Analytical CRM Technology Options

Transcription

1 EXTRAPRISE Company Services Alliances Case Studies Extraprise Report Analytical CRM Technology Options Volume 5 No. 4 Executive Summary We described the evolution of the customer relationship management market in an earlier Report ( Living Through the CRM Life-Cycle, Vol. 4, No. 6.). A frequent refrain among commentators on this market has now become the unmet promise of sales, marketing and customer support systems. Many analysts point to a number of major initiatives that have failed to achieve their financial or operational objectives. A few in the press now decry CRM as a failing concept. With their focus on fad and fashion, the press are an unreliable indicator of the success or failure of any technology market. The real world has witnessed an increased focus on return on investment and successful customer relationship management applications have become the norm. As better technologies, processes and practices have come to market, success rates are increasing dramatically. Companies are becoming adept at defining financial objectives for their initiatives, managing changes in organization and process, deploying successful projects and measuring the returns. The success rate of projects is now high enough that companies are trying to find new ways to leverage their investments. Positive returns on investments in customer relationship initiatives do not end when projects are delivered successfully. There are many opportunities to use the systems in ongoing process of continuous improvement. Mountains of customer information contain many secrets than can generate more revenue and decrease operating costs. Somewhere in this mound of data are key insights into buying patterns, churn rates and profitability that can drive customer loyalty and retention. At the center of this process are customer analytics. One of the greatest challenges companies face is that analytical projects are too often confined to marketing and done as an infrequent batch process. Analyzing and disseminating information to the local sales or service representative or the customer in real time can have tremendous impact on the business. The effective use of analytical processes and software in the front office (what has become known as analytical CRM ) is rapidly becoming the next major trend in the market. Many concepts, approaches, processes, vendors and technologies constitute this area. As a result, it can be a confusing place to navigate. This Report discusses the overall analytical CRM market, defines the basic issues and approaches and highlights its leading software vendors. It analyzes three leaders in the market Siebel, SAS and NetIQ and contrasts their approaches to customer analytics. As customer relationship management becomes a common infrastructure choice for most companies, workflow and information management will continue to be at the core of most initiatives. However, without the ability to analyze customer data and use the resulting information to inform and improve business planning, CRM will deliver on only a small portion of its promise. EXTRAPRISE 2002

2 Introduction Many corporations have automated one or more of their customer channels over the last few years. A few have begun the sometimes arduous task of integrating two or more of them to create a unified view of their customers. These initiatives have had a positive impact on both revenue production and operational savings. Developing a panoramic view of customers across marketing, sales and support helps any company deliver personalized service. Providing consistency across these operational CRM systems can, in turn, help drive retention and decrease the cost of new customer acquisition. Automating points of customer interaction is useful in any company. Decreasing the cost of managing customer relationships, even more so. If these two insights were the total contribution customer relationship management solutions had to make to business, it would be a significant gain. However, this purely operational approach to CRM realizes only a fraction of the potential benefit. The concept of analytical CRM is relatively new, and as a result, inadequately defined. We view it as the conjunction of three technology trends applied to three distinct sets of problems. Analytics Analytical CRM Web CRM Suites Figure 1. Technology trends creating the analytical CRM market. Source: Extraprise, Three major technologies are competing (and in many cases, cooperating) to create a market for analytical CRM software. Traditional CRM software and suites (from Siebel Systems and others) have always had analytical modules in their catalogs. CRM vendors are increasing their investments in these products to provide more sophisticated analysis of customer data across multiple channels. Their great strength is the tight integration of these capabilities within their architecture of communications, workflow and data storage. Newer Web analytics vendors such as NetIQ, have built analytical software on an Internet infrastructure. They are particularly adept at analyzing and interpreting online traffic, marketing campaigns, site utilization and e-commerce initiatives. NetIQ, since its acquisition of WebTrends, provides a complete suite of site management and program analysis tools. Traditional analytics companies, like market leader SAS, include customer analytics as a component in an enterprise business intelligence system. The SAS products cover employees, the supply chain and enterprise business intelligence in addition to customer analytics. 2

