Learn about potential of Social Network Analysis in achieving better understanding of inter customer relationship, influence and management of big data. Take a sneak peak at SNA implementation at Mobilink in this case study.

1. 50 Years of Growth, Innovation and Leadership
The Role of Social Network Analysis in Data-Centric
Vertical Markets: How KXEN’s InfiniteInsight™ Social is Paving the Way
in the Use of Social Relationships to Drive Cross-Industry Marketing Results
A Frost & Sullivan
White Paper
Saverio Romeo
www.frost.com

3. Frost & Sullivan
1. EXECUTIVE SUMMARY ........................................................................................................... 4
2. ANALYZING DATA-CENTRIC VERTICAL MARKETS WITH SOCIAL NETWORK
ANALYSIS .................................................................................................................................. 5
A Brief Introduction on the Role of Social Data in Various Vertical Markets................................. 5
An Introduction to Social Network Analysis ................................................................................. 5
The Use of SNA in Vertical Markets ............................................................................................. 6
Boosting Predictive Power with SNA ............................................................................................ 6
3. INFINITEINSIGHT™ SOCIAL – SNA SOLUTION BY KXEN ................................................... 7
Overview of InfiniteInsight™ Social .............................................................................................. 7
The Main Features of InfiniteInsight™ Social ............................................................................... 8
The Benefits of InfiniteInsight™ Social… ...................................................................................... 8
4. SNA IN THE COMMUNICATIONS INDUSTRY ......................................................................... 10
The Adoption of SNA in the Communications Industry ................................................................. 10
The Applications of SNA in the Communications Industry ............................................................ 11
InfiniteInsight™ Social for the Communications Industry ............................................................. 11
5. SNA IN THE BANKING AND FINANCE INDUSTRY ................................................................ 13
The Adoption of SNA in the Banking and Financial Services Industry ........................................... 13
The Applications of SNA in the Banking and Financial Services Industry ...................................... 13
InfiniteInsight™ Social for the Banking and Financial Services Industry ....................................... 14
6. SNA IN THE RETAIL INDUSTRY .............................................................................................. 15
The Adoption of SNA in the Retail Industry .................................................................................. 15
The Applications of SNA in the Retail Industry ............................................................................. 16
InfiniteInsight™ Social for the Retail Industry…........................................................................... 16
7. CUSTOMER CASE – MONOTARO – SEGMENTING AND TARGETING
CUSTOMERS WITH INFINITEINSIGHT™ SOCIAL .................................................................. 17
Overview of MonotaRO................................................................................................................. 17
MonotaRO’s Challenges with a Growing Customer Base ............................................................... 18
InfiniteInsight™ Changed MonotaRO’s Approach to Customers ................................................... 19
InfiniteInsight™ Social for Enhancing Customer Segmentation and Product Recommendations ... 20
Conclusions ................................................................................................................................... 20
8. CUSTOMER CASE – THE SUCCESSFUL CASE OF MOBILINK – USING INFINITEINSIGHT™
SOCIAL IN EMERGING COMMUNICATIONS MARKET ........................................................... 20
Brief Overview on Mobilink ........................................................................................................... 20
Why Mobilink Needs Social Network Analysis ............................................................................... 21
How Mobilink Uses InfiniteInsight™ Social ................................................................................... 21
Conclusions ................................................................................................................................... 22
9. CONCLUSION ........................................................................................................................... 23
CONTENTS

