Tsuruha HD's decision to adopt SalesSensor and the new NEL partnership add to a "decision-making OS" vision that pairs breadth of customer coverage with AI cross-selling to drive gross margin back up

Summary

1Q FY3/27 revenue came in at JPY 489M (+19.4% YoY) with operating income of JPY 12M (+13.0% YoY), a profitable start versus the initial plan for an operating loss. On the call, management disclosed two new wins not included in the results materials: Tsuruha Holdings' decision to adopt SalesSensor, the AI-based new-store sales forecasting service, and an agreement to supply purchase-segment data for advertising to NEL's new Trade Lift Ads service. The decline in gross margin to 51.2% (-9.1pt) was framed as a deliberate strategic choice to expand customer touchpoints as quickly as possible via partners, followed by cross-selling high-margin AI solutions into that installed base to lift ARPU and gross profit — a three-stage model. Three investments have already been executed through the CVC and related vehicles, and the company has entered the search phase for M&A, explicitly targeting qualitative research data, shopper behavior data, and POS-adjacent technologies.

Key Points (Results Highlights and Growth Initiatives)

• Management Strategy and Market View
  • Management aims to evolve from a marketing SaaS company into a company that supports corporate decision-making, with purchase data x AI insights at the core.
  • The advance of generative AI is seen as a tailwind; the competitive axis is not whether you have AI, but whether you have the data that allows AI to make correct judgments.
  • The moat lies not in data volume alone but in the 15-plus years of painstaking data generation processes that standardize and consolidate inconsistent product descriptions across 2 billion receipts annually.
  • In advertising, the company will not become a seller of ads itself, but will own both ends: pre-campaign targeting design and post-campaign effectiveness measurement.
• Current Business Progress and Drivers
  • Manufacturer-facing installations reached 201 companies (+39 YoY, +21 QoQ), with the fiscal-year-start contract switchover and FOODATA wins overlapping to boost net additions.
  • On the retail side, the large-cap retailer solution launched last fiscal year has taken root at the operational level and full-scale usage has begun, driving revenue growth.
  • Retail media standalone revenue landed above the already-strong 4Q level, supported by repeat orders from major consumer goods manufacturers and a strengthened sales structure at partners.
  • 1Q profitability reflected the build-up of recurring contracts plus the deferral of some expenses into 2H.
• Key Strategic Initiatives and Inflection Points
  • Tsuruha Holdings has decided to adopt SalesSensor — a large order from a major drugstore chain, following Welcia Pharmacy.
  • Purchase-segment data for advertising will be supplied to NEL's new Trade Lift Ads service, verifying the effectiveness of social media ad creative against purchase data.
  • The price simulation service will first accumulate use cases in a bespoke report format, then move to a cloud-based offering.
  • Three investments in AI/deep-tech funds and related vehicles have been executed via the CVC; the startup pipeline build-out is nearing completion.

Outlook and Strategy

• Full-year guidance (revenue JPY 2,200M, operating income JPY 80M) is unchanged. 1Q progress of 15.6% on operating income is in line with plan given the concentration of spot revenue in 2Q and 4Q and the ongoing recurring revenue build.
• Guidance was left unchanged despite acknowledged upside because the company is concentrating investment this year on shifting to a business base where costs do not rise proportionally with data volume and customer count.
• Gross margin is planned to recover over the long term via a three-stage model: capture breadth → deepen with AI → lift ARPU and gross profit.
• Net additions to manufacturer installations may not match the 1Q pace from 2Q onward; growth will increasingly come from customer count x ARPU.
• M&A is not about spending the investment budget; the company will opportunistically pursue deals that layer qualitative research data, shopper behavior data, and POS-adjacent technology onto ID-POS.
• The FY3/29 targets of over JPY 3B in revenue and JPY 300–400M in operating income are to be achieved through four drivers: customer count growth, ARPU uplift, gross margin improvement, and M&A.

Positive Factors

• Recurring revenue of JPY 440M (+26.4% YoY, +8.6% QoQ), marking another step up in the stability of the earnings base.
• Retail solutions revenue of JPY 117M (+118.5% YoY), as major retailer projects begin contributing on a full-year basis.
• Net cash of JPY 852M at end-June, an equity ratio of 76.8%, and zero interest-bearing debt provide ample M&A capacity.
• Recurring revenue accounted for 81.4% of FY3/26 revenue; the annual-contract-centric structure underpins predictability even through economic cycles.
• The retail media network market is projected at roughly JPY 276B by 2028, with the company's Revenue Opportunity estimated at approximately JPY 36B.
• FreakOut's DSP "Red" carries ad inventory exceeding 850 billion monthly impressions, offering substantial room for data fee revenue expansion.