3 The definitions of customer analytics and analytical CRM vary widely, and are used inconsistently. The traditional definition of customer analytics incorporates three distinct functions: Program analytics. Captures performance or results of a program or other set of actions. These include things like response rates for marketing campaigns, hits on a Web site or calls per hour in a contact center. This campaign resulted in a 2% response rate with 42% falling within our most profitable demographic. Descriptive analytics. Identifies the state of one or more business metrics. These are typically a higher order analysis, or aggregation of data across customer channels and often include data from the manufacturing and supply chain. Examples include such things as sales pipeline, retention rates, customer satisfaction, close rates, revenue, profitability, etc. Banks with less than $10 billion in assets yielded 60% of our revenue and 75% of our profit. Prescriptive analytics. Defines a new business rule or action designed to improve a business metric. These close the loop to identify major triggers or actions that should be taken based on the analysis of operational and descriptive data. Have the sales representative call individual brokerage accounts that show no activity for 270 days since they have a 60% chance of being closed. Most commercial analytical software provides some capabilities in the first two areas. Whether the products are CRM-centric, like those from Siebel Systems, Web-centric capabilities in NetIQ or enterprise analytical software from SAS, all provide features for measuring performance and monitoring business metrics. From a program perspective, many analytical tasks and business metrics are common from company to company, and easily supported in a commercial software product. A company that manages a marketing program of 1,000 s a week and a corporation that does a million differ primarily in the scale of data to be captured, analyzed and processed. The same can be said of direct mail, telemarketing, Web-based marketing and advertising. Processes and workflow may differ significantly, but basic analytical tools are easily adaptable to handle these kinds of problems for both large and small companies. Program analysis often measures a single customer channel, marketing program or other metric to determine the results of a discrete event. Integration of other data is typically limited to time-series data on past performance. This kind of analysis is critical to determining future investments in marketing programs, contact center staffing or sales force effectiveness. On their own, however, program measurements provide limited insight into the state of customer relationships. Analyzing data to identify key business metrics or trends (what is referred to here as descriptive analysis ) is typically done at pre-determined intervals daily, monthly, quarterly and yearly. The goal is to get an overall view of the company s business and the key operating metrics that determine success. Descriptive analytics often involve data that span customer channels and may include information from the supply side of the value chain. The focus is typically on the aggregation of data from multiple sources and their consolidation into a critical metric revenue, pipeline, satisfaction, average sales price, cost of sales, etc. This kind of analysis can provide useful input into critical business decisions. To do so, it must also integrate data from the back office, as we have discussed elsewhere ( Extending Customer Relationships to the Back-Office with Siebel Software, Vol. 5., No. 1.). Automatically calculating a revenue forecast or sales pipeline that spans all sales channels provides a real benefit. However, if the company has the capacity to manufacture a smaller amount of product or the supply chain can not meet its delivery commitments, customer satisfaction will decline. 3

4 Prescriptive analytics identify actions that can improve business metrics or triggers that define such actions. They involve a complex interpretation of the many sources of data to provide input into predicting customer actions. Most existing analytical software provides some tools to perform prescriptive analysis. Given the differences in companies, even in the same industry, there are few turnkey solutions. There are some, especially in financial services and telecommunications, which analyze customer data to identify the key metrics that determine customer loyalty. In the telecommunications industry, for example, Amdocs Ltd. provides advanced data mining capabilities for analyzing and understanding of the causes of customer churn. The approach includes predictive modeling, analysis and forecasting techniques. The software analyzes data such as how many calls each customer initiates and receives, what percentage are long-distance, the duration of the calls and many other variables. It then creates a statistical model that gives subscribers a score defining their likelihood of dropping service. This provides information to create unique incentives to help keep individual customers as well as develop broad-based retention campaigns. The software is currently in use at many of the major carriers in the U.S. and Europe. Trends in Analytical CRM As the market for analytical CRM evolves, new demands are being placed on customer analytics. Historically, these kinds of initiatives were locked in the marketing or finance departments or in individual customer channels. They were performed infrequently as batch processes and distribution within the company was very limited. Aggregating information across channels and integrating it with manufacturing, order and supply data was, and still is, usually a manual process. The speed, scope and complexity of global businesses demand a different approach to customer analytics. Four major trends, outlined below, are fundamentally changing they way companies analyze data to create actionable information. Aggregating Multi-Channel Data Many of our Reports have focused on the importance of rationalizing multiple customer channels (see for example, Only the Ubiquitous will Survive, Vol. 3, No. 5). Developing a panoramic customer view is only possible through an analytical approach that spans these channels. Since marketing, sales and support channels have been (and in many cases still are) separate, developing a complete customer profile can be difficult. This has driven the traditional analytical approach of extracting structured (and mostly alphanumeric) data from multiple databases, normalizing and transforming multiple data sets into a common format, and loading the results into a data mart or data warehouse. This ETL process is laborious and has traditionally been an infrequent batch process. When unstructured data from the Web (HTML, tracking logs, click streams, etc.) are added to the mix, any difficulties are compounded. Companies that aggregate multi-channel data into a shared physical or virtual customer repository typically perform the ETL process once as part of a batch load and then update customer data in real-time. As a result, this approach can leverage the costly investment in ETL over time. This approach also can add a real-time dimension to customer analytics. 4