4. Frost & Sullivan
1. EXECUTIVE SUMMARY
Data is like a river that continuously expands as it is constantly fed by numerous
tributaries. The increasing complexity and volume of data evident today is driven by
a variety of events happening across a broad range of industries - mobile phone
calls, credit card swipes, website clicks and retail purchases are some of the
thousands of data points that are growing at an exponential rate. In addition, the
astonishing growth of social networks has empowered ‘word of mouth’, making the
data held by organisations inherently social. Today, marketers in almost every
organization recognize the social value of their data in terms of understanding
consumers’ behaviour and identifying new business opportunities. Social Network
Analysis (or SNA) is an advanced technique that enables companies to analyze
social data and make sense of it.
During the 1950s and 1960s, SNA was almost exclusively used in academic
environments to research the behavior of specific social groups. But it was only
with the advent of digital social networks - and the astonishing developments of the
realm of data computing – that the value of social network analysis became evident
outside the academic environment. The communications industry started to
understand the value of social network analysis. But, today, SNA extends far beyond
its telecoms routes, being used by companies such as banks, retailers, and online
gaming providers. In general, every industry characterized by large event streams,
and therefore rich of customer and transaction data, can benefit from using Social
Network Analysis.
This paper explores the use of Social Network Analysis in vertical markets through
the experience of KXEN’s SNA solution, InfiniteInsight™ Social. KXEN launched its
first Social Network Analysis solution in 2009. KXEN’s approach to SNA gained
immediate traction in various verticals, notably communications, banking, and retail.
Today, KXEN’s InfiniteInsight™ Social solution offers an intuitive and effective way
of building and analyzing social networks.
InfiniteInsight™ Social rapidly builds social networks independently of the size of
the data. Once the social network is built, InfiniteInsight™ Social creates a number
of social attributes such as:
• Social position of a node
• Network roles of the node (for example, bridge, leader, local leader)
• Social pressure
• Social influence
• Community profiles
• Diffusion simulation
In combination with KXEN’s leadership in predictive analytics and their flagship
product, InfiniteInsight™, these attributes are then used to describe and predict the
behaviour of customers.
InfiniteInsight™ Social has been used successfully in communications providers,
banks, finance organizations, on-line retailers and traditional retailers for a variety
of purposes such as churn management, product recommendations, and fraud
detection.
4 frost.com

5. Frost & Sullivan
This whitepaper illustrates two case studies of KXEN’s customers, MonotaRO and
Mobilink, using the solution successfully. These two case studies show that KXEN’s
“Today, marketers in
solution is a user-friendly, rapid, and efficient tool and its native integration to the
any organization
company’s predictive capabilities powerfully enhance the analysis of customer base
recognize the social
behavior.
value of their data
for understanding
2. ANALYZING DATA-CENTRIC VERTICAL MARKETS WITH SOCIAL consumers' behavior
NETWORK ANALYSIS and spotting business
opportunities. Social
A Brief Introduction on the Role of Social Data in Various Vertical Markets Network Analysis
(SNA) is an advanced
In a famous interview, Hal Varian, professor of Information Sciences, Business and
technique that enables
Economics at the University of California and Google’s chief economist, said: “I keep
companies to analyze
saying that the sexy job in the next 10 years will be statisticians.” The amount of
social data and make
data available in the economy and society is tremendous. Its growth is exponential.
sense of it”
Its value is unimaginable. The ability to extract this value, process it, interpret it,
deliver it and use it for strategic decisions is the big challenge for any business
today and in the future.
Statisticians have coined the term “big data” to describe the explosion of huge
amounts of data that various types of organizations are dealing with. The term
describes data sets that are becoming increasingly complex and difficult to manage
and analyze. The complexity of “big” data is driven by a variety of events happening
in different industries, such as mobile phone calls, credit card swipes, website clicks
and purchases in stores and virtual shops. In addition to that, “big data” has a strong
social nature.
The astonishing growth of social networks and digital communications devices and
applications has extraordinarily empowered the word of mouth making
organizations’ data inherently social. Today, marketers in any organization recognize
the social value of their data for understanding consumers’ behavior and spotting
business opportunities. Social Network Analysis (SNA) is an advanced technique
that enables companies to analyze social data and make sense of it.
An Introduction to Social Network Analysis
Social network analysis is used to analyze customer base behavior and spot revenue
opportunities. It is based on the concept of social networks, a set of relationships
between entities (consumers, products, etc.). The social network is represented
through a graph of nodes and links. Each node represents a customer or entity.
Links represent the relationships between customers or between entities.
Once the social network is built, social network analysis uses graph theory
algorithms to detect and interpret social ties between nodes and groups of nodes.
Typical analysis includes detection of specific communities and their structure as
well as identification of different roles within the network and communities
analysis. This information can then be used to optimize operational activities that
can lead to cost reduction and new revenues streams.
frost.com 5