Concerns and Risks

• Gross profit margin of 51.2% (-9.1pt YoY), weighed down by mix deterioration from the increase in partnership contracts and gross-basis accounting of development costs on spot projects.
• FY3/27 operating income is guided down 21.3% YoY; the outcome will swing depending on how much of the strategic investment budget is deployed.
• Additional development spot revenue from a major retailer booked in 2Q is scheduled for September, leaving some timing/slippage risk.
• The plan calls for the majority of operating income to be earned in 4Q, creating 2H-weighted progress risk.
• Management's bad-case scenario centers on geopolitical shifts — regulation or changes in the cross-border environment — that would affect the entire data platform structure.
• ID-POS data supply is concentrated among a handful of top retailers; changes in commercial terms could impact earnings.

Performance Highlights

1Q FY3/27 revenue was JPY 489M (+19.4% YoY), gross profit JPY 250M (+1.4% YoY), operating income JPY 12M (+13.0% YoY), and recurring profit JPY 13M (-12.4% YoY). An operating loss had originally been planned for 1Q, but the build-up of recurring contracts and unspent expenses secured a profit. Progress against full-year plan was 22.3% for both revenue and gross profit, and 15.6% for operating income.

Segment Results

• Manufacturer solution installations: 201 (+24.1% YoY, +39 companies)
• Recurring revenue: JPY 440M (+26.4% YoY)
• Gross profit margin: 51.2% (-9.1pt YoY)
• Operating income margin: 2.5% (-0.1pt YoY)
• EBITDA: JPY 28M (+6.4% YoY)
• Quarterly net income: JPY 6M (vs. JPY 8M a year earlier)
• Cash and deposits at end-1Q: JPY 852M; equity ratio 76.8% (our estimate)

