Drivers – Not Just for Golf or Cars

What causes something else to happen? In the field of management the popular term for the “cause” is a driver. An example would be that the more sales calls that a sales person makes on prospects, the more likely a sale will be made.

What causes something else to happen? In the field of management the popular term for the “cause” is a driver. An example would be that the more sales calls that a sales person makes on prospects, the more likely a sale will be made.

Last week I enjoyed a presentation on this topic by Pierre Guillame, a consultant with Beyond EPS Advisors. In his past, Pierre led the original implementation of customer profitability analysis using an activity-based costing system for the giant credit card company Capitol One. In his presentation, Pierre took the application of drivers to a much higher level than they are traditionally used for. He connected them to financial projections – a form of predictive analytics.

As background, Pierre and I were both honored to be selected as presenters at the 10th annual North America Beyond Budgeting Round Table conference. (I described this conference in my blog I’m Dancing with the Performance Management Stars.)

Chain of Drivers
Pierre illustrated that a cause-and-effect relationship can have many more than a single relationship. Similar to tipping domino blocks cascading against other blocks, divers affect other drivers. In the sales call example, Pierre described several intermediate and subsequent drivers from the sales call event. For example, some sales led to a request for more other information whereas some led to a meeting with the prospect. And subsequent to a successful sale, some drivers led to high profit margin product sales while others to low profit margin ones.

By visually mapping these relationships into a tree diagram, several benefits result. The specific product or service-line “downstream” sales provides information for sales and profit forecasting. By analyzing the “upstream” drivers, their yield effect can result in identifying which type of driver more or less resources and time should be devoted to. For example, which type of sales prospect might have a greater propensity to purchase products or services?

Too often intuition and gut feel are used to develop plans. Better results come from understanding cause-and-effect relationships – and even better results when statistical correlation analysis is applied. Executives and managers wonder why business analytics is getting so much media press now. Why is it a “hot” topic? It is because fact-based information, modeling with logical relationships, and probabilistic scenarios are better ways than relying on hunches. Do you know? Or do you think you know?