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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IntroductionCompanies 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 BusinessThe 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.

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Figure 6.1

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

peoplewho are called in to support him when he calls in.

If he does not speak English and is a registered customer for locallanguage support, he should not have to explain to the operator thathe wants local language support.

Despite the fact that numerous customers would have reported thesame problem, each call goes through yet another problem-solvingcycle.

If he called in earlier regarding a problem and if it is unresolved, theCSR 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 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:

1. The processing load of reporting reduced the response time of theoperational systems.

2. The database designs of operational systems were not optimised forinformation analysis and reporting.

3. Most organisations had more than one operational systems, socompany-wide reporting could not be supported from a single system.

4. Development of reports in operational systems often required writingspecific computer programs which was slow and expensive.

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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 learningand 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.Decision trees2.Rule induction3.Case-based reasoning (CBR)4.Visualisation techniques5.Nearest neighbour techniques6.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. What do you understand by data warehousing? How is this done?

2. What is meant by data mining? How is this done?

3. Discuss the tools and techniques of data mining.

4. "Effective database management is a key for success of e-CRM.“ Comment.

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