• Strategy
  • CFO IT

ROI: Results Often Immeasurable?

Despite a torrent of interest in ROI calculations, truly workable solutions are just beginning to emerge.

In the face of such attitudes, vendors and consultants have redoubled their efforts to put hard numbers on soft benefits. Hubbard, for example, has created Applied Information Economics (AIE), an ROI methodology he bills as being “truly scientific and theoretically sound.” He offers ROI services through his own firm, Hubbard Decision Research, and also licenses the AIE methodology to other consultants.

Steven Hausheer, chief operating officer at Investrics in Chicago, which is building the precepts of AIE into an ROI analysis software program, explains how it works. The basic concept is that things that don’t seem measurable actually are. Consider something intangible such as “better employee access to information.” Based on input from groups of employees, the Investrics program would try to answer such questions as: Would better information access result in faster decisions? Would it produce better decisions on pricing? Would it produce faster decisions that actually close sales and produce more revenue? Questions can be industry-specific; for insurers, would getting an answer to a prospect within one hour increase the chance of a sale, and, if so, by what percentage?

Similarly, for an oft-invoked intangible such as employee empowerment, the software would use input from employees (gathered by asking questions that have a scaled response, such as 5 for very likely, 1 for not at all likely, and so on) that zeros in on, say, the time that managers spend on supervisory tasks. If a project has, as one benefit, a reduction in such requirements, the process will be able to assign a hard-dollar value to at least one aspect of employee empowerment.

Hubbard says a system that assigns a probability-weighted range of values acknowledges the inherent uncertainty of input regarding projected benefits. Rather than yielding a single projected value, AIE calculations yield a probability-weighted range of outcomes. This range is reflected in the final ROI assessment, which is essentially a collection of such outcomes. Hubbard might, for example, conclude that a client has a 40 percent chance of a 50 percent ROI on a new document-management system. But recognizing such risks as project cancellation and the possibility that users might not fully embrace the system, he might conclude that there is also a 10 percent chance that the project will produce a negative return. Based on input from managers, Hubbard calculates the level of risk that a client will tolerate for a given projected return. If, in this case, managers have indicated they would tolerate as much as a 15 percent risk of negative return in exchange for a 50 percent ROI, the project would get a green light. If management will tolerate only a 5 percent negative return risk, that project would be rejected.

It is in the assessment of overall project risk that Hubbard and those who have drawn on his methodology distance themselves from vendors and some consultants who, to borrow from Will Rogers, have never met a tech project they didn’t like. Hubbard says that his analysis results in a red light about 20 percent of the time; of the remaining projects, about 60 percent require modifications before getting a green light.


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