This article is featured in the 2000 Resource Guide, a supplement to the December issue of DM Review.

Traditionally, organizations have relied on mass marketing techniques as represented in Figure 1. They have purchased volumes of demographic, socioeconomic and historical data from national sources, segmented it and applied it to their own customers, assuming throughout this process that all customers behave the same. Mass marketing and target marketing methods are quickly becoming antiquated.

In today's world of pathologically short attention spans and virtual relationships formed through e-mail and chat rooms, people's attention is no longer held and interest is insufficiently piqued by the old marketing techniques. Consumers are more sophisticated, and they have less time than ever. Companies must work "smarter" to keep customers, not just win them. This new and smarter way of marketing is called enterprise customer relationship management (ECRM). Companies must maintain consumers' interest by providing them with clever, convenient and innovative ways of doing business. They must offer the individual customized services with convenient means of access. For example, online users get new product notices and financial information from their bank via e-mail and make investment moves and change checking options online. Traditional banking customers receive financial news and product news via circulars in their printed monthly bank statements and, subsequently, execute financial decisions by visiting their branch office.

There is evidence of this trend in every industry. In the financial industry, banks have embraced customer relationship marketing. They offer packaged checking, loan, mortgage and investment account solutions based on usage patterns. Customers that bank on the Internet, via telephone, debit card and ATM might pay more for excess check usage and less or nothing at all in monthly fees. People who commonly use traditional teller services for their banking needs and rarely or never bank via telephone or online are offered solutions that provide low-priced or unlimited check writing with a more substantial monthly fee or a high per- transaction cost.

Organizations must nurture their client relationships to ensure customer satisfaction and subsequent loyalty. While this might sound like considerably more work, it is much more costly long term to focus solely on winning customers than it is to concentrate on keeping them. In the end, the cost of doing business is less because your customers are reached more successfully. Companies are able to target the right customer with the most appropriate solution, ensuring the most profitable promotions and highest propensity to buy.


Figure 1: Mass Marketing Techniques

By 2001, customized products, new distribution and communications channels, and multiple pricing options will drive 70 percent of enterprises to reengineer and automate their marketing process. Efficient management, analysis and use of customer information yield the competitive advantage necessary among companies fighting for the same customers. The competitive advantage is not reached just by understanding the optimal customer segmentation. Instead the competitive edge is gained by linking to the customer via the optimal delivery channel (the World Wide Web, direct mail, outbound call center, etc.), organizing customer information and mining customer information for innovative product and service ideas that competitors haven't thought of yet.

The key to successfully making this switch in marketing focus is altering the way in which organizations analyze their customer data. Most of the data organizations need in order to yield integrated, accurate customer information comes directly from the customers themselves: their demographics, their purchasing patterns, their likes and dislikes, their means and methods of purchasing, their buying history. The good news is most organizations are likely to have most of this data. The bad news is that most of this information is in bits and pieces and lives in disparate data sources across an organization. To compound the problem, those systems do not speak the same language; and many organizations have no means of efficiently consolidating and analyzing the data into meaningful decision support information. This may cause organizations to think that from a marketing or customer relationship management perspective, they are back at square one. That is not the case. Knowing that the data is within reach is the first step to the solution. Next, it must be found, run through the data quality process and integrated based on business rules – then it can be analyzed.

Organizations need different tools to achieve these "smarter" marketing goals. They must build an infrastructure to support technology- enabled marketing that is information- and customer-driven, rather than product- driven like the old style of marketing. Organizations must invest in an enterprise customer relationship management solution based on an effective customer information architecture (CIA), decision support systems (DSSs) and delivery channel applications, such as e-commerce applications.

Imagine ...
A Customer-Centric Environment

An enterprise customer relationship management (ECRM) solution enables decision-makers to recognize and understand patterns and trends based on the integration of complex customer transaction data. It also helps organizations understand how relationship management can optimize customer value. Despite the benefits and usually due to lack of education, many organizations still have not invested in this technology. Meanwhile, their competitors are finding ways to collect, translate, analyze and use customer information in unlimited ways, which results in strengthened relationship marketing and more focused promotional campaigns and brand management programs. Companies with effective ECRM solutions better understand buying behavior, cross-selling opportunities, customer retention and acquisition, customer loyalty, upselling opportunities, product trends and promotion effectiveness. Plain and simple – they just know their customers better.

