Contact Management Software

This section of our technical library presents information and documentation relating to CRM Solutions and Customer relationship management software and products. Providing customer service is vital to maintaining successful business relationships. Accurate and timely information provided in a professional manner is the key to any business and service operation. Telemation, our CRM software application, was built on this foundation. But the flexibility to change is just as important in this dynamic business environment. Telemation call center software was designed with this concept from the very beginning. That is why so many call center managers, with unique and changing requirements, have chosen and continue to use Telemation CRM software as their solution. Our Telemation CRM solution is ideally suited for call center service bureaus.

By Jeanne G. Harris

CRM2day.com

Realizing the Promise of Customer Relationship Management

Most savvy executives recognize that forging long-term relationships with their key customers is the route to success in an increasingly competitive and dynamic marketplace. Executives have long envisioned a world where customers would replace products or channels as the centerpiece of their business. Yet for many senior managers, realigning their organization to support a customer-driven strategy remains an elusive goal.

This article explains why transforming customer transaction data into business results is a critical and challenging element of any CRM strategy. It also describes how successful companies foster an analytic capability, which accelerates the creation and impact of customer insights. Finally it concludes with four critical success factors essential to creating an organization that can support a successful CRM strategy.

Data is the Lifeblood of Customer Relationship Management

Customer Relationship Management is only as good as the data and knowledge that it relies upon. Organizations have devoted extensive IT resources to implementing sales planning systems, knowledge management systems, marketing decision support, data warehouses, and call centers. The CRM software market continues to grow rapidly, up to $16 billion by 2003, according to AMR market research. And the market for analytic systems is growing just as quickly.

According to one market research organization (survey.com), today's market for "business intelligence and data warehousing" is growing at an annual growth rate of more than 43 percent, and is expected to exceed $148 billion by 2003. So insufficient investment in technology to create more data is not the problem. Every day, transaction data is created which has the potential to help companies understand their customers better, and make better decisions to enhance customer relationships. But simply amassing customer transaction data doesn't assure results. The real problem is that, despite the terabytes of data coursing throughout organizations today, not enough of it is actually being used to create, enhance and sustain customer relationships and create business value. In short, these CRM initiatives often fail to generate business results.

It's not that data doesn't get turned into knowledge and results at all; nor is it that we don't really know how to do it. Some companies are achieving significant benefits from improved decisions and better customer interactions. Transforming raw data into useful knowledge for decision-making just does not happen often enough, primarily because organizations overlook the most important step in the process - the human realm of analyzing, interpreting and acting upon data.

Learning from Success

There are companies who have been successful using the principles of CRM to create tremendous value for their organizations. Accenture's Institute for Strategic Change recently completed a study to learn how firms realize value from their transaction data. The study looked at over 100 companies to identify those that had successfully demonstrated their ability to transform data into knowledge and results. We studied 20 companies that had made significant progress towards unleashing the power of their transaction data. These companies routinely use their transaction data to identify new customer segments, improve the profitability of existing customers, improve the effectiveness of direct mailings, enhance each customer interaction, and build customer relationships. On the surface, these companies have little in common. They are in different industries and have vastly different customers and market strategies. But what this diverse set of companies has in common speaks volumes about what executives need to know in order to translate data into results. Some of the companies who focused on generating and acting upon customer insights included:

Definitions

Building a Capability, Not a System

What these companies have in common is a focus on creating an analytic capability that enables professionals to leverage customer information - through data aggregation and analysis - to gain customer insight, make better decisions, and shape future customer interactions. If data is the lifeblood of CRM, it is the employees who are the brains, arms and legs. Without a high level of human performance, a CRM initiative is "brain dead". Data may be flowing throughout the company, but employees lack the knowledge and resources they need to effectively analyze data, make decisions, and take the actions needed to achieve results. Thus, building an analytic capability to achieve a well-defined set of business outcomes is the key to a thriving CRM effort.

Unlike companies who simplistically view CRM as an IT project, the companies in our study recognized that they needed to consider how CRM would impact many different aspects of their business. As one US West vice president noted, "It's not a linear, staged process. It's more like managing a 15-level chess game because there are 15 CRM-related projects happening concurrently, and their interdependencies must be managed."

The companies in our study shared four common success factors:

- Employees share a common understanding of strategic objectives and the outcomes desired.

