Transcript Information Technology implementation in CRM

If you are serious about customer relationship management (CRM), you must
consolidate your data.
-STEVE CLARKE,
-ACCOUNT DIRECTOR, CDMS
Learning Objectives:
After studying this chapter, you should be able to
learn:
 Assess and analyse the role of database in e-CRM.
 Create an understanding of database management in
e-CRM.
 Inform about the technological dimensions of data
warehousing
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Introduction
 Companies are increasingly aware of the need to implement a CRM
solution to manage information about their customers.
 Right from outbound telemarketing capabilities to tele-servicing, a
technology capability exists to manage every possible customer
scenario. Despite this, the moments of truth at many interaction
centers end up being disastrous because they access customer
information product-wise and not customer-wise. It is a rare moment
when a customer service representative (CSR) can help the customer at
one go with all transactions that he may have with an organisation.
This ensures immediate fulfillment of customer queries and
requirements through simple procedures.
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Customer Interaction Issues of Business
 The
customer
interaction
problem
rests
not
with
technology but with the thought
process that goes behind
technology
selection
and
deployment.
 Most of the tools and techniques
used, such as work force
scheduling, are focused on
increasing the efficiency of the
interaction
centres.
The
techniques are similar to a
supervisor's role.
Figure 6.1
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There are a number of ways to make this customer experience more
delightful and memorable:
 Not having to repeat his problem again and again to different people
who are called in to support him when he calls in.
 If he does not speak English and is a registered customer for local
language support, he should not have to explain to the operator that
he wants local language support.
 Despite the fact that numerous customers would have reported the
same problem, each call goes through yet another problem-solving
cycle.
 If he called in earlier regarding a problem and if it is unresolved, the
CSR should be able to trace this during the subsequent call.
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Database Management
 A company's competitive advantage lies in how well it can understand its
customers. Building a comprehensive customer database is the founding
step towards this. Various analyses are then run on the data to determine
patterns in customer behaviour with regard to products, prices and sales
channels. It is not necessary to invest in expensive, highly sophisticated
data mining systems to employ the CRM approach.
 Data mining begins after this—when analysis attempts to predict future
customer behaviour based on past patterns, the company takes action
accordingly.
 A database also acts as corporate memory about customers.
 Even though products or staff may change, well-developed consumer
information enables service quality to continually improve. Identifying and
targeting high value customers also becomes easy. In constructing the
database, it is vital to keep the data detailed, for only then it can be
effectively mined. Summary data means average information, average
decisions and average performance.
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Database Construction
 Database construction is the heart of Customer Relationship Management
(CRM). Most of the data would come from transactional systems such as
billing and accounting, promotions and campaigns. These operational
systems are typically fragmented, inconsistent and unsuited for managing
relationships.
 To have a 360-degree view of the customer the CSR would require the
assistance of software tools such as next generation integration and
transformation platforms that are capable of handling the complexities of
transforming bare facts into useful data, to enable efficient customer
service.
 A unified view of the customer would further mean maintaining
hierarchical views of customers, linked to their transaction histories and,
of course, enhanced with external demographics, dates and behavioural
patterns obtained through the various interactions and transactions that
the customer previously had with the organisation. This will strengthen
the quality of relationship and probably increase the lifetime value of the
customer.
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Data Warehousing
 A data warehouse is the main repository of an organisation's historical

1.
2.
3.
4.
data, its corporate memory. It contains the raw material for the
management's decision support system.
Data warehouses had become a distinct type of computer database
during the late 1980s and early 1990s. They were developed to meet a
growing demand for management information and analysis that could
not be met by operational systems for a range of reasons:
The processing load of reporting reduced the response time of the
operational systems.
The database designs of operational systems were not optimised for
information analysis and reporting.
Most organisations had more than one operational systems, so
company-wide reporting could not be supported from a single
system.
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Development of reports in operational systems often required writing
Data Warehouse Architecture
 Based on analogies with real-life warehouses, data warehouses were
intended as large-scale collection/storage/staging areas for corporate data.
From here, data could be distributed to "retail stores" or "data marts"
which were tailored for access by decision-support users (or "consumers").
