Integration of ATPCO data as a core module of Jupiter, our Pricing Decisions Support System. FlyNava’s innovative airline pricing decision support solution leverages ATPCO data to empower airlines to create optimal fares that consider an unprecedented range of factors. It provides a high level of automation using an integration model in MongoDB with sophisticated connectors, algorithm and workflows.

2. Mahesh Shastry
FlyNava

3. THE PRICING PROBLEM WE SET OUT TO SOLVE
Lot of manual work with large volumes
Not able to leverage real time market opportunities
Most decisions are guess and/or experience based
Leaving money on the table, can I make 1-3% more
Nothing new, everyday is a new situation/challenge but same actions
Not able to manage competition, stakeholders, sales team, all markets
Where are the science and the models of ecommerce?

4. OUR APPROACH

5. JUPITER PERFORMANCE PARAMETER PROFILE

6. PRICING MODELS
Integrated Pricing model for each market,
Primary
Factors
Competitor Match
Classic Oligopoly
Market Share based
Optimal Price Points
Inventory/Forecast based
Secondary
Selections
RBD
Channel
Fare Brand
Corporate
Flight
General
rules
Increase/decrease
Time based
Logical rules
Price

7. MANAGING COMPETITORS
Unique Competitor mapping algorithm for each market
Aspect Weight Components Weight Points Points Points
Product Rating 20 6.6 8 4.4
Pre-booking 20 6 8 5
At-Airport 20 7 9 4
In-flight 20 8 8 4
Post Flight 20 5 8 6
Value adds` 20 7 7 3
Capacity/Schedule 15 7.3 9.15 6.8
Frequency 20 8 10 6
Type of Aircrafts 15 8 10 7
Capacity 25 6 9 7
Connecting time (convenience) 20 6 8 5
On time performance 20 9 9 9
Airline Rating 15 8.2 8.4 7.05
Safety 25 10 10 9
Brand Image 5 7 10 6
Reliability/ baggage, on-time 15 8 8 8
Service Recovery 15 9 6 6
Ground handling 10 9 9 7
Corporate 10 3 9 5
Skytrax rating for Hub airport 10 9 9 7
Skytrax rating for Airline 10 7 6 5
Market Rating 20 5.8 6.6 5.4
Market Share 40 2 4 1
Size of Market 10 10 10 10
Growth of market 10 8 8 8
No: of competitors 10 9 9 9
Market Elasticity 10 6 6 6
Currency export restrictions 10 8 8 8
countertrade 10 9 9 9
Fares Rating 20 5.8 5.8 6.6
Fare Value 40 7 5 9
Price Availability Index 20 4 8 3
Price agility index 20 6 8 5
Restriction Index 20 5 3 7
Distributors Rating 10 6.2 9.75 5.3
Number of channels/distributors 25 8 10 9
Agents/incentive commission 15 6 9 3
Number of friends 25 7 10 5
Number of foes 25 5 10 5
Corporates 10 3 9 1

8. ATPCO INTEGRATION
• Connectors 150
• Data Lake creation
• Base validations
Raw Data
(Full Subs)
• All Records Processing
• Connectivity and
linkages
• Core validations
• Change/Add included
Transformation
Phase 1
• Single Record
Structure
• All Categories are
embedded
documents in
MongoDB
• Optimized for UI
usage of millions of
records
Jupiter Fares
Phase 2
• Full links to
records
• UI for rules/fare
changes
• Real time
generation of
Work packages
• Core Snap
database
• Fully automated
processes
Reusability
Phase 3

9. JUPITER LANDING PAGE

10. JUPITER DECISION CENTER L1
ATPCO Integration with single line fare record with full traceability of Records 1-9

11. JUPITER DECISION CENTER L2 & 3
3 levels of deep insight aligned to typical though process of decision making!!

12. FARES REVIEW MANAGEMENT
Integrated Fare Review and Update

13. FARES REVIEW MANAGEMENT
Flexibility in Changing using models

14. ATPCO FILING MADE EASY
Automation with simplicity

15. ANALYTICS WITH 200 QUESTIONS
Answers with reason, click widget to more details and resolution

16. INNOVATION IN FARES MANAGEMENT
ATPCO Integration with single line fare record with full traceability of Records 1-9
12 fare update models for price recommendation
Templates and forms for global & tactical updates of fares, surcharges, discounts
Fare Optimization and enrichment algorithms
Snap, trend and analytics for core elements; market, booking/sales/flown, fares
Full Fares Integration with Performance and all operational screens
Curve, History, Heatmap, Characteristics, Performance, Biometrics

17. JUPITER SOLUTION PROFILE
• TranConnectors (245)
Algorithms
sformers
• Collections 220
Triggers (45)
• Data Change
• Data Level
• Business rules
• Events
• Global
Algorithms (50)
• Market/Competitor
• Fare Recommendations
• KPIs & Performance
Dashboards (28)
Booking, Sales,
Competitor, Market,
Customer…
Analytics
Answers to
200 questions

18. NEXT PHASE OF DEVELOPMENTS
Offers
Auto Pilot
Advanced Analytics
Mobile
Enhance real time pricing capability
APIs can be easily integrated
Choice of Rule/Fare/Fare Brand enrichment
ML for auto decisions
ML for best fit pricing model for markets
Auto Filing and integration with ATPCO
ML & AI for more creativity
More global data leverage
Models for customer specific pricing
Decision making on mobile
Higher usage of multi-media sources
Direct integration to sales process
Higher impact promotions

19. FLYNAVA USP
Jupiter Product is ready, implemented at an airline in ME
Release 2.0 in October 2017
Integrated service including data, implementation and customization
Ideal value as startup with low overheads
Effective use of new technology like Cloud, Analytics and Data Science
Fresh talent meets experience concept success
Focus on Pricing and Innovation

20. FLYNAVA TEAM
Fresh Talent + Experience
Pricing, sole domain focus
New Technology
Work Life Balance
15 fresh engineering graduates out of 32
Leads with experience in airline pricing
World class mentors and advisors
Detailed in-house training
Videos, onsite, thesis and web
Real life work experience
All open source with self learning
All development on the cloud
Data Science, Java, UI/UX, Product teams
Work Smart Party Harder
Blend of Corporate and Start-Up
Mahesh Shastry
CEO & Founder
Strong Advisors
Dr. Prem Kumar
Head of Analytics
Raghunand Arcot
Head of Data
Meghna Mehra
Head of Talent

21. Thank you
Meet us for a chat!!