6. Frost & Sullivan
Figure 1: The Social Network is a Set of Nodes and Links
Source: KXEN
The Use of SNA in Vertical Markets
During the 1950s and 1960s, social network analysis was almost exclusively used in
academic environments to research the behavior of specific social groups. But, only
with the advent of digital social networks and the astonishing development of data
computing did social network analysis become known outside academic
environments.
The communications industry, and primarily the mobile communications industry,
started to understand the value of social network analysis. Mobile network
operators’ data is naturally social. SNA has been used to understand the structure
of the networks of mobile users and uses this analysis to re-design marketing and
customer churn campaign. As the results in the communications industry revealed
the power of SNA, other verticals have started looking at SNA for analyzing their
customer base.
Today, the applications of SNA have gone beyond telecoms and are used in banks,
financial organizations, retailing organizations, online gaming organizations, and
government, mainly in the areas of security. In general, every industry characterized
by large event streams, and therefore rich of customer data and transaction data,
can use SNA.
Boosting Predictive Power with SNA
Social network analysis does not just enable a more powerful understanding of
social networks and communities, but it also empowers predictive models. SNA
enables marketers to predict behavioral changes of communities, and also to
identify specific communities of interest. The output of SNA, made of different
social variables and aggregates of variables, is used to empower predictive
modeling. For example, SNA can help mobile network operators predict the viral
diffusion of churn, detecting the likelihood of customers to be influenced by recent
churn behavior in their communities.
6 frost.com

7. Frost & Sullivan
3. INFINITEINSIGHT™ SOCIAL – SNA SOLUTION BY KXEN
Overview of InfiniteInsight™ Social
KXEN launched its first social network analysis solution in 2009. KXEN’s approach
to SNA got an immediate traction in various verticals, including communications,
banking, and retail. Today, InfiniteInsight™ Social, largely relying on that experience,
offers an intuitive and effective way of building and analyzing social networks.
InfiniteInsight™ Social rapidly builds social networks independently of the size of
the data. Once the social network is built, InfiniteInsight™ Social creates a number
of social attributes, such as:
• Social position of a node (centrality measures)
• Network roles of the node (bridge, leader, local leader)
• Social pressure and influence
• Community profiles
• Diffusion simulation
These attributes are then used to describe and predict behavior of customers and
communities of customers. The InfiniteInsight™ Social workflow is illustrated in
Figure 2.
Figure 2: InfiniteInsight™ Social Workflow
Source: KXEN
frost.com 7

8. Frost & Sullivan
1. The user specifies the various network filters (SMS, voice, off-peak
networks, etc.).
2. The corresponding filtered transactional data (CDRs or else) is loaded and
social graphs are produced.
3. The user decides which interesting attributes to produce.
4. The user can run queries on the produced networks, modify them.
5. The corresponding variables are joined to the existing customer
information, and a predictive model analyzing all attributes (including social
ones) can be built with InfiniteInsight™ Modeler. InfiniteInsight™ has been
successfully used in diverse industry contexts, from communications to
banking, and retail.
The Main Features of InfiniteInsight™ Social
Social network analysis is largely based on graph theory. Its complexity has
historically affected the speed of adoption of SNA outside academic environments.
KXEN has developed InfiniteInsight™ Social with the aim to make SNA simple and
accessible to professionals without a specific and detailed knowledge of the
underlying mathematics. InfiniteInsight™ Social offers a very intuitive user interface
for building and analyzing social networks.
Despite this, the solution also presents various modules for advanced users of SNA.
For example, InfiniteInsight™ Modeler and its best-in-class predictive technology
enhances the value of InfiniteInsight™ Social, as it is able to quickly build predictive
models based on a large number of social extra-variables. The idea is simple, but
powerful. The combination of the two software modules does not only enable the
user to identify the influencers present in that moment in the network, but to use
that information to predict the influence that they will have on their communities’
behavior, as well as to predict individuals who are about to become influencers.
The Benefits of InfiniteInsight™ Social
The features of InfiniteInsight™ Social have clear benefits for the users:
• Powerful predictive capabilities
• User-friendly interface
• Very low computational time when building social networks and rapidly
creating social attributes
• Detailed community detection and analysis
8 frost.com

9. Frost & Sullivan
But, InfiniteInsight™ Social users can gain more insights. They can easily create sub-
networks by setting filters in the interface. They can set various conditions for link
InfiniteInsight™
creation and different modes to explore social graphs. Some of the key features of
Social is a fast,
InfiniteInsight™ Social are listed below:
scalable and dynamic
software product with
powerful visualization
1. InfiniteInsight™ Social has powerful visualization capabilities
capabilities and
• Offers both bottom-up and top-down graph exploration combined with the
best-in-class KXEN
• Switch to “Community mode” and depicts customer roles in the network predictive technology
provides a large
• Superimpose social graphs and pinpoint the differences in the graph spectrum of social
evolution network capabilities
for understanding and
predicting social
2. InfiniteInsight™ Social is fast, scalable and dynamic
influence across
• Takes 20 minutes to load 16M nodes with 59M links diverse customer
communities
• Scales up with several million nodes and links, billions of CDRs rows
3. InfiniteInsight™ Social identifies “influencers” for specific business problems with
a patented methodology
4. InfiniteInsight™ Social detects hidden links in your data and links individuals
using multiple identities
5. InfiniteInsight™ Social’s unique approach is to combine SNA and predictive
technology, using InfiniteInsight™ Modeler
• The targeting accuracy is improved (usually by 50 percent-plus in the first
decile)
6. InfiniteInsight™ Social is a software product, not a piece of software on top of a
service; no out-sourcing of data or knowledge is required
In conclusion, InfiniteInsight™ Social provides a spectrum of social network analysis
capabilities so that you can understand social influence and behaviors across your
customer communities.
frost.com 9