 While the data warehouse was designed to manage the bulk supply of data
from their suppliers (e.g. operational systems), to handle the organisation
and for storage of this data, the retail stores or data marts could be focused
on packaging and presenting selections of the data to end-users, to meet
specific management information needs.
 Storage: Data warehousing literature suggests that data be restructured
and reformatted to facilitate query and analysis by novice users. Online
Transaction Processing (OLTP) databases are designed to provide good
performance by rigidly defined applications built by programmers fluent
in the constraints and conventions of the technology.
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Approaches in Data Warehousing
 While
the
dimensional
approach is very useful in data
mart design, it can result in a rat's
nest of long-term data integration
and abstraction complications
when used in a data warehouse. In
this approach, transaction data is
partitioned into either a measured
"facts" which are generally
numeric data that captures
specific values or "dimensions"
which contain the reference
information that gives each
transaction its context.
 The normalised approach uses
database normalisation. In this
method, the data in the data
warehouse is stored in third
normal form. Tables are then
grouped together by subject areas
that reflect the general definition
of the data (customer, product,
finance, etc.) The main advantage
of this approach is that it is quite
straightforward to add new
information into the database—
the primary disadvantage of this
approach is that because it
involves a number of tables, it can
be rather slow to produce
information and reports.
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Data Mining
 Large companies generate gigabytes of data daily through their daily
transactions. Analysing such large quantities of data requires approaches
that are very different from the traditional data analysis approaches. This
need has led to the discovery of data mining.
 CRM technologies are particularly in data storage capabilities, data
warehousing applications, and data mining techniques (Berry and Linoff
1997). Although a large part of CRM is technologically driven, it is not just
about computer software and hardware.
 For most small businesses, CRM occurs naturally (Coyle 1999). Customer
loyalty and profitability are derived from the closely knitted relationships
that small community businesses have with their customers. As businesses
expand, however, that degree of intimacy is no longer available. As it is not
realistic and cost-effective for big corporations to know each customer
individually, CRM must be achieved in an indirect manner for such
organisations. They must predict the behaviour of individual customers
through the available transactional, operational and other customer
information they have.
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Characteristics of Data Mining
 It is an interdisciplinary field taking inputs from diverse but related
disciplines such as statistics, artificial intelligence, machine learning
and large databases.
 The data mining tools and techniques operate on very large databases.
Therefore, many techniques that were available to researchers earlier
cannot be used without modification to suit large datasets.
 The data mining techniques give the search methods some degree of
search autonomy resulting in automated or semi-automated nature of
discovery.
 It is usually done on the data that have been collected while
undertaking the day-to-day transactions of the company. Such data
usually have less bias than those specifically collected for the purpose
of analysis.
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Data Mining Tools and Techniques
 A variety of data mining tools, techniques and algorithms are available
to support the above five data mining tasks (operations). They are the
following:
1.
2.
3.
4.
5.
6.
Decision trees
Rule induction
Case-based reasoning (CBR)
Visualisation techniques
Nearest neighbour techniques
Clustering algorithms
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Conclusion
 In a world of intense competition where the customers are more
demanding and the competitors are just a dick away, better Customer
Relationship Management is the only source of competitive advantage.
Creation of strong relationship is the essence of CRM, which, in turn,
results in revenue optimisation, profitability and customer satisfaction.
However, due to increase in product offerings, increased competition and
compressed marketing cycle time, managing customer relationships is
becoming more complex.
 CRM means moving from "inside-out", the seller-driven enterprise, to
"Outside-in" the customer-driven enterprise. e-CRM is the combination of
business process and technology that seeks to understand a company's
customer from a multifaceted perspective. It involves capturing and
integrating all customer data from anywhere in the organisation, analysing
and consolidating it into information, and then distributing the results to
various systems and customer contact points across the enterprise.
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Project Assignment
Create a database of
customers of a retail
store and apply the
required information for
managerial
decision
making for superior
customer service.
 REVIEW QUESTIONS
1.
2.
3.
4.
What do you understand by data
warehousing? How is this done?
What is meant by data mining? How is
this done?
Discuss the tools and techniques of data
mining.
"Effective database management is a key
for success of e-CRM.“ Comment.
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