Cross-Sell Offer Failure
Not only do an estimated 75 percent of all cross-sell offers fail, a high percentage of the offers destroy value as well. Banks often invest a significant amount of time and money to craft the offer and choose the segment and delivery channel. Additional costs are also associated with the lost opportunity of making an offer that would have been accepted and developing a stronger customer relationship, as opposed to an offer that was not accepted and may cause damage to the relationship.
Many of the underlying problems contributing to the 75 percent failure rate reflect that in many banks portfolio managers represent independent silos. For practical purposes, each silo within a bank engages the market separately, representing a missed opportunity to build a true picture of what each customer wants and needs at an organizational level. This also illustrates that silos within banks are actually competing among themselves for a customer's wallet share. When combined with the underlying organizational barriers caused by mergers, acquisitions, and incompatible legacy systems, the enormity of the problem becomes apparent. Based on the pervasiveness of the failure rate for cross-sell offers, it appears that there is no ready solution.
In an attempt to solve the problem, many financial service institutions have invested heavily in systems that enable them to make more offers to current and prospective customers. From a workflow perspective, these systems have delivered on their promise of consistently presenting more offers to customers. The difficulty inherent in their success is that they only contribute to the problem. Now it is possible to make even more offers, a significant percentage of which should never have been made in the first place.
Given the extent of their relationship with their banks, most consumers would believe that their bank knows a great deal about them. The bank has visibility across a broad spectrum of their financial profile, transaction patterns, and likely future needs. The customer's expectation of his bank's ability to tailor offers and deliver personalized service is elevated by his interactions with companies that have mastered the art of personalization. If the bank provides the customer with an experience that fails to match his expectations, it only contributes to the failure rate for cross-sell offers.
What's the Answer?
Initially analytics were used by banks to improve their collections capabilities. Banks needed to be able to collect the maximum amount of money, in the shortest time, at the lowest cost. It was crucial to know who would pay, when he would pay, and how much would be paid. Since this information could not be derived from intuition or policy, banks required the ability to analyze all of the customer information, including call center notes, email logs, and Web interactions.
For several years, analytics have proven to be extremely successful in customer interactions where the bank has the greatest degree of risk. The success banks have experienced using analytics to guide them in more effective interactions with delinquent customers can now be achieved earlier in the customer life cycle to acquisitions, marketing, and customer service. A more accurate picture of the customer can be built, which will provide far better guidance for future offers, product development, and service delivery decisions.
Satisfaction Determines Success
Satisfaction of customer needs, in terms of providing relevant products and high-quality services, will ultimately determine the success of the financial services industry. A January 2006 Gartner report states, "Two-thirds of CEOs believe their competitors make better use of customer information to create business opportunity than their own organization does." CEOs recognize that CRM systems were designed to gather, store, and report on information contained in the structured fields of customer records--not to decipher the reason for the customer's call or determine how that call is related to the customer's lifetime value to the business.
Predictive analytics and decision management software make it possible to gain the necessary level of insight and provide actionable results from a broad mix of data sources. For the first time, businesses can analyze all of their customer data, regardless of volume or type, in order to discover business-changing metrics and develop strategies for more successful future segmentation and customer management.
About the Author
Debbie DeGabrielle is CMO at Intelligent Results. Please visit www.intelligentresults.com