Despite the generally acknowledged importance of having a good understanding of the customer, in most organizations the customer's profile is a sedimentary amalgam of the results of previous data collection initiatives.
Information typically is retained or continues to be collected because it was used in the past, or because someone thought it might be useful in the future. While this approach results in impressively sized data warehouses, it can mean that emerging categories of data are often overlooked, and time and resources have been wasted collecting, storing, and managing low-value data. This results in unreliable decisions, poor customer service, and lost revenue.
Categorizing the elements of a customer profile into seven groups enables enterprises to begin a rigorous process of identifying which data will be valuable in supporting their business strategies. The starting point is to work with line-of-business managers to identify which insights are required to define the appropriate customer treatment across functions and channels. The exact form of insights varies by industry and company, but they fall into three categories:
Customer needs: A reflection of the customers' requirements and desires. It tells you what they need, why they need it, and how they will attempt to get it. A needs and wants analysis is the most critical dimension of segmentation.
Satisfaction: Reflects the status of the customer's relationship with your organization, prompting questions such as: "Do they like you?" and "Why?" Satisfaction may be tracked in direct terms such as a Net Promoter Score, or it may be inferred by customer behavior. Most organizations struggle to agree on the best measure of satisfaction between different business units.
Value/profitability: Tells you their worth to you now and in the future. The challenge is usually in defining what is good enough when CRM functions on a wider range of loose estimates (revenue and lifetime value) and finance is keen on rigorous cost allocation.
The four categories of raw data
Working with business analysts, these insights are then derived from combinations of four categories of raw data. They include:
Descriptive: Data that clearly defines the customer. Tracked items have traditionally included fixed variables (gender, date of birth) and slow-changing variables (home address, income, marital status). In some cases they may include anecdotal information (subscriptions, car ownership) that extends the profile of the customer. The biggest change in this category of data is the availability of real-time status infomation, such as location.
Relationship: This is data that tracks the history of the relationship between the company and the customer. It could include customers' transaction histories, as well as a history of interactions (such as website visits, marketing offers sent but ignored, and service requests). The biggest change in this category comes from a growing ability to track the operational history of the relationship (for example, physical devices are increasingly including tracking of how, when, and why they are used).
Social: The idea of social networks is not new, but what is growing is the ability of and interest from organizations to track and leverage these networks. To be effective in a customer profile, social network data requires the knowledge of the existence of a social network and an understanding of the influence that the network is likely to exert over the customer. For example, knowing that a close relationship exists between a customer in a network and a recently lost customer probably indicates that any influence the social network is exerting is negative.
Psychological: This element reflects the values and opinions of the customers, as well as how they make decisions. For example, does the customer make decisions on principle ("I will buy this hybrid because it is good for the environment"), or is the customer pragmatic ("I will buy this hybrid because it gives better fuel economy, saving me money on my commute")? The potential of speech analytics and text mining to capture explicit voice-of-the-customer data opens the possibility for this category to be tracked directly instead of inferred.
All organizations must consider insights about customer needs, satisfaction, and value (the basis for segmentation schemes) to be required elements of their customer profiles. These attributes will be derived from the four elements of raw customer data. The appropriate mix of this data will be determined by the nature and purpose of the segmentation scheme the enterprise is creating.
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