Report Creation from "2 Hours" to "5 Minutes"
Push-Based Tourism Analytics
with FTAS × Generative AI

Background: Data Exists, but Remains Underused — Tourism's "Last Mile" Problem

Fukui Prefecture had accumulated valuable data including accommodation booking trends and visitor surveys. However, three barriers stood in the way of practical utilization at the operational level:

Since 2022, we have been working to overcome these barriers through hands-on support and knowledge sharing, conducting pilot programs and coaching for data collection and utilization.

However, the barriers proved persistently high, and data utilization had not spread sufficiently — a reality that became increasingly clear.

Meanwhile, generative AI advanced dramatically, reaching a level where users could "read a report and immediately grasp the situation for decision-making." We saw an opportunity to leverage this technology to build systems that would genuinely be adopted on the ground.

Solution: Push-Based Tourism Intelligence — From "Go Find the Analysis" to "Analysis Comes to You"

At the core of this pilot was a system that automatically generates standardized analytics reports from FTAS and other open data, then distributes them via push delivery.

The system was launched as the "FTAS-AI Analytics Report Site."

Operators don't need to interact with any dashboard. Simply by reading the reports delivered periodically via email, they can stay current on tourism trends and shifts.

We provide this capability as the mitsumonoAI Analytics & Reporting Extension. Its key differentiator is the seamless integration of analysis, report generation, and distribution into existing daily workflows.

Results: Report Creation from "2 Hours" to "5 Minutes" — Sharing Frequency from Monthly to Weekly

In the Awara Onsen area, reports were previously created by downloading CSVs from FTAS and manually processing them in Excel. Each report took approximately 2 hours, limiting sharing frequency to once per month.

After the pilot deployment, AI-generated analytics reports reduced creation time to approximately 5 minutes. As a result, weekly information sharing became realistic, transitioning to a state where decision-relevant information could be circulated on a regular cadence.

The value goes beyond speed alone. Weekly cadence elevates decision-making granularity — enabling timelier pricing adjustments, promotional timing, and strategic responses.

User Feedback — Evaluating "Was It Used?" Not Just "Is It Usable?"

A survey was conducted among actual users of the FTAS-AI analytics reports. Respondents included tourism operators, tourism associations, DMOs, municipal officials, and private-sector executives and staff — spanning both decision-making and operational roles in destination management.

Respondent Profile — Who Evaluated This System?

With both decision-makers and frontline staff represented, the survey captures evaluations from a management and policy perspective — not merely tool usability assessments.

Overall Evaluation and Key Metrics

This demonstrates that the system meets "production-ready" standards not just for dedicated analysts, but for users across diverse roles.

How It Was Actually Used — Implementation Impact

A notable finding was that reports moved beyond "confirmation" into active integration within decision-making processes.

Given the high proportion of executive/management respondents, a significant practical benefit was the reduction of effort needed to "explain analysis results to others."

Evaluation Patterns by Role

Case Study: Awara Onsen — "We Can Now See the Whole Area as Our Own Concern"

In Awara Onsen, by overlaying area-wide accommodation booking data published on FTAS with their own performance data, operators have begun embedding "comparison-based" rather than "intuition-based" decision-making into daily operations.

For pricing and promotional decisions, what matters is not whether "the numbers are good or bad," but how they compare to the area-wide trend. The ability to make this relative assessment on a weekly basis has fundamentally changed decision-making quality.

Interview

Mr. Tsukasa Yagi — President, Hotel Yagi / Marketing Committee, Awara Onsen Cooperative

Mr. Yagi has been involved since the initial FTAS development and leads the Awara Onsen Cooperative's Marketing Committee. We spoke with him about the changes observed through this pilot.

"You Can't Make Company Decisions Without Seeing the Whole Area"

Mr. Yagi describes comparing FTAS data against his own accommodation performance for the same period, always checking whether his hotel is outperforming or underperforming the area average before forming hypotheses.

"Whether You Can Analyze Matters Less Than Whether It Keeps Running"

Previously, analysis was a significant burden. Downloading CSVs from FTAS, processing in Excel, creating charts — each report took about 2 hours, making monthly analysis the practical limit.

After the FTAS-AI analytics report deployment, the situation transformed.

The real significance goes beyond time savings.

"The Value of Everyone Seeing the Same Information"

The Marketing Committee now distributes auto-generated analytics reports to ryokan cooperative members, the tourism association, and relevant municipal departments.

Mr. Yagi identifies this as the core value of the "push-based" approach.

Weekly Cadence Changed the Decision-Making Time Horizon

With reports circulating weekly, pricing adjustments and promotional strategies shifted from "monthly retrospectives" to "in-progress decisions."

Next Steps: Evolution Driven by User Feedback — Establishing a Continuous Improvement Cycle

Feedback from the pilot surfaced clear improvement themes:

These are not mere feature requests. They emerged from concrete use cases around "how we want to use this for decision-making."

Mr. Yagi of the Awara Onsen Cooperative Marketing Committee shared this perspective:

From Feedback to Implementation — Improvement Speed as a Pilot Outcome

A distinctive feature of this pilot was that feedback was not deferred to "next fiscal year" but acted upon during the pilot period itself. File export support and visual enhancements were partially deployed shortly after feedback collection.

This agility is enabled by the generative AI-based reporting platform, which allows flexible tuning of analysis logic and presentation.

At the same time, differences in data volume by area emerged as a challenge. In regions with limited data collection, single-source analysis faces inherent precision constraints. Going forward, we plan to evolve the platform by integrating multiple open data sources and regional datasets to absorb area-level differences while enabling broader deployment.

Distribution channels are also being expanded beyond email to include LINE, Slack, and other tools commonly used in daily operations. Additionally, in coordination with the Fukui Prefecture Tourism DX Consortium, we are designing sustainable delivery models including potential service monetization.

Conclusion: The Essence of Tourism DX Is Not "Analysis" but "Keeping Decisions Moving"

This pilot made clear that the success or failure of tourism DX is not determined by the sophistication of analysis itself.

What matters is:

Only when all three conditions are met does data become a genuine tool supporting on-the-ground decisions.

The FTAS × Generative AI × Push-Based Sharing Platform configuration has been validated through this pilot as a realistic, reproducible approach for overcoming the "last mile" in destination management.