Undoubtedly, an organization that is not using information technology to understand and serve its customers better will be out- performed, out-sold, out-smarted and probably out of business before the end of the next decade. Competition within a global marketplace dictates that companies distinguish themselves from the competition through their customer relationships. It is impossible for employees to provide the level of service customers expect if information about them is scattered throughout an organization or is just plain unavailable. A good customer information architecture can effectively integrate data from many sources (e.g., financial, claims, customer and call center systems).

For example, if a financial institution's legacy systems – credit card, installment loan, investments, trust account, mortgage, etc. – are isolated within each division, how can that institution leverage its customer information? The financial institution with fragmented customer information is bombarding customers with redundant, confusing propositions that often don't apply to their individual situations. Meanwhile, the competitor who is using an ECRM solution – supported by integrated information from a customer information architecture – to share customer information across the enterprise can approach customers with large balances about opening a trust account or urge those with excessive debt to consolidate. Today's customers want to be able to glance at their entire financial portfolio and see how they can optimize their financial future. The financial industry has responded to this with consolidated monthly statements and e-commerce solutions that give clients real time access to their portfolios. Furthermore, financial institutions that enable customers' access to personal account information, loan payment and money managing services online will have a clear advantage over their competitors who do not offer sophisticated, e-commerce solutions.

Imagine ...
A World-Class ECRM Solution

Figure 2: ECRM Solution

An ECRM strategy must include effective use of data warehousing technology and sophisticated delivery channel support systems – many of them Web-enabled. Three key elements to a successful enterprise customer relationship management solution, as shown in Figure 2, are as follows:

Customer information architecture for accurate and integrated customer information. An ECRM solution is ineffective without accurate and integrated customer information. The best method for integrating data cross-functionally is with data warehousing technology, and the best solution for accuracy is taking data through the data quality process.

Delivery channels that provide sophisticated analysis tools and customer interfaces. When the dust settles, the clear market leaders will be the organizations taking advantage of the Internet age by implementing e-commerce solutions on top of robust analytical solutions that are supported by data warehousing technology.

Decision support systems for analysis of customer information. Decision support systems (supported by the data warehouse) are the tools that enable decision-makers to analyze their data. These systems are typically functionally specific (i.e., product performance analysis, risk analysis, call pattern analysis, etc.).

Developing and implementing an enterprise customer relationship management solution, as depicted in Figure 2, facilitates business communication by collecting and combining customer-driven data into an easily accessible operational data store and data warehouse that makes information available to the enterprise and, potentially, to customers accessing organizations via the Internet.

Imagine ...
Exceeding Customer Expectations

Whatever the industry, customers' expectations of how their business relationships are managed have already been set by services as simple as their local pizza parlors. On any given night, customers phone their favorite pizza place and discover that the person answering the phone knows who they are. They know their address, phone number, their usual order, most recent order and whether or not they regularly pick up or get delivery – and all this before a word is spoken. This kind of relationship management on a small scale has set expectations for businesses in industries across the board.

E-commerce is changing the face of enterprise customer relationship management and marketing, enabling customers to purchase items and be catered to virtually 24 hours a day. When you sign-on or call-up, customer preferences, profile, value, issues and history should be known. This provides customer service representatives with the information needed to make suggestions on additional services that can be configured for the individual customer. It is the tools behind the knowledgeable sales associate and the fancy Web site that help organizations cater to their customers' needs and wants.

In the financial market, customers expect their bankers to know who they are when they walk in the door or call or visit online. In retail, customers of Home Depot Expo expect to be known when they visit Home Depot, because both are owned by one parent company. A Rich's bridal registry customer wants to be known at Bloomingdale's – both being Federated Stores. In the financial industry, customers assume that the necessary information is gathered when they open their account, apply for a mortgage or secure a car loan. But, in most cases, customers are required to present their financial picture and needs again and again. Over time, they become annoyed and dissatisfied, and this dissatisfaction is reinforced with each visit to the financial institution, either in person or via the Web site.

