True Data Inc. Q3 FY2026 earnings call

• New Medium-Term Management Plan (2026-2029 March Fiscal Years):

  • Core strategic concept: "Retail Data × AI Insights", expanding the company's scope from traditional data analysis to supporting corporate decision-making directly, aiming to build a cross-channel decision-making operating system (OS) that combines the company's massive proprietary retail data asset with AI.
  • Strategic framework: "Partner collaboration × Pattern Standardization": Build standardized solution patterns through custom development for large enterprise clients, then scale patterns broadly via partner distribution networks, while updating organizational structure and human capital strategy to support this growth model.
  • Core value focus: Keep core processes including evaluation metric standardization and AI insight derivation in-house, while leverage external partners for customer acquisition, ad operation, and system operation to maximize scaling speed.
  • Growth pipeline: Accumulate knowledge and build generalizable solution patterns from custom engagements with large clients (such as price increase impact prediction AI and advanced supply-demand management AI), then roll out these patterns to 13,000 potential clients across major wholesale partner networks covering food, healthcare, and daily goods/COSMECTics sectors.
  • Retail media is positioned as the largest growth engine: The company targets the OS position in retail media by building unified cross-channel performance evaluation metrics to address the industry challenge of fragmented metrics amid rising demand for first-party retail data driven by cookie regulations. The company estimates the total Japanese retail media market will exceed 1.1 trillion yen by 2028, with the company's serviceable addressable market (SAM) reaching 36 billion yen, representing significant growth upside versus the company's current annual revenue of ~1.85 billion yen. The company positions itself as a foundational OS provider rather than an ad seller, supporting the market by providing pre-campaign targeting insights and post-campaign effectiveness measurement.
  • 2029 March fiscal year financial targets: Total revenue of at least 3 billion yen, operating profit of 300 million to 400 million yen, with 1.17 billion yen of incremental revenue built from the core business, AI solutions, and retail media, plus an additional 1 billion yen M&A investment allocation for further upside growth.
  • 3-year roadmap: Year 1 (current period) focuses on structural transformation and building scalable winning solution patterns, with full-scale launch of wholesale partnerships and large projects, plus investment to build the AI ecosystem. Year 2 focuses on horizontal pattern expansion and diversifying revenue streams, integrating Year 1-built AI solutions into the core SaaS platform. Year 3 focuses on achieving organic growth targets and pursuing breakthrough growth via M&A, with the company actively pursuing good M&A opportunities throughout the 3-year period, targeting at least one transaction.

• Organizational and Strategic Updates:

  • Transition from traditional functional organization to an in-house company system: Delegate significant authority to on-site department heads and business leaders regardless of age to speed up decision-making. The company is also introducing paid stock options and engagement phantom stock as a third compensation scheme aligned with shareholder interests, to foster a culture committed to financial target delivery, with all employees eligible for the program that pays cash linked to stock price based on KPI goal achievement.
  • CVC initiative launched on February 12: The company has committed LP investments to specialized funds focused on early-stage AI startups to gain access to innovative AI technology and build early partnerships. LP investments have already been completed in funds managed by ON&BOARD and ANOBAKA, with co-development of new services with AI startups for large clients already underway.
  • Shareholder return commitment: The company targets turning retained earnings positive within the medium-term plan period, and will initiate shareholder returns as soon as this milestone is achieved.

• Recent Operational Highlights:

  • A strategic business partnership with Arata Co., Ltd. was signed in 3Q, completing the company's top-tier domestic wholesale network covering three major categories of food, pharmaceuticals, and daily goods, building a solid foundation for growth starting from the next fiscal year.
  • Growth of Eagle Eye contracted customer count has accelerated, driven by three key internal initiatives: establishing a fully patterned sales process from target selection to closing to improve reproducibility and shorten time-to-productivity for new sales staff; creating a dedicated sales team focused specifically on Eagle Eye serving consumer goods manufacturers to shorten lead times and improve new customer acquisition efficiency; and proactively strengthening customer success to maximize LTV and reduce churn, building a more solid cumulative growth foundation. This successful sales model will be rolled out to other product lines including AI solutions.

The third quarter (3Q) of the 2026 March fiscal year recorded total revenue of 456 million yen, a 25.1% increase year-over-year, maintaining strong top-line growth. The Eagle Eye core SaaS segment saw accelerating growth in the number of contracted customers, driven by internal operational improvements. The Shopping Scan and other related services segment grew 43.4% quarter-over-quarter. Spot revenue from large custom development projects recorded a decline quarter-over-quarter, as no large projects were recognized in 3Q following large spot revenue recognition in the first and second quarters. Operating profit for 3Q was 7 million yen. Gross profit and cost of sales were impacted by delayed ramp-up of recurring revenue from large projects, with upfront operating costs recognized in other costs. Selling, general and administrative expenses (SG&A) increased by 18 million yen year-over-year, driven by strategic hiring to strengthen management and sales infrastructure ahead of the new medium-term plan, representing upfront strategic investment for future revenue growth.

