predictive analytics & cross border marketing

Making advertising more targeted, more relevant, more effective

Predictive Analytics to Target Cross-Border Travelers

OUTLINE

Why cross border travelers

Explaining predictive analytics and the relevance of different analytics to a cross border marketing scenario

Leveraging the new world

01

The possibilities created by dataand technology

02

0304

Why cross border travelers

Chinese Overseas Travellers

According to China Tourism Research Institute, China had 61.9 million outbound visitors in the first half of 2015an increase of 12.1% compared with the same period in 2014.. 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 H1 2015 64% of those travelers were women.

Traveller Arrivals by Countries

Indon

esia

China

Malays

iaInd

iaJa

pan

Philipp

ines

Hong K

ong S

AR

South

Korea

Thaila

nd

Vietna

m

Taiwan

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

Number of Arrivals by Country in Singapore from January 2015 – October 2015

Source: Vpon Big Data Group

Accommodation

49%Shopping

26%Sightseeing,

Entertainment & Gaming

9%Food and Beverage

16%

Source: Vpon Big Data Group

Chinese Travelers contributing 15% of total Tourism Receipts, with 49% spent on Shopping

Explaining predictive analytics and

the relevance of different analytics

to a cross border marketing scenario

Traveller Arrivals by Countries

Valu

e

InnovationLow

High

High

Business Value

Com

plex

ity

Passive

Descriptive

Predictive

Prescriptive

What happened?

Why did it happen?

What will happen?

How to make it happen ?

Pre-trip reach

In-trip reach

Post-trip reach

Prescriptive Predictive DescriptivePassive

Target travelers based on the travel related apps on theirphone BEFORE they plan a trip

Target travelers while in Singapore to get them to visit Specific attractions / retail

1.Target travelers who have been to OTHER locations to suggest Singapore as next destination2.Target travelers who have traveled to Singapore so that they can share to their friends and families too

Pre-trip reach

In-trip reach

Post-trip reach

Target travelers based on the travel related apps on theirphone BEFORE they plan a trip

Target travelers while in destination to get them to visit specific attractions / retail

1.Target travelers who have been to OTHER locations to suggest Singapore as next destination2.Target travelers who have traveled to Singapore so that they can share to their friends and families too

Prescriptive Predictive DescriptivePassive

Pre-trip reach

In-trip reach

Post-trip reach

Target travelers based on the travel related apps on theirphone BEFORE they plan a trip

Target travelers while in Singapore to get them to visit Specific attractions / retail

1.Target travelers who have been to OTHER locations to suggest (Singapore) as next destination / market travel product2.Target travelers who have traveled (to Singapore / on an airline / hotel) so that they can share to their friends and families

Prescriptive Predictive DescriptivePassive

The possibilities created by

data and technology

Researching segments, understanding responses, optimizing target audiences

• Device ID• Brand• Size• Time• Location• GPS• IP• Ad Click

…

• SDK• Data

Partnership• First-hand

Resource

Data Sources Data Analytics Segment

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

Identifying relevant, high reach segments

High CTR

High Impression

All Segment Performanceon Finance campaign

LBS for Descriptive & Predictive Analytics

Mainland Chinese

Hongkongers

+35%

+13%

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

Where do they come from ?

28%

20%16%

11%

10%

5%

3% 3% 3%

Taipei New Taipei CityTaichuang KaohsiungTaoyuan TainanHsinchu Pingtung

Where do they go ?

> 20 %

< 2%

2%~5%

5%~10%

10%~20%

1. Yilan City2. Zhushan

Township3. Fengshan District4. Zuoying Distric5. Puli Township6. Pingtung City7. Xingying District8. Dongshan

Township9. Annan District10.Budai Township11.Shuishang

Township12.Hengchun

Township13.Qianzhen District14.Toucheng

Township15.Qingshui District

Tracking Travel over CNY

Where do people go ?

Where do people go ?

And where do they stop moving ?

And where do they stop moving ?

陽明山

National Highway

MRT Routes

Yangmingshan

Tracking traveler movement along MRT routes

Not just a marketing gimmick

What comes after the data?

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

Xaxis Chairman on DCO and it’s magical powers

Leveraging the new world

AB

C

•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?

•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?

• 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?

Segments, Scenarios

KPI / Outcome

Creative matching

Unlocking the possibilitiesfor your business

Thank You!

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