- Employees have the knowledge, skills, and experience necessary to use the data.

- Senior management is committed to building a "fact-based culture."

- IT infrastructure and quality data exists to support an analytic capability.

- Beginning with the End in Mind (Focus on Strategic Objectives and Outcomes)

As Yogi Berra once noted, "You've got to be very careful if you don't know where you are going, because you might not get there." The most successful companies focus on a few key strategic objectives, along with clearly defined quantifiable outcomes. They place a high priority on ensuring that everyone in the organization is "on the same page" and understands what they are striving to accomplish.

Having a common understanding is important in determining how to allocate resources and how to implement customer insights. For example, an executive at Kraft said, "The reason why we have done so well (in category management) is that we focus on what is truly critical." By maintaining a laser-sharp focus on this objective, Kraft can analyze a product category using a fraction of the data (and time) of their competitors, with superior results.

Most successful companies, we found, take pains to ensure that the conclusions of their analyzes are implemented. "These insights only have value if the business owner takes action," noted a Fleet Bank senior vice president. One way that these organizations focus management attention on implementing the results of their analyzes is to ensure that their performance metrics and incentives are aligned with strategic objectives. The right decision for the company won't be implemented if it conflicts with the sales force's incentive compensation.

The Right Stuff (Knowledge, Skills and Experience)

An unusual combination of skills and experiences are needed to transform data into actionable decisions. Even the most sophisticated analytic software needs to be used by employees with the right knowledge, experiences, and insights into their company's objectives. Nearly two-thirds of the firms in our study cited recruiting, developing and retaining highly skilled employees with analytic capabilities as a major challenge to effectively using transaction data for decision-making. Having the right mix of skills and experience is crucial, since no single individual can "know it all." Most companies are aware of the need for knowledge about using analytic software tools. But just as important is an understanding of statistical techniques, and how to apply them to a given problem.

Another often overlooked but vital expertise is an understanding of the sources, relationships, and meaning of the data itself. Most organizations have someone who has a deep understanding of how their company's data is produced and transformed. This expertise only comes after long experience of working at a company. "[Roger] knows the tribal lore," said one marketing information manager. "We rely on him to understand the data, and it's all in his head." Such an employee would obviously be difficult and costly to replace. Identifying, rewarding and sustaining key individuals with this company specific knowledge is one of the most effective ways to speed the transformation of customer data into meaningful insights.

Finally, the most sophisticated analysis is useless if it is not effectively communicated to decision-makers. A good analyst adjusts his or her communication style to fit the needs of the decision-maker. When analysts lack effective teaming and communication skills, there can be a breakdown in the decision-making process. Companies with effective analytic capabilities recognize that analysts and decision-makers must work together to achieve a common end. "To be successful, you have to learn the lingo and be willing to talk about what the data is saying," said a marketing program manager at Hewlett-Packard. "Now, instead of two hour conversations, we do it in 15 minutes, and we get a better decision."

Creating a Fact-Based Culture

By far, the greatest challenge to creating a thriving CRM analytic capability is developing a "fact-based culture." In one survey at Accenture's Institute for Strategic Change, more than 62 percent of executives viewed cultural resistance as the greatest hurdle to achieving significant return on major investments. Kraft Foods has largely met this challenge. "Data is like oxygen at Kraft. Our executives don't question the need for it, because we use it daily," said the vice president of marketing information services. But for most companies, data-driven analysis is not the driving force behind decision-making. Few employees would openly object to a CRM initiative that is intended to improve customer relationships, yet we repeatedly found that the culture of an organization can suffocate a CRM analytic capability. "At times the culture can seduce you into thinking you're making progress selling CRM," said a US West employee. "Only to find out later that nothing has changed. Trying to change this culture is like hitting up against a Jell-O wall."

So how do you create a fact-based, analytic-friendly culture? First of all, everyone applying the data must believe in the value of having high quality, credible data, and in the value of analysis in decision-making. Secondly, decision-making processes need to be based upon evidence rather than opinions.

Culture change of this magnitude is not easy to accomplish. It requires a major commitment from top management. The CEO of Earthgrains made a concerted effort to change the culture of his organization by challenging anyone who presented a recommendation that was unsupported by facts. "In God we trust, but all others need data" became his creed. Senior management commitment to creating a fact-based culture is one of the most crucial factors for a successful CRM analytic capability, because both decision-makers and analysts must believe in the importance of data analysis and in the value of having high quality, credible data.

