A SAS White Paper: Implementing the Customer Relationship Management Foundation Analytical CRM

A SAS White Paper: Implementing the Customer Relationship Management Foundation Analytical CRM

Table of Contents Introduction.......................................................................... 1 A Changing World.................................................................... 1 What is Customer Relationship Management?............................................ 1 The Technologies Behind Analytical CRM................................................ 3 Data Warehousing................................................................. 3 Data Mining and OLAP............................................................. 4 Decision Support and Reporting Tools................................................ 4 Summary............................................................................ 4

Introduction In today s highly competitive business environment, companies must continuously learn from interactions with customers and respond to the knowledge gained from those interactions. A shift from product-driven business strategies to customer-driven business strategies has become essential. This white paper discusses the technological and business issues involved in implementing progressive techniques in managing customer relationships. It details the practice of using advanced data management and analysis techniques to transform a large amount of carefully chosen customer data into reliable information to support strategic and tactical business decisions. A Changing World The business world is changing. Widespread deregulation, diversification, and globalization have stimulated a dramatic rise in competition between companies. And customers themselves are changing natural customer loyalty is a thing of the past. The more consumers understand the marketplace, the more they want to be recognized and understood as individuals, and they will give their business to the companies that do the best job of understanding and responding to their needs. In response to these changes, competitive companies are abandoning ineffective business philosophies of the past and adopting new and innovative ways to maintain customer loyalty and profitability. Rather than focus on internal considerations like reducing costs and streamlining operational systems, companies are seeking to maximize profitability by focusing on the many facets of the customer relationship. Guided by the concept of customer profitability, an economic view of customers that measures how profitable a customer is to a company rather than measuring the profitability of product lines, companies are adopting a customer-centric strategy, stressing the importance of optimizing the value of each customer relationship. In this significant shift from product-driven strategies to customerdriven strategies, customer retention is becoming more and more important, in some cases eclipsing customer acquisition as the primary focus. Reacting to increasing acquisition costs and acknowledging the value of analyzing customer profitability, many enlightened companies have realized that the key to success is this: know everything you can about the customer. The goal is to define the profitability of customers based on customer lifetime value (LTV) a measurement based on the fact that the past value of a customer, though useful information, does not itself determine future profitability. It is what that customer is going to do and spend in the future that will contribute to the bottom line for the next year and beyond. This enables companies to not only manage profitable customers in a way that optimizes the customers LTV, but also manage nonprofitable customers. A notion of future or lifetime value 1 is essential when prioritizing and focusing resources to ensure they are directed where they will benefit most. One key approach in transitioning to a customer-centric strategy is to gather enough information to group customers into categories and tailor your interaction to each group. Segmentation is an important starting point for understanding individual customers. Through analytical segmentation techniques, customer information such as demographic data and lifestyle information can be combined with historical customer information to help identify differences in behavior among various groups, or segments, of customers. For key segments, these can continually be filtered and differentiated to become segments of one, with the goal of creating unique, individual customer profiles. So why do many companies settle for a fuzzy definition of who their customers are, when a detailed profile of individual customers allows for vastly improved opportunities to meet the customers needs? The answer for some is an unwillingness to tackle what may appear to be an incredibly complex data management and analysis task. After all, if you gather 100 pieces of information about each of 1000 customers, you then have to store, manage, retrieve, and analyze 100,000 pieces of data in real-world, operational scenarios. Even in a small-scale example like that, the numbers can be a little daunting. This is where data warehousing technology comes into play it s the foundation on which successful CRM strategies are built. What is Customer Relationship Management? Customer Relationship Management (CRM) is a process by which a company maximizes customer information in an effort to increase loyalty and retain customers business over their lifetimes. The primary goals of CRM are to build long term and profitable relationships with chosen customers get closer to those customers at every point of contact maximize your company s share of the customer s wallet. CRM combines a progressive approach to gathering customer data with advanced database and decision support technologies that help transform that data into business knowledge. By maximizing the use of customer information, companies can better monitor and understand customer behavior. In response to newly gained customer intelligence, companies retool their frontand back-office systems to ensure that they are providing what the customer really desires. CRM calls for increasing customer share that is, retaining customers and selling them an expanded set of products that has been tailored to their wants. This requires amassing data from every customer contact point, analyzing that data to find ways to better serve customers, and then effecting changes based on the new business knowledge.

