Are You Ready for Customer Analytics?

By Geoffrey James

Customer analytics, a relatively new technology, analyzes data about an organization’s customers and presents this data in such a way that sales reps are able to make better and quicker decisions. According to the high-tech research and consulting firm AMR Research, the infusion of customer data into business processes and decisionmaking both reduces costs and increases revenue. In fact, some companies have been able to achieve an average increase in revenue of 2 to 5 percent while reducing marketing, inventory and other costs by 5 to 50 percent. Two forms of analytics exist:

1. Simple analytics. When a customer is on the phone, simple (sometimes called “single threaded”) analytics tools provide information to the sales rep or support agent based on past buying patterns, along with behaviors of other customers in their segment. For example, a customer calls with a service request for product ABC. While solving the customer’s problem, the support agent notices on the analytics sub-screen that the customer also purchased product XYZ, but has not yet purchased a service contract. The analytics sub-screen also shows that 95 percent of the companies in that customer’s industry have purchased service contracts for this product. The support agent now has value-added information with which to open a conversation with the customer about the wisdom of purchasing a service contract for product XYZ: e.g., “I’ve noticed that of the hundred customers who purchased product XYZ, you are one of only three who didn’t purchase a service contract. Do you mind if I ask why?”

2. Multiplex analytics. Queries and models are applied to a group of data (from thousands to millions of records) at the same time. The system then suggests actions for entire segments rather than for individual customers. For example, an analytics program might identify buying patterns in a particular geographical region and compare them to the mailing list for a recent direct mail lead-generation offer. The program would then point out potential customers who fall into the right demographic and spawn an action item for a sales rep to make a sales call.

AMR Research cautions that the goal of collecting customer data should not be to generate reports, but to significantly increase revenue while reducing costs. AMR researchers recently conducted in-depth interviews with over two-dozen companies that have customer analytics capabilities. They discovered that revenue growth resulted from three processes:

  1. Better information. Companies using customer segmentation, buying patterns and historical interaction data were able to achieve anywhere from a 2 percent to a 600 percent increase in acceptance rate, which translates into an average increase in revenue of 3 to 5 percent.
  2. Better segmentation. The ability to compare similar customers within a segment to determine why buying patterns are different and target them with specific offers significantly increased the number of closed sales.
  3. Better targeting. Customers’ online viewing habits provide a basis for targeted offers via email and call centers. For example, if a customer views a product more than once but does not buy it, the company sends a coupon for that item.

Cost savings resulted from three other processes:

  1. Marketing effectiveness – The time required to generate a campaign decreased from two weeks to two days.
  2. Smaller campaigns – Campaign costs decreased by an average of 50 percent, based primarily on the use of segmentation and targeting to reduce the number of customers and prospects in each campaign. Cost savings in the $1m to $3m range were common.
  3. Inventory reduction – Midsize manufacturing companies (less than $1B in revenue) used point-of-sale data to determine what is selling to help develop future manufacturing schedules.

AMR’s research indicates that, despite the potential, 85 percent of customer analytics projects help companies to access data but do not achieve Return on Investment (ROI). Instead, many organizations foolishly use customer analytics data to validate decisions that have already been made rather than using that data to improve sales performance.