ROI Case File No.062: Vanishing Regulars, Unreached Transformation
📅 2025-06-28
🕒 Reading time: 4 min
🏷️ DX 🏷️ Retail 🏷️ Digital Strategy 🏷️ Dependency 🏷️ Regional Revitalization
- Chapter I: The Regional Chain's Lonely SOS
- Chapter II: Numbers Tell the Story of "Lost Voices"
- Chapter III: The Starting Point of Change Lies in the Field
- Chapter IV: KPT Framework (Retail Rediscovery)
- Chapter V: The Detective's Retail Diagnosis
- Chapter VI: The Eye for Change
- Chapter VII: The Invisible Thread
Chapter I: The Regional Chain's Lonely SOS
"We want to advance digitalization, but... what should we do and how?"
The visitor to Baker Street at dusk was the Sales Operations Manager of Oceantrail & Co. In his hands were sales trend graphs from multiple stores and bundles of survey results placed beside cash registers.
"Elderly regulars still visit us. But we hardly see the younger generation anymore."
He continued with drooping shoulders.
"At this rate, we won't last another five years. Please give us some kind of 'solution.' AI, DX—anything. We're grasping at straws."
However, as I listened to his story, I felt a sense of déjà vu. Between yesterday's Espol case and others, why were there so many recent cases of corporate decision-making confusion?
Chapter II: Numbers Tell the Story of "Lost Voices"
"Interesting situation, Watson."
Holmes pointed to the graphs. Year-over-year sales slightly down, visitor numbers trending downward. But per-customer purchase amounts were rising.
"This is 'aging of regular customers.' And 'new customer departures' that aren't visible."
He flipped through more surveys.
- Product satisfaction: ◯
- Friendly service: ◯
- Visit frequency: Monthly → Several times per year
"The problem isn't 'bad things.' It's 'not noticing changes.'"
I found a concerning line in the corner of the materials: 'Previous marketing manager departed suddenly six months ago'—wasn't this the same pattern as yesterday's Espol case?
Chapter III: The Starting Point of Change Lies in the Field
"What should AI learn? It's proportional to 'what humans have overlooked.'"
Holmes began describing a virtual scenario of analyzing in-store camera footage with AI.
- Young demographics stop at the entrance and immediately leave
- Elderly demographics have long in-store stays and frequent conversations
- "Non-response rate" to promotional POPs exceeds 80%
"These aren't things AI teaches us. We need humans with the awareness to observe."
"So... we should sharpen field observation skills before AI?"
But then I noticed—among the competitive analysis materials he brought were documents that seemed to be from other regional chains' internal sources. As if someone was intentionally leaking information.
Chapter IV: KPT Framework (Retail Rediscovery)
| Item | Current State | Direction for Improvement | Long-term Vision |
|---|---|---|---|
| Keep | • Close regional relationships • Loyal elderly customer base • Network with local suppliers |
• Continued strengthening of service quality • Community-rooted services |
• Store evolution with community function |
| Problem | • Declining young customer visits • Store-by-store operational dependency • Unmeasurable promotional effects |
• Undeveloped customer journey • Non-standardized workflow |
• Structure that doesn't accumulate operational data |
| Try | • Digitization of register-side surveys • Camera data behavioral analysis PoC • Promotion→Purchase behavior linkage measurement |
• Standardization manual for personalized responses • Pre-AI implementation inventory |
• Reconstruction as central hub for regional DX |
Chapter V: The Detective's Retail Diagnosis
"They sought 'reasons for poor sales' only in sales figures."
Holmes gazed at the worn shopping baskets in front of the register.
"Here lies the security regulars choose. But young people can't find value in this 'security.' So what do they value?"
Without that question, any AI would be powerless.
However, I was concerned about something else. His descriptions of "competitor success stories" sounded far too specific—as if only insiders could know such information.
"Holmes, there's something bothering me."
"Ah, I was thinking the same thing. His story contains unnatural 'knowledge bias.'"
Chapter VI: The Eye for Change
Oceantrail's manager concluded:
"DX isn't about technology, is it? It's about reviewing what we've been overlooking."
Holmes smiled and replied:
"Precisely. To 'accept' change, you must first 'notice' it."
Change begins with questioning.
And only beyond that questioning do "truly effective AI" and "reasons to be chosen" exist.
But after he left, I voiced a suspicion:
"Holmes, yesterday's Espol case and today's Oceantrail case—both had predecessors' sudden departures. Is this coincidence?"
Holmes stared at the fireplace and quietly replied:
"Coincidence becomes intention the moment it becomes a pattern. We may be walking within some larger flow."
Chapter VII: The Invisible Thread
That night, driven by curiosity, I reviewed past case files. Astonishing commonalities emerged.
In many companies that had consulted us over recent months, key personnel departures and decision-making confusion occurred simultaneously. And what invariably followed was "sudden policy shifts toward DX and AI implementation."
As if someone was intentionally dulling corporate judgment, promoting irresponsible decisions.
"Is this just the tide of the times, or..."
Looking out the window, I saw black shadows swaying under the streetlights. It might have been a mistake. But something was definitely beginning to move.
"Response to change is a company's lifeline. But if that change is artificially orchestrated—"—From the Detective's Notes
