Heroz, Inc. · TSE:4382
Heroz — Q3 FY04/2020 Earnings Presentation
25 slides from Heroz's earnings presentation, Mar 6, 2020
Slide 1: Presentation Material for FY04/2020 Q3 Financial Results
Notice This document is an excerpt translation of the original Japanese document and is only for reference purposes. In the event of any discrepancy between this translated document and the original Japanese document, the latter shall prevail. Presentation Material for FY04/2020 Q3 Financial Results
Slide 2: 1. Business Overview
1. Business Overview 2. FY04/2020 Q3 Performance 3. Growth Strategy 4. Reference Materials
Slide 3: Company Overview
Company Overview 2019 Listed on the First Section of Tokyo Stock Exchange (TSE) Name HEROZ, Inc. 2018 Listed on Tokyo Stock Exchange (TSE) Mothers Location PMO Tamachi 2F, 5-31-17 Shiba, Minato-ku, Capital and business alliance with Netmarble Games Corporation Tokyo 2017 Established April 2009 Capit
Slide 4: Our Vision: Creating the future through AI revolutions
Our Vision Creating the future through artificial intelligence (AI) revolutions 4
Slide 5: Global Leader in Mind Game AI
Global Leader in Mind Game AI Chess AI Shogi AI Go AI Deep Blue (’97) AI developed by our Google acquired Deep Mind defeated a engineers defeated a (’14) professional Chess professional Shogi AlphaGo, developed by player (Japanese Chess) Deep Mind, defeated a player (’13) professional Go player (’16
Slide 6: B-to-B Services: Machine Learning by Replacing Shogi Game Records with Other Data
B-to-B Services: Machine Learning by Replacing Shogi Game Records with Other Data Structural design data, Shogi game records financial data, etc. HEROZ Kishin Deep learning and other machine learning: MLaaS (Machine Learning as a Service) Shogi AI Construction AI, Finance AI, etc. MLaaS： What is Mac
Slide 7: B-to-B Services: Profit Model and High Switching Costs
B-to-B Services: Profit Model and High Switching Costs Research/develop Shogi AI Standardize AI technology accumulated through shogi AI Establish “HEROZ Kishin”=MLaaS （Machine Learning as a Service） Establish an infrastructure which allows HEROZ to provide AI services efficiently Output after machin
Slide 8: B-to-B Services: Engines included in HEROZ Kishin and Examples of Applications
B-to-B Services: Engines included in HEROZ Kishin and Examples of Applications ◼ Create AI products by combining engines and achieve an efficient operating structure Know-how on AI-related technology development and and R&D capabilities service operations (deep learning, etc.) (massive servers, etc.
Slide 9: B-to-B Services: Areas of Focus and Examples of Services Provided
B-to-B Services: Areas of Focus and Examples of Services Provided ◼ Focusing on construction, finance, and entertainment for the foreseeable future ◼ Earning structure is initial setup fee and ongoing fees *Examples of clients served Construc Fi- Enter- -tion nance tainment Creation of high-quality
Slide 10: B-to-C Services: Fees from Application Users
B-to-C Services: Fees from Application Users Charging fees for B-to-C content which user AI ◼ Leveraging AI-related technology to provide mind games such as shogi, chess, and backgammon worldwide ◼ Shogi Wars has remained cash cow backed by increase of paid users compared with FY04/2019 Q3 Shogi War
Slide 11: 2. FY04/2020 Q3 Performance
1. Business Overview 2. FY04/2020 Q3 Performance 3. Growth Strategy 4. Reference Materials
Slide 12: Our Key Performance Indicator: EBITDA
Our Key Performance Indicator ◼ We believe that EBITDA, as calculated below, is our key performance indicator EBITDA = Operating Profit + Depreciation/Amortization Expenses ◼ In AI-related businesses, server investments, etc. are required to engage in advanced machine learning, leading to various de
Slide 13: Performance Overview (May 2019-January 2020)
