Chapter 1 of a thesis. The Impact of CRM on Customer Retention By Amal Shawky 

From The MckinseyQuarterly.com

Connecting CRM systems for better customer service

Also

An Introduction

 (Please see The ABCs of CRM from CIO)

What is CRM?

What Every Exec Should Know About Customer Retention
 

Leveraging Value With a More Effective Customer Interaction Center (CIC)

The traditional call center continues its battle to prove its value within the organizational structure. For many leading companies, the shift from a cost center to a revenue generator is already underway. See how leading companies can achieve success, the people, processes and technologies required to make that transition successful by aligning with the company's customer vision, including its ability to differentiate customers by their value and needs. (Please see Leveraging Value With a More Effective Customer Interaction Center (CIC)   Our Server. Peppers & Rogers Group)

Successful Customer Relationship Management - Why ERP, Data Warehousing, Decision Support and Metadata Matter  SAS  

The year 2000, then what? Many companies aren’t thinking past the new millennium. But smart organizations are considering initiatives and strategies to increase profits and revenues. Many have invested heavily in enterprise resource planning (ERP) and supply chain (SC) systems to automate the back office and cut costs through more efficient and streamlined financial, inventory management, and order-entry systems.
Cutting costs alone is a tried-and-true route to return on investment. But why not increase revenue while also reducing costs? While many of these ERP and SC systems improve external processes, such as distribution, these initiatives remain
largely internally focused. Yet addressing external issues and pressures have the greatest impact on corporate profitability. That is why so many smart businesses have turned their attention to customer relationship management (CRM) strategies.

CRM recognizes that customers are at the core of the business, and the company’s success depends on effectively managing relationships with them. To correctly manage those relationships, the company must first know who its customers are, not just as groups or segments of customers, but each individual customer. Is this customer a good customer? Is he or she profitable? Why does he or she do business with me? What does this person like about my business? Does this person also do business with my competitor?

Data warehousing technology makes one-to-one interactions with customers possible because they sit at the core to consolidate information and turn data about customers into customer intelligence. Smart organizations know that they have to think beyond automating processes and figure out how to better understand their customer base to increase revenues and profits. Information technology plays a crucial role in ensuring that the data and metadata associated with these various systems is kept clean, accurate, and valid. There is an increasing realization that a metadata strategy
is central to an organization’s future. The demand for readily accessible and reliable information to support strategic and tactical decision making has grown steadily in recent years. As a result, organizations have dedicated substantial resources
(in some cases massive resources) to building data warehouses and data marts.

The Case for CRM

A high level of customer knowledge is crucial to organizations today because of competition in shrinking markets. Deregulation, diversification, and globalization have
stimulated a dramatic rise in competitiveness, making it more imperative than ever to better manage customer relationships at every point of contact, and to acquire and build loyalty among those customers deemed most profitable. This has focused intense scrutiny on front-office applications, where the company interacts most often with the customer directly or indirectly via such outlets as branch offices, kiosks, call
centers, sales representatives, marketing, the Web, email, and order entry. Customers are defined in many different ways. For example, the customer can be the direct consumer or an organization, such as a business partner, distributor, or shareholder. The point is, today’s customers have many choices, and it is up to the company to make sure profitable customers remain loyal and new prospects grow in loyalty
and profitability.

Customer Satisfaction and Customer Loyalty:
Not the Same Thing

Monitoring and capturing customer satisfaction information isn’t new. But is this information really telling companies what they need to know about their customers? Is satisfaction alone good enough? Not according to a recent survey from the Meta Group:

“Fifty percent of your company’s satisfied customers will do business with your competitors. Twenty-five percent of your company’s VERY satisfied customers will
do business with your competitors.”

Percentages like that mean companies need to discover much more about their customers than their satisfaction level. Building loyalty among customers involves understanding the various ways that they are different and using that knowledge to tailor appropriate behaviors towards those customers. To be customer focused and customer centric, the company must continuously learn from interactions with each individual customer and be prepared to dynamically respond to information and knowledge gained from those interactions. The company must be well equipped with the information and knowledge to engage proactively with the customer versus reactively.