Situations like these illustrate the need for an enterprise customer relationship management solution that can help businesses understand, manage and foster customer relationships. As a tool for setting information free across the organization, a data warehouse-based ECRM solution can help businesses meet and exceed customer expectations by letting customers know that the organization knows what they want and need.

Customer Information Architecture (CIA)

Imagine ... The Challenge

The piece of the enterprise customer relationship management puzzle that turns disjointed data into cohesive information is the customer information architecture – the tools and methodologies that deliver integrated and accurate data for analysis.

Information within companies evolves over time. In companies that have collected scores of data over years or decades, the challenge of handling and sharing information across the organization can be overwhelming. Even in younger companies, there is no guarantee that the then-state-of-the-art billing system installed two years ago can interface with the order entry system that went in last month or the new acquisition under negotiation.

Figure 3: The transfer (or non-transfer) of information across dozens, sometimes hundreds, of legacy systems is a tangled web that creates information roadlock at every turn.

As depicted in Figure 3, an organization whose information is tangled in a web of isolated systems seems, to its customers, disorganized. Although customer information is stored in one or more locations, if there is no way to marry that information into a portrait of the customer – his or her needs, level of satisfaction, likes and dislikes – then there is no real way to manage that customer relationship.

Imagine ...
The Best CIA Capabilities

With the customer information resident in disparate databases across disparate platforms, traditional data management processes are inadequate to support a best-in-class customer information architecture (CIA). Because customer information repositories are developed iteratively and the operational environment is constantly changing, the interface between the operational and data warehouse environments can become unmanageable if one relies solely on hand-coding efforts. Best-in-class customer information architectures (CIAs) are supported by a suite of scalable data management packages (i.e., extraction, transformation and loading software) which automate the design, construction and maintenance of operational data stores, data warehouses and marts as shown in Figure 4.


Figure 4: Customer Information Architecture

Often many attempts are made to aggregate customer information. Most attempts only make data available on a departmental basis and provide incomplete or inconsistent information. These pseudo-architectures don't support enterprise-wide access, nor do they provide information such as the customer's net worth, lifetime value, current and potential service opportunities, buying capacity, demographics or preferences. Additionally, most organizations have between 50 and 100 legacy systems that currently store and disseminate customer information. Even when the need to integrate these systems is realized, many organizations just don't have the time or resources to undertake such a task.

The best in class data management solution for a CIA provides a flexible, automated approach to data warehouse or mart initiatives, allowing customers to build on their initial investment by growing in any direction. They are based on a unique architecture that lets organizations collect and process information only once and then organize it for delivery to any point of use within their organizations. Multiple capabilities required to support this environment include:

  • Capability to support projects requires integration of data from multiple, heterogeneous source databases to provide consistent information across departmental organizations. Normally this includes sourcing multiple legacy files and targeting one or more relational database environments.
  • Capability to build and maintain data warehouses or operational data stores serving a wide variety of users across the enterprise. The capability must be provided to integrate data from several subject areas from cross-enterprise data sources, as well as managed deployment of warehouses on multiple heterogeneous databases scaling from NT SQL to MPP configurations.
  • Capability to support rapid, economical construction of scalable data marts for a workgroup or specific subject area.
  • Capability to build a mart consisting of a single subject area of data within a single target database, commonly sourcing UNIX operational systems and creating a data mart on a UNIX or NT platform to support specific customer-facing solutions or populating a customer service or sales management system.
  • Capability to convert data for migrating data across one or more application domains. It integrates data from multiple legacy sources into a single target database or distributes to multiple sources.
  • Capability to identify changes that occur in operational systems so that ongoing data warehouse/data mart/operational data store maintenance is more efficient, by capturing and applying changed data to the warehouse or mart environment.

The core components of a best- in-class CIA include software which provides integrated capabilities for design, construction and maintenance of scalable data warehouses and marts; an intuitive, graphical user interface; and workflow model for complete support of the implementation and maintenance cycles. The environment must provide an open platform to integrate and manage both business and technical meta data about warehouse versions across the enterprise.

