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Pricing Analytics: What can Retailers learn from Airlines ? (and vice versa)

Explore the potential for cross-industry insights in pricing analytics, as retailers and airlines learn from each other. Discover strategies for personalized services, market-sensitive optimization, dynamic pricing, and more.

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Pricing Analytics: What can Retailers learn from Airlines ? (and vice versa)

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  1. Pricing Analytics: What can Retailers learn from Airlines?(and vice versa) Arne K. Strauss Associate Professor of Operational Research Warwick Business School

  2. Press Cuttings

  3. In-Seat Retailing Source: R. Kollau: Increasing onboard ancillary revenues through data, connectivity and a retailing mindset. FutureTravelExperience.com, 15 Aug 2013

  4. In-Seat Retailing Source: R. Kollau: Increasing onboard ancillary revenues through data, connectivity and a retailing mindset. FutureTravelExperience.com, 15 Aug 2013

  5. A Retailer is Not an Airline; but… Personalised Services

  6. Independent vs. Choice-based Demand • Product-sensitive optimization means ‘rejecting or accepting demand’ • Market-sensitive means to additionally consider buy-up/down/across effects Source: Kemmer, P; Winter, T; Strauss, A: Decomposition Techniques for Market Sensitive Revenue Optimization. AGIFORS Symposium 2010.

  7. Example: Assortment Optimization Airline Retail Retail

  8. Choice Modelling: Complexity Data • What was when offered to whom at which price? • Unconstrain sales data – “first choice demand” • Data density Constrained Unconstrained Demand Source: Strauss, A, Talluri, K. (2013): Tractable consideration set structures and new inequalities for choice network revenue management. Working paper. Not offered Source: Strauss, A; Vakil, D.: Predicting Demand from Sales Data: Unconstraining in the Car Rental Industry. RM Society, Oct 2012 Optimisation • Hard combinatorial optimisation problems • Structure of the choice model and customer segmentation can be exploited

  9. Dynamic Pricing Decisions Airline Retail Constraints Time Constraints CLEARANCE

  10. Example: Clearance Pricing at Zara’s Study background • Designed and implemented forecasting and price optimisation model motivated by dynamic pricing research • Conducted controlled field experiments in Belgium and Ireland to measure revenue impact Impact: • About 6% increase of clearance revenues over previous manual markdown practice • Implemented world-wide by Zara Source: Caro, F; Gallien, J. Clearance Pricing Optimization for a Fast-Fashion Retailer. Operations Research 60(6):1404-1422 (2012)

  11. Case Study Managing Attended Home Deliveries in Online Grocery

  12. UK Leadership in Online Grocery % of individuals buying groceries online in past 12 months, 2013 UK online grocery sales by major online grocers as % of all grocers' sector sales • Ocado’sCEO Tim Steiner expects ultimately 40-60% of grocers sales to be online Eurostat (c) European Union, 2013 Source: Online Grocery in Europe – January 2014, Mintel Source: Online Grocery Retailing - UK - March 2014, Mintel

  13. Challenges • To maintain strong growth, • barriers such as delivery costs will need to be removed, • and incremental conveniences such as 30-minute delivery slots will be needed “Grocery retailing over the next 20 years is going to be driven by technology” Source: Online Grocery Retailing – UK – March 2014, Mintel Source: Forbes, 16 April 2014 Source: Ocado’s CEO Tim Steiner, CNBC, 13 March 2014

  14. Implications Competitiveness / Customer Satisfaction Fulfilment Costs

  15. Example: Attended Home Deliveries Delivery Cost Customer segments (e.g. by location) Delivery Day Time

  16. Apply RM Concepts to Home Delivery Problem Study background • Real online shopping data (June-Nov 2011) from major retailer • Method to control the booking process by dynamically setting incentives to steer customers’ time slot choices towards slots that are expected to be cheap to serve • Currently under implementation at our retail partner Findings: • Opportunity for significantincrease of profitability • Insights on relative impact of different incentives; non-monetary incentives can be as strong as monetary ones Source: Yang, X, Strauss, A, Currie, C and Eglese, R. Choice-Based Demand Management and Vehicle Routing in E-fulfilment. Forthcoming in Transportation Science

  17. Personalisation Airline • “Passengers who feel understood and valued at a personal level are more likely to be receptive to up-selling and cross-selling” • “A guideline for each airline could be to find its retail ‘twin’ [..] and behave like that retailer in targeting customers.“ Source: Lam, K-Y; Ng, J;Wang, J-T: A business model for personalized promotion systems on using WLAN localization and NFC techniques. IEEE 27th International Conference on Advanced Information Networking and Applications Workshops. March 2013. Source: P Coby. How airlines can learn from retail on sales personalisation. Flightglobal.com, 25 Jun 2013 Retail: Promotions via NFC

  18. In Conclusion • Analytics idea exchanges between different sectors can stimulate development of better decision support • Future developments will focus on context-dependent, personalised experiences • Potential for innovation through collaboration between industries and academia

  19. The Future? Source: R. Kollau: Increasing onboard ancillary revenues through data, connectivity and a retailing mindset. FutureTravelExperience.com, 15 Aug 2013

  20. THANK YOU Email: arne.strauss@wbs.ac.uk Web: go.warwick.ac.uk/astrauss/

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