5 Integrating Back-Office Information As we discussed in an earlier Extraprise Report ( Extending Customer Relationships to the Back- Office with Siebel Software ), it is often difficult to get a complete picture of the customer without considering information outside the basic channels of interaction. A financial services customer, for example, may inform his broker about his investment plans (or aspirations), but information in the back office indicates what the customer has actually done. The same is true for product companies that have significant manufacturing operations and supply chains. Developing a comprehensive view of the customer is only possible if order history, margins and contractual terms are available in the analytical process. Many companies now integrate basic ERP and financial information into their customer analytics processes. The trend is to link more and more databases, applications and repositories to the mix. Each piece of data about the customer helps create a rounded picture that can increase loyalty, share of wallet and trend analysis. Adding the Real-time Dimension Analytical processes are often performed regularly, but infrequently. Information in disparate databases is consolidated on a weekly, monthly, quarterly or yearly schedule and analytical processes executed on the same schedule. This has real value, but misses countless opportunities to influence customer behavior or improve interaction processes through an immediate feedback loop. Many companies have become adept at providing near real-time feedback in their contact centers or on their Web site. Similarly, they gather information regularly from their sales force, lead generation programs and service representatives. The real-time dimension, however, is often lacking in two important ways. First, data arrives at the data warehouse or customer repository on different schedules and as a result analytical processes tend to be either run frequently with incomplete data, or infrequently with comprehensive data. Analyzing this data frequently and with as much relevant information as possible is often as challenge. Second, even if analytical processes are executed frequently the results are often closely held and not shared widely in the customer-facing groups. This is discussed in greater depth below. Analytics Everywhere While analysis is frequently done in batch mode on an infrequent basis, the results are often more static. Typically, customer data that has been through an analytics process never gets out of the sponsoring organization usually the marketing or finance groups. There are two major issues here. First, analytical processes tend to be centralized in the marketing or finance organizations. Sales, service and contact center organizations often provide basic operational analysis calls per agent, revenue per sales representative, discounts per channel but these are typically focused on the efficiency of the channel, not the benefits to customers. Second, data that has been produced by the marketing and finance organizations are often isolated from the decision-making processes in other functional groups. Customer intelligence is shared at sales meetings or on fiscal boundaries, but not integrated into daily business. As the following diagram illustrates, both customer data and analytical processes should be available throughout the organization. 5

6 Business Units Customer Analytics Service Sales Figure 2. Customer data and analytical processes should be distributed throughout the company. Source: Extraprise, Three Approaches to Analytical CRM The challenges outlined above, and others, have created a tremendously innovative phase in analytical software. The following sections investigate three different approaches to the issues defined above with the analytical capabilities from looking at Siebel, NetIQ and SAS. Siebel is representative of those companies that have added customer analytics to their existing suite of operational CRM software. The Siebel strategy, described below, has been evolutionary. Siebel originally provided software to automate individual customer channels and over the last several years added analytical software tailored to the requirements of each channel. With the advent of Siebel 7 (the company s first completely Web-based suite) operational and analytical CRM software are tightly integrated. With its acquisition of WebTrends in 2001, NetIQ added Web analytics to its existing suite of systems management and security software. The NetIQ suite is indicative of current state of the art of Web analytics. The company s WebTrends products provide both program and descriptive analytics for any company s Internet channel. The NetIQ suite monitors Web site performance as well as customer interactions with the site. The company s software creates an interesting distinction between products that are the best at an individual customer channel (NetIQ) and those with the ability to aggregate data across many channels (Siebel and SAS). SAS is the industry s leading vendor of enterprise analytical software. Their suite of products spans not just the demand side of the corporate value chain, but also the supply chain and company operations. As a result, the SAS Intelligence Architecture integrates customer intelligence with information across the enterprise. This can create a complete picture not just of customers, but of the company s overall business. The final section of this Report takes a closer look at each of these vendors. The Siebel Approach In less than a decade, Siebel Systems has become the market leader in customer relationship management software that spans marketing, sales, support and e-business applications. Siebel has always had an extensive product suite in operational CRM: sales force automation, contact centers and field service. In the last two years, the company has increased its investment in analytical CRM with the introduction of Siebel Analytics. 6

