Customers—even bad ones—are our best loyalty teachers. In fact, the lessons gleaned from "problem" customers are often rich and long-lasting. Consider the following less-than-ideal customer types and some of the loyalty-making insights they provide.
Rule Breakers: Not Playing Fair
Consider the case of a home shopping channel, which religiously applied the industry's RFM (recency, frequency, monetary value) model for scoring customer-buying behavior. A long-time customer had graduated into buying roughly $1,000 a month in merchandise and was now dubbed a "top customer" per the RFM model. In fact, her "stair-stepped" purchasing trend was exactly what the company strove toward.
But, six months later, the bloom was off the rose. When the customer's revenue data and returns data (which were stored in different databases) were matched, a surprising finding was revealed: Her returns were sky high! Digging deeper, the company was shocked to discover that the customer owned a small gift shop and was using the shopping channel's merchandise on a consignment-type basis while carefully complying with the company's 60-day return policy. Sadly, the company's data silos masked this "top" customer's true value for too many months.
Kelly Cook, who was the director of CRM for Continental Airlines, recalled a similar awakening when she worked at the airline. The first year that the airline's new data warehouse was in operation (it consolidated 45 or so separate customer databases into just 2), the company saved $5 million in security and fraud detection. Kelly recalled, "We found one customer who got 20 bereavement fares in 12 months off of the same dead grandfather!"
Before the data warehouse, for example, all sorts of fraud was possible. A devious-minded ticket holder with a canceled flight might get a replacement flight voucher from the airport customer service agent, and then immediately go to the phone and call the Continental call center and get a second reimbursement voucher for the same canceled flight.
Not anymore. Continental's data warehouse quickly consolidates customer files across channels, dramatically reducing compensation fraud.
Loyalty lesson: A timely merging of customer data across silos and channels is a must for detecting unusual buying patterns, diagnosing flawed buyer transactions (and the systems that allow them), and "encouraging" customers to play by the rules.