Imagine ... Guaranteed Success


Figure 5: Biggest Warehouse Challenges

Many debates exist over why CIA or data warehousing projects fail. Some say it's the technology; others say it's the people. As shown in Figure 5, the study conducted by the META Group indicates that data quality is the overriding issue. Another study, conducted by The Data Warehousing Institute (TDWI), surveyed 21 data warehouse project managers on their most difficult challenges. Methodology was the third biggest menace – technology and education were number one. The three represented 87 percent of the problems identified. Interestingly, solving the third biggest problem – methodology – often minimizes technology and education issues. Another study conducted by Dr. Barbara Haley, a professor at University of Virginia, regarding data warehouse successes and failures clearly showed that unclear business drivers, sponsorship and dysfunctional project teams where the chief contributors to the failed projects. Dr. Haley's study revealed that while technological challenges existed, the technical teams were persistent and found resolutions, but they were not as adept at solving the organizational issues.

The critical success factors driving CIA solutions include:

Sponsorship, leadership and management. The number one cause of warehousing project failure is the lack of sponsorship from the business and the leadership in IT. This alliance must initiate the project and provide leadership throughout. The sponsors must remain involved as strong and vocal proponents throughout the project. The sponsor should identify and make available highly qualified and motivated resources, as identified and scheduled in the project plan. Most resources should be dedicated full time to the project.

Data quality and availability. Data quality and availability issues must be addressed at the highest levels of the organization. The sponsors must initiate and support data quality and capturing initiatives, as well as educate pilot users regarding these issues. CIA will not fix all data quality and availability issues; rather, the initiative will bring these issues to light. Some issues may be resolved through the CIA technology; however, most data quality and availability issues must be addressed by the organization through improvements to operations, changes in operational level systems and the potential integration of a data hygiene tool.

Requirements gathering and setting expectations. "Build it and they will come" has proven to be the wrong approach. The correct method is to understand what information drives the measurement of the key processes that deliver the highest payback and what data drives the process. This not only insures buy-in but also defines the priority data sources that drive the key business decisions. One critical component of requirements gathering is defining the business rules and getting agreement by cross-functional teams. When the business drives the process and sets the direction and priorities, expectations are clearly understood by all and success rates are significantly improved.

Strategy and architecture. Taking the right approach for the development of the strategy and architecture will ensure that the initial CIA investment yields high returns up front and on subsequent initiatives. Building incrementally with the future in mind is a key ingredient to success. The requirements will drive which customer information data sources are included in each iteration of the CIA implementation. The projects with the highest degree of success deliver periodic results, 4 to 6 to 9 months, and deliver 4 to 6 additional data sources with each iteration.

Enterprise data model. Wise institutions look at information about their customers across the enterprise. Unfortunately, many organizations do not follow this model. For example, some organizations still provide access to customer information residing in disparate mainframe applications by using inflexible and costly mainframe-based reporting tools. This often requires extensive support from the information technology staff. The enterprise data model should reengineer the data collected from the legacy application so that all customer relationships, products, services and profitability can be viewed consistently and accurately. The correct design of the data model will ensure success and provide a competitive advantage for years to come.

Methodology. The methodology is a necessary tool for this overwhelming task. Following a proven methodology, either in part or in parcel, will get the project past some common stumbling blocks that might otherwise trip up the project.

Meta data driven. The meta data repository contains the organization's business models and rules, data view definitions, data usage model, report dictionary, user profiles, physical – and usually logical – data model, source file data dictionaries, data element descriptions and data conversion rules. In effect, the meta data repository contains the blueprints or schematics for the CIA system to function. Without integrated meta data, the maintenance and support costs rise significantly. It is the nerve center and a critical component to success.

Imagine ...
World-Class Technology

Technology components that should be included in a customer information architecture are depicted in Figure 6.

