Hurricane Irene is moving through eastern Canada and out to sea just as fast as the storm moved into North Carolina, New Jersey and New England, leaving behind a mess to clean up. Over 20 people died in only the third hurricane to make landfall in New Jersey in the past 200 years. There was an estimated $7 billion in damages; however, it could have been much worse. As bad as it was, some of the predictions and fears for destruction in highly populated areas were not realized. This photo is of flooding in New Jersey.What lessons should business take from this? Our view is that predictive metrics played a key role in reducing the deaths, injuries and destruction. The forecasting of the Hurricane Center was very accurate and timely. People had warning. Flights were cancelled. Airports were closed. Transportation systems were shut down. Every attempt was made to save life, property and infrastructure. People throughout the region were asked to evacuate low lying areas, for example. Public officials were able to predict where flooding was likely and thereby prevent loss of life. Damage occurred, to be sure, but the precautions reduced the impact of Irene significantly.
What we have advocated for businesses is to have internal predictive metrics that can predict behaviors and outcomes with customers. One prime example from our presentations and book is the idea that if popcorn is less than 10 minutes old in a movie theater, it will taste fresh and the customers will like it. That metric was derived through research and used to predict how customers would react to the popcorn based upon how old it was. We are not comparing the impact of Irene to popcorn, but the idea is that predictive metrics can save disasters and minimize negative impacts for businesses.
Developing predictive internal metrics for businesses is both an art and a science, just as it is for hurricanes, but it can be done. Our book outlines the basics of approaching this problem. For every issue that is important to your customer (like the freshness of the popcorn in a movie theater), there should be at least one internal predictive metric (such as a kitchen timer that is set for 10 minutes near the popcorn popper in the movie theater) that can warn the business when things are going awry, so you can take action (throw the old popcorn away and pop another batch), hopefully before your customers notice a problem. To see a couple of videos about this process, click here for Part 1 and here for Part 2.
Are you prepared for disaster? If a Hurricane Irene type of event were to approach your business, what precautions would predict that you have minimized potential damage to you and to your customers? What internal predictive metrics would you monitor? What actions would you take?
As we also discuss in the book, the House of Quality and other techniques can be used to not only find and document those predictive metrics, but also to decide which ones to act upon and how. We will leave that discussion for a future blog entry.
