked up: Average 5.2 - Tried on: 1.8 - Purchased: 0.3 (30% leave without buying)

Observed Behavior Patterns: - Pick up item → check price tag → return (repeatedly) - Hesitate 5+ minutes before fitting room - Search for similar products on smartphone - Look for staff but can't find them, give up and leave

Phase 3: Insight Extraction

From interviews and observation, truths invisible to data emerged.

Insight 1: Customers buy context, not products - Not "I want a blouse" - But "I want clothes that support my first day after promotion"

Insight 2: Too many choices become obstacles - Product count: Store average 1,200 items - Customer concern: "I don't know which fits me"

Insight 3: Staff sell products but don't ask for context - Staff question: "What design do you like?" - What customers want to answer: "This is the occasion I want to wear it for"

Chapter 4: Problem Redefinition—What Customers Really Want

Phase 4: Problem Articulation (Define)

Based on discovered insights, we redefined the true problem.

Conventional Problem Definition: "Customers aren't satisfied with product selection"

New Problem Definition: "Customers lack a way to find 'clothes that fit their story'"

Further Specified Personas:

Persona 1: "Promotion Sarah" - Age: Late 30s - Situation: Career turning point - Need: Clothes supporting new self - Obstacle: Lost in vast choices

Persona 2: "Father Mike" - Age: 40s - Situation: Gift shopping for daughter - Need: Confidence daughter will be happy - Obstacle: Doesn't know daughter's preferences

Phase 5: Idea Generation (Develop)

Based on problem definition, we diverged solution ideas.

Idea 1: "Story-Based Service" - Change first staff question - "What design do you like?" → "What occasion will you wear it for?" - Context-based suggestions

Idea 2: "Situation-Based Display" - Conventional: "Tops" "Bottoms" "Outerwear" - New method: "Promotion/Job Change" "Weekend Casual" "Special Days" - Search products by context

Idea 3: "Gift Concierge" - Hear about gift recipient - Narrow to 3 optimal products - "Refund if not pleased" guarantee

Phase 6: Prototyping and Testing (Deliver)

We tested the most promising ideas on a small scale.

Test 1: "Story Service" Implementation at Boston Flagship (3 stores, 1 month)

Conventional Service: Staff: "Welcome. Looking for anything?" Customer: "Looking at blouses" Staff: "Here are new arrivals"

New Service: Staff: "Hello. What occasion are you looking for clothes for?" Customer: "Actually, starting in a new department next week" Staff: "That's wonderful! Congratulations. What impression do you want to make in the new environment?" Customer: "Professional but approachable" Staff: "Understood. Let me suggest these three styles. Each gives this kind of impression"

Results (after 1 month): - Average purchase count: 0.3 → 1.8 items (6x) - Average transaction: 2,400 yen → 12,500 yen (5x) - Customer satisfaction: 3.8 → 4.7 - Repeat rate: 18% → 42%

Test 2: "Situation-Based Display" (1 store, 2 months)

Reorganized part of store into three zones: "Promotion/Job Change," "Dates," "Weekend Relaxation."

Results: - Zone dwell time: +30% - Purchase conversion: 8% → 22% - Customer voices: "Easier to find" "Found clothes that fit me"

Results after 12-month Company-wide Rollout:

Business Metrics: - Annual sales: 28 billion yen → 38.5 billion yen (+37%) - Transaction size: 2,400 yen → 8,900 yen (3.7x) - Visit frequency: 1.8 times/year → 4.2 times/year - Customer satisfaction: 3.8 → 4.8

Organizational Changes: - Store staff training: Product knowledge → "Story listening skills" - Store design: Product categories → Situation categories - Data analysis: Purchase history → "Customer story" pattern analysis

Chapter 5: The Detective's Design Thinking Diagnosis—Empathy Transcends Specifications

Holmes compiled the comprehensive analysis.

"Emily, the essence of design thinking is 'empathy.' Numbers teach facts, but empathy teaches truth. Entering customers' stories, understanding context, discovering what they really want—that's true customer understanding."

Final Report after 24 months:

BlueHarbor Retail gained attention as an "innovator in customer understanding" in the North American retail industry.

Final Achievements: - Annual sales: 28 billion yen → 52 billion yen (1.9x) - Operating margin: 5% → 18% - NPS (Net Promoter Score): +12 → +68 (industry top) - Industry award: "Most Customer-Empathetic Retailer"

Emily's letter contained deep gratitude:

"Through design thinking, we transformed from 'a store selling products' to 'a store supporting customer stories.' Most important was seeing people, not data. Now all new product development and store design begins with customer interviews. Numbers are tools for measuring results, but empathy is the source of innovation—we understood that."

The Detective's Perspective—The Magic of Empathy

That night, I contemplated the essence of customer understanding.

The true value of design thinking lies in human-centered thinking. Data analyzes the past, but empathy creates the future. When you enter customers' stories and discover true needs, solutions previously invisible become visible.

Observe, listen, empathize. These simple acts hold the power to fundamentally transform business.

"Data tells you 'what.' Empathy tells you 'why.' And only those who know 'why' can create true value."

The next case will also depict a moment when empathy opens a company's future.

"Customers don't buy products. They choose products to move their own story forward"—From the detective's notes

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