InferringCustomerPreferences3A-FinalPresentation
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The document discusses Sabre's Preference Inference Engine, which aims to personalize customer experiences for airlines. It does this by inferring individual customer preferences using data from travel histories and machine learning. This helps airlines better target customers with bundled ancillary offers and increases both customer spending and airline revenue. The Preference Inference Engine integrates with Sabre's Intelligence Exchange and Customer Insight profiles to continually update inferred customer preferences.
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- 1. 1confidential | ©2016 Sabre GLBL Inc. All rights reserved. Preference Inference Engine Zachary Escabedo Bradley Bain Rahul Jadhav Stephen Pallone Vivek Chander Selva Kumar Jie Yang
- 2. 2confidential | ©2016 Sabre GLBL Inc. All rights reserved. Personalization When customers are faced with more choices, personalization is key Customer Centricity Customer Data Hub Dynamic Retailer Customer Experience Manager
- 3. 3confidential | ©2016 Sabre GLBL Inc. All rights reserved. Present State of Personalization •Trends statistically inferred on a broad level •Ability to manually create rules •Previous customer itineraries and purchase history recorded Future Direction of Personalization •Preferences inferred on an individual level •Automated rule suggestion •Portability of knowledge of customer preferences Prior to Personalization •Trends statistically ascertained on industry- wide level •No ability to tailor rules to customers •Past customer history unwieldy and uncentralized Intelligence Exchange is changing the industry standard
- 4. 4confidential | ©2016 Sabre GLBL Inc. All rights reserved. Other industries are benefiting from personalized offerings UNTARGETED ADVERTISING TARGETED ADVERTISING4.5% Increased Conversion* *according to Amazon, Facebook, and Microsoft’s studies
- 5. 5confidential | ©2016 Sabre GLBL Inc. All rights reserved. Airlines can similarly leverage inferring ancillary preferences Note: Amadeus forecasts growth of ancillary market to $180B in 2020 contingent upon the right retailing and merchandising technologies. Source: IdeaWorks, Amadeus $- $50 $100 $150 $200 2015 2020 $60B $100B-$200B Billions Air Ancillary Market
- 6. 6confidential | ©2016 Sabre GLBL Inc. All rights reserved. Segment level Traditional way to segment Machine learning based segmentation Find favorite bundle Willingness-to- pay calibration Individual level (Personalization) Past purchase Experiment Probability of preferring bundle Optimizer Goal: apply machine learning technology to target individual customers
- 7. 7confidential | ©2016 Sabre GLBL Inc. All rights reserved. Dynamic Retailer Customer Experience Manager Customer Data Hub Customer Centricity Existing Preference Summary Individual’s preference information is updated in Customer Insight profile. No Preference Summary Preference information added to corresponding Customer Insight profile. This is possible thanks to our solution – the Preference Inference Engine Preference Inference Engine
- 8. 8confidential | ©2016 Sabre GLBL Inc. All rights reserved. Preference Inference Engine updates knowledge as an IX analyzer Intelligence Exchange DCS Domain DCS Analyzer Ticketing Domain Ticketing Analyzer Booking Domain Booking Analyzer Customer Domain Customer Analyzer Linker Preference Inference Engine Updated Data Store
- 9. 9confidential | ©2016 Sabre GLBL Inc. All rights reserved. What the Preference Inference Engine looks like – Dashboard Integration
- 10. 10confidential | ©2016 Sabre GLBL Inc. All rights reserved. What the Preference Inference Engine looks like – Dashboard Integration
- 11. 11confidential | ©2016 Sabre GLBL Inc. All rights reserved. A win-win situation for airlines, customers and Sabre! 2015 PB’s 35 Million Estimated Incremental Revenue $0.32 / PB Estimated Revenue Increase $11.1 Million Estimated Cost $200,000 Sabre’s Estimated Revenue $1.75 Million ($0.05 / PB) Preference Inference Engine
- 12. 12confidential | ©2016 Sabre GLBL Inc. All rights reserved. 12 Questions?