Performance Overview (May 2019-January 2020) ◼ Although new and ongoing AI (B-to-B) projects increased, some of the projects are delayed compared with the original plan in Q3 ◼ EBITDA, our key indicator, progressed almost as planned although it is negative on a year-on-year basis due to increased la
Slide 14: Net sales and EBITDA Trends
Net sales and EBITDA Trends ◼ We forecast that revenues from AI (B-to-B) services, which is the core pillar of our growth strategy, will grow faster than the industrial average, CAGR 43% calculated in Reference Materials, and exceed revenues of AI (B-to-C) services in FY04/2020 ◼ We expect revenues
Slide 15: COGS and SG&A Trends
COGS and SG&A Trends ◼ Labor cost has increased on a year-on-year basis and is expected to keep growing to increase our engineering capacity, which will lead to our medium-to-long term growth ◼ Size-based business tax and one-time incurred expenses associated with the listing market change and capit
Slide 16: Balance Sheet (as of January 31, 2020)
Balance Sheet (as of January 31, 2020) ◼ Cash and deposits increases via public offering in December 2019. We plan to invest them in efforts to achieve the medium-to-long term growth strategy (e.g. hiring for AI engineers, in-house computing servers and external cloud computing services, expansion o
Slide 17: 3. Growth Strategy
1. Business Overview 2. FY04/2020 Q3 Performance 3. Growth Strategy 4. Reference Materials
Slide 18: Medium-to-long term Growth Strategy
Medium-to-long term Growth Strategy ◼ We aim to achieve EBITDA growth by investing our cash flows and leveraging our know-how from our AI (B-to-C) services to our AI (B-to-B) services, which ・EBITDA are positioned as our key medium-to-long-term pillar of growth ◼ In our AI (B-to-B) services, we aim
Slide 19: Initiatives Aimed at Achieving the Growth Strategy (1)
Initiatives Aimed at Achieving the Growth Strategy (1) ◼ Listing of our shares was changed to the 1st section of the Tokyo Stock Exchange on December 25, 2019. At the same time, we completed a capital increase via public offering and secondary sale of shares aimed at achieving medium-to-long-term gr
Slide 20: Initiatives Aimed at Achieving the Growth Strategy (2)
Initiatives Aimed at Achieving the Growth Strategy (2) ◼ Commenced full-scale operation of the housing loan abuse detection system “ARUHI Hawkeye” ◼ We co-developed “ARUHI Hawkeye”, the housing loan abuse detection system leveraging our deep learning capabilities, with ARUHI Corporation (“ARUHI”). A
Slide 21: 4. Reference Materials
1. Business Overview 2. FY04/2020 Q3 Performance 3. Growth Strategy 4. Reference Materials
Slide 22: Global Market Forecasts: AI Software (B-to-B)
Global Market Forecasts: AI Software (B-to-B) ◼ AI demand is accelerating globally in each industry, leading us to believe that our AI (B-to-B) service carries strong growth potential ◼ AI software market in Japan is also required to grow so rapidly due to the decrease of the domestic working popula
Slide 23: Japan: Potential AI Demand is Among the Largest Worldwide
Japan: Potential AI Demand is Among the Largest Worldwide ◼ Potential AI demand in Japan can be expected to be among the largest worldwide, as the working population is expected to decrease significantly going forward Projection of Population Age 15-64 (Median Prediction Interval, 2015=100) 140 ◼ Wh
Slide 24: Potential AI Demand in Our Areas of Focus
Potential AI Demand in Our Areas of Focus ◼ We believe there is also significant potential demand for enhancing operational efficiency through AI in the construction industry, which is one of our areas of focus ◼ There will be a shortage of 110,000 construction technicians by 2025 (source: Human Tou
Slide 25: Disclaimer
Disclaimer The contents set out in this material are prepared based on generally acknowledged economic, social and other conditions, and on certain assumptions deemed rational by HEROZ, Inc. and may therefore be subject to change without prior notice due to changes in the managerial environment or f