Figure 6: Technology Components

  • Scheduling capability, which creates a data warehouse production blueprint to automate job scheduling and file transport from operational sources into a data warehouse or mart.
  • Loading capability, which replicates data from mainframes to servers, or between servers, reducing the amount of time and disk space needed to copy and load data into target databases.
  • Updating capability, which performs incremental, high-speed replication of changed data from mainframes to servers.
  • Ability to generate programs to extract data from disparate legacy and client/server source systems, and generate the output files to load target data warehouses or marts on a broad range of platforms.
  • Ability to identify and extract source database changes from relational database log tapes, speeding the process of warehouse refreshment.
  • Web access, which allows users to access and navigate the meta data stored in the meta data repository.
  • Quality management, which provides the ability to audit, monitor, improve and certify data quality at key points throughout the CIA life cycle.
  • Comprehensive financial services data model, which provides templates for future iterations and insures enterprise-wide data integration.
  • Householding algorithms that match accounts for households prior to loading into the operational data store and support data standardization and management functions that eventually approach 100 percent reliability.
  • Meta data management that integrates both technical and business meta data from numerous locations (i.e., source systems, data management software, database technology, analytical and modeling tools).
  • A data management methodology, which provides a detailed process road map for developing the CIA in concise, repeatable steps.
  • A relational and potentially a multidimensional data management solution that supports efficient and effective organization of data and accessibility via numerous platforms and networks.

Numerous tools from IBM, NCR, CA (Platinum), Ardent Software and others are available to accomplish these tasks. The return on investment for this capability is significant. Productivity gains are generally 100 percent and maintainability of a dynamic CIA environment would be impossible without the technical capability described.

Imagine ... High Data Integrity

With the consolidation of data and the accessibility via the Internet, quality issues are exposed – leaving the organization vulnerable when errors occur. It is important to address data quality from source to destination, which includes everything from implementing standards for proper data entry to careful analysis prior to sourcing a data warehouse. Clean and accurate data is critical to accurate analytical information.

A CIA data management function defines the roles and responsibilities of the organization and defines the overall level of acceptance for the organization. First, the data management function should establish the guidelines for data quality for the organization. Data quality embraces four levels of data analysis, and each level is designed to analyze the quality of the data environment from a different perspective using appropriate criteria to identify the various types of data quality problems that may be present. The methodology of data quality analysis provides a structured framework in which to plan, organize and perform a systematic assessment of the data quality condition of the data environment. The four levels of analysis, as shown in Figure 7, are completeness and validity, structural integrity, business rules and conversion rules.


Figure 7: Data Quality Analysis Methodology

Data Quality Analysis Methodology

The methodology is based on performing sequential analysis that progresses from simple tests of data quality to more rigorous, complex and subtle tests of data quality. By following this methodology, the data quality analysis results in a comprehensive and complete assessment of the data quality that exposes and quantifies both the strengths and weaknesses of the data environment.

Each level of the analysis is designed to address relevant types of data quality problems and requires that appropriate quality criteria be available or established. The actual data quality condition is measured against those criteria. Analyzing each level in sequence is important because it is useful in understanding the problems at one level and how they can have a compounding effect on the results at a higher level of analysis.

Imagine ...
Outpacing the Competition

The financial services industry has been a leading adopter of leveraging the vast amounts of customer information locked in legacy systems. Financial institutions have various goals and objectives when implementing a CIA system. Some organizations want to maximize their cross-selling opportunities, understand customer buying patterns and demographics, and analyze loan performance. Others want to better increase the effectiveness of their customer service and conduct risk analysis. Mergers and acquisitions often present the need for a CIA to integrate financial, claims, customer and application data from companies that have merged. Some companies want to assess their credit risk, better understand market segmentation and more effectively detect fraud. Some organizations' main goal is to service their customers better. For example, they want to conduct credit line analysis so they can offer better consolidation rates and/or raise or lower credit lines. Other organizations want to streamline and maximize their marketing efforts. All of these business issues can be solved and objectives can be reached by using and analyzing information appropriately. An effective CIA enables the storage of decades of historical data and integration of data from numerous sources.