Proactive customer engagement and interaction involves much collaboration throughout the organization. CRM is a concept, and it is also a process because a company has to organize itself such that all information coming from customer touch
points and other customer related information is consolidated to facilitate a learning process. Bestselling author, Dr. Fred Wiersema speaks of five essential steps to help organizations reach their customer oriented potential. Step five involves establishing trust as the key to building stronger, lasting relationships with customers. “First, you have to communicate clearly what you are and what you are not going to do for that
customer and follow through. Second, customers must know that you are trying to understand what is important to them —the critical aspects of their business and their life. And third, in many cases, companies hold large amounts of very personal
information about customers. They must trust that you will protect their privacy. Ultimately, if you develop trust, you should be able to get a customer to outsource part of their decision-making to you.” This involves support from many lines of business units and information technology.

Marketplace Pains…CRM Remedies

The issues that motivate organizations to seek a CRM solution can take many forms, and the needs originate from many functional areas of the business. The following scenarios indicate the need for an overhaul of an organization’s customer management strategy:

• No real understanding of who the customers are, which are the most profitable, or what their lifetime value is.
• Communications efforts with customers are not effective enough. Customers often receive conflicting messages from the company’s different business units, and the sales and marketing cycle is too long.
• Inability to get a handle on the market in general, and, in particular, what customers need or want.

The best CRM solutions allow a company to bring together the most complete repository of information about customers and competitors—including market research data from both internal and external sources—within a single, customer-centric data warehouse. Exploiting such a repository leads to a solid understanding of market trends and competitive marketing activities. With this understanding, a company is in a good position to develop new products or reposition or abandon existing ones to satisfy customer demands and gain competitive advantage in the marketplace.

The best CRM processes also incorporate newfound knowledge into existing customer-related operational systems and analyze the results of previous actions to predict which customer segments are more likely to respond to which type of
campaign or product offering. They also use multidimensional data analysis and data mining techniques to understand what drives profitability by identifying the most profitable current customers and predicting future customer profitability. Finally,
the best CRM solutions manage and store all customer-related data, from every point of customer contact, in a single data warehouse. This information is surfaced to every customer contact point through Web-enabled dynamic reporting systems and executive information systems. This provides business managers with timely reports on any aspect of customer relations, allowing them to refine resource allocation to
maximize profits. For example, marketing managers can manage campaign efficiency (profitability, impact, percentage of respondents to a given offer) as well as sales activities (average sales cycle length, cost of sales, sales profitability).

The Role of Metadata

Until very recently, a surefire way to provoke an embarrassed silence at any meeting between business and information technology (IT) people was to mention the word metadata. Even if they knew what you were talking about, business peoples’ eyes would glaze over. IT people might think, “Someone ought to do something about metadata, but please, not me!”

Organizing all customer data into an integrated warehouse environment is one of the biggest challenges faced by information technicians. However, an equally important
challenge is to create an integrated environment for telling everyone what data is available and how it can be exploited. Without data about data (metadata) the organization will, at best, fail to get the full return on its investment in data warehousing. At worst, there is the risk that as the amount of data in the data warehousing infrastructure rises exponentially, business users will despair at the time it takes to find useful information. Other trends are also contributing to the need for a tighter inventory of information resources. For example:

• Changes in the business environment create constantly evolving business definitions.
• Data marts are proliferating, often without central planning.
• Business units and teams create their own terms for similar data elements.
• Trans- and multinational ventures create language difficulties.
• Increasing staff turnover means a constant outflow of undocumented business knowledge.
• Valuable but unstructured external information (such as Web-based) is adding to data volumes.

Develop a Methodology Early

One of the great advantages of a data warehouse is that it provides a repository of data, independent of operational systems, and that is centered around the business users’ needs. But inevitably, when these users begin accessing the information within a warehouse, certain questions arise:

• What information is available in the warehouse?
• What do the definitions mean (for example, exactly how is a
customer defined)?
• Is the data current and can I trust it?

