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Real-time CRM analytics: the future of BI?

This article appears in the issue February 2002 (100 Companies) [Volume 11, Issue 2]

By Helena Schwenk

Tough market conditions are forcing companies to examine all sources of information that can help improve bottom-line performance. In this context, the idea of real-time CRM analytics is set to spur the next phase of growth in the business intelligence (BI) market. It brings together the need to respond quickly to changing market conditions and to exploit customer data to maximum effect. This article examines the component elements, potential benefits and real challenges involved in real-time CRM analytics.

Why now?

The Web has changed the dynamics of decision making. In a faster, more complex and competitive environment, organizations are being forced to make decisions in “Web time.” It is becoming increasingly important for them to react to a customer request with increased speed while still maintaining the integrity of the decision-making process. CRM analytics can provide intelligence in a more timely and personalized fashion to support those customer interactions in real time.

Furthermore, an average customer typically has a multitude of contact points with any one organization; the Web, a retail store and a call center are just a few examples. The challenge facing companies is to build an integrated view of the customer, to understand how each touchpoint relates to the others and to use the resulting customer intelligence to better understand and service that customer.

Likewise, user organizations, which face difficult market conditions at the moment, are focusing on the value they can get from customer data. Many companies that have invested in CRM systems in recent years are now under pressure to see real value from them. Knowing which customers are profitable or unprofitable is valuable, but it is also important for organizations to know what kind of customers they are. That is particularly true in highly competitive markets such as financial services, retailing and telecommunications, where companies are increasingly using CRM analytics to understand customer preferences, buying patterns and trends, and to identify those with high value.

Those drivers have contributed significantly to the growth of real-time CRM analytics and are responsible for bringing the worlds of BI and CRM even closer together.

The major drivers contributing to the growth in real-time CRM analytics

  • The high priority of integrating the customer viewpoint across all touch points;

  • A need to respond to customer demands in "Web time";

  • A requirement to derive more value from CRM investment;

How does real time CRM analytics work?

If a banking customer applies for a personal loan over the Web, the bank needs to be able to authorize or reject that initial loan application within a number of mouse clicks. Or if a valuable credit card customer uses his or her card to pay a hotel bill that exceeds the credit limit, the issuing company needs to be able to authorize payment while the transaction is being progressed by hotel staff. Both examples involve the application of real-time CRM analytics, and can go a long way to improve the satisfaction and loyalty ratings of the customer.

From a high-level viewpoint, real-time CRM analytics works by passing an incoming customer request and processing it against a set of predefined business rules and/or data mining algorithms to determine the best course of action to take or recommendations to make. The resulting answer, which is typically derived from the analysis of real-time operational data and summarized historical decision support data, is then passed back via the necessary channel to front-office staff or the customer in real time.

Real-time analytics and CRM analytics

The terms real-time analytics and CRM analytics are sometimes used interchangeably; however, there are overlaps and subtle differences between both terms, and it is important to distinguish between them. Real-time analytics is used to describe the technologies that facilitate automated decision making. CRM analytics, on the other hand, refers to the branch of BI that analyzes data to provide meaningful insight to better understand the customer. Like other BI solutions it involves using a variety of tools and applications, but in this case, analysis is centered on customer data. The overlap of the two areas comes from providing CRM analytics in real time.

Web analytics—first-generation real-time analytics

One area in which real-time analytics has been applied more conclusively is through the analysis, interpretation and feedback of Web data used to enhance and personalize options offered to customers. Web analytic companies such as WebTrends and Net Genesis, ( now owned by SPSS), provide solutions that can interpret Web traffic data to provide more insight to online customer behavior. The resulting analysis is then used to highlight areas for improvement, such as Web site design, clickstream navigation and real-time personalization of Web content.

Real-time analytics—where the online and offline worlds merge

The attractions of providing decision support information in real time are not hard to see; the possibilities are almost endless. Leading adopters of CRM analytics such as those in the telecommunication, financial and retail industry have already started to apply the concept of real-time analytics.

However, the outstanding challenge for those organizations is more often than not one of data integration. The basis for any CRM-based initiative is the single integrated view of the customer across all lines of business and customer contact points used by an organization. It’s a headache and costly problem for companies. The challenge comes not only in solving the complexities of data integration but also in understanding the relationship and interdependencies between each different communication channel used within an organization.

For instance, from a competitive standpoint, it is extremely beneficial for a retail company to understand a customer's in-store buying habits, so that they can service them in the most effective way when the same customer purchases online. However, the situation becomes more complex when shortly afterward, the customer telephones to amend an existing order. Having a complete view of the customer’s buying patterns and purchases makes it easier to offer the most appropriate level of service to them. The advantage of having such a holistic view of the customer is very appealing to organizations. So while there have been some limited applications of real-time analytics in recent years, we are now seeing a more concerted effort by a wider range of organizations to develop such capabilities. There has also been a corresponding development of the core BI technologies needed to make real-time analytics a reality—for example, Web delivery platforms, alerts, analytical tools, packaged analytical applications and back-end data integration.

What are the vendors offering?

Several constituent technology components contribute to a real-time analytics solution. Often a CRM solution is combined with infrastructure components such as enterprise application integration (EAI) technology and data warehouses, as well as tools for OLAP and data mining that can analyze real-time data. A simplistic view of the key constituent technology components inherent in a real-time CRM analytics solution are illustrated in figure 2. BI and CRM vendors have responded to the challenge of real-time analytics in a number of ways.