Imagine ... Cost Justification

Although the initial investment in this technology is expensive, companies must focus on the long-term benefits. CIAs are the core of enterprise customer relationship management and e-commerce solutions. Therefore, not only will organizations save money and resources in the long run, they will be able to use the insight and information gained from the CIA to make money by effectively managing relationships with their customers. With the development of CIA solution, one financial organization uncovered $3 million in unfulfilled contract commitments.

Decision Support

Imagine ...
Unlocking the Secrets

ECRM solutions differ from the traditional "just-build-it-and-they-will-come" philosophy. ECRM integrates customers into the business architecture. To effectively integrate decision support into ECRM, organizations must focus on how key business drivers in the corporation relate to customers. One caution with implementing a data warehouse is to avoid creating a "data dump." It is critical to ensure that the data that goes into the warehouse comes out as strategic information. If the data is organized, aggregated and correlated in an efficient manner, then it is much easier to extract and analyze. Decision support is the process of analyzing databases to uncover new and valuable information, usually in the form of previously unknown relationships between variables.

There is a difference between trend analysis and basic analytics commonly supported by online analytical processing (OLAP) tools. Where OLAP enabling technology can uncover trends, decision support can find the correlations between data elements.

For example, an OLAP query might answer the following question: Do sales of product X increase in November? A data mining query, on the other hand, must answer: What are the factors that determine sales of product X? OLAP is highly tied to time dimension, whereas decision science is more customer centric. Decision support or data mining is the process in which analytical models are created that are predictive, descriptive or both. Data mining techniques are becoming more and more critical for companies to remain competitive. Direct applications of data mining techniques include:

Response modeling. Based on demographic attributes and purchasing history, which consumers are most likely to respond to a particular product or advertising campaign? This can include buying patterns, service opportunities or shifts in behavior.

Customer lifetime valuation. Based on the number of repeat purchases, dollars spent or longevity, which consumers are likely to become my most valuable customers?

Customer segmentation. What are the characteristics common to my customer base, and do they fall into identifiable groups?

Cross-selling. Given data on whether a customer has purchased product A, B and C, which ones are likely to purchase product X?

Marketing programs. These programs include relationship management, thank you programs, incentive programs, retention programs, etc.

These examples demonstrate how data mining techniques provide useful business information. Each instance can be further correlated. For instance, one could determine which customer segmentation provides the highest margin or value. There are many technological solutions on the market. However, prior to investing in a technical implementation, it is important to define the type of information that would be useful in driving the corporate strategy.

The justification for investing in integrated customer information lies in the improved ability to manage customer relationships and develop appropriate products and services based on customer preference. All areas of a business that impact customers or potential customers can reap the benefits. A recent study conducted by International Data Corporation (IDC) found that the ECRM market would be worth $8.7 billion worldwide by 2003. A GartnerGroup survey of companies using a data warehouse in their direct marketing activities reported, "Companies utilizing a data warehouse to perform this task have reported an increase from a hit rate of two to three percent to a rate of 20 to 30 percent."

Imagine ... A Proven Solution

As previously mentioned, one key to successful decision support implementation is to determine the business drivers first, followed by the data required to make the right interpretations. Executives at one major banking institution knew they where losing loan customers, but they were not clear why. Some initial analytical diagnostics using OLAP tools revealed that the concentration of customer decrease was in the area of car loans. An analysis was run on customers with car loans, and it revealed that customers were paying off their loans up to six months early. Based on further decision support studies, these customers were profiled, enabling the organization to identify other customers that were at risk of being lost. Additional studies were done using external behavioral information which revealed that most customers meeting the profile usually financed their next car within a few months. In response to the knowledge they gained, the bank began a retention program that offered customers a special rate if they financed a new car within a given time frame. In order to gather the necessary customer profiling information, this effort required integrating several legacy systems and correlating the information correctly. After implementing the customer retention program, this organization was able to dramatically increase their customer loyalty, thereby driving their top-line growth.

