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Press Cuttings 2 In-Seat Retailing 3 Source: R. Kollau: Increasing onboard ancillary revenues through data, connectivity and a retailing mindset. FutureTravelExperience.com,

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Presentation on theme: "Press Cuttings 2 In-Seat Retailing 3 Source: R. Kollau: Increasing onboard ancillary revenues through data, connectivity and a retailing mindset. FutureTravelExperience.com,"— Presentation transcript:

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2 Press Cuttings 2

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

4 In-Seat Retailing 4 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… Choice Modelling Demand Unconstraining Forecasting Dynamic pricing of perishable items Assortment optimisation Optimisation 5 Personalised Services

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

7 Example: Assortment Optimization Airline Retail 7

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

9 Dynamic Pricing Decisions 9 Airline Retail Time Constraints CLEARANCE Constraints

10 Example: Clearance Pricing at Zara’s 10 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)

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

13 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 Challenges 13 Source: Online Grocery Retailing – UK – March 2014, Mintel Source: Forbes, 16 April 2014 “Grocery retailing over the next 20 years is going to be driven by technology” Source: Ocado’s CEO Tim Steiner, CNBC, 13 March 2014

14 Implications 14 Fulfilment Costs Competitiveness / Customer Satisfaction

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

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 significant increase of profitability  Insights on relative impact of different incentives; non- monetary incentives can be as strong as monetary ones 16 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.“ 17 Retail: Promotions via NFC Source: Lam, K-Y; Ng, J;Wang, J-T: A business model for personalized promotion systems on using WLAN localization and NFC techniques. IEEE 27 th 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

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 18

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

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


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