From the enterprise level, most likely the level at which the success of the data warehouse ultimately will be judged, metadata is designed to help answer these sorts of questions. These questions usually can be answered relatively easily early in the life of a data warehouse initiative. However, the role of metadata becomes more complex as the enterprise information matures and users’ demands increase. Consequently organizations can save themselves a lot of trouble if they structure a complete environment for metadata management and approach the issue methodically from day one.

The Functions of Metadata

Metadata serves several functions for both technical and business users. Technical metadata describes or documents data and information items, providing details such as what an object is named; when, how, and by whom it was created; its type and size; its sources; and transformations. It also provides an integral record in data quality management practices — establishing naming and other standards, documenting source to- target transformations, and measuring and reporting compliance to systems of record. The true value of metadata emerges when it is used to guide information update and access processes.

Additionally, metadata can be used to enforce security on the data in a data warehouse. Although it may be desirable to allow broad access to all the data elements in a warehouse, special-purpose data marts and operational data details may require restrictions. Moreover, security of access to the data stores may be necessary to minimize redundancy.

For business users, metadata is essential to the management, organization, and exploitation activities that occur throughout the evolution of a truly active information environment. Things change quickly in the business world. It is very helpful for
business users to be able to see, for instance, how the profit variable was calculated, or that perhaps sales territories were divided differently prior to a certain date, or even just when the data was last updated. This type of metadata, such as documented business rules, helps extend the value of data warehouse information. People feel that they can trust it.

Given the complexity of the relationship between operational and information delivery/decision support systems within increasingly close-knit enterprise information systems, metadata should document all data elements from data source to data
exploitation as completely as possible. And, as more and more data warehouses and data marts are created, an integrated, effective metadata management strategy is key to avoiding confusion and chaos later. Inheriting data warehouses and data marts without metadata would be worse than just a surprise for IT technicians. It would be a real pain.

Think Big and Plan for More

Getting data warehouse projects off the ground with the impetus needed to deliver an early return on investment invariably involves overcoming skepticism and raising
expectations. A methodology that emphasizes the benefits of starting small and building the warehouse iteratively is highly recommended. That way, people see an early reward for their efforts and trust. Metadata represents the key element on the
other side of the equation. When planning a metadata environment, it is advisable to think big and build an environment that allows for never-ending evolution.

Organizations need to realize that ERP vendors know online transaction processing (OLTP) technology and that transaction processing is their core competency. And although ERP vendors’ data warehousing solutions will offer the appeal of easy integration with their own ERP modules, it’s less obvious that their toolsets will be as sophisticated as competing data warehousing and decision support solutions from integrated data warehousing/decision support vendors. ERP vendors are good at managing and maintaining OLTP systems and delivering static reports. However, ERP vendors are just beginning to offer first-generation solutions to their customers’ data warehousing needs. The point is, there’s a fundamental disconnect between OLTP and decision-support systems (DSS) systems.

As organizations continue to develop enterprise-wide information delivery mechanisms to support decision support via databases, data marts and data warehouses, the next logical step is to extend these information stores from ERP system and other legacy links to data exploitation. Organizations must maintain their legacy systems to evaluate past data to make better business decisions (many of
these legacy systems are still active and gathering new information). The challenge remains in tying legacy data into a complete information architecture.

Begin by developing an enterprise decision support architecture that contains metadata and integrates ERP environments. Managing metadata is not only a key component of a well architected IT infrastructure,it also represents a valuable road map that can provide an organization’s decision support users with a strong competitive advantage. It requires a customizable solution that offers a single point of control, making it easier to respond to the ever-changing needs of the business community.

Is a Data Warehouse the Same as a
Decision-support System (DSS)?

