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
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
Web
Analytical
CRM
CRM
Suites
Figure 1. Technology trends creating the “analytical CRM” market. Source: Extraprise, 2002.
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
EXTRAPRISE REPORT
2002
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 email 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 emails 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.
EXTRAPRISE REPORT
2002
3
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
EXTRAPRISE REPORT
2002
Integrating Back-Office Information
As we discussed in an earlier Extraprise Report (“Extending Customer Relationships to the BackOffice 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.
EXTRAPRISE REPORT
2002
5
Business
Units
Customer
Analytics
Service
Sales
Figure 2. Customer data and analytical processes should be distributed throughout the company. Source: Extraprise, 2002.
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
EXTRAPRISE REPORT
2002
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 email, 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.
Marketing
Sales
Support
Contact Center
Internet
Operational CRM
Program Analytics
Descriptive Analytics
Prescriptive Analytics
Figure 3. Capability scorecard for Siebel Systems. Source: Extraprise, 2002.
EXTRAPRISE REPORT
2002
7
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, 2002.
8
EXTRAPRISE REPORT
2002
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
EXTRAPRISE REPORT
2002
9
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.
Marketing
Sales
Support
Contact Center
Internet
Operational CRM
Program Analytics
Descriptive Analytics
Prescriptive Analytics
Figure 5. Capability scorecard for SAS. Source: Extraprise, 2002.
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
EXTRAPRISE REPORT
2002
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. 5.
EXTRAPRISE REPORT
2002
11
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 617-880-4017 or david.winterhalter@extraprise.com.
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 +1 617 880 4000
tel +44 (0)20 7758 7500
email info@extraprise.com
email infoUK@extraprise.com
www.extraprise.com