7 Siebel Analytics is an integrated business and customer analytics solution. It offers prebuilt topical and industry specific analytic applications that deliver best practice analyses across the ebusiness value chain. The suite also includes a configurable data warehousing solution that transforms data from Siebel and other internal and external sources into actionable intelligence. It also provides an open and scaleable analytic platform to deliver insight from multiple data sources across the enterprise. These analytic applications are tightly integrated with Siebel operational applications, and enable organizations to quickly provide users key business metrics in ready to use reports, tailored to their business function and industry. The Siebel Analytic Applications family includes: Siebel Sales Analytics Siebel Marketing Analytics Siebel Service Analytics Siebel Partner Analytics Siebel Executive Analytics Siebel Answers Siebel Analytics focuses on delivering the right intelligence to find the right person at the right time. Siebel Analytics uses agent technology to proactively monitor and deliver business intelligence to the right person on the device of their choice including , pager, PDA, RIM or mobile phone. The information delivered and capabilities provided are formatted for the recipient s device. For instance, a sales representative can be notified if an urgent service request is submitted for one of their top 10 deals for the quarter. In this way, they can head off potential problems that could disrupt the deal. The Siebel Analytics architecture is scalable to thousands of users, large data volumes and multiple heterogeneous data sources. It provides efficient, simultaneous access of information spanning large and complex data warehouses and operational sources both within and external to Siebel in relational, host and XML-based systems. Siebel Analytics also provides an open and platform that allows organizations to leverage existing investments in data warehouses and business intelligence tools. The following chart highlights the capabilities of the Siebel Analytics suite. In this, and the following charts, a indicates market leading capabilities, a means some capabilities and a means limited capabilities. Operational CRM Program Analytics Descriptive Analytics Prescriptive Analytics Marketing Sales Support Contact Center Internet Figure 3. Capability scorecard for Siebel Systems. Source: Extraprise,

8 As the chart indicates, Siebel s strength lies in its close linkage of operational and analytical capabilities. Operational data can come directly from a Siebel repository or be aggregated with other sources of information to do descriptive analytics. Siebel provides basic tools for prescriptive analytics. With their increased focus on market segments and named accounts, the company may begin to provide custom analytical capabilities in its vertical applications (finance, communications, pharmaceuticals, etc.) The NetIQ Approach NetIQ (formerly Mission Critical Software) is leading provider of software solutions for systems management, security and Web analytics. With its acquisition of WebTrends in 2001, NetIQ established a leadership position in analytical software for Web and e-commerce sites. NetIQ's Web Analytics and Management solution delivers key insight into every element of Web visitor activity as well as improved site performance and availability. NetIQ s products span Marketing analysis, e-commerce analysis, advanced visitor analysis,web traffic reporting, Web server monitoring and diagnostics and firewall and proxy server reporting. NetIQ has established a leadership position in the Web analytics market with products like: WebTrends Intelligence Suite is focused on Web analytics, based on customer activity on the Web.The suite helps employees throughout a company to access information over the Web by analyzing user experience, marketing campaign success, e-commerce performance and customer self-service effectiveness WebTrends Reporting Center enables company management to define and distribute predefined reports on Web traffic, visitor activity, campaign effectiveness, site performance and shopping behavior WebTrends Live is a hosted Web analytics service designed for organizations that outsource their Web sites or analytical functions. The service provides Web traffic analysis, campaign management and revenue tracking in real time WebTrends Analysis Suite provides site management and log analysis tools designed for small businesses. The suite includes modules for traffic, link and proxy server analysis along with problem alerts and recovery for Web servers AppManager for Web Servers provides modules for managing, monitoring and reporting on the status, performance and availability of Web servers WebTrends Firewall Reporting offer firewall and proxy server traffic reports that identify suspicious activity and report on the state of the network In short, there are many Web site analysis tools, click stream analysis tools and site performance monitors. NetIQ is one of the few to integrate systems management and analysis capabilities. As a result, its approach to analytics combines three functions: performance statistics on site usage, statistics on customer behavior and preferences and descriptive analytics for the Web and e-commerce channel. NetIQ provides best-of-breed products in this area, with little or no capabilities for other channels. Marketing Sales Support Contact Center Internet Operational CRM Program Analytics Descriptive Analytics Prescriptive Analytics Figure 4. Capability scorecard for NetIQ. Source: Extraprise,

