The document discusses an AI-powered pricing tool called PRIX that analyzes signals like historical prices, inventory levels, and customer location to dynamically optimize prices in real-time. It notes that while pricing is currently manual and retroactive, automated real-time experimental pricing is the future. PRIX aims to use machine learning on product and sales data to beat humans at optimizing pricing and revenue, similar to how AI has beaten humans at games. Simulation results show PRIX increased revenue over fixed and reactive human pricing. The market is ready for such a tool across e-commerce, travel and events.

1. PRIX
[preeh]

2. Line-pricing
Gut-feel Feedback
Excel
CompetitionCost-based
Market-
research
Value-based
Price-promise
Experience
Analysis

4. SIGNALS
• Historically successful price-points
• Time remaining
• Inventory remaining
• Predicted demand
• Endogenous signals: geo-location, browser-profile etc
• Exogenous signals: weather, seasonality, holidays etc

5. PRICING - NOW & THEN
NOW
• Manual
• Retro-active
• Big data-requirement
• Long cycles
FUTURE
• Automated
• Real-time
• Experimental
• Configurable

7. API Agents
StoresCustomers
Price
Sales
Price
Price Sales
Control
Panel
Settings
PRIX
[preeh]

8. SHARE OF COMPANIES BY SIZE
Micro
Small
Medium
Large
# Businesses GDP
< 1 % 42%
1% 18%
7% 18%
92% 21%

9. PROBLEM

10. Price ≠ Value

11. SOLUTION

12. We’ve already seen it

14. Airlines have been doing this for
years. Now it’s Uber, AirBnB…
Even Disney

15. If computers can beat humans at
Go and chess why not at pricing?

16. Product = Game
Revenue = Points
Signals = Moves

17. SIMULATION-DATA

18. LIVE DEMO

20. RESULTS OF SIMULATION
# Viewed # Sold € Revenue € Avg. Price
Fixed Price 468 317 63,400 200
Tit-for-tat 468 354 69,363 196
Machine 468 ? ? ?

21. MARKET

22. Technical - Computing is feasible
Science - Machine learning is ready
Data - Backends are opening up
Cultural - Attitudes are changing

23. SHARE OF COMPANIES BY SIZE
Micro
Small
Medium
Large
# Businesses GDP
< 1 % 42%
1% 18%
7% 18%
92% 21%

24. INDUSTRIES
• E-commerce
• Travel (bus & train)
• Events

25. 5% revenue lift
10% profit-share

26. €500 billion volume
€2.5 billion revenue

27. COMPETITION

28. COMPETITION - INCUMBENTS
• Vendavo -
• ModelN -
• PROS. -
• IBM Demandtec -
• Zilliant -
• Revionics -

29. COMPETITION - DISRUPTORS
• Wiser -
• PRICER -
• Boomerang Commerce -

30. TEAM

31. FOUNDING TEAM
• Jens Munch
Chief Strategy Officer, iZettle
5 years of Strategic Partnerships, Google
• TBD

32. BOARD OF ADVISORS - TENTATIVE
• Galina Shubina
10 years of engineering at Google
PhD Computer Science, Brown University
• Jason Pinto
LP at Amadeus Capital
10 years in Venture Capital
Ph.D., Physics, Cambridge
• John-Paul Clarke
Professor Georgia Institute of Technology
Sc.D., Aeronautics and Astronautics, MIT

33. BOARD OF ADVISORS - TENTATIVE
• Nick Firoozye
Managing Director at Nomura International
25 years in financial quantitative research
• Mitra John Groff
Head of Pricing and Product, National Express
15 years in yield management
• Dr Rupal Rana
BSc (Leicester), MSc (Nottingham), PhD (Warwick)
Lecturer in Management Science