[DSC DACH 25] Nuno Antonio_BigDataDrivenStrategies.pptx

In the competitive hospitality industry, harnessing Big Data Analytics is crucial for enhancing profitability. This concise presentation explores practical strategies for leveraging predictive and prescriptive analytics using data lakes to transform hotel performance. It discusses data and methods to accurately forecast room occupancy, anticipate reservation cancellations, and optimize pricing to maximize revenue. The talk also highlights real-world use cases, demonstrating how internal and external stakeholders can effectively access insights through APIs and dashboards, facilitating timely and informed decision-making. By showcasing targeted predictive models, including demand forecasting and revenue simulation, participants will understand how analytics-driven decisions significantly impact occupancy rates, pricing strategies, and overall profitability. Participants will gain actionable insights into the application of advanced analytics tools, enabling them to improve efficiency, optimize inventory management, and boost profitability in a rapidly evolving market.

1. Big Data-Driven Strategies: Improving Hotel Profitability Through Predictive and Prescriptive Analytics Nuno António DSC DACH 25 Vienna, Austria, 15 Oct

2. Data Scientist | Professor Nuno António 2

3. The hotel industry is transforming 3 source: https:// insights.shijigroup.com Tourism and Hospitality: One of the most data-rich industries

4. Challenges 4 Low margins High competition Digital disruption 01 02 03 04 Data is underused for decision-making

5. The Intelligence Hierarchy 5 INTELLIGENCE DATA INFORMATION KNOWLEDGE Power of Information Raw Data Standard Reports Ad Hoc Reports & OLAP Descriptive Modeling Predictive Modeling Prescriptive Modeling ROI (€) adapted from Cokins (2012)

6. Two-real world cases of big data-driven strategies 6 #1 Improving guest satisfaction and website conversion #2 Reduce cancellations and improve demand management decisions

7. Improving guest satisfaction and website conversion #1 7

8. How can we improve the hotel's social reputation ? How can we enhance our website's conversion rate ? Business Problem 8

9. Solution Descriptive Model 9 23 322 ENG, POR & SPA 56 Hotels (8 hotels + competitors)

10. Ratings per Language 10

11. 11 ENG City

12. 12 ENG Resort

13. 13 SPA City

14. 14 SPA Resort

15. 15 POR City

16. 16 POR Resort

17. Decisions To improve guest satisfaction Introduce more “ S panish ” breakfast dishes Change bathroom textiles Upgrade food service counters Update wi-fi coverage 17 To improve website conversion rate Adapt content to the customers location Change landing pages according to the hotel's best facilities

18. Reduce cancellations and improve demand management decisions #2 18

19. How can we decrease cancellations ? How can we decrease uncertainty when making demand management decisions? Business Problem 19

20. 20 20% to 60% Cancellations in hotels

21. 21

22. 22 High Risk Honor bookings Make demand-management decisions with imperfect information Bear with the marketing costs for reselling canceled rooms Bear with the opportunity costs

23. 23 To Lower Risk Overbooking Restrictive cancellation policies

24. 24 Overbooking Harms social reputation Generates reallocation costs Loss of immediate and future revenue

25. 25 Restrictive cancellation policies Reduce the number of bookings Decrease revenue

26. Solution Predictive Model 26 PRICES & INVENTORY SOCIAL REPUTATION EVENTS HOLIDAYS WEATHER FORECAST

27. Predictive Model Prototype 27 4 ~200 Rooms PMS data only 3 Months RESORT R1 CITY C1

28. 28

29. 29 37 perc. points Decrease in cancellations € 39 000 Room revenue from prevented cancellations

30. Decisions Demand management decisions Better informed decisions Redefinition of cancellation policies Decrease in overbookings Decrease in the number of days “closed to sales” 30 Customers Decrease in cancellations Cancellations made earlier Improvement of customer service

31. Wrapping it up… 31

32. Data-Driven Value Creation Cycle 32 Build Culture Collect Data Foster a data-driven
organizational culture Gather internal and
external data Redefine Strategies Analyze Data Adjust pricing , adjust cancellation policies , improve marketing strategies , and improve service Apply predictive and
prescriptive analytics

33. 33 Organizations that turn data into decisions don't just predict the future — they shape it.

34. Thank you! Q&A Nuno Antonio [email protected] https:// www.linkedin.com /in/ nunoantonio /