2014 International
Workshop on Data Scienceand Service Research
CHALLENGES FOR BIG DATA IN OUR SOCIETY: STATISTICALANALYSIS OF LARGE SCALE, HIGH DIMENSIONAL DATAFOR SOCIO-ECONOMIC PROBLEMS

Abstracts

Date: July 18, 2014

Time: 10:00-17:30

Venue: Graduate School of Education Administration Office 11F Large Conference Room

Organized by: TOHOKU FORUM for CREATIVITY
Center for Data Science and Service Research
East Asia Project

Supportedby: TOKYO ELECTRON

Program

10:00〜10:15

Nobuhiko Terui (DSSR, Tohoku University)
Welcome remarks

10:15〜11:00

P.K.Kannan (University of Maryland)
Impact of Attribution Metrics on Return on Keyword Investment in Paid Search
Advertising ...................................................................................................................2

11:00〜11:45

Jaehwan Kim (Korea University)
An Economic Model for Charitable Donations ......................................................... 3

11:45〜12:30

William Rand (University of Maryland)
The Complex Network of Social Media .....................................................................4

14:00〜14:45

So Young Lee (Hoseo University)
Hyang Mi Kim (KT Economics & Management Research Lab)
Jae Wook Kim (Korea University)
Is Loyalty Transferable? An Evidence from Partnership Loyalty Program
Network ........................................................................................................................5

14:45〜15:30

Hiroshi Maruyama (The Institute of Statistical Mathematics)
Developing Data Analytics Skills in Japan: Status and Challenge ........................ 6

16:00〜16:45

Tsukasa Ishigaki (Tohoku University)
A Large-Scale Marketing Model using Dimension Reduction and Variational
Bayes Inference...........................................................................................................7

16:45〜17:30

Akifumi Kira (Tohoku University)
Rich Vehicle Routing Problems and Our Challenges ............................................... 81

Impact of Attribution Metrics on Return on Keyword Investment in Paid Search
Advertising
P.K.Kannan (University of Maryland)This study analyzes the impact of attribution metric used for imputing conversion credit tosearch keywords on the overall effectiveness of keyword investments in search campaigns.
Recently, firms have been experimenting with different attribution metrics to assign conversion
credits to search keywords appearing in the consumersʼ journey to purchase. These attributionbased credits affect a firmʼs future bidding decision and budget allocation for keywords, and inturn determine the overall return-on-investment of future search campaigns. Using a six-monthpanel data of several hundred keywords from an online jewelry retailer, we empirically model
the relationship among the advertiserʼs bidding decision, the search engineʼs ranking decision,and the consumerʼs click-through and conversion decision, and analyze the impact of theattribution metric used on the overall return-on-investment of paid search advertising. Our
analyses account for the simultaneity and endogeneity in the decisions made by the advertiser,
the search engine and the consumer. In our data, the focal advertiser changed the attribution
metric from last-click attribution to first-click attribution half-way through the data window. This
allows us to estimate the impact of the two alternative attribution metrics on budget allocation,
which in turn influences returns realized for keyword investments under different attribution
regimes. Given the mix of the keywords bid by the advertiser, the results show that the firstclick leads to lower overall revenues and this impact is stronger for the more specific keywords.
In the policy simulation, we find the advertiser would be able to improve their overall revenue
by more than 5% by appropriately changing the attribution metric for individual keywords toaccount for their actual contribution.
Keywords: attribution metrics, paid search advertising, ROI, keyword specificity, budget
allocation, simultaneous equations system.

2

An Economic Model for Charitable Donations
Jaehwan Kim (Korea University)
This study examines the effect of message characteristics on donation behavior using an
economic model of giving behavior. The utility for giving can come from one's own contribution
and possibly from the combined contributions of others. Donors are assumed to be constrained
utility maximizers, and the message attributes affect the degree to which they react altruisticallyor egoistically. The model is estimated with data from an incentive-aligned study of South
Korean consumers, and implications for message optimization and donor targeting are explored.
The study found that for purpose of maximizing the fundraising performance, the ad consistingof recipient-focused message with absence of happiness mood and the presence of arousal in
the add is the best, The optimal scheduling of different campaigns moving from altruistic toegoistic motive-emphasizing ad brings enhanced donation performance for non-profit
organization.
Keywords: Direct Utility, Hierarchical Bayes, Altruism