• Full-year 2026 March fiscal year guidance was revised downward on February 12, 2026: New full-year targets are total revenue of 1.83 billion yen, and operating profit of 60 million yen. The downward revision from the original plan reflects a timing shift: the company prioritized client-side operational optimization for large projects, leading to upfront resource investment and delayed revenue recognition. The downward revision is considered temporary pain to maximize future recurring revenue, and the company still maintains a year-over-year increase in revenue and profit for the full year.
  • The company expects the SG&A increase driven by strategic hiring for organizational strengthening will peak in 3Q, with 4Q SG&A expected to land at the same level as 3Q, ending the rising trend.
  • Going forward, the company will target annual 20% revenue growth, while strictly building a profit structure where revenue growth outpaces SG&A growth, and aims to increase operating profit margin to 10% or higher as early as possible within the 3-year medium-term plan period.
  • For the Arata partnership: The initial foundational construction phase is already complete, with activities scheduled to start from April 2026, and full-scale launch planned for the second half of the 2026 March fiscal year, with faster ramp-up than earlier initial wholesale partnerships. The partnership will enable growth at twice the historical pace, building a solid foundation for sustained 20% annual growth.

• Large custom projects experienced timing delays, with upfront resource investment leading to near-term profit pressure and the full-year 2026 guidance downward revision, though management views this as temporary and necessary to maximize long-term recurring revenue.
  • The commoditization of software functionality driven by generative AI has created industry pressure for SaaS businesses, though management views its proprietary large-scale retail data asset as an un replicable moat that mitigates this risk.
  • M&A success is dependent on available market opportunities, and there is uncertainty regarding the timing and completion of suitable transactions, so M&A is positioned as incremental upside to the base organic growth plan.
  • The previous functional organizational structure created cross-departmental coordination delays and reduced revenue ownership accountability among business leaders, a risk that is being addressed via the transition to the in-house company system.

Q: Amid discussion of 'the death of SaaS' driven by the rise of AI, how do you view your company's competitive advantage?

A: The narrative of SaaS death reflects the rapid commoditization of software functionality driven by generative AI progress, and competing on functionality alone will become increasingly difficult going forward. In this environment, the source of competitive advantage is not tool functionality, but the high-quality real-world data that AI uses for learning. In particular, actual measured data that shows what market outcomes are generated by corporate decision-making is an extremely valuable learning foundation for AI. True Data has accumulated massive proprietary retail purchase data covering 60 million consumers and 5.5 trillion yen in total transaction value over more than 20 years, capturing real consumer behavior that reflects the actual market results of client marketing and product strategies. This data asset cannot be built in a short period of time, and is the core asset of the company's growth strategy. The company is evolving from a marketing SaaS company to a decision support company that supports corporate decision-making, building a unique growth model that combines rare retail data with AI insights to increase both delivered value and average selling price simultaneously. The company views generative AI innovation not as a threat, but as a tool to dramatically increase delivered value, and will maintain overwhelming market advantage as an indispensable partner for client decision-making while further solidifying its retail data foundation.

Q: Given the full-year guidance downward revision this term, is there a risk that collaboration with large wholesalers including Arata and Alfresa Healthcare will take longer than expected? What is the structure of the partnership with these partners, and how much revenue can be generated via these large partner networks over the medium-term plan period?

A: For earlier collaborations including with Itochu, the company had to build joint sales structures and integrate system architectures from scratch, which caused delays. That 0-to-1 foundation phase is already complete, so the new collaboration with Arata can start from the 1-to-10 scaling phase, so ramp-up is expected to be much faster. The foundational infrastructure is already largely in place at the initiation of the Arata partnership, so future growth will have a much faster pace. Initial activities are scheduled to start after April 2026, with full-scale launch in the second half of the current fiscal year, and the Arata partnership has already begun initial activities, with Arata's CEO publicly noting high expectations for the collaboration. The company will leverage this completed robust partner sales network to scale both existing core business and new AI solutions at twice the historical pace, which has already built a foundation for sustained 20% annual growth. The medium-term plan's revenue breakdown already incorporates expected sales from solutions distributed via the large wholesale network, and the combination of existing services and AI added via this network creates significant growth potential.

Q: The growth pace of Eagle Eye contracted customer count has accelerated. Did you change any initiatives including sales approach to drive this?