ETL vendors

ETL vendors such as Informatica, ETI and Systemfabrik claim to provide solutions for real-time analytics. Many of those vendors position their products as the central data integration hub and platform for extracting, transforming data from real-time data feeds and loading it continuously into a data warehouse. There have been some significant developments in that area. Specifically, vendors have started to provide support for continuous data pipelining, data replication, “always-on sessions” and parallel transformation pipelining. Additionally, there has been a move by vendors such as Informatica to integrate with enterprise application integration products and message queues, such as WebSphere MQ from IBM, Tibco, Vitria and webMethods in an effort to integrate real-time transactional data with traditional data streams.

As a consequence, ETL vendors increasingly see their product development overlapping with the EAI market. ETL vendors have discovered that their products can offer quick data connectivity options and a high level of data analysis functionality that can be used instead of, or to complement, existing EAI architectures. In contrast, EAI software vendors tend to focus on the requirement for heavyweight, pervasive infrastructure solutions and on the intricacies of application integration and intelligent message routing.

Most ETL vendors are in the early stages of releasing products for real-time analytics. Of the solutions being offered, most are based on the concept of real-time data feeds to and from the data warehouse. The missing part in the jigsaw is the analytics engine, which makes use of the data to deliver a specific set of actions or recommendations to the front office. Front-end analytical BI vendors typically provide that piece of the jigsaw.

Analytic vendors BI vendors such as SAS, Cognos, Business Objects, Oracle and Hyperion all offer some sort of CRM solution in one guise or another, usually in the form of a packaged analytical application. Those solutions provide off-the-shelf answers to specific business problems such as churn management, customer retention or marketing automation. Those vendors have started to respond to the need for real-time analytics by evolving their offerings, specifically through the development of data mining technology and real-time alerts.

The real-time components of those solutions are provided by building key performance indicators that track certain business metrics against predefined limits and automatically notify users of any changes. For instance, Hyperion has recently announced “real-time triggers.” Through a partnership with iSpheres (ispheres.com), the company claims to have an offering that enables the analysis and modeling within its Essbase OLAP product to be driven by real-time events. Other BI vendors, like Cognos, have also ramped up their offerings in the areas of performance management, visualization software and multi-channel real-time alerts in response to the trend. However, the key decision-making technology component that will provide the greatest value for real-time CRM analytics solutions is data mining. It is the mechanism that allows companies to make sense of their data by analyzing, segmenting and using predictive techniques that help support the customer interaction process through quicker and more informed decisions.

SAS, one of the leading data mining and advanced analytics vendors, is attempting to re-position itself as a CRM solutions provider, in response to the growing demand for CRM applications requiring data mining tools. Other vendors such as Oracle,Microsoft and IBM are also increasing their efforts in that area by introducing data mining components in their core database technology. However, those vendors must learn from the experiences of other vendors in the marketing of a data mining technology. The complexities of the technology and the limited applicability for many companies (in its raw form at least) have proved major barriers to adoption. To gain more mainstream acceptance, vendors must continue to make data mining more usable by embedding the technology in specific applications.

CRM vendors

In contrast to BI vendors, CRM vendors have concentrated on making the organizational front office more effective, and have only recently become interested in offering analytical capabilities to their customers.

Conventional CRM vendors—such as Siebel, Clarify [acquired by Amdocs] and Vantive (acquired by PeopleSoft)—originated in the sales automation and help desk/service management software market. Most of those players have already or are in the process of integrating analytic capabilities. They have done that through two routes: developing their own analytical capabilities or partnering and/or buying other vendors.

Likewise, eCRM vendors that originally offered additional Web and e-mail technologies to the CRM environment, have started to offer analytical capabilities in their products. A proportion of those vendors were directly involved in marketing analytics, and so analytics became the main USP for them. Companies such as E.piphany and Blue Martini, for instance, use reporting, OLAP and data mining technology to provide analytics. They take the form of prompts to front-office staff and/or the customer, with suggestions based on the customer's past behavior, current selections and patterns of behavior mined from customer records.

CRM real-time analytics--the challenges ahead

BI and CRM vendors are providing the individual pieces to the CRM real-time analytical jigsaw, with their tools and applications or infrastructure components. With the technology foundations in place, there is plenty of potential to make this concept a widespread reality, but as yet no one vendor is offering a completely integrated solution.

In addition to that, there are still major challenges that need to be addressed by organizations considering real-time CRM analytics. First, the concept of real-time analytics relies on having a consolidated view of the customer across all lines of business and customer contact points. That is vital for companies wishing to offer the correct level of service relevant to a customer at a particular point in time. Unfortunately, it is still an ideal rather than a reality for most organizations and an area that needs to be addressed to ensure it does not wreck the success of real-time CRM analytics.

Second, it requires both cultural and organizational change. A lot of the issues that surround the implementation of real-time analytics will not arise from technical difficulties but from political and cultural issues. Real-time CRM analytics implies a significant change in responsibilities and priorities for the staff involved. For example, front-office staff may find themselves being shifted from a focus on customer service to a more sales, utilizing the capabilities of real-time analytics for cross-selling and up-selling opportunities. That will pose significant cultural challenges for many organizations. It will be necessary to get buy-in from front-office staff to ensure success.


Helena Schwenk is an analyst with Ovum Ovum, e-mail hms@ovum.com.


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