CRM + customer intelligence = customer relationship strategy

A successful customer relationship strategy maximizes corporate goals by managing customer service and leveraging customer data. Find out why both pieces of the puzzle are crucial.

Ask a hundred people in your organization to define customer relationship management (CRM), and you’ll get a hundred different responses. Such is the state of understanding regarding one of the hottest business paradigms today. One cause of the confusion is the evolutionary nature of CRM systems. CRM systems have evolved from the clear business recognition that the customer, and hence management of customer relationships, should be a fundamental element in any corporate strategy. Industry pundits loosely call this strategy the customer relationship strategy (CRS). In this article, we’ll discuss the components of effective CRS and the importance of analyzing customer intelligence.

Goals of the CRS model
The primary goal of the CRS business model is to maximize corporate strategic goals by gaining in-depth understanding of the organization’s marketplace and acting on this knowledge. According to today’s best practices, it is imperative that companies embrace the principles of the CRS paradigm at all levels of the organization, from senior management to the line worker. Only then will the company move to a consumer-centric business model that can effectively analyze information and strategically act to achieve its goals.

The establishment of an enterprise-wide CRS strategy has many components. These include:
  • Tactics to address customer inquiries and issues.
  • A system or program to develop and maintain customer relations.
  • Support for front-end customer systems.
  • A process to farm or mine these relations for data.
  • Analytics to process this data for strategic decision making.
  • Mechanisms to evaluate the results of customer-related decisions.
  • Feedback mechanisms leading to changes in tactics and strategy.
  • Action based on new or changed tactics and strategy.

Companies must understand the component elements to fully achieve value in a CRS system.

CRS consists of two components: traditional CRM systems aimed at maximizing interaction with the customer and customer intelligence (CI) systems aimed at analyzing information to gain knowledge of customer attributes. The technology marketplace has helped blur the lines between these components. However, it’s necessary to understand the difference between the goals of these two systems in order to build a successful CRS business model.

Traditional CRM systems developed as an evolutionary process originating from corporate customer support systems. These systems help manage customer service by placing customer demographic information in corporate databases, which in turn are utilized for applications such as:
  • Sales force automation.
  • Marketing and campaign management.
  • Supply chain management.
  • Call or service center operation.
  • Contact or customer prospecting.

On the other hand, CI systems evolved from the need to analyze customer information to gain expanded insight into the enterprise marketplace. Traditional business intelligence (BI) systems led the way by creating tools to mine data from traditional databases as well as nontraditional systems such as e-mail, personal contact histories, and office documents.

CI systems add the crucial concept of information analytics to the mix. These tools process mined information with respect to the environment from which it was gathered. By continuously analyzing data gained from customer contact points or touches, CI systems provide insight into customer behaviors, preferences, operations, loyalty, assets, and more. In essence, they aim to answer the how and why parameters of the sales process. With these newfound insights into customers’ preferences and habits, enterprises can better target the cross-selling of products and refine marketing initiatives.

Gaining expertise via CI systems
Key vendors in the CI marketplace include Broadbase (KANA iCARE), Business Objects, Cognos, Siebel Systems, and SAS. In addition, several new vendors are delivering a next generation of analytical solutions.

Any CI system should address these four key attributes:
  • Value—CI systems need to clearly identify information of value.
  • Context—CI systems must clearly identify the context in which the data was gathered or processed. For instance, an increase in umbrella sales may be due to an increase in local precipitation rather than a fashion trend.
  • Granularity of identity—CI systems need to clearly distinguish and associate between data instances. For example, information surrounding the attributes of customer A may not apply to customer B.
  • Action—The results of CI analytics should point to a course of action.

Unfortunately, achieving these CI objectives is difficult. Skepticism, reflected in articles printed in recent trade journals, appears to be growing as enterprises struggle with implementation issues and lack of ROI. Additionally, the symbiotic nature of CRS component systems has led to confusion over terminology. Much of this disillusionment can be traced to the overhyping of product abilities in CRM and CI vendor marketing material. On the plus side, the emergence of Internet- or intranet-based solutions has shown promise, at least in their ability to rapidly communicate with all levels of the organization. Before you sign off on the purchase order for CRM or CI software, keep your sights focused on what new insights you expect to collect from applications.

While the process of gathering customer intelligence hasn’t reached maturity, enterprises should think twice before bypassing CI or giving up on it altogether. It’s impossible to build an effective customer relationship strategy on the strength of CRM alone. Enterprises must leverage customer data to gain an in-depth understanding of the marketplace and thus maximize corporate strategic goals.