Operational CRM Call Centers Campaign Management Customer Service Quality of Service Call Behaviour Behavioural Modeling SAS Analytical CRM Monitoring Integrated customer view Campaign Customer Valuation Segmentation and Profiling Risk Sales Force Automation Web Profitability E-Commerce Web Sales Needs ERP Other Figure 1. CRM Overview Effective CRM depends on data data about who customers are, their preferences, their behavior, their status with the company (both past and current), their purchase history, and their classification, based on demographic and psychographic information. Much of the data-intensive work involved in CRM initiatives begins with back-office processes. A repository of customer data must be created within a single, customer-centric data warehouse. Then, this integrated customer view can feed a wide variety of data analysis processes, from call behavior analysis and needs analysis to segmentation and campaign analysis. In addition to the application of state-of-the-art data management and analysis techniques, CRM implementation often involves a significant change in a company s business strategy changes that require not only retooling back-office processes, but in many cases, reorganization of the business functions closer to the customer. Operational processes designed to support a product-driven strategy become ineffective when the company s marketing focus shifts to a customer-centric strategy. Companies must adjust those processes to allow all information coming from customer contact points and other customer related information to be consolidated and analyzed. Then, newly acquired customer intelligence is translated into real-world changes in how the company manages customer relationships. From implementing Web-based solutions for communicating with customers to initiating automated campaign management strategies, effective and flexible front-office processes are vital in any 2 CRM implementation, serving as both the initial conduit for customer data, and then, after the data is analyzed, affecting changes based on the resulting customer intelligence. As we explore the central concept of CRM the notion that customer information fuels analytical processes that in turn yield business knowledge used to improve company processes a clear division begins to emerge between the primary elements of a CRM initiative. Two halves of CRM, one focusing on operational initiatives and the other focusing on analytical initiatives, can be further defined as follows: Operational CRM, which, building on the notion that customer management plays an important role in a company s success, calls for the automation of horizontally integrated business processes involving customer contact points via multiple, interconnected delivery channels and integration between front- and back-office operations. Analytical CRM, which involves the implementation of the advanced data management and analysis tools that make progressive customer relationship management possible. This is the analysis of data created by Operational CRM for the purpose of business performance management. As this paper focuses primarily on Analytical CRM, let s take a closer look at the advanced analytical technologies that contribute to an effective CRM initiative.

The Technologies behind Analytical CRM The technologies most commonly involved in the analytical portion of a CRM initiative are data warehousing data mining online analytical processing (OLAP) advanced decision support and reporting tools. Before we examine each of these technologies, let s first examine the general flow of data and information in a CRM initiative to get a feeling for where each technology comes into play. Initially, operational data (records of business transactions that have occurred between the company and the customer) is gathered from customer contact points. This operational data, along with legacy customer data and market data from external sources, is compiled into a data warehouse. At this point, OLAP tools and data mining techniques are used to extract relevant patterns or trends in the data. Finally, utilizing sophisticated reporting tools such as Web-enabled dynamic reporting systems and executive information systems, the business knowledge gleaned from this process is used to improve operational effectiveness at customer contact points, and the knowledge is also deployed to back-office business activities for use in tactical decision making. Data Warehousing A data warehouse is an implementation of an informational database used to store shareable data that originates in an operational database-of-record and in external market data sources. It is typically a subject database that allows users to tap into a company s vast store of operational data to track and respond to business trends and facilitate forecasting and planning efforts. Typically, before a data warehouse can be created, a data cleansing phase must occur. Data cleansing is the process of manipulating the data extracted from operational systems to make it usable by the data warehouse. When loading data from existing operational systems, it is likely that few if any of the operational systems will contain data in a format that is compatible with the data model developed for the warehouse. For example, a product number may be stored as a numeric field in one system, while a second system appends an alphabetic suffix to the number for reporting purposes. Data cleansing is an extremely important part of creating the integrated customer view, often exposing unexpected inconsistencies in operation data from disparate sources. It is during the creation of the data warehouse that an IT department adds metadata, or data about the data. Metadata includes descriptions of what kind of information is stored where, how it is encoded, how it is related to other information, Purchased Demographic Data Legacy Systems Campaign Management M E T A D A T A Standard Reporting Merge - Clean - Enrich Customer Data Warehouse Customer Profitability ERP Systems Customer Segmentation Front Office Automation Figure 2. A Closed-Loop CRM Architecture 3 Closed Loop Analytical Applications