Imagine ...
Increasing Your Top-Line Growth

Decision support, if implemented properly, can help companies market the right product to the right person at the right price in the right time frame to drive the top line. In order to remain competitive in the future, organizational focus must be directed at the top-line revenue growth. Positive bottom-line impacts will be realized through the implementation of an ECRM solution. However, the bottom line should not be where efforts are focused. Decision support technologies can yield huge returns, but often they are difficult to understand and implement. It is critical to carefully select a tool that is robust and will easily integrate into the overall ECRM solution. Many of the enabling technologies are awesome standalone tools that read flat files and provide statistical insight into the data. However, such tools require significant experience in statistical analysis. Instead, a tool should be chosen that will enable the dissection and analysis of data by an average business user. Decision support capabilities need to be made available to middle to upper management with support to strategize on how to use the information to position the company overall.

The support systems of the ECRM architecture facilitate information sharing that increases productivity and profitability, untangling the web that once blocked the transfer of information. ECRM systems should support decision support teams analyzing the following:

Predictive modeling. Determine how to market the right product to the right person at the right price and the right time (fact-based decisioning).

Customer, product and business line profitability. Identify the customer, product, organization and business line profitability bottom line.

Risk management. Risk management includes management of these risk factors: market, interest, reputation, strategic, compliance, transactional, credit, systems and operational.

Event- triggered marketing and selling. Deliver customer/product- level information to individuals in the sales process based on predefined parameters established in predictive modeling and other processes.

Scoring and decisioning. Make business decisions based on empirically derived statistical information and judgmental factors (loan to value ratio). This also includes predictive modeling.

Cost, pricing and fee analysis. Match appropriate revenues with resources and expenses used in generating those revenues.

Product development and creation. Determine what products will sell; define the distinctive product characteristics and pricing.

Target marketing. Sell the right product to the right person at the right time at the appropriate return (subjective-based decisioning).

Sales execution and tracking. Collect information pertaining to who sells what product to whom, when and where.

Credit card behavior analysis. Track, at a segment level, all account management strategies and tactics. Specific focus on utilization, delinquency (charge off and charge back) and change in balance (profitability, payment, etc.).

Forecasting/budgeting/tracking. Quantify estimates of future business activities based upon historical data, predictive models and economic forecasts within a framework of corporate strategic goals.

VRU event tracking. Enhance the VRU menu selection for event tracking, customer service, contact management and tracking. This includes the ability to capture VRU usage data by individual menu selection and provide account-level detail.

Credit card tracking and analysis. Track and validate business case assumptions at actionable levels of segmentation/detail.

Competitive and product infor-mation delivery. Deliver competitive and product information to enhance the sales process and retain customers. Included is the process of triggering and responding to customer requests, as well as delivering information to customers, customer service representatives and sales persons.

Collection and recovery. Take action to minimize loss while retaining customers and providing information to the front- end process. This includes resolving customer problems and helping customers find ways to pay.

Bankruptcy and loss analysis. Identify characteristics, events or attributes that would be present when a bankruptcy occurs on a loan or line relationship. Analyze to determine who is likely to declare bankruptcy or charge off.

Distribution channel reporting. Deliver products and services that meet customer needs and add value.

Fraud analysis. Identify characteristics, events or attributes that would be present when fraud occurs on a loan or line relationship. Apply fraud actions to new marketing opportunities.

Advertising. Communicate product offerings with disclosure and allow customers to purchase.

Periodic credit review. Analyze specific customer transactions to resolve issues. Non-modeled, detailed look at business, addressing issues of compliance, policy and regulatory issues at a customer or portfolio level.

Delivery Channel Integration

Imagine ...
Aligning Delivery Channels

The goal of each ECRM solution is to operationalize world-class best practices while minimizing risk, maximizing effective and efficient customer contact and reducing overall costs. To have a successful ECRM solution, organizations must align their service channels to their customers. Not all customers can be afforded the same level of service. Organizations must determine how to segment their customer base and delivery channels. Once segmented, they need to align customer segments with the most appropriate delivery channel or channels.