3The Complex Network of Social Media
William Rand (University of Maryland)
The dramatic feature of social media is that it gives everyone a voice; anyone can speak outand express their opinion to a crowd of followers with little or no cost or effort, which creates aloud and potentially overwhelming marketplace of ideas. The good news is that theorganizations have more data than ever about what their consumers are saying about theirbrand. The bad news is that this huge amount of data is difficult to sift through. We will look atdeveloping methods that can help sift through this torrent of data and examine importantquestions, such as who do users trust to provide them with the information and therecommendations that they want? Which tastemakers have the greatest influence on social
media users? Using agent-based modeling, machine learning and network analysis we begin toexamine and shed light on these questions and develop a deeper understanding of the complex
network of social media.

4

Is Loyalty Transferable? An Evidence from Partnership Loyalty Program Network
So Young Lee (Hoseo University)
Hyang Mi Kim (KT Economics & Management Research Lab)
Jae Wook Kim (Korea University)
In recent years, there has been a renewed interest on partnership loyalty programs (PLP) run
jointly by multiple firms and stores. The present study examines the formation and diffusion ofcustomer loyalty within the PLP network to understand the role of the program within thenetwork. We collected two sets of data from the members of a real-world PLP with a year
interval. Our analyses of two data sets show that: (1) loyalty towards a specific store/firm
affects loyalty towards the PLP itself, (2) loyalty towards the PLP does transfer to other partner
stores and firms in the network, validating the argument that customer loyalty does diffuse
through the PLP, (3) loyalty towards the PLP feeds back to the first store (i.e. a specificfirm/store). Understanding the loyalty diffusion mechanism within the PLP network can help
marketers create effective strategies for relationship-marketing management and support
deciding for the right partner with which they can share a loyalty program. Also it can help apotential partner firm in deciding whether to join the PLP network or not.
Keywords: Partnership Loyalty Program, Loyalty Diffusion, Customer Relations Management,Value, Social Exchange Theory, Mere Exposure Theory, Balance Theory5

Developing Data Analytics Skills in Japan: Status and Challenge
Hiroshi Maruyama (The Institute of Statistical Mathematics)
Japan needs to develop data analytics talents quickly to catch up with the trends of using datafor business decisions. The Ministry of Education, Culture, Sports, Science, and Technology
started a three-year project to develop so-called data scientists. This talk reviews the findingsof the first year of the project, and discuss the future challenges.

6A Large-Scale Marketing Model using Dimension Reduction and Variational
Bayes Inference
Tsukasa Ishigaki (Tohoku University)
In this talk, we propose a marketing model that incorporates complete consumer and productdatasets, rendering it scalable to large-scale databases. Marketing models accommodating
consumer heterogeneity are extended from sampled panelists to the model with entire item
categories and consumers. Our study fills a gap between theoretical modeling in academics andpractice demanded by direct marketing practitioners. To achieve this goal, we combine the latentvariable model for dimensional reduction with a choice model for description of consumer
behavior. For computational feasibility, we employ variational Bayes inference that hascomputational efficiency compared to the resource-intensive Markov chain Monte Carlo
inference in large-scale problem. We show that the model is applicable to datasets involving
tens of thousands of consumers and hundreds of product items and it is useful for targeting
infrequent consumers to predict their choice. In addition, we discuss insights gained from themodel that will assist marketing practitioners.

7

Rich Vehicle Routing Problems and Our Challenges
Akifumi Kira (Tohoku University)
The vehicle routing problem (VRP) is a combinatorial optimization problem which designs
optimal delivery or collection routes from one or several depot(s) to a number of geographicallyscattered customers. It has been extensively studied in the field of Operations Research,transportation, and logistics. Determining the optimal solution is known to be NP-hard, but we
can obtain acceptably good solutions by using local search heuristics in many practical settings.
However, real-world VRPs have a wide variety of additional constraints, so it is not so easy todevelop an algorithm handling these constraints. In this talk, we introduce our challenges totwo rich VRPs and show our successful results. One is a case study for the vending machine
replenishment. We use a road network data of Miyagi Prefecture (large-scale network) as aninstance. The other is a case study for the disaster restoration scheduling of lifeline networks.
We propose an indirect search method for handling precedence and synchronization constraints.

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