10. Frost & Sullivan
4. SNA IN THE COMMUNICATIONS INDUSTRY
The Adoption of SNA in the Communications Industry
The increasing penetration of communications networks and devices has driven the
use of social network analysis (SNA) in the communications industry. This
phenomenon has principally emerged in the mobile communications industry, where
customer data has an explicit social nature. SNA was first employed intensively in
developed mobile communications markets, including Western European and North
American, where the number of mobile subscribers per head of population is
typically more than 100 percent. Today, almost all Mobile Network Operators
(MNOs) in Europe and North America have either introduced SNA or have
considerable experience in the use of SNA.
The European and North American experience of SNA underlined the power of
such solutions for mobile network operators, prompting the larger Asian MNOs to
introduce SNA into their data analytics suites. Asia—particularly the Far East—is
now a rapid adopter of social network analysis.
Figure 3: SNA in Communications – State of Adoption
Consolidating Growing Emerging
Source: Frost & Sullivan
10 frost.com

11. Frost & Sullivan
The Applications of SNA in the Communications Industry
“InfiniteInsight™
With mobile communications markets becoming highly saturated, mobile network Social puts the power
operators started to observe ARPU declines and a migration of subscribers toward of social networks
rival operators and service providers. The immediate desire of MNOs was to have analysis in-house”
a better understanding of their customers’ behavior in order to reduce the impact
of these negative developments. Jaroslaw Kosinski,
Corporate Project
Manager, TPSA
MNOs have used a range of analytical tools for predictive purposes for some time. Poland.
However, the accuracy of these models was largely unsatisfactory and,
consequently, the churn management techniques were not effective. Social network
analysis introduced the community perspective to the existing churn prevention
methods. Using Call Detail Record (CDR) data, SNA detects and interprets
communities of subscribers. This, in combination with predictive models, vastly
improved churn management activities.
The use of social network analysis with regards to churn management remains the
key application within the communications industry. However, as mobile network
operators’ experience of using SNA has evolved, the applications of SNA have
moved toward different directions. MNOs are able to analyze specific churn
management problems such as rotational churn. They are also able to perform
detailed customer segmentation, community behavior analysis, detection of specific
customers (i.e., multi-SIM users) and identification of community leaders or
influencers. All of this has been used to design and optimize viral marketing
campaigns for new products and services, refer-a-friend campaigns, and to detect
fraud. The overall result is that an MNO can use SNA to both prevent revenue
decline and spot new opportunities for ARPU growth.
InfiniteInsight™ Social for the Communications Industry
KXEN is a key provider of social network analysis solutions for the communications
industry. KXEN combines social network analysis and predictive analytics to explain
the social structure of the mobile network and uses its dynamics to better predict
customer behavior and specific roles in the network.
frost.com 11

12. Frost & Sullivan
InfiniteInsight™ Social brings that concept into the hands of communications
providers, enabling them to:
InfiniteInsight™
Social has been used
in a large number of • Reduce churn
communications • Identify specific roles in the social network
providers across the • Pinpoint influencers
world reaching • Optimize the viral adoption of new products and services
excellent results in • Acquire new customers
churn reduction and • Segment customers
viral adoption of new • Detect rotational churners
products and services • Identify and profile multi-SIM users
• Gain insight on competition
• Track past churners
InfiniteInsight™ Social has performed the above tasks in a large number of
communications providers across the world, reaching excellent results, such as in
the example in Figure 4.
Figure 4: InfiniteInsight™ Social Identifies Influencers in a Community of
Mobile Subscribers
The software built a social network of 14 million nodes in one hour. Using these results,
the company was able to identify influencers with the potential to adopt new products
three to seven times better than before using SNA .
Source: KXEN
12 frost.com

