Can an increase in contacts to the right customers create high value from low-value customers when all other drivers are similar?

Database Marketing at the Royal Bank of Canada :

 At least once a month, Toronto-based analysts at the Royal Bank of Canada (the country’s largest bank) use data odelling to segment its base of 10 million customers. The segmentation variables include credit risk profile, current and projected profitability, life stage, likelihood of leaving the bank, channel preference (i.e., whether customers like to use a branch, self-service machines, the call center, or online banking), product activation (how quickly customers actually use a product they have bought), and propensity to purchase another product (i.e., cross-selling potential). Says a senior vice president, “Gone are the days when we had mass buckets of customers that would receive the same treatment or same offer on a monthly basis. Our marketing strategy is [now] much more personalized. Of course, it’s the technology that allows us to do that.” The main source of data is the marketing information file, which records what products customers hold with the bank, the channels they use, their responses to past campaigns, transactional data, and details of any restrictions on soliciting customers. Another source is the enterprise data warehouse, which stores billing records and information from every document a new or existing customer fills out. Royal Bank analysts run models based on complex algorithms that can slice the bank’s massive customer database into tightly profiled microsegments that are based on simultaneous use of several variables, including the probability that target customers will respond positively to a particular offer. Customized marketing programs can then be developed for each of these microsegments, giving the appearance of a highly personalized offer. The data can also be used to improve the bank’s performance on unprofitable accounts by identifying these customers and offering them incentives to use lower-cost channels. An important goal of Royal Bank’s segmentation analysis is to maintain and enhance profitable relationships. The bank has found that customers who hold packages of several services are more profitable than those who don’t. These customers also stay with the bank an average of three years longer. As a result of the sophisticated segmentation practices at Royal Bank, the response rates to its direct marketing programs have jumped from an industry average of only 3 percent to as high as 30 percent.

2.    Can an increase in contacts to the right customers create high value from low-value customers when all other drivers are similar?