A data warehouse is best used to enable analytical information processing. Since this analytical information processing is performed “online” and interactively (though not necessarily over the Internet), the discipline is called Online Analytical Processing (OLAP). OLAP is an analytical processing technology, which creates business information through a robust set of business transformations and calculations upon existing data. For a DSS to supply valuable information, it has to represent the information the way the users perceive it in real life. Users look at the information in the form of business events and the different ways the events are analyzed.

How Does a Data Warehouse Feed a DSS?

The suite of products that could be called DSS products consists of: OLAP, query and reporting, data visualization, data analysis, data mining, executive information systems
(EIS), and business solutions, including financial consolidation and reporting, human resources, and Web enablement.

A data warehouse designed for DSS armed with the appropriate OLAP software tools can give users easy and intuitive access to their data for analysis — making users self
sufficient and getting developers out of the report writing business. Users should be able to combine their data in any order they desire, at any level of aggregation and over several time periods. Users should be able to analyze the data by clicking on the dimensions and the data itself.

Definition of OLAP: Short for Online Analytical Processing, a category of software tools that provides analysis of data stored in a database. OLAP tools enable users to analyze different dimensions of multidimensional data. For example, it provides time series and trend analysis views. OLAP often is used in data mining.

The chief component of OLAP is the OLAP server, which sits between a client and a database management systems (DBMS). The OLAP server understands how data is organized in the database and has special functions for analyzing the data. There are OLAP servers available for nearly all the major database systems.

ERP vs. DSS?

Enterprise Resource Planning (ERP) software is a set of applications that automates finance and human resources departments and helps manufacturers handle jobs such as order processing and production scheduling. The main mission of ERP systems is integrating the processes of an organization. ERP systems involve processes that in most organizations that have evolved over the years, so are notoriously complex to install and maintain. A data warehouse, on the other hand, has an “information out” mission. The major mission of a data warehouse is to enable the users to “take out the data” for analysis.

Data warehouses have different design criteria and need to be judged by different performance monitoring criteria than OLTP databases. The fundamental assumptions of data warehouse design are that data is stored so as to be readily accessible in ways that are of interest to users and that the design of the data model is driven by usage. In general (and this can drive a traditional DBA crazy), data redundancy is favored over data model complexity. Because a simple model is favored over a complex model, most standard relational database modeling tools, without modifications for data
warehouse usage, are of little use in deriving a data warehouse design. Unfortunately, most cost-based database optimizers aren’t well suited for data warehouse queries. There is a fundamental disconnect between OLTP and DSS systems. Although ERP customers can take advantage of those systems’ built-in reports, they often discover how hard it is to get a “global,” unified view of all their data which may be distributed
across multiple ERP servers vendors. The functionality of ERP systems is not easily transferable to decision support. ERP systems, generally haven’t been built to integrate data from external sources, or even integrate legacy data on an ongoing basis. They weren’t designed to support and manage data marts or data warehouses for DSS applications. They weren’t designed with a data transformation engine to store the types of complex data transformation rules often associated with populating and freshening data warehouse data. They weren’t built with tools to let data architects design and model data warehouses and marts. ERP systems didn’t ship with OLAP viewers that let end users slice and dice. Therefore, bridging the gap between that front office and the back office ERP systems through integrated DSS approach is crucial for a successful CRM solution. But there’s one other element.

Collaboration of Customer Knowledge

Collaboration of this customer intelligence and customer knowledge becomes another key issue for organizations as they expand the customer channels. How can customer
knowledge be shared in such a way that each customer touch point has the most accurate and up-to-date customer information independent of which channel that customer chooses to interact with?

Collaborative Business Intelligence (CBI) allows companies to unlock the power of their data stores by creating collaborative, contextual information environments. Analysts who traditionally use business-intelligence tools to look for important business factors can now save their findings into a knowledge repository, making it easily accessible to the rest of the organization over the Web. Decision makers become more engaged in the knowledge-building process as they add to the thread, and everyone can search for similar projects and re-use ideas that have worked in the past.