13. Frost & Sullivan
5. SNA IN THE BANKING AND FINANCE INDUSTRY
The Adoption of SNA in the Banking and Financial Services Industry
Fraud detection has been the initial driver of adoption of social network analysis in
the banking and financial services industry. Initially, the solution was tested in North
American and Western European banks, where the cases of fraud from credit cards
and debit cards were more frequent. However, the use of social network analysis
rapidly spread in other financial organizations, such as money transfer companies,
not necessarily located in the Western world, but also in Africa, Latin America, and
Asia.
Today, it can be said that the value of social network analysis for fraud detection is
recognized almost worldwide. However, the use of social network analysis for other
business purposes has gained momentum in the past three years, particularly in
North America, Europe, certain Asian markets and wealthy Middle East countries.
Figure 5: SNA in Banking and Finance – State of Adoption
Spread and using Spread and
SNA for different exploring Emerging
purposes different usages
Source: Frost & Sullivan
The Applications of SNA in the Banking and Financial Services Industry
Social network analysis can be beneficial for banks and financial organizations in
many activities of their business. Money transfers, check transactions or credit card
purchases are the “links” building such social networks.
As seen, fraud detection is an important application of SNA, but applications in
customer relationship management (CRM) and risk management are gaining
traction.
frost.com 13

14. Frost & Sullivan
In the area of CRM, banks can use SNA for acquiring new customers, improving
customer retention techniques, and cross-selling activities through viral marketing.
InfiniteInsight™
SNA can identify networks of non-existing customers that are connected with
Social can rapidly
existing customers. This knowledge can optimize acquisition campaigns. In cross-sell
address different tasks
activities, communities of customers and relative roles, from influencers to
for banks and other
potential buyers, can be detected and their behavior can be predicted in order to
financial
optimize new product marketing campaigns. As in the communications industry,
organizations such as
SNA can help financial organizations reduce churn rates by combining the analysis
customer relationship
of the social networks with the power of predictive models.
management, risk
management and
fraud detection InfiniteInsight™ Social for the Banking and Financial Services Industry
InfiniteInsight™ Social can rapidly and easily address different tasks for banks and
other financial organizations. Using transaction data, the KXEN solution builds
social networks based on direct links—bank transfers and check transactions—and
on indirect links—linking customers to products purchased and agencies. These
networks are then analyzed for addressing problems in customer relationship
management, risk management and fraud detection.
InfiniteInsight™ Social has successfully enabled banks and financial companies to:
• Optimize new customer acquisition campaigns like refer-a-friend
• Detect communities of interest for new product launch and communities
of risk for churn prevention
• Identify customers with high potential for purchasing new products
• Identify customer with high potential of churning
• Identify influencers
• Detect and predict potential credit defaults
• Identify fraudsters and communities of fraudsters
Figure 6: InfiniteInsight™ Social Identifies High Communities of Fraud
The chart shows communities of merchants linked by credit cards. The bigger the
community is in this graph, the more merchants are in it; the closer it is to red, the
more fraud there is in it.
Fraud!
Source: KXEN
14 frost.com

15. Frost & Sullivan
6. SNA IN THE RETAIL INDUSTRY
The Adoption of SNA in the Retail Industry
The retail industry has different sources of customer data. These can take the form
of credit card transactions and information from locality programs in traditional
outlets and stores to digital communities’ data in online shopping environments.
Social network analysis helps retail companies make sense of all this data for better
understanding their customer base and for better satisfying their purchasing habits
and desires. In fact, customer segmentation and product recommendation are areas
where social network analysis is used. This is happening primarily in Europe, North
America, Australia and part of Asia, such as South Korea and Japan.
Figure 7: SNA in Retail – State of Adoption
Increasing use of SNA Emerging use of SNA
Source: Frost & Sullivan
frost.com 15