- 13. 13confidential | ©2016 Sabre GLBL Inc. All rights reserved. Special Thanks • Mentors – Matthew Lane – Dorothee Irisson • Contributors – Sandra Opdahl – Robert Cook – John Terrell – Jason Godfrey – Chris Fry
- 14. 14confidential | ©2016 Sabre GLBL Inc. All rights reserved. • 4 customer-centric considerations for airlines to compete less on price and more on product • Customer Centric Analytics Tool • Dynamic Retailer – Release Kickoff • CDH & CEM – Knowledge & Skill Building Session References
- 15. 15confidential | ©2016 Sabre GLBL Inc. All rights reserved. Assumptions for Revenue Calculation Additional Ancillary Purchases • 35 M PBs / Year • 30% of customers purchase via direct channel • 10.5M Direct Channel PBs / Year • 4% Purchase Ancillaries • 421K customers purchasing ancillaries / year • Increase customers purchasing ancillaries by 40% • 168K additional customers purchasing ancillaries • Average ancillary revenue of $22 • Additional annual revenue of $3.7M through new ancillary purchases Additional Direct Channel Bookings • 10.5M Direct Channel PBs / Year • Increase Direct Channel customers by 0.4% • 42K additional customers purchasing through direct channel • Average revenue of $170 / customer • Additional $7.4M of annual revenue through new customers Total added annual revenue of $11.1M from Preference Inference Engine
- 16. 16confidential | ©2016 Sabre GLBL Inc. All rights reserved. • Competitors are moving towards personalized marketing solutions that have proven success in E-commerce • Drive to combine big data from past history with latest learning models • Intelligence Exchange’s ability to dynamically consolidate and enhance data gives Sabre the best opportunity to lead the industry in individual customer inference Competitive Analytics
- 17. 17confidential | ©2016 Sabre GLBL Inc. All rights reserved. Product process to sell more ancillaries Dynamic Retailer Direct channel Indirect channel Up sell Price elasticity Cross sell Product bundling Product redesign Quick-win Travel pattern Social network analysis Win-win Profit sharing Segmentation
Editor's Notes
- Zach Initially our findings showed us that for airlines, every trip the customer takes essentially belongs to a “new” individual, because assembly and analysis of extensive customer profiles have yet to be deployed at scale. However, through the power of technology Sabre is changing this with four unique products. Customer Data Hub – Uniquely creates true differentiation through data-driven personalization. Customer Experience Manager – Solution enabling airlines to automate rules using near real-time data to personalize and deliver positive customer experience Dynamic Retailer – creates the ability to offer ancillary discounts to eligible customers. Customer centricity analytics tool –provides the ability to extract and analyze data to identify customers preferences and buying patterns IX is used to create incredible personalization With so many incredible initiatives coming through innovation within Sabre we believe we are on a clear path for success. However, we know that we can provide more customized solutions for our airlines while improving the overall customer experience. PRODUCT DETAILS EXPANDED ----- DO NOT READ FOR PRESENTATION Customer Experience Manager – Solution enabling airlines to automate rules using near real-time data to personalize and deliver positive customer experience throughout the journey, generating more revenue, growing loyalty, and creating brand differentiation Customer Data Hub – Uniquely creates true differentiation through data-driven personalization. Identifies The True Customer Behind Every Transaction and creates true differentiation through data-driven personalization Dynamic Retailer – creates the ability to offer ancillary discounts to eligible customers. Offers based on traveler information (profile), flight information (selected itinerary) and shopping context (search criteria). Customer centricity analytics tool –provides the ability to extract and analyze operational, marketing and revenue data for the purpose of identifying customers preferences and buying patterns
- Zach Prior to coming up with a personalized solutions for airlines to improve the experience of the customer, the market trends were statistically determined at industry wide level. It was not possible to create rules for an individual customer or even group of customers falling in a specific category. The customer history although significant in size was not usable for any personalization solution. Now we at Sabre are able to categorize individual customers into broad level of categories and have the ability to make rules with a specific criteria and discount in Dynamic Retailer(DR), we also have all the past customer itineraries and purchase history ready to be exploited to make value for the customer, airlines and Sabre. In the coming future we should provide an opportunity for the airline to provide their customers with a personalized offer, tailored just to cater their individual need, making them feel that they are of utmost importance to the airline. To achieve this we should have the ability to infer preferences of customer at individual level. In order to make the task of creating rules for customer easy for the airline, we should to a certain degree automate the process, giving the airline automated rule suggestion with a specific criteria and discount value. Once we have the ability to classify a particular customer and are able to infer which specific criteria the customer satisfies, we should be able to make this knowledge portable and easy to use across all the products.
- Brad Amazon studied in depth the effectiveness of untargeted to targeted advertising, as that’s essentially what their core business is founded upon. They found that such a shift increased conversion rates by a whole 4.5%. Facebook, Yahoo, and Microsoft found similar conversion rate increases. If Sabre can tap into targeting as effective as that, then that represents a large potential to increase ancillary sales.