Creating Stronger Technical and Data Infrastructures

Although we found that the differentiating factor between successful and less successful firms was a more holistic approach encompassing more than just technology and data initiatives, technology and data are still important first steps. At times companies feel that they simply don't have the data they need to even begin to build a CRM analytic capability. Indeed, the first thing a firm needs to do is to assess whether it has a transaction data environment that is sufficiently robust and of sufficient quality to provide data for analysis. If not, it makes sense to begin putting a new one in place immediately. As a practical matter, however, no organization has all the data needed to answer every question about their customers and their relationships with them. So, instead of deferring the creation of an analytic capability to support CRM, our research suggests that an organization need only have sufficient quality data to use as a starting point.

As valuable as analyzing transaction data can be, some of the most insightful information and knowledge doesn't even reside in a transaction system. Intelligently and selectively integrating transaction data with externally produced data can lead to new markets and opportunities. For example, in order to identify new customer segments, knowledge about existing customer behavior must be compared to the characteristics of potential customers. Typically such an analysis requires extensive external information. Similarly, linking transaction data to other forms of corporate knowledge (often contained in memos, presentations, or meetings) can produce far greater customer insight than either approach alone. At Harley Davidson, for example, the most valuable customer knowledge was derived from close observation of customer behavior. Data analysis is viewed as a "tie-breaker" used by decision-makers when they are unable to achieve consensus any other way.

Assuming that some quality data is available, most companies focus the majority of their CRM resources and attention on establishing the right technology and data environments for analysis and decision-making. The companies in our study each used different technical architectures and tools. Choosing a specific technical architecture or product is far less important than recognizing the need to create a technical environment that supports all aspects of a company's analytic capability. Companies generally underestimate the challenges of establishing an analytic technical architecture that must anticipate and support a vast array of problems, ranging from "Who are our best customers?" to "How effective was our last marketing campaign?" Creating an analytic architecture requires different software, tools, skills, and even methods than a traditional transaction application architecture. These human performance challenges were common to even the most successful organizations in our study.

Although performing a complex analysis may require a sophisticated and highly integrated analytic environment, communication of the analysis to decision-makers and customers should be in a form that is "decision-ready." At Kraft Foods, for example, highly skilled analysts use complex statistical tools for their category management analyzes. But the sales force receives the results in a business-focused slide presentation that addresses the implications for each category for an individual grocery manager.

Another ongoing challenge is creating and maintaining a high quality data environment. Lack of confidence in the data can fatally undermine any analytic capability. Maintaining data that is accurate, complete, reliable, accessible, current and comprehensive is a costly and time-consuming effort. According to industry estimates, 60-80 percent of the total cost of a data warehouse project is spent on cleaning up and integrating data.

A seemingly simple activity like determining a list of all customers is rarely as straightforward as it seems. Within a bank, for example, the retail customer is the individual or household; the institutional trust customer is often an institution or group; the corporate lending customer is a division or corporation; and the investment customer is very likely another financial intermediary. The labor-intensive process of mapping inconsistent data often requires the time of the very people who need to be spending more time with customers-sales and relationship managers. As a result, the problems of data quality are often organizational, cultural, and human performance issues, rather than purely technical ones. Maintaining high quality data remains a challenge for even the most successful CRM initiatives.

Summary

As long as computers have been used in business, organizations have generated transaction data. Yet this data remains one of our most underutilized assets. With the growing importance of CRM, transforming data into meaningful insights is more critical than ever. Despite the hype of technology vendors, computers don't make and carry out the decisions in an organization. Skilled people will continue to be the heart of any CRM initiative. Employees need to be armed with the right skills, knowledge, and experience. They must have a clear understanding of what outcomes they are trying to achieve, and be supported by an organization (culture, infrastructure, and metrics), which effectively implements fact-based decisions. Building an analytic capability now is the difference between a "brain-dead" organization and one that will thrive in the new reality of the customer-driven marketplace.

To request a free copy of the Accenture Institute for Strategic Change white paper, "Data to Knowledge to Results: Building an Analytic Capability," please contact the author at jeanne.g.harris@accenture.com