16. Frost & Sullivan
The Applications of SNA in the Retail Industry
InfiniteInsight™
Social enhances The key aim of using SNA in the retail industry is to understand which products
product interest customers the most and which is the best way to recommend these
recommendations by products. Searching a product from numerous choices and then making a decision
introducing product can become a tough task. Retail companies have used different recommendation
communities as a agents to make that easier for customers. However, many of these agents do not
building block of the take into consideration the social nature of the shopping. In the recommendation
recommendation rules. context, the interest of two friends tends to be related and, therefore, their
purchasing modes can be similar. Social network analysis is able to detect patterns
of relationships between individuals and between products and individuals, and all
this information can improve recommendation techniques.
InfiniteInsight™ Social for the Retail Industry
InfiniteInsight™ Social aims to enhance product recommendation by introducing
product communities as a building block of the recommendation rules. The analysis
of these communities can reveal relationships between products that were not
possible to detect with other techniques. In addition to that, the analysis of these
communities can identify influential products, but also highlight other types of
products such as the not-so-frequent products. Recommendation rules can then be
built around influential products, but also around other types of products and
entities relevant for the retailer (“bridge products,” for example). All this can be
done in a scalable way and faster than any other method. In addition to that,
InfiniteInsight™ Social visualizes the rules, and this appears to be extremely helpful
for companies.
The overall approach of InfiniteInsight™ Social to the development of
recommendation rules is the following:
• Two products are linked because they share some common features—
clicks, purchases, bids, tags and others
• The software analyzes these links and then creates rules
• It then identifies communities of products
• Finally, personalized recommendations are built using the community of
interest each authenticated customer belongs to (see graph below)
16 frost.com

17. Frost & Sullivan
Figure 8: InfiniteInsight™ Social Detects Communities of Interest for
Product Recommendations
“InfiniteInsight™
Social is increasing
If the number of customers purchasing product A is greater than the average, then that
stickiness on our
community of customers is called “the community of product A.” Therefore, product A
website by
can be recommended to all of the community.
personalizing dynamic
movie
recommendations. For
us it is the best choice
we made this year.”
Frédéric Krebs, COO
Allociné France.
Source: KXEN
InfiniteInsight™ Social has been successfully used for the generation of
recommendation rules in various retail contexts, but its power also has been
demonstrated for identifying influencers for detecting habits of shopping
communities.
7. CUSTOMER CASE – MONOTARO – SEGMENTING AND
TARGETING CUSTOMERS WITH INFINITEINSIGHT™ SOCIAL
Overview of MonotaRO
MonotaRO was established in 2000 with the aim to become a leading player in the
Japanese market for direct marketing of indirect materials and consumable items
for enterprises through the Internet, fax, and the phone. Since then, the market
growth of the company has been exceptional. At the end of 2010, the total revenues
were 22bn Yen, e.g., about 300mln US$. MonotaRO is also publicly quoted on Tokyo
Stock Exchange.
frost.com 17

18. Frost & Sullivan
The number of SKUs (items) available for MonotaRO’s clients has grown
continuously over the years, reaching 1,500,000. These items are grouped in 60
categories, and they are available for 640,000 customers. The growth rate in terms
of new customers is also astonishing: 10,000 new customers per month. This pace
has made the customer growth rate almost exponential, as shown in the chart.
Figure 9: MonotaRO Customer Growth Over The Period 2002-2011
700,000
600,000
Number of Customers
500,000
400,000
300,000
200,000
100,000
0
1Q 1Q
3Q 002
1Q 002
3Q 003
1Q 003
3Q 04
1Q 004
3Q 005
1Q 005
3Q 06
1Q 006
3Q 007
1Q 007
Q 008
1Q 008
Q 009
1Q 009
Q 010
10
20
20
20
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
Time Source: MonotaRO
MonotaRO’s Challenges with a Growing Customer Base
As the customer base was rapidly growing, MonotaRO started to face the
challenges to cope with a large customer base and a vast product portfolio. The
main concern regarded the risk to misinterpret or miss customers’ needs. At that
time, MonotaRO’s customers chose their products from the catalog, but there were
not appropriate marketing tools in place for advising customers about new items.
MonotaRO needed a systematic marketing approach for providing customers with
personalized recommendations without being invasive and inappropriate.
In 2005, MonotaRO introduced a data analytics system based on IBM’s SPSS. The aim
was to identify customers’ lists for specific marketing activities. However, the
approach soon appeared to be time consuming and difficult for a company that did
not employ any statisticians. The only activity MonotaRO could do was identify
groups of customers for delivering catalogs once a year. Clearly, the timeline was
not satisfactory.
18 frost.com