A: The acceleration is not yet driven by wholesale collaboration effects (those are expected to come in future periods), but is the result of changes to internal operations and organizational specialization implemented this term. Three key initiatives drove the improvement: First, we established a fully patterned sales process, moving away from individual-dependent sales to fully pattern the entire process from target selection to closing, embedded this repeatable sales model into internal training to improve overall closing rates and speed up time-to-productivity for new sales staff. Second, we created a dedicated sales team focused specifically on the Eagle Eye core service serving consumer goods manufacturers, which improved issue-specific expertise in client proposals, shortened lead time from prospecting to contracting, and enabled more efficient new customer acquisition. Third, we proactively strengthened customer success to maximize LTV: we repositioned customer success from a pure support function to a team that proactively supports client data utilization, which allows us to prevent churn proactively, reduce churn rate, and strengthen the cumulative growth foundation. This proven winning sales model will be rolled out to all other service lines including AI solutions going forward, and the company expects this supports a high-probability growth trajectory of 20% annual growth to hit the medium-term plan targets.

Q: Can you share the specific strategy and current progress for your CVC investment, and any early results?

A: The primary goal of CVC investment is to dramatically accelerate the company's AI solution strategy, because speed is the core source of competitiveness in the fast-changing AI sector. The company has two core investment policies: First, invest in globally competitive, distinctive AI implementations that have not yet penetrated the Japanese market. Second, build early collaboration with promising domestic seed-stage AI startups. To gain access to seed-stage startups, the company is pursuing LP investments in specialized AI-focused funds, as this makes it easier to build connections with early-stage companies that are hard to reach directly. The company has already completed an LP investment in a fund managed by ON&BOARD Co., Ltd. in June last year, and has recently committed to an LP investment in a fund managed by ANOBAKA, a firm with strong expertise in seed and early-stage startup investment, and is already in a collaboration framework. Through these fund connections, the company will expand its network of promising startups, and will make direct co-investments from its own CVC to startups that have particularly high synergy with the company's data, solutions, and client base. Already, the company has won large client project engagements that are being developed in collaboration with AI startups, so early results are already emerging. Going forward, the company will build a strong ecosystem that enables non-linear growth by combining its massive retail purchase data with startup AI technology, and will cultivate a virtuous cycle where higher quality deals and technology attract more opportunities to drive overall ecosystem growth.

Q: SG&A has increased due to management and sales infrastructure strengthening. Will this upward trend continue after the fourth quarter?

A: The SG&A increase driven by strategic hiring for management and sales team strengthening has already completed its main phase in the third quarter. SG&A in the fourth quarter is expected to be at the same level as the third quarter, so the rising trend will not continue. Going forward, the company will target scaling without relying on pure headcount expansion. While strategic human capital investment will continue to hit the new medium-term plan targets, the company will strictly ensure that revenue growth outpaces SG&A growth to build a profitable structure and operating leverage. Three initiatives will drive efficiency improvement: First, leverage the partner ecosystem to maximize use of strategic partner and wholesale distribution networks to penetrate the market while keeping sales and promotion costs low. Second, improve productivity via solution pattern standardization: reduce individual custom work, scale standardized packaged AI solutions to cut down on human costs required for implementation and operation. Third, embed AI deeply into internal operations to automate back-office and sales processes, which drives organizational efficiency. The company is committed to prioritizing capital efficiency and profit margin, not just scale expansion. The organizational building phase via upfront SG&A investment is largely complete. The company will continue to target 20% annual revenue growth going forward, while making necessary investments and continuously improving operating profit margin, with the goal of reaching 10%+ operating profit margin as early as possible to drive steady profit growth through efficient delivery of high value-added services.

Q: What were the issues with the current functional organizational structure, and what are the goals of transitioning to the in-house company system?

A: The goal of this organizational change is not just to redraw the organizational chart, but to build a system to mass produce managerial talent that can create new revenue sources while leveraging the company's core data platform advantage. The previous functional organizational structure was effective for building functional expertise, but in the fast-changing data business, it created issues: cross-departmental coordination took significant time, and individual business leaders did not have strong ownership of revenue and cash flow outcomes. The in-house company system will deliver three key changes, via delegating significant authority and responsibility to leaders including mid-career and younger managers: First, it dramatically speeds up decision-making: each in-house company operates as an independent management entity, enabling rapid on-site-led decision-making. Second, it fosters a management mindset regardless of age: even small organization leaders take responsibility for resource allocation and financial metrics, which builds a culture focused on maximizing return on investment. Third, it accelerates monetization of the company's unique assets: each in-house company can freely combine the company's largest-in-Japan ID-POS data and wholesale partner distribution networks to test and launch new businesses, creating an environment that supports faster innovation. The new system will also function as a succession platform to培育 next-generation management, increasing the depth of the leadership pipeline. By combining the agility of an AI startup with the company's massive retail data asset, the company will capture continuous new business opportunities, while培育 a deeper next-generation management team to drive medium and long-term corporate value growth.