9 Clearly, NetIQ has an almost exclusive focus on analytical software for the Web channel. The capabilities are deep, but narrowly focused on this important, but single channel. With this focus, however, they have the opportunity to increase their prescriptive analytics capabilities to help identify buying patterns, customer preferences and churn potential. The SAS Approach SAS is one of the world s largest software companies and has established a leadership position in enterprise analytics. SAS software spans the enterprise with modules for both the demand and supply sides of the corporate value chain. SAS Customer Relationship Management Solutions are designed to extract and deliver the information needed to implement smarter customer strategies and maximize customer profitability. The SAS analytical CRM technologies are built on the company s overall Intelligence Architecture, and combine proven methodologies and services to enable companies to understand customers across all channels including the Web. The products help identify problems and proactively define solutions. By providing customer intelligence, SAS CRM solutions support a complete, closed-loop marketing process. They also support a comprehensive, closed-loop CRM process: Plan, Target, Act and Learn. The SAS CRM solutions include: SAS marketing automation Customized analytical CRM Customer retention for telecommunications Credit scoring for financial services IntelliVisor for retail SAS solutions deliver the functionality to develop a focused customer strategy, to target high value prospects, communicate with those prospects and learn from each customer interaction. SAS Marketing Automation is one of the few products to combine warehousing and data mining with campaign management. The products help plan and manage every aspect of multi-level, multi-channel campaigns from the desktop. SAS Marketing Automation enables companies to: Measure the return on each campaign, learn from the results and improve the next one Target communications at either individuals or households, and apply intelligent rules to identify the right person in each household to receive your offer Schedule key campaign activities and automatically initiate regular communications at the frequency of your choice Coordinate and optimize inbound and outbound communications over multiple channels including traditional and digital media Build predictive models and combine them with other database criteria for more accurate results Automatically update models for any changes to customer records, ensuring that predicted outcomes always reflect the latest information Run analysis, predictive modeling and customer communication activity from a single environment 9

10 In addition, SAS Marketing Automation provides an open software architecture that includes integration with front- and back-office applications, enterprise scalability, and implementation tailored specifically to company goals and requirements. Operational CRM Program Analytics Descriptive Analytics Prescriptive Analytics Marketing Sales Support Contact Center Internet Figure 5. Capability scorecard for SAS. Source: Extraprise, As this chart illustrates, SAS is particularly strong in analytical software that spans each customer channel. Its strength lies not just in developing this data, but combining it with other sources throughout the company to provide a rounded picture of operations. SAS Marketing Automation (described above) combines operational and analytical capabilities for the marketing channel, but the company is not focused on other operational issues. The Bottom Line Analytical CRM processes and software products have the potential to significantly increase the return on investments in customer relationship management initiatives. In our experience, when customer relationship management initiatives are perceived as failing, analytics provide the missing functionality that can both identify and quantify returns. In the early phase of the market, companies played fast and loose with definitions and descriptions of functionality. Three classes of vendors traditional CRM suites, Web analytics suites and expanded enterprise analytics software are bringing distinct approaches to the problem. All three kinds of software have different design centers, different functionality and solve an interlocking set of problems. There is no single software suite that provides all of the functionality to perform every program, descriptive and prescriptive analysis. Indeed, prescriptive analytics often requires a level of market-specific understanding that can only be provided by specialized vendors. Fortunately, while there is fierce competition among many vendors, there is also a great degree of cooperation. The Siebel Alliance program, for example, includes NetIQ and SAS as members. Our view is that best-of-breed products (such as NetIQ) that are strong only with a single channel or function have their place in the enterprise. Most of the multi-channel analytics products are note best-of-breed at every channel. However, the list of enterprise analysis suites while limited, is growing rapidly and today includes Siebel and SAS. Companies making strategic investments in analytical CRM will be well served to select these companies as their foundation. 10

11 Further Reading Maximizing the Return on Siebel Software, Extraprise Report, Vol. 5. No. 1. Living Through the CRM Life-Cycle, Extraprise Report, Vol. 4, No. 6. Only the Ubiquitous Will Survive, Extraprise Report, Vol. 3, No

12 Further Information Extraprise is an international technology services company specializing in customer relationship management. We combine industry best practices, proven technologies and rigorous processes to enable our clients to become more effective at customer acquisition, retention and service. Extraprise solutions span analytics, direct sales, marketing, field service, customer support, e-commerce, contact centers and other points of interaction. For further information on Extraprise services or the material featured in this Report, please contact David Winterhalter at or Extraprise Reports may not be duplicated, reproduced, stored in a retrieval system, or retransmitted without the express permission of Extraprise. The information contained in this publication is believed to be reliable, but not necessarily complete and its accuracy cannot be guaranteed. This Report originated in Boston. All content Copyright 2002, Extraprise Group, Inc. All trademarks are property of their respective holders. Boston London Amsterdam Chicago Dallas New York San Francisco tel info@extraprise.com tel +44 (0) infouk@extraprise.com