19. Frost & Sullivan
InfiniteInsight™ Changed MonotaRO’s Approach to Customers
MonotaRO decided to introduce InfiniteInsight™ in May 2008. The impact was
immediate. It took just three months to deploy, and in August 2008, MonotaRO
started its daily operations with the predictive analytics solution. First off,
IntiniteInsight™ was easy to use. MonotaRO did not need any employees with a
statistics background. Existing MonotaRO’s employees were in charge of
InfiniteInsight™, and they became familiar with software without major difficulties.
Today, MonotaRO is able to produce 42 propensity models in a month, practically a
revolution.
One of the first effects of this revolution was the creation of specific catalogs for
specific groups of customers. Before the introduction of InfiniteInsight™,
MonotaRO had only one catalog with nearly 1,700 pages. InfiniteInsight™ enabled
MonotaRO to segment their customer base per interest and create several catalogs.
Currently, MonotaRO develops seven catalogs for a total of 4,000 pages. The
customer segmentation also enables MonotaRO to target specific customers with
advertising materials on new products and new offers.
Figure 10: MonotaRO’s Catalog Shift
Safety, cleaning, Tool, measuring, FA, machine
material handling welding parts
previous big catalog
1700P
Automotive parts Construction Laboratory items
Source: MonotaRO items
In 2009, the predictive analysis introduced by InfiniteInsight™ was combined with
UNICA, a campaign management tool. This combination makes the marketing cycle,
from planning to measurement, short in time and almost entirely automatated.
frost.com 19

20. Frost & Sullivan
InfiniteInsight™ Social for Enhancing Customer Segmentation and Product
Recommendations
MonotaRO is able to
build better product
categorization four In 2010, MonotaRO introduced InfiniteInsight™ Social to empower its marketing
times faster and define cycle with the use of Social Network Analysis (SNA). SNA has enabled the company
product to improve the detection of communities of customers who purchase common
recommendation rules products and communities of products which tend to be purchased by similar
that are relevant for customers. This information has enabled MonotaRO to improve customer
customers. segmentation and create associate rules for recommendations.
Conclusions
InfiniteInsight™ has played a critical role in the growth of MonotaRO. It has
transformed the way the company analyzes and engages with its customers. Today,
MonotaRO detects communities of customers, designs marketing campaigns and
measures their effects on a daily basis. With the use of SNA, MonotaRO has
introduced another level of refinement to their customer base analysis, improving
recommendation rules.
8. CUSTOMER CASE – THE SUCCESSFUL CASE OF MOBILINK –
USING INFINITEINSIGHT™ SOCIAL IN EMERGING
COMMUNICATIONS MARKET
Brief Overview on Mobilink
In April 2011, the number of mobile subscribers in Pakistan reached 108 million,
bringing the mobile penetration in the country close to 60 percent, one of the
highest penetrations in the South Asian region. The market is expected to further
grow rapidly, creating opportunities for the large competitive landscape. In fact, the
number of mobile network operators in Pakistan is surprisingly high: six GSM
operators, three CDMA providers and also WiMAX providers. In this crowded
landscape, Mobilink is the undisputable market leader, with 32 million customers.
Two key features of the subscriber base in Pakistan justify the high number of
mobile network operators in the market. The subscriber base is highly volatile. They
move across operators looking for better deals, leaving a mobile network operator
and coming back to the same one when there is a good offer in terms of service
and pricing. Therefore, the churn rate is very high. The other relevant feature is the
multi-SIM phenomenon (whereby a customer will have multiple phones, typically to
take advantage of new customer pricing with each purchase). Pakistan mobile
market is estimated to have between 30 percent and 40 percent subscribers with
multi-SIM.
20 frost.com

21. Frost & Sullivan
Why Mobilink Needs Social Network Analysis
In the complex mobile communications market described previously, understanding
the behavior of the subscriber base becomes essential for reducing churn, better
serving customers and identifying new revenues. Understanding the attitude of the
single subscriber is not enough. Instead, gaining insights on the relationships
between subscribers is extremely valuable. In fact, information and analysis about
the social sphere of subscribers provide insights on how these subscribers are
influenced by others, if they feel the pressure from other subscribers and if they
influence other subscribers in adopting a new service and purchasing a new
product. This information helps Mobilink retain customers, acquire new ones, and
promote new products. Mobilink soon realized that social network analysis is the
only tool that enables the development of such detailed knowledge about the
subscriber base.
How Mobilink Uses InfiniteInsight™ Social
Mobilink started using InfiniteInsight™ in 2007. The initial aim was to segment the
customer base and develop predictive modeling on those segments. Soon, Mobilink
appreciated the flexibility and usability of the KXEN software solution and it has
recently looked at the combined use of social network analysis with predictive
modeling using InfiniteInsight™ Social.
The use of InfiniteInsight™ Social has enabled Mobilink to address two key business
challenges: increasing the accuracy of prediction models for churn and better
promoting the adoption of value-added services. In the case of churn, Mobilink can
predict that within the top 10 percent of subscribers, 50 percent of them are
potential churners. The improvement in accuracy is shown in the chart below.
Figure 11: Comparing SNA-Based Churn Model with Other Models
Random Inactivity Based Model NextGen Perfect
100
Cumulative Churn Percentage
90
80
10% Base - 50% Churners
70
60
50
16% Base - 28% Churners
40
30
20
10
0
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Percentage Population Source: Mobilink
frost.com 21