Q&A List

• Q: Recurring revenue grew in 1Q, and if that trend continues, earnings look set to beat plan this year. Why leave guidance unchanged?
  A: Recurring revenue expanded a solid 26.4% YoY in 1Q, and operating income started ahead of our initial assumptions. That said, this year we place greater weight on shifting to an earnings structure that can generate profit sustainably over the medium to long term than on chasing short-term upside. Specifically, we will concentrate investment this year in automating and upgrading the system infrastructure that supports growth in customer count and data volume, and in the technical talent driving AI solution development. These are not simply cost increases; together with AI integration, they are investments to build a business base where costs do not rise in proportion to data volume or customer count, so that revenue growth translates into disproportionately larger profit growth. Therefore, while 1Q progress alone suggests potential upside, we believe it is appropriate at this point to maintain full-year guidance inclusive of these growth investments. We view this year as one in which we capture near-term growth while building the foundation to further accelerate profit growth from next year onward. We currently see progress toward the full-year plan as on track.
• Q: 1Q gross margin fell sharply from 60.2% a year ago to 51.2%. You say this is in line with the 51.0% full-year plan, but will gross margin stay at this level over the long term? As a SaaS company, declining margins are a concern.
  A: To answer directly, we plan to lift gross margin again over the long term. The current decline reflects the fact that we are precisely in a strategic investment phase aimed at capturing breadth. Through a powerful partner network including ITOCHU Corporation, we are expanding customer touchpoints rapidly under revenue-share models. In the short term, a higher proportion of partner-routed revenue lowers gross margin, but our priority is first to broaden the footprint where our data and services are used. On top of that acquired customer base, we will cross-sell high-value-added, high-margin AI solutions such as price simulation to lift ARPU and gross margin. In other words, the model is: capture breadth, then deepen with AI, then raise ARPU and gross profit. Rather than looking at the current gross margin in isolation, we ask investors to understand that we are building a business structure that expands the customer base and maximizes total LTV and profit.
• Q: You say upselling and cross-selling to customers acquired through the partner network is the key to future growth. Specifically, what services will you sell?
  A: The flagship example is the price simulation service announced this time. For manufacturers, we use our ID-POS data to visualize price elasticity: how much of a price increase maximizes gross profit, and whether a price increase causes shoppers to switch to another of the company's own products or to a competitor's. For retailers, we will roll out demand forecasting and SalesSensor for new-store sales prediction. Historically our services centered on analyzing what is happening. Going forward, we will move into why it is happening, what will happen next, and therefore how you should decide. In short, evolving from a company that looks at data to a company that makes decisions with data is our largest growth opportunity.
• Q: Looking beyond the current medium-term plan to the next mid-term and long-term plans, what does President Yonekura see as the good-case scenario from further AI evolution, and conversely the bad-case scenario — environmental changes that would be problematic?
  A: Our current strategy is to combine AI with retail data — ID-POS, which captures purchasing on a person-level axis. What lies beyond that is becoming a data platform that understands consumers better than anyone, by combining many other data sets. When making a decision, you check with True Data and you can see how consumers are behaving and where they are heading, so you can decide. We believe we can become that kind of one-of-a-kind platform. Assuming such a future, no one can today foresee exactly how far or in what direction AI will evolve, but if that value is fully realized, we may be able to deliver value in ways we cannot even imagine — that is the good-case scenario. Conversely, I cannot think of a bad-case scenario at this point, but if something occurs beyond current assumptions that affects the entire structure of AI or data platforms — regulation, changes in the cross-border environment, or geopolitical shifts — we could be impacted. Our infrastructure is built on global platforms, so we believe what we build here can be deployed globally, including overseas. If some constraint arose that made global deployment difficult, that would be the bad-case scenario.
• Q: You've laid out a direction of analyzing purchase data with AI to support customer decision-making, including price simulation. As AI functionality expands, will these be positioned as add-on features to existing services, or do you envisage a revenue model that prices in the AI value-add and raises ARPU?
  A: Fundamentally, SaaS is the most efficient way to grow customer count and is our strength for expanding breadth. But AI services combine the AI, data, and insights each customer needs, and we will aggressively cross-sell to existing customers and upsell rather than only acquiring new logos. As a result, per-customer ARPU will rise. There will also be cases where attaching functionality as an option to an existing service is the better fit and strategically advantageous, or easier for the customer to use, and cases where we sell it separately with a value-added price. So it will be a combination — whichever approach fits best.
• Q: Your connections are broadening — Rakuten, Hakuhodo DY ONE, SMN, MBK Digital, and now FreakOut. Rather than becoming a player that sells advertising itself, are you aiming to be the purchase data infrastructure commonly used by multiple ad platforms? If so, where do you think you can capture the most value?
  A: We have no intention of becoming a player that sells advertising ourselves. Rather, there are many agencies and firms that create and sell advertising, and when they do targeting upstream, they come to True Data to determine whom to target and which messaging will resonate most. We then provide the data pathways to reach those audiences — that is the front end. Other companies then create the creative, sell the ads, and execute. After that, we believe we have another role to play: effectiveness measurement, conducted quickly and with considerable precision. So at the front end and the back end of creating, selling, and executing advertising, we want to establish the position where the answer is always "ask True Data." That is where we believe we can most easily capture value, and we are building the ecosystem with that strategy in mind.
• Q: Partnerships such as with ITOCHU are significantly increasing manufacturer client counts, but revenue sharing depresses gross margin in the short term. Once the customer base reaches a certain scale, do you envisage raising per-customer revenue through upselling AI functionality and lifting gross margin again? Or will you continue to prioritize customer count growth?
