Download NTT DoCoMo: CellulAr INTellIgeNCe

CUSTOMER STORY
NTT DoCoMo:
Cellular Intelligence
Competency center integrates analytics into organizational culture,
leads to low churn rates
Akira Kubota
Director, NTT DoCoMo
Information Systems Group
Business Issue
Keeping existing customers and persuading
them to increase cell phone use.
Solution
Implemented SAS data mining technology to
help anticipate individual customer behavior
and needs and established a BICC to promote
the use of business intelligence throughout the
organization.
Benefits
Lowered churn rates amounting to a retention of
9.9 billion yen (US$84 million) annually.
As the cell phone market matures and
subscriber numbers increase in Japan,
business challenges for telecommunications providers shift from getting new
customers to keeping existing customers and persuading them to increase cell
phone use.
At NTT DoCoMo, the leading mobile
communications provider in Japan, SAS
data mining technology was introduced
to fine-tune strategies that increase
customer satisfaction. A Business
Intelligence Competency Center (BICC)
was established for the same reason.
We recently asked Akira Kubota,
Director of NTT DoCoMo Information
Systems Group, how effective the BICC
is as an organizational structure to
support the companywide use of
business intelligence.
Most people use cell phones these
days, and the cell phone market is
seen as saturated. What steps is NTT
DoCoMo taking in order to improve
business performance?
AKIRA KUBOTA: It is certainly true that
responding sensitively to customers’
needs and increasing customer
satisfaction is now vital. The best way to
achieve these objectives is to implement
strategies that anticipate individual
customer behavior and needs, using
sophisticated analytical tools. We are
using SAS data mining technology and
have established a BICC as the
organizational structure responsible for
promoting the use of business intelligence throughout the organization.
Based on the results obtained from our
analyses, including data mining, we are
now introducing programs that will lead
to greater levels of customer satisfaction. Actions we took in fiscal year 2005
resulted in a churn rate of 0.77 percent,
which is 0.24 percent lower than the
previous year. This amounts to a retention of 9.9 billion yen (US$84 million)
annually, assuming 0.24 percent of our
50 million users were using 6,900 yen
worth of services per person per month.
That’s an amazing figure. I understand
it was the BICC that brought about that
sort of benefit, but what was behind the
actions you implemented?
KUBOTA: To use data mining – and
other sophisticated analytical tools –
and link the results to improvements in
business performance, not only do you
need IT to do the analysis, but one
must also think about creating an
organizational structure to support the
analytical work. Knowledge of information systems, data, tools and bulk dataprocessing are all essential to realizing
sophisticated analytical work.
The business divisions did not have
enough of this knowledge when we
introduced data mining in 2000, so we
were unable to carry out sophisticated
analysis smoothly. Furthermore, there
was inadequate general support from
the IT division. The BICC was established in 2003 as an organizational
structure for data analysis support and
recommendation purposes, as well as
for promoting the effective use of business intelligence across the organization.
How Mature Is Your Organization?
What is the actual form of the NTT DoCoMo BICC?
KUBOTA: Our BICC is in our Information
Systems Department. Inside is the Analysis
Group, which is responsible for data mining as well as other types of sophisticated
analysis, and a Data Extraction Group, which
extracts complex data. Head count in those
two groups adds up to approximately 30
people. We also have a Data Warehouse
Development Group doing development
work on the data warehouse. This BICC
organization effectively meets the business
intelligence needs of our business divisions.
The BICC does the planning and development for the data warehouse and data
mining systems and manages data analysis
projects. For each issue that needs to be
analyzed, such as preventing contract termination, the BICC and the related business
divisions, such as the Corporate Strategy
and Planning Department and the Corporate
Marketing Departments, collaborate to find
the best answers. Each of the business divisions thus benefits from the BICC and is able
to implement projects using the intelligence
derived from data.
The business divisions and the BICC start
working through all the conceivable scenarios by topic. There may be more than a hundred scenarios for each topic. From several
hundred scenarios, the BICC creates several
hundred or several thousand variables for
the project and builds a mathematical model
that can be used for prediction. Using this
prediction model, the business divisions
can then devise measures that can be taken
to increase levels of customer satisfaction,
stop contract cancellations and promote the
uptake of new services.
What measures are you working on?
KUBOTA: The strategies developed based
on the prediction models will enable us to
make business recommendations and improve customer service based on individual
customer needs through DoCoMo stores and
call centers. For example, when customers
who are likely to cancel their contracts visit
a DoCoMo shop, staff will have three or so
options displayed on their terminals to help
them decide what the best recommendation
might be. Suggestions may include introducing a suitable payment plan or applying for a
DoCoMo credit card.
As far as customer service is concerned, for
members of “DoCoMo Premier Club,” we are
now providing an extension of the period covered for repairs and free battery replacement.
Using the predictive models, we can estimate the ROI of the steps that will be taken
to prevent contract cancellation, and we
can conduct timely pre-emptive measures
against contract cancellation. Moreover, the
validity of models is constantly verified – and
repeating the verification cycle makes it possible to further increase the accuracy of the
correlation models.
Is there a formula for success of the BICC? It
seems that simply setting up a BICC would
not necessarily mean BI would be enhanced.
SAS Institute Inc. World Headquarters The Information Evolution Model, developed
by SAS and detailed in the book Information Revolution, classifies organizations
into five levels of information management
maturity. NTT DoCoMo identifies itself as
a Level 4 organization for two reasons: It
has optimized its internal processes, and it
feeds results back into the system to make
processes more efficient and effective over
time.
To learn more about the Information
Evolution Model – and how to move from
one level to the next – buy the book
Information Revolution: Using the Information
Evolution Model to Grow Your Business at
www.sas.com/sascom-iem.
KUBOTA: Because the business divisions
have the needs to introduce more appropriate
strategies based on deeper analyses, I think
it is important for the BICC to actively support
their initiatives and make suggestions.
Finally, how did you use the Information
Evolution Model proposed by SAS for a BICC?
KUBOTA: The organizational structure for BI
deployment and business performance improvement is described in Information Revolution written by Jim Davis, Gloria J. Miller
and Allan Russell. Using this Information
Evolution Model, we can objectively explain
to our top executives and related organizations the various BI issues and initiatives,
such as how our company is now on Level
4 and is aiming for Level 5, etc. I also think
that when it comes to exchanges of opinion
concerning BI with other businesses in Japan
and overseas, this Information Evolution
Model will probably be useful as a practical
global benchmark.
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