Customer relationship management is often an exercise in sophisticated chaos. Massive resources are poured into “Star Wars” technology. Advance-degreed data miners develop complex algorithms to drive overall contact strategy. Agency creatives jump into the fray with their own unique contributions.
Unfortunately, few companies have successfully implemented a cutting-edge CRM program. The data miners have little real-world business experience, the agency creatives are suspicious of technology and statistics, and the technologists resent the meddling from both parties.
The success of any CRM initiative can be ensured by appointing a data-leveraging architect – a seasoned individual with the practical expertise and vision to coordinate the multiple functional disciplines required for the success of any complex, data-driven strategy. The best architects command the respect and cooperation of the data miners, creatives and technologists.
For example, a major financial institution recognized the need for its credit card division to acquire better customers, and to spend less in doing so. To that end, it embarked on the construction of a massive prospecting data mart. A well-known services firm was engaged to build and maintain the mart. The services firm operated a research and consulting group that provided predictive modeling as well as quantitative and strategic consulting services. Because of existing commitments, however, no one from the group participated in the building of the mart. Also, no data-leveraging architect was appointed to supervise the project. Instead, the technologists assumed total control.
After six months, the prospecting data mart was ready to launch. Reps from the financial institution, anxious to display immediate payback to senior management, requested a two-day summit meeting to develop a comprehensive, data-driven strategy. Several members of the service firm’s research and consulting group were asked to attend. One hour into the meeting, the brainstorming came to an abrupt and premature end. The technical folks, in their quest for processing efficiency, had not included in the mart a running history of several fields that were critical to the execution of any analytical work. Instead, the values comprising these fields were overwritten each and every month. This necessitated a redesign of the mart. The unfortunate result was a two-month delay, a loss of credibility in the eyes of senior management, and a substantial decline in momentum.
At the same time, the financial institution retained a prestigious strategy firm to review its entire operation. During the mart’s rollout, senior management was presented with the results of the seven-figure study. An entire section of the report described in detail a state-of-the-art CRM program.
Senior management, impressed with the strategy firm’s recommendations, asked it to implement the vision. The goal was to build a cutting-edge, multi-faceted, statistics-based algorithm that would drive all of the institution’s prospect and customer contacts. A team of statisticians was assigned to the project. The prospecting data mart would be a key input to the algorithm.
The project team’s army of statisticians descended on the financial institution with a mandate to revolutionize the way business was conducted. Unfortunately, no one on the team had any substantial real-world experience. Therefore, when constructing their cutting-edge algorithm, they neglected to consider mundane data processing realities, such as computer run times. This was a critical issue with the prospecting data mart, which was maintained in a legacy mainframe environment.
The services firm that was maintaining the data mart asked to be apprised of the project team’s strategy, but to no avail. The project team was particularly wary of the data-leveraging consultants employed within the service firm’s research and consulting group. These experts, although privy to invaluable insights about the limitations of the systems under which the mart was operating, were perceived to be a competitive threat. Unfortunately, no overall data-leveraging architect had been appointed to insist on the cooperation of all parties. As a result, the project team’s statisticians worked for seven months in a virtual communications vacuum.
The lack of coordination spawned a total disaster. A benchmark test of the cutting-edge, statistics-based algorithm indicated it would take 360 CPU days to execute against the entire prospecting data mart. Because mainframes process multiple jobs at once, this translated into an elapsed time of between two to four years – an absurdly impracticable situation.
Panic-stricken, the financial institution assembled an emergency team of data processing experts to develop a solution. Ultimately, about 95% of the algorithm was discarded and the balance rewritten. The remaining code was just a shell of the strategy firm’s original vision. Senior management, again, was not impressed.
Another example: A multibillion-dollar company manufactured products with price points in the thousands of dollars. These products were marketed to large businesses primarily through a dedicated sales force, and to small firms and consumers via direct mail and space ads. Concerned about a recent loss of market share, the manufacturer retained a consulting firm to develop a comprehensive CRM strategy. The first step was to construct a data mart containing robust purchasing information and promotion history.
It was critical the design reflect the way in which the manufacturer viewed its customers. Specifically, there were three levels at which customer transaction information was aggregated: the company, the location and the individual.
The manufacturer saw its customers primarily from the perspective of location. Accordingly, this was reflected in the design specifications for the data mart. However, the data-leveraging architect recognized that the project scope should be expanded to track activity at the individual level. The vision was to maintain a relationship with customers as they changed locations and companies during the course of their careers.
The data-leveraging architect realized that the manufacturer’s sales representatives were privy to virtually all of their customers’ career changes. The challenge was to provide the mechanism and incentive for them to input this information into the data mart in an accurate and timely manner. The solution? Create a structured graphical user interface and ensure weekly input via “carrot” and “stick” incentives. The result was a dramatic enhancement of the CRM strategy’s effectiveness.
The graphical user interface proved to be especially valuable whenever a loyal customer would move to a company that had never been receptive to the manufacturer’s products. Such an event would trigger a marketing action/reaction system, which, in turn, would unleash a flurry of hyper-targeted prospecting activity. Likewise, when an individual with a history of hostility toward the manufacturer would jump to a historically loyal customer, the system would instantaneously activate a series of preventive promotional contacts.
State-of-the-art CRM requires “Star Wars” technology as well as highly trained specialists. Individuals must be hired to work directly with the technology. Without such an architect, database marketers often have to explain to senior management why most – if not all – of their efforts have been failures.
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