TELECOM SERVICES: I.T. & ANALYTICS

A customer centric IT infrastructure based on telecom specific metrics and metadata, enabling behavioral
modeling, advanced customer segmentation & analytical techniques for marketing, CRM and also decision
making processes. This structural representation of your customer base using composite segmentation
schemes, customer attributes & demographics, behavioral metrics and scores, is critical in understanding both
your customer base and each single customer within the dynamic telecom market. Datamine models this
complexity, and provides solutions that simplify customer understanding: IT infrastructure enhancements
encapsulate data handling complexities and implement procedures and policies so that the business commu-
nity retrieves rich, reliable customer information.
The (telco)
Marketing Database
Proven record of accomplishment
and experience in the Telecom sector
Datamine provides Telecommunication consulting services in both IT and Analytical CRM - BI arena. Our
professional services range from requirement gathering, business processes analysis, system architecture to
business layer modeling and user interface design, using approaches that comply with well known standards
and specifications such as UML, RUP or the latest eXtreme Programming (XP) model. Concerning Customer
Analytics and CRM, our data consultants demonstrate sound business understanding and outstanding data
manipulation capabilities that can easily add value to your data warehouse and data-centric infrastructures.
TELECOM SERVICES:
I.T. & ANALYTICS
<<TELECOM SERVICES: I.T. & ANALYTICS>>

www.datamine.gr info@datamine.gr
CUSTOMER DATA MIGRATION, CLEANSING & ENHANCEMENT
Datamine emphasizes in the quality of data used in any modeling attempt. Quality Assurance processes are an intrinsic part of
any data-aware solution we deliver. Advanced matching techniques along with standardized libraries and data objects are used
in order to assess the quality of your data and perform automated normalization where possible. Additional data-quality report-
ing supports the cleansing process until the desired quality level has been achieved. A common situation, the physical
customer identification problem is critical for functions related to CRM or Credit Risk Assessment since it makes it difficult or
expensive to get an overall, objective picture of the (physical) customer.
TRAFFIC MODELING
Our Traffic Processing Engine for Telecoms generates, on a monthly basis, hundreds of traffic variables (directly in your
data warehouse); ready to be used as OLAP dimensions or measures, data mining modeling input or simple cross tabula-
tion reporting. A critical set of metrics in identifying how each customer uses your services. Different typologies of custom-
ers, with a wide range of needs, interests and habits: service sensitive versus price sensitive, passive versus active, local
versus international are some very basic examples from the telecom industry. This kind of typology is not only an excellent
way of understanding and monitoring your customer base versus time but also a key input for further modeling processes.
CREDIT SCORING
Assessing customer risks regarding ‘bad-payment’ behaviors is a complex but usually oversimplified process; disjointed
from a global ‘customer treatment policy’. This is due to the large volumes of customer data (internal such as transactions,
requests, demographics or service-related data or external such as credit bureau information) and the multiple aspects of
customer behaviors. Datamine deals with consumer credit risk as another dimension of the customer: Our Credit Risk
Scoring models produce a set of metrics, at the physical customer level, expressing the underlying risk at a certain time
point. Statistical and data mining processes consume large volume of usage, billing, and payment information in order to
generate patterns that are finally combined with demographics and socioeconomic profiles that provide an overall,
unbiased assessment of this specific customer aspect.
CHURN MANAGEMENT
Churn modeling is a challenging task due to the large number of factors that affect customer loyalty: Pricing policy, Quality
of Service, Customer Service level combined with market trends, competition, technology advances may have significantly
different effects among subsets (clusters or segments) of your customer base. Our approach in modeling churn always
begins as a process of (re) defining churn-related metrics against a suitable customer segmentation scheme and a well
defined analysis of the customer-base evolution. This is a critical (though often omitted) phase in order to obtain an overall
picture of the synthesis of the customer base and the inner dynamics (KPIs monitored versus time for different segments).
datamine specializes in the following:
Billing systems; integration, extension and quality assurance
POS (Point Of Sales) systems
CRM and Customer Analytics (Customer Assessment, Loyalty platforms)
Traffic processing
Data warehousing
Campaign management
Predictive Modelling
<<TELECOM SERVICES: I.T. & ANALYTICS>>

TELECOM SERVICES: I.T. & ANALYTICS

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TELECOM SERVICES: I.T. & ANALYTICS