Provide strategy for Ctrip on consumer perspective

1. Strategy for Ctrip
MSTM Advancement Track
Jason Han, Joonwan Myung, Jialiang Zhong, HaoZhe Cui, Yu-Chen Su, Yiyo Lin, Honey Jeong

2. 2
Agenda
#4
Strategy
Suggestion
#3
Important Variables for
Revenue Prediction
#2
Customer Behavior
Understanding
#1
Goal Framing

3. Framing Goal and Approaches
3

4. 4
Background
Goal | What channels and variables are most effective for revenue
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0.50
1.00
1.50
2.00
2.50
3.00
8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
2012 2013
Online offline
M RMB
Through the descriptive analytics,
What variables affect predicting revenue
§ Which variables are related with revenue?
§ Are those making certain pattern for revenue?
§ Which channels get highlight?
Then, we analyzed variables for predicting
both “Online” revenue and “Total” revenue
§ Which variables and channels are meaningful
for Online / Offline respectively?

5. Approaches
Methods Variables
Target
Online, Total Revenue
Channel Perspective
§ Product (Owned Media)
§ Community (Earned Media)
§ Search (Paid Media)
§ Membership
§ Mobile (9/12/13-12/31/13)
Consumer Behavior Perspective
§ Unique Visitors
§ Unique Pageviews
§ Average Time on Page
§ Bounce Rate
Others
§ Investment in Advertising
Descriptive Analysis
- Using R, Excel
Goal
Predictive Analysis
- Linear Regression Model
- Using R
What variables affect
predicting revenue
What variables for
predicting both
online and total revenue

6. Understanding of Customer
Descriptive Analytics
6

7. 7
Channel Effectiveness on Consumer Behavior
Product plays a significant role to increase Awareness
Community works effectively to make clients Interest
Membership shows best media for Desire
Channel
Total
Unique Visitors
Aver. Visitors
per day
Aver. Pageviews
per days
Aver. Pageviews
per Visitors
Aver. Time on Page
Per day
Ave. Bounce Rate
per day
Product 116,623,365 225,142 (64%) 686,926 3.05 98 47%
Community 47,745,973 92,174 205,108 2.26 102 62%
Search 10,759,227 20,771 32,240 1.54 66 56%
Membership 3,051,132 5,890 13,892 2.33 90 28%
Mobile 823,146 7,416 34,781 1.01 40 36%
Period : 2012/08/01 – 2013/12/31
Awareness Interest Desire
[ Consumer Behavior based on AIDA Model ]

8. 8
Behavior Analysis Affected to Revenue
[ Consumer Behavior based on AIDA Model ]
Product _ Unique pageviews
Online
Revenue
Correlation
0.8
Search _ Unique pageviews
Online
+
Offline
Revenue
Correlation
0.7
Correlation
0.8
Product _ Unique pageviews
Online
+
Offline
Revenue
The Unique Pageviews are very significant for action
than other three behavior variables.

9. 9
Behavior Analysis affected to Revenue
[ Consumer Behavior based on AIDA Model ]
Period : 2012/08/01 – 2013/12/31
Total Unique Visitors
Total
Revenue
Correlation
0.84
Unique Pageviews and Unique Visitors are significantly working for Revenue (Action)
Total Unique Page Views
Total
Revenue
Correlation
0.87

10. 10
Behavior Analysis affected to Revenue
Total Unique Visitors
Total Unique Pageviews
Total Ave. Time on Page
Total Bounce Rate
Revenue
(Online+Offline)
0.84
0.87
0.29
-0.37
Unique Pageviews& Visitors : Significant
Aver. Time on Page & Bounce Rate : Less Significant
Total Unique Visitors Total Unique Pageviews
Total Average Time on Page Total Bounce Rate
Total
Revenue
Total
Revenue
Total
Revenue
Total
Revenue

11. 11
Effectiveness of Mobile Service
Mobile Booking Service Launching : 2013/09/12
Before : 2012/09/12 – 12/31
After : 2013/09/12 – 12/31
Mobile service affected the online sales revenue.
Before After Before After
[ Online ] [ Online + Offline ]
4.2M
0.5M
1.4M
7.5M
Online Offline
206% 75%
Increased
as compared to the
same period a year ago.

12. Important Variables for Revenue
Predictive Analytics
12

13. 13
Model for Total(Online+Offline) Revenue
At the beginning, our group used total Revenues as y variable, and put all variables into x variable.
However, the result was not as expect, R-squared only 0.82.
Then we modified the model and choose the most significant variable. The result is still not good.
- Target : Online+Offline Revenue
- Excluded : Offline Revenue, Offline/Online, Online+Offline Revenue, Investment
This result aligned to the result we found.
Then, Online Revenue as the target variable and try to create a new model to see.

14. 14
Model for Online Revenue
All independent variables are based on “Online” that we focused on predicting “Online” revenue.
By doing so, we could figure out that Unique Visitors and Unique Pageview are the commonly
important variables for predicting revenue R-squared value, 0.90.
Product and Mobile channels are useful for explaining the revenue
§ “Average Time on Page” and “Bounce rate” were not highly important like the descriptive analytics.
- Target : Online Revenue
- Excluded : Offline Revenue, Offline/Online, Online+Offline Revenue, Investment

15. Strategy Suggestion
15

16. 16
Strategy for Immediate Growth
Product, Community and Mobile channels are effective to the Revenue.
§ Specifically, total unique visitor in product channel, average time on page per day in the community
channel, shows significance in consumers’ Awareness and Interest stage in AIDA model.
§ Executable strategy by strengthening those two stages and mobile services.
Increasing Awareness
Help customers easily find the
websites by pay per click
advertising.
§ This will allow the product media
to appear on the top of search
results.
Increasing Interest for
community channel
Sponsor well-known travel
bloggers, youtubers, and
Instagram accounts for
website promotions.
Encourage Action
Fasten the check-out (final
decision processes) to
enhance mobile experience

17. Focus on Product channel with Mobile
§ As a primary channel, strengthen more on product channel by focusing on own web site, blog,
brand community and social fan page.
§ Among several product channel, Ctrip also need to utilize more on Mobile. Unique Visitors in
Mobile is soaring up and online revenue improved 205% (YoY)
Strategy for Long-term Growth
Product
(Owned Media)
Use Community / Search Channel to attract new customers
§ While strengthen Community channel, Ctrip also need to reinforce Search channel.
§ As the online revenue goes up, Community/Search channel
become another source for securing new customers
ex) Word of mouth, Power blogger, Paid Influencers
Community
(Earned Media)
Search
(Paid Media)
à Increase
Awareness!

18. Thank You!
MSTM Advancement Track
Jason Han, Joonwan Myung, Jialiang Zhong, HaoZhe Cui, Yu-Chen Su, Yiyo Lin, Honey Jeong