Finding Drivers Without Data
Many times when a problem surfaces, it doesn't arrive with enough data to determine exactly what is causing it. The data vacuum is a breeding ground for opinions and theories about how to fix the problem.
Imagine a marketing promotion for a wireless telecom company that is failing due to inconsistent implementation amongst internal and third-party sales channels. A team of people representing the areas of marketing planning, sales, channel support, and finance have boiled down dozens of possible reasons for the failure into the following possible root causes:
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| A. | Program not clearly defined |
| B. | Program not integrated with other elements of the marketing plan |
| C. | Rapid introduction has stretched resources |
| D. | Lack of channel support capacity |
| E. | Lack of time and resources |
| F. | External factors driving inconsistency of execution |
| G. | Planning approach non-standardized |
| H. | Poor communications between business units |
Sound familiar?
A simple way the team can test the relative strength of each possible cause is to assess the relationship between each pair of variables in turn to determine whether there is a causal or influence relationship between them and in which direction the relationship might be stronger.
A simple way the team can test the relative strength of each possible cause is to assess the relationship between each pair of variables in turn to determine whether there is a causal or influence relationship between them and in which direction the relationship might be stronger.
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For example, if it compared A to B, the team might surmise that B would tend to cause A because lack of integration with other plan components would make it difficult for people to know how it fit in with the current activities and therefore make the program more confusing. If team members next compared A to C, they might find no particular causal relationship. The team would then compare A to D, A to E, and so on, drawing arrows to indicate causal relationships (see figure above). After finishing with A, the team would do the comparisons with B until it had completed the entire network of relationships between the variables.
The final graphic (see figure at left) would then show how many arrows were coming out of and going into each variable's box. The relationship between arrows out and arrows in is a good indicator of which variables are the drivers (most arrows out) and which are the outcomes (most arrows in). The highest priority is then placed on addressing the drivers, knowing that outcomes would improve simultaneously.
This approach, sometimes called an Interrelationship Diagraph, can be a powerful way to get different perspectives aligned behind a common approach when facts and data are in short supply.