22. Frost & Sullivan
In the case of the adoption of value-added services, the results show that for each
targeted influencer, you could bring four potential adopters.
Mobilink has seen an
eight-fold increase in
campaign response Despite the positive results, the implementation of SNA in Mobilink is just in the
rates using predictive beginning stages. The market leader in Pakistan has at least three clear objectives
analytics from to achieve in the near future.
InfiniteInsight™. It
expects those numbers
to increase when 1. Using insights on the subscriber base for acquiring critical users and likely
combined with the use influencers from competitors.
of social variables
derived from 2. Preventing the churn of influencers from Mobilink network.
InfiniteInsight™
Social.
3. Stimulating influencers in the area of value-added services with new
content, products and promotions in order to enable virality in the
communities.
Conclusions
Mobilink is strongly aware that subscriber data is a key competitive asset for
managing a volatile subscriber base. The market leader also recognizes that
exploring the social sphere of subscribers is more insightful than just analyzing
behavior of single subscribers. In light of all this, Mobilink has adopted
InfiniteInsight™ Social for looking into communities of subscribers for preventing
churns, identifying influencers and enabling product virality. The company has seen
an eight-fold increase in campaign response rates using predictive analytics from
InfiniteInsight™. It expects those numbers to increase when combined with the use
of social variables derived from InfiniteInsight™ Social. For Mobilink, this is just the
beginning of a valuable journey in the power of social network analysis.
22 frost.com

23. Frost & Sullivan
8. CONCLUSION
We live in a data-centric world. Data describes business processes, and individual
and community behaviors. The analysis of this data is fundamental to making
business operations more efficient and more profitable. Social network analysis is
an intelligent tool that looks inside user behavior and communities, and in
combination with predictive analytic techniques, allows businesses to identify
patterns and predict trends, regardless of context. InfiniteInsight™ Social from
KXEN is a software product that makes this possible in a user-friendly, rapid and
efficient way, and its native integration to the company’s predictive capabilities is a
unique and powerful solution.
frost.com 23

24. London Oxford Silicon Valley
4, Grosvenor Gardens, 4100 Chancellor Court 331 E. Evelyn Ave. Suite 100
London SWIW ODH,UK Oxford Business Park Mountain View, CA 94041
Tel 44(0)20 7730 3438 Oxford, OX4 2GX, UK Tel 650.475.4500
Fax 44(0)20 7730 3343 Tel: +44 (0) 1865 398600 Fax 650.475.1570
Fax: +44 (0) 1865 398601
+44 (0)20 7730 3438 • enquiries@frost.com
http://www.frost.com
ABOUT FROST & SULLIVAN
Frost & Sullivan, the Growth Partnership Company, partners with clients to accelerate their growth. The company's
TEAM Research, Growth Consulting, and Growth Team Membership™ empower clients to create a growth-focused
culture that generates, evaluates, and implements effective growth strategies. Frost & Sullivan employs over 50 years
of experience in partnering with Global 1000 companies, emerging businesses, and the investment community from
more than 40 offices on six continents. For more information about Frost & Sullivan’s Growth Partnership Services,
visit http://www.frost.com.
For information regarding permission, write to:
Frost & Sullivan
Sullivan House
4 Grosvenor Gardens
London SW1W 0DH
United Kingdom
Auckland Dubai Mumbai Sophia Antipolis
Bangkok Frankfurt Manhattan Sydney
Beijing Hong Kong Oxford Taipei
Bengaluru Istanbul Paris Tel Aviv
Bogotá Jakarta Rockville Centre Tokyo
Buenos Aires Kolkata San Antonio Toronto
Cape Town Kuala Lumpur São Paulo Warsaw
Chennai London Seoul Washington, DC
Colombo Mexico City Shanghai
Delhi / NCR Milan Silicon Valley
Dhaka Moscow Singapore