where it comes from, and how it is related to your business. The disparity in content and format of the data, coupled with the sheer volume of information, requires the creation of metadata that will tell users and analysts important details about the source, format, and purpose of the data. For business users, metadata is essential to the management, organization, and exploitation of the data that feeds a CRM initiative. 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 your repository of customer information. Given the complexity of the relationship between operational systems and decision support systems, metadata should document all data elements from data source to data exploitation as completely as possible. SAS software is widely recognized as the de facto standard data warehousing technology, highlighted by its ability to capture and integrate data from a large number of sources and its support for a broad range of hardware platforms and operating systems. SAS software allows you to access, manage, and organize all relevant customer data from operational systems, legacy systems, value-added data providers, and market research sources both internal and external. Data Mining and OLAP Data mining involves specialized software tools that allow users to sift through large amounts of data to uncover data content relationships and build models to predict customer behavior. For many years, statisticians have manually mined databases looking for statistically significant patterns. Now, data mining uses well-established statistical and machine learning techniques to build models that predict customer behavior. Predictive modeling can segment and profile customers, and this information can be integrated into the data warehouse and with other marketing oriented operational applications. SAS Institute s data mining offerings, including the recently released Enterprise Miner software, are acknowledged market-leading technologies that enable analysts to model virtually any customer activity and find patterns relevant to current business problems. This technology enables you to define a set of comprehensive and consistent customer profiles to better understand customer needs, behavior, and profitability and construct predictive models of customer behavior to fuel target marketing and campaign management activities. OLAP (Online Analytical Processing), also known as multidimensional data analysis, offers advanced capabilities in querying and analyzing the information in a data warehouse. In some CRM initiatives, OLAP plays a major role in the secondary analysis that takes place after initial customer 4 segmentation has occurred. For example, in CRM-based campaign management systems, OLAP is an excellent tool for analyzing the success or failure of the promotional campaigns. A pioneer in OLAP technology, SAS Institute offers the SAS/MDDB server, a powerful tool that allows analysts to work with multidimensional views of the data and surface information that will aid in decision support processes. Decision Support and Reporting Tools Web-enabled reporting tools and executive information systems are used to deploy the business information that has been discovered. This enhanced customer knowledge is distributed to executive decision-makers as well as to the operational customer contact points. Applications equipped with some of the same sophisticated modeling routines developed in the data mining phase are applied to individual contacts in real time. In some companies, the World Wide Web and Internet/intranet technology are critical components in managing customer relationships. They not only use Web technology to share business knowledge from the CRM process, giving every employee a better understanding of customers at every point of contact, but also to collect additional information about customers and prospects, thus fueling further CRM activities. You can create advanced information delivery systems like Web-enabled OLAP, query, and reporting tools using SAS/IntrNet software. Additionally, SAS/EIS software viewers and our Web-based data warehouse exploitation tool called MetaSpace Explorer software are also powerful reporting tools that help put fresh, valuable business intelligence into the hands of those who need it. Summary As corporate competition continues to increase due to external market factors and a market-savvy customer-base, companies must discard their business philosophies of the past and adopt new and innovative ways to maintain customer loyalty and profitability. Building loyalty among customers involves understanding the various ways that they are different and using that knowledge to tailor appropriate behaviors towards those customers. Companies must adopt a customer-centric strategy that stresses the concept that success over time comes from customer loyalty that long-term profitability lies in fostering unique lifetime relationships with small numbers of carefully chosen customers. Companies must continuously learn from interactions with each individual customer and be prepared to dynamically respond to information and knowledge gained from those interactions. What makes this possible is CRM, a method of processing a large amount of carefully chosen customer data in order to obtain reliable information to support strategic and tactical business decisions. Utilizing advanced data warehousing, data mining, OLAP, and decision support technologies, companies can create an

integrated customer view and extract relevant patterns or trends in the data. Business decisions based on complete and reliable information about your customers are very difficult for your competitors to replicate and represent a key and sustainable competitive advantage. SAS Institute offers the only software and services solution that spans the entire decision support process for managing customer relationships. SAS software is recognized as the de facto standard data warehousing technology, providing the capability to capture and integrate data from a large number of sources on a broad range of hardware platforms and operating systems. Our data mining offerings are acknowledged marketleading technologies that enable analysts to model virtually any customer activity and find patterns relevant to current business problems. As a pioneer in OLAP technology, SAS Institute offers powerful tools that allows analysts to work with multidimensional views of the data and surface information that will aid in decision support processes. And finally, SAS software enables you to create advanced information delivery systems like Web-enabled OLAP, query, and reporting tools so you can deploy the valuable business intelligence reaped from your CRM implementation into the hands of those who need it. 5

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