- Brad Ancillaries make up a significant amount of airline revenue worldwide: $59.2 Billion worldwide, or 8%! Since 2010, ancillaries have experienced a 21% YOY Growth. I think it’s safe to say…Merchandising is here to grow. Global airline capacity is growing at a current rate leading towards an additional 1 billion travelers in 2030, which gives us very large potential to grow our revenue even larger. By focusing even more of its resources on inferring customer preferences, Sabre and its customer airlines can better capture an even more significant part of this $60 billion in worldwide revenue.
- Machine learning technology offers customer highest value bundle We infer customers’ ancillary bundle and price from two perspectives: segment level and individual level. At the segment level, we want to predict each customer segment’s willingness-to-pay for each favorite product bundle. So, we start from segmenting customers. We keep the traditional segmentation way such as segment them into business, leisure, vfr. But inside each traditional segment, we apply machine learning algorithms to classify customers. Then within each customer segment, we find their most favorable bundle and each segments’ willingness-to-pay distribution to each bundle. At the individual level, we infer each person’s probability to prefer each bundle with his/her past purchase experience. Then we input this with the previous calculated distributions to the optimizer to get the best price for the best bundle for this customer.
- Steve If there already exists an associated Preference Summary in the Customer Insight profile, then this information is updated. If not, then the Preference Inference Engine creates a summary using previous itinerary and purchase information, fetched from Customer Data Hub. This updated information then available for use for current and future Sabre’s customer-focus products. The Preference Summary is a similar to the existing array scores in Customer Data Hub and Customer Experience Manager, but focuses on determining the likelihood of a customer purchasing any given itinerary bundled with ancillaries.
- Steve The solution is to integrate a Preference Inference Engine as an analyzer in Intelligence Exchange. We consider four primary domains when learning the preferences of a given traveler: Customer, Booking, Ticketing and DCS. When any new information in these four domains is pushed, Intelligence Exchange uses Linkers to merge the data sources. This is a core strength of IX. Afterwards, Consolidation Analyzers enhance the merged, raw data. The Preference Inference Engine will use all such raw data to refine our current knowledge of the customer, and the updated Preference Summary will be stored with the respective Customer Insight Profile.
- With a cost of roughly $200,000 the Preference Inference Engine could add value to existing products. So based on the annual reports of Jetblue, Air China, and Brussels Airlines and their results of adopting Datalex analytical software products. We believe the value produced by our the added enhancements from Preference Inference Engine would result in the overall ancillary revenue growth of $0.125 for every PB Jet blue specifically who had 35.1 million PB’s in 2015 could have a total revenue increase of 4.39 million dollars _________________________________________________________ In Datalex’s annual report and there are some interesting figures inside. Basically, they helped JetBlue to build a digital commerce platform, Air China to build a digital promotion channel and Brussels Airline to launch a digital pricing platform. I think these three aspects are what we are trying to build in Sabre’s IX system, and we can use Datalex’s impact as a benchmark for computing our value.
- Customer Centric Analytics Tool
- 1.28 Million Additional PB/s based on Total passengers boarded 2014: 28.4 million https://www.alaskaair.com/content/about-us/newsroom/as-fact-sheet.aspx 774,000 Additional PB’s based on Total Passengers boarded 2014: 17.2 million Acquiring new bookings Targeting Bundling of Ancillaries Loyalty Acquisition
- Datalex provides a unified Digital Commerce Platform which combines pricing, shopping and order management with customer insights to deliver a competitive and differentiated retail experiences on every device, across every digital sales channel and at every touch point in the travel journey. Amadeus uses Data intelligence tool to extract new opportunities for growth from existing market information data types (MIDT) from over 600 airlines. Digital and Direct Retailing Systems • Global Distribution and Indirect Retailing • Global Merchandising System • Travel Intelligence and Personalization Systems • Airline Core Systems Navitaire offers a broad suite of integrated airline and high-speed rail management solutions. Reservations, Ancillary Revenue and Loyalty Solutions Data Analysis, Planning and Revenue Accounting Solutions Customers who visit Mercator see a personalized view that reflects the individual customer’s unique purchase history and interests. Big data analytics for these leading e-retailers is focused on the customer - what does he/she want? – not solely on supplier needs like inventory levels, fare buckets or booked load factors. SITA Horizon Passenger Management and Distribution offers the most comprehensive range of passenger management services available. Its infrastructure is built on a flexible platform of core systems required by every airline, including reservations, ticketing, inventory, check-in, departure control and weight and balance. Airline controlled Flexible selling Responsive service Agile design