Predictive Analytics to Target Cross-Border Travelers. Making advertising more targeted, more relevant, more effective

Transcription

1 Predictive Analytics to Target Cross-Border Travelers Making advertising more targeted, more relevant, more effective

2 OUTLINE Why cross border travelers Predictive analytics and cross-border marketing 03 The possibilities created by data and technology 04 Leveraging the new world

3 Why cross border travelers

4 According to China Tourism Research Institute, China had 61.9 million outbound visitors in the first half of 2015 an increase of 12.1% compared with the same period in Additionally, Boston Consulting Group (BCG) estimates that by 2030, tourists from China will make up about 40% of outbound Asian travelers, taking 1.7 billion trips annually, up from 500 million trips taken in 2012, the latest year of which data were available.they travel internationally for leisure (92%) vs 56% Business (CITM 2015), and in H % of those travelers were women.

5 Number of Arrivals by Country in Singapore from January 2015 October ,500,000 2,000,000 1,500,000 1,000, ,000 0 Source: Vpon Big Data Group

6 Accommodation 16% Shopping Food and Beverage 9% Sightseeing, Entertainment & Gaming 26% 49% Source: Vpon Big Data Group

7 Predictive analytics and cross-border marketing

8 Complexity High Descriptive Prescriptive Predictive Why did it happen? What will happen? How to make it happen? Passive What happened? Low Business Value High

9 Pre-trip reach Target travelers based on the travel related apps on their phone BEFORE they plan a trip In-trip reach Target travelers while in Singapore to get them to visit Specific attractions / retail Post-trip reach 1. Target travelers who have been to OTHER locations to suggest (Singapore) as next destination / market travel product 2. Target travelers who have traveled (to Singapore / on an airline / hotel) so that they can share to their friends and families Passive Descriptive Prescriptive

10 Pre-trip reach Target travelers based on the travel related apps on their phone BEFORE they plan a trip In-trip reach Target travelers while in destination to get them to visit specific attractions / retail Post-trip reach 1. Target travelers who have been to OTHER locations to suggest (Singapore) as next destination / market travel product 2. Target travelers who have traveled (to Singapore / on an airline / hotel) so that they can share to their friends and families Passive Prescriptive

11 Pre-trip reach Target travelers based on the travel related apps on their phone BEFORE they plan a trip In-trip reach Target travelers while in Singapore to get them to visit Specific attractions / retail Post-trip reach 1. Target travelers who have been to OTHER locations to suggest (Singapore) as next destination / market travel product 2. Target travelers who have traveled (to Singapore / on an airline / hotel) so that they can share to their friends and families Passive Descriptive Prescriptive

12 The possibilities created by data and technology

13 Researching segments, understanding responses, optimizing target audiences Data Sources Data Analytics Segment SDK Data Partnership First-hand Resource Device ID Brand Size Time Location GPS IP Ad Click

14 During the 3-day Labor Day holiday, the volume of international roaming increased with the number of oversea travelers.

15 High CTR High Impression All segment performance on Finance campaign

16 Pre-trip reach Target travelers based on the travel related apps on their phone BEFORE they plan a trip In-trip reach Target travelers while in destination to get them to visit specific attractions / retail Post-trip reach 1. Target travelers who have been to OTHER locations to suggest (Singapore) as next destination / market travel product 2. Target travelers who have traveled (to Singapore / on an airline / hotel) so that they can share to their friends and families Passive Descriptive Predictive Prescriptive

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18 During the 3-day Labor Day holiday, the volume of international roaming increased with the number of oversea travelers. +35% Mainland Chinese +13% Hongkongers

19 3% 3% 3% 5% 10% 28% 11% > 20 % 10%~20% 5%~10% 2%~5% < 2% 17% Taipei Taichuang Taoyuan Hsinchu 20% New Taipei City Kaohsiung Tainan Pingtung

20 1. Yilan City 2. Zhushan Township 3. Fengshan District 4. Zuoying Distric 5. Puli Township 6. Pingtung City 7. Xingying District 8. Dongshan Township 9. Annan District 10. Budai Township 11. Shuishang Township 12. Hengchun Township 13. Qianzhen District 14. Toucheng Township 15. Qingshui District

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22 Sept Oct GPS Cell Tower IP

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25 Bidding engine uses past data to segment, experiments with ad variants Learns from each iteration to further refine segments and ad variants that produce results Constantly optimizes effectiveness by extending this knowledge to additional members of each segment and further refining the data over time

26 Understanding the consumer journey is key Data helps to segment and find high potential scenarios, then make predictions on likely high response audiences and scenarios Making messaging relevant and specific drives response

27 Leveraging the new world

28 A B C Segments, Scenarios Define your consumer segments and important characteristics In what situations are they most likely to be responsive? What kinds of data can help to focus on your segment and identify the specific scenarios in which they re most receptive? KPI / Outcome What do you really need consumers to do? Is there a way to make this an online / phone based activity so it flows easier? If it s offline how can you connect the consumer outcome to your advertising input? Creative matching What kind of messaging is most appropriate for each segment? How can we refine the message to make it even more relevant in different scenarios what elements work with DCO?

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