Linking Structured and
Unstructured Information

Creating CBI environments will allow organizations to expand the way they are already doing business by knowledge enabling their existing and future business processes. Organizations will be able to link structured data — the reports, and graphics
created by business-intelligence tools, for example — with unstructured information, the documents, email, and on-line discussions that support structured data. By surfacing this linked information through a Web browser, everyone in the organization can benefit from the capture, organization, and re-use of corporate knowledge. As a result, knowledge remains available regardless of employee availability or mobility, and current systems are better utilized.

CBI takes that customer information, stores it in a common knowledge repository, and allows anyone with proper Web clearance to locate information quickly, dynamically use linked business intelligence tools, and contribute their own ideas. Users can set up subscriptions to be notified of additions to the knowledge repository and can email directly into the repository.

Conclusion

A CRM system is only as successful as the quality of data and data-management processes supporting it. Organizations planning wisely beyond the next millennium are thinking beyond process automation and are focused on getting better acquainted with customers to increase revenues and profits. Maintaining a sound metadata strategy — as well as understanding the roles of ERP, decision support, and data
warehousing systems — is crucial for attaining this higher level of understanding.

(Please see Successful Customer Relationship Management - Why ERP, Data Warehousing, Decision Support and Metadata Matter  SAS   Our Server )  & (Please also see The ROI of CRM STRATEGIES FOR MEASURING AND MAXIMIZING CUSTOMER RELATIONSHIPS )

Enabling Partner Value Networks Through Partner Relationship Management

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Data sheet: Siebel Partner Relationship Management (PDF)  Our Server
White paper: Enabling Partner Value Networks Through Partner Relationship Management (PDF)  Our Server

Today, the business drivers for partnering focus less on cost savings and more on revenue growth, brand awareness, and customer loyalty. Organizations are consequently shifting their use of partners from a tactical focus to a more-overarching strategic focus.

INTRODUCTION
Many companies recognize the importance of managing their customer relationships as a means to achieve dramatic improvements in business performance. These customer-focused strategies often fall short, however, when they fail to consider the growing impact of business partnering.

Increasingly, many brand owners are leveraging partners to drive revenue and meet customer demands. In fact, partner-generated revenues now account for a growing share of total brand owner revenues. Many industry segments drive significantly more than 40 percent of revenue through the partner channel. The high-technology industry, for example, sells as much as 60 to 70 percent of products through indirect channels; for financial services, the figure is 80 percent; and for automotive, the figure is 90 percent.
As competition intensifies and globalization continues, the use of partnering will only continue to increase, helping brand owners increase revenue and profits, decrease costs, and enhance customer satisfaction and loyalty. In a survey conducted by Accenture, approximately 82 percent of executives believed alliances would be a prime vehicle for future growth. The same study predicted that by late 2004, the average company would derive 16 to 25 percent of shareholder value from partnerships, representing between US$25 trillion and US$40 trillion.1 As former General Electric CEO Jack Welch has observed, “If you think you can go it alone in today’s global economy, you are highly mistaken.”2
The expanding role of partnering in today’s business environment brings with it a new set of opportunities and challenges. This white paper examines these facets of business partnering and identifies the key business benefits of enabling a partner value network by deploying a comprehensive partner relationship management (PRM) strategy.
1 Charles Kalmbach Jr. and Charles Roussel. “Outlook: Dispelling the Myths of Alliances,” Accenture, October 1999.
2 Ibid.
PARTNERING FOR COMPETITIVE ADVANTAGE
Partnerships have flourished in recent years even as the reasons for business partnering have changed in the new economy. Today the business drivers for partnering focus less on cost savings and more on revenue growth, brand awareness, and customer loyalty. Organizations are consequently shifting their use of partners from a tactical focus to a more-overarching strategic focus. Tactically focused partnerships tend to be oriented toward product or service distribution; whereas, strategic partnerships are geared more toward achieving higher-level goals, such as increasing brand awareness and growing customer loyalty.

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