  A: This repeats what I said earlier, but in the short term a higher proportion of partner-routed revenue lowers gross margin, and that is because we are prioritizing broadening the footprint where our data and services are used. On top of that acquired customer base, we then cross-sell high-value-added, high-margin AI solutions such as price simulation. Only then do ARPU and gross margin rise. That is the structure. So the base model is: capture breadth, deepen with AI, raise ARPU and gross profit. We will keep growing customer count continuously, but combine it with deepening via AI to lift ARPU and gross margin.
• Q: Looking at other newly listed AI and big data analytics companies, my impression is that many of the people working there, including management, are academically strong but poor at business. I don't get that impression from True Data, yet the current market cap seems to reflect concern that you will be poor at monetizing in the future. I also feel recent earnings calls haven't done much to address the market cap level. To the extent you can discuss it, what does President Yonekura see as necessary to convert the company's true capability and future potential into the market cap?
  A: First, I believe companies and businesses have stages, and we must clear them one by one. It may be easiest to frame it in terms of fundamentals and valuation — the combination of building genuinely strong services and businesses that continuously expand earnings, and having the market evaluate that. On the fundamentals side, unless investors trust the strength of the business, the moat we are building amid market and environmental change, the barriers to imitation, and how we will grow, there are limits to what valuation alone can do. I see this as the stage where we must build those fundamentals as quickly as possible, and as the numbers show, organic revenue growth has risen to a 20% pace. On top of that, with growth investment, we have finally begun to add growth that leverages the balance sheet — that is our current stage. As these materialize, we will of course pursue IR, and we are reaching the point where we can finally show the intent behind the strategy, the KPIs, and how those KPIs will evolve. So on both fundamentals and valuation, the range of what we can do has expanded and is now in place, and we are finally entering a phase where we can strengthen forward-looking communication, including IR. Please look forward to it.
• Q: Manufacturer solution installations rose sharply, with net additions of 39 companies YoY to a cumulative 201. What is the outlook for the pace of net additions and revenue?
  A: 1Q saw large net additions because the new fiscal year budget and contract switchover timing coincided with FOODATA contract wins. As a result, the pace of net additions from 2Q onward may not be as steep as in 1Q. However, going forward we will grow along two axes: customer count and ARPU. First, expanding customer count through deeper collaboration with major wholesalers and trading companies. Second, raising ARPU by adding AI functionality and new solutions. So growth will come not simply from adding logos, but from the multiplication of customer count x ARPU.
• Q: Recurring retail solutions revenue grew 13% QoQ in 1Q. What drove that, and what is the outlook?
  A: The main driver was that the solution for a major retailer launched last fiscal year has taken root at the operational level and is now being used in earnest. That said, some services are affected by seasonality and by customers' own sales expansion policies, so quarterly revenue may fluctuate to some degree. Overall, however, it is steadily expanding as a stable recurring revenue base. Going forward, we will use these retailer touchpoints as a starting point to horizontally deploy high-value-added AI solutions such as SalesSensor and lift ARPU.
• Q: Retail media and other was up only slightly QoQ. Should we read retail media standalone revenue as roughly in line with the strong 4Q? And what is the outlook?
  A: 1Q retail media standalone revenue actually landed above the very strong 4Q of last fiscal year, helped by repeat orders from major manufacturers. The backdrop is repeat orders from major consumer goods manufacturers plus the strengthening of sales structures at our partners. We are also adding new collaboration partners and improving the efficiency of our delivery structure to handle rising deal volume. What we are aiming for is not simply supporting the sale of ad inventory. We will evolve this into a data business that provides end-to-end support using purchase data: whom to reach, what to communicate, and whether it actually converted into purchases. Through this, we will further expand retail media as a key growth area.
• Q: On the new price simulation service, what differentiates it from competitors, and what is the go-to-market strategy?
  A: The biggest differentiator is the combination of SAS's latest AI platform with our ID-POS data. Typical demand forecasting predicts demand fluctuation from POS and weather data. By using ID-POS, we can analyze consumer price tolerance — which customers stay with the brand and which switch to competitors when prices rise. In other words, our strength is predicting not just how sales change, but how customers move. Our roadmap is to first accumulate use cases via bespoke reports for individual companies, then expand into a highly scalable cloud-based service.
• Q: Generative AI is advancing rapidly. Is there a risk that manufacturers and retailers use generative AI in-house and no longer need True Data's analytics tools and services?
  A: On the contrary, we see the advance of generative AI as our biggest tailwind. However high-performance the generative AI engine becomes, correct judgments require high-quality data as fuel. Data held internally by any single company cannot capture overall market movements or competitive dynamics. And if inconsistencies in product naming remain, AI cannot analyze correctly. For over 15 years we have matched, cleansed, and refined nationwide purchase data into an AI-ready state. What will matter going forward is not whether you have AI, but whether you have data that lets AI judge correctly. On that point, we believe we are in a very strong position.
• Q: Could you restate your growth strategy going forward?
  A: What we are aiming for is to evolve from a mere marketing SaaS company into a company that supports corporate decision-making. To that end we have four growth drivers. First, growing customer count — leveraging a powerful partner network including ITOCHU Corporation to broaden the footprint where our data and solutions are used. Second, raising ARPU — cross-selling AI-driven decision support services such as price simulation and SalesSensor into the existing customer base. Third, raising gross margin — enhancing scale benefits through automation and sophistication of our data infrastructure while increasing the share of high-value-added AI services. Fourth, growing non-organically through M&A. With the JPY 10 trillion-scale ID-POS data built over more than 15 years as our core asset, we will combine AI, qualitative data, and shopper behavior data to evolve from looking at data to predicting the future and supporting optimal corporate decision-making. The mid-term plan targets for FY3/29 — revenue above JPY 3B and operating income of JPY 300–400M — will be achieved through the multiplication of these four growth drivers: growing customer count, raising ARPU, raising gross margin, and growing non-organically through M&A. This 1Q was precisely the quarter in which this structural transformation began in earnest. We view this year not as one to maximize profit but as one to build a structure in which future profit grows substantially. Connecting that structure to revenue and profit growth from next year onward is the essence of this medium-term plan.