One method that helps companies identify appropriate distribution channels is to consider using customer touchpoints as a guidepost for establishing channel functionality. If the analysis identifies significant customer contact for multiple products across single distribution points (i.e., outbound sales), the process design and technology support will be designed differently than if only a single product were to be identified for a specific channel. Conversely, if a particular product were to be distributed through several channels, designs would focus heavily on consistency of message and tight integration of live information. In either event, these operational touchpoint decisions should be tightly aligned to corporate vision and strategy.

The ECRM design needs to be holistic or enterprise wide with a whole company end-to-end process view. Only then can you design and execute effectively for the benefit of customers and the organization.

Imagine ...
Access on Your Customers' Terms

Customers now demand service at their convenience. It is imperative that organizations make accessing its products and services as easy as possible. To achieve world-class results, organizations should enable their customers to have multiple access methods, such as phone, interactive voice response, Internet, ATM/kiosk, branch and mail; and the type of access should be linked to customer-segmented needs. Toll-free access numbers should be provided to all customers. Companies must exemplify a single-image perspective by providing one number to call for all inquiries; and they should have a common architecture to support multiple channels, which maximizes system components and reusability and leverages existing systems. The sales and service philosophy needs to be one of "anytime, anywhere," resulting in high customer satisfaction.

Imagine ...
One-to-One Marketing

Organizations' future success will be tightly linked to their ability to target and attract their chosen customer groups. By offering differentiated product offerings, they allow products to be targeted at specific market segments. By employing data mining, companies can analyze market data to identify target markets and develop predictive models around purchasing behaviors. Based on that information, businesses can promote brand awareness campaigns and advertising to select markets, maximizing the effectiveness of their marketing dollars. Instead of seeking new customers, organizations must leverage their existing customer relationships by maximizing cross-selling opportunities.

Imagine ...
Accurately Predicting Your Customers' Needs

Acquiring and cross-selling to targeted customers while maintaining control over costs is critical. ECRM helps organizations know their customers. By determining affinity groups, sales campaigns can be personalized and timely and pointed product messages can be sent. The complete customer profile provided by the customer information architecture will reveal cross- selling opportunities with clients whose buying patterns are already established.

The single most important focus of relationship management is to generate demand from your customer base. The key to creating this demand is personalizing service by segmenting customer type and building customer relationships. The flexibility of decision support enables professionals to provide personalized customer support, and billing options can be customized to suit customers' needs.

Imagine ...The Total Solution

Through the CIA, decision support and delivery channel integration, a total ECRM solution can be realized. When reviewing all of these strategies, it is possible to realize positive results by implementing portions of these components. However, the maximum competitive advantage and ROI are gained when the majority of the components are combined into an enterprise solution. To that point, the overall approach can be segmented to realize benefit by prioritizing and implementing those elements that realize the most initial benefit. When a segmented approach is taken, it is important to keep the overall future vision in place and insure that all of the initiatives help realize that future vision.

For example, consider a technology implementation. It is possible to separate the enterprise solution by module or business area. The total solution cannot be realized until all the modules are implemented; however, certain modules can be rolled out prior to the total system being completed. This would yield benefit to a given business unit, but the full extent of the enterprise ROI would not be realized until all modules are implemented.

Imagine ... Happy Customers

Many customers want the benefit of hassle-free, Web-based services – shopping, bill payment, investment account management, etc. Time continues to be an increasingly important consideration for many people, and those time-sensitive customers (a population that is growing exponentially) that potentially could be lost are now retained by tailoring solutions to their lifestyles. Companies with effective ECRM solutions know who their customers are and they know how to service them. Without integrated data, none of this is possible. Customer relationships remain the foundation of any organization's ability to achieve significant gains in productivity, profitability and competitiveness. Meeting ongoing customer needs is only possible when organizations can provide the right information to the right people at the right time. ECRM allows them to do just that.

ECRM will benefit the customer by providing convenient means of commerce and establishing customer loyalty and satisfaction. But organizations will realize tremendous benefit, as well. It has been estimated that U.S. companies, by the year 2002, will achieve profit improvements of $360 billion to $480 billion from the cost-side benefits of e-commerce (Giga, July 26, 1999). The key word in ECRM is "relationship." Customers and businesses are getting to know each other, meeting each other's needs, resulting in a win-win situation.

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