The term “analyzing customer data” brings to mind which of the following:
- A brightly lit, static-free room at company headquarters filled with whirring computers storing terabytes of customer data
- Thick reports generated by a 1970s era dot-matrix printer on oversized green and white striped paper
- A technology professional who is responsible for “protecting” customer data and loathes out-of-the-ordinary requests for information
- All of the above
If you answered D, you are probably thinking about analyzing customer data in a business-to-consumer (B2C) environment, where customer data is stored in a centralized location and accessible by a chosen few who have the ability to complete detailed customer analyses. In the B2C environment, decision makers can be frustrated by a lack of access to customer data they know would be valuable to achieving their goals. My fellow ClickZ columnist Melaney Smith referenced those frustrations, and how to overcome them, in her column “The Three Brick Walls Between You and Your Data.”
In the business-to-business (B2B) world, conditions are different. Technological advances have altered the way customer data is analyzed in environments that rely on sales professionals to establish and grow relationships with prospects and customers. The relationship between sales professionals and prospective clients is truly one-to-one. Sales professionals must know every aspect of a prospect’s business (e.g., size of market, competitors, customers) and intricate details of a prospect’s personal life (e.g., marital status, family, birthday). However, knowing this information is cost-of-entry and does not truly create a competitive advantage. Competitive advantage is created when sales professionals have instant access to data that allows them to accurately anticipate the needs of prospects. This, in turn, allows sales representatives to make recommendations that satisfy those needs, many times before a competitor even realizes the needs exist.
The growth of inexpensive, Web-based database applications and SFA software has provided companies the power to instantly distribute customer data to the edges of the corporation, allowing sales professionals to analyze real-time customer data right from their laptops. This eliminates the frustration created by a lack of access to customer data — no more requests to corporate, no more waiting for reports, no more waiting for leads from marketing campaigns. Putting real-time customer data directly into the hands of sales professionals eliminates the waiting and allows relevant data to be used to close new business.
Sales professionals can benefit from access to three types of customer data:
- Transactional data. This data represents historical purchases by the prospect’s company. Historical purchases might include the number of seat licenses for a specific software program or the number of consulting hours contracted in the prior month. A sales professional may use transactional data to develop a target list of prospects based on previous purchase history. If a client purchased Software Product X, it is likely to be an excellent target for purchasing complementary Software Product Y.
- Observed data. This data represents trackable actions taken by prospects. The action might be any of a variety of trackable online or offline activities: a visit to a Web site, a click-through on an email newsletter, an online response to a direct mail piece, attendance at a seminar, download of a topic-specific white paper. A sales professional may use observed data to identify products or services in which a prospect has demonstrated a recurring interest. If a client clicked on a link in an email newsletter regarding corporate portals and downloaded a white paper on the value of corporate portals, a sales professional will infer the client is interested in purchasing a corporate portal.
- Self-reported data. This data represents responses collected directly from a prospect. The responses are typically collected through an offline or online survey. The survey can be either the one-question variety that is popular in email newsletters or the more traditional multiple-question survey. Self-reported data can give sales professionals insight into a variety of areas, depending on the topic of the survey. If a prospect responded to an economic survey that asked questions regarding when the economy will turn around and by what percentage IT budgets will increase, a sales professional will be able to infer the client’s buying horizon.
Although all of this data is valuable in isolation, it becomes even more valuable when viewed together. A sales professional can begin connecting the dots between transactional, observed, and self-reported data to create a robust profile on each prospect that identifies the prospect’s unique needs. Over time, new data is added to the prospect record as she makes additional purchases, takes future trackable actions, and answers new survey questions. This new data allows the sales professional to monitor the evolving needs of the prospect and continues to make ongoing recommendations to satisfy those needs.
The profile a sales professional develops for a prospect by analyzing data is not unlike the profile Amazon.com develops on its customers. Amazon knows what its customers have purchased (transactional data), the products they have been looking at but not purchasing (observational data), and the ratings they have assigned to products (self-reported data). Amazon uses this data in combination to develop a customer profile and make product recommendations.
Analyzing customer data is not always about reviewing terabytes of data, running regression analyses, and understanding statistical distributions. Sometimes it is about building the infrastructure to provide decision makers with the data they need to achieve their goals. Providing sales professionals real-time access to transactional, observational, and self-reported customer data creates competitive advantage and leads to incremental revenue.
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