How many times have you received a cold-call on a Sunday afternoon from an uninformed sales rep? Or received a barrage of e-mails that undoubtedly find your "DEL" key? The truth is that most companies know very little about their customers. Fortunately, enterprise relationship management (ERM) applications are enabling enterprises to implement the technologies to capture interactions with customers through a variety of different "touchpoints" including phone, mail, Internet and personal sales calls. By building data warehouses and data marts that integrate customer contact information with product history and customer service information, organizations can truly begin to build a complete picture of their customers and really start to understand their needs and tailor specific messages for each customer. This one-to-one marketing will provide many benefits including increased customer lifetime value, increased customer satisfaction and greater customer loyalty. Organizations that undertake the development of data warehouses and data marts using data from ERM applications will surely have an advantage over their competitors. A company drives profits through its customer value streams those processes that generate significant benefit for an organization's customers. Thus, a company would benefit most by analyzing value streams and improving upon them. But how do companies begin to understand the effectiveness of those processes? Organizations have some gauge of their success through standard measures such as sales and revenue, but how much do these metrics really say about how a customer is interacting with the organization? To truly improve customer value streams, organizations need to analyze each interaction with a customer and understand how those interactions impact things such as product sales and customer retention.
ERM applications capture customer interaction with an organization. When a customer calls a representative to purchase a product, the interaction is captured within the ERM application. When a sales rep visits a prospective client, that interaction is recorded within the ERM application. When a customer calls a service representative to report a problem, that interaction is recorded. Over time, these interactions collect and begin to paint pictures about the customers within an organization. However, within ERM applications, these interactions are just transactions. For example, on a particular day, customer X ordered product Y. The application does not tell the organization why customer X ordered product Y or which other customers may want product Y based on customer X's purchase. However, the data warehouse will integrate this information and provide an environment to analyze customer interactions. A word of caution here this may signal to some that it is simply enough to dump information from an ERM system into a data mart and analyze it. While this approach may give a limited picture of the customer, the better approach is to augment and integrate information from existing legacy applications with ERM data to provide a more complete view of the customer.
Once the warehouse has been built with the ERM and legacy data, there is a wealth of opportunity for analysis. Simple channel-based analyses can be performed to understand the effectiveness of sales channels or more complex product ownership analyses can be performed to identify product-bundling opportunities. Almost all aspects of a customer's interaction with a company can be analyzed. Perhaps the richest opportunities exist in the area of data mining. Companies can use the data warehouse to generate profiles about each customer, their preferences and their habits. Additionally, companies can cluster similar customers together based on a variety of attributes including product ownership, demographic attributes or life events. Data mining also provides the tools to perform lifetime behavioral analysis to understand a customer throughout his or her life with an organization. By using data mining against ERM information, companies will begin to get a better handle on why customers leave, who are the best customers and what types of customers are similar to each other. Organizations can learn a great deal about their customers by thoroughly analyzing the information within the data warehouse.
To some, the vision stops here. Once the customer has been analyzed, the marketers simply need to tailor their campaigns; the salespeople need to target the right customers; and mailings should go out to the right people. This is certainly more effective than what most companies do today, but there is a vision of something far more grand. This grand vision is what many industry leaders refer to as a "closed-loop" environment. In a "closed-loop" environment, the analyses feed back to the application, and then the application feeds back to the analyses creating a learning environment between the application and analysis engine. In the ERM world, the ERM application first captures information about individual customers and passes it on to the data warehouse. The data warehouse, in turn, creates customer profiles based on pre-defined data mining algorithms about each customer within the ERM application's environment. When the customer calls into the call center and a representative is interacting with them, the system will automatically generate suggestions for product purchase to the sales representative based on customer profiles. After a day's worth of interactions, those interactions are fed back into the data warehouse and are used to refine the previous customer profiles. The process then becomes ongoing. This is just one instance of a closed-loop opportunity within an ERM environment. A closed-loop system can be used to generate everything from personalized e-mails to customized interactions through call centers.
Customer interaction data from ERM applications offers great potential to all organizations. While most organizations have built some kind of customer data warehouse, they often do not contain the detailed information to study customer behavior patterns. To really perform one-to-one marketing, the lowest level of interaction needs to be captured. By building a data warehouse using ERM data and existing legacy information, organizations will enable continuous improvement of their customer value streams, effective development of their closed-loop processes, and stronger and deeper understanding of their customers. It is these attributes that will help keep organizations ahead of their competition today and ensure that they remain leaders in their field well into the next century.
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