Traditional business metrics include standard financial and managerial accounting categories, such as quarterly statements of net cash flow, profits and losses, and changes to balance sheet items such as shareholder's equity. Traditional business metrics have their origins primarily in paper and pencil after the fact reporting. Some of these metrics were once innovative too, like 500 years ago, when bankers in Florence, Italy invented double entry book keeping. Business decisions, that might be made based on these metrics, will happen usually after long deliberation, sometimes on a scale of months to years. They remain extremely important and worth of our study, but we just don't have time for that. We want Dynamic Business Metrics that are defined and can be communicated in a manner that conveys urgency. Metrics that address the right question, what change in our business processes can we make right now to increase revenues, maximize profitability, or reduce risk? Two attributes make a business metric dynamic. First, will the metric change significantly over intervals of a month or less? If not, it's not very dynamic. For example, the monthly rent a stand alone retail store in a mall pays on its three-year real estate lease is of course a business metric related to its efficiency and profitability, but it is not a dynamic business metric. There's no point in tracking it because it won't change anytime soon. On the other hand, if a national retail chain with 1,000 mall-based stores is individually negotiating and signing an average of about seven new three-year leases each week, it can and should track average monthly rent per square foot on new real estate leases as an important dynamic metric, against which to set goals and track progress. Second, are their specific actions the company can take that can visibly or significantly impact the metric in the short term? If not, then the metric doesn't lend itself to dynamic tracking. Whether the metric is dynamic, may also depend on the business context. For example, if the 1,000 store retail chain we just mentioned is neither adding nor closing stores and is simply renewing leases on current space, where the old leases had pre-negotiated terms to be extended, It will be difficult to make much impact on the average monthly rent per square foot. On the other hand, if that retail chain publicly announces that it is going to close 25% of its US retail stores, as the GAP chain did in June of 2015, it may be able to go back to landlords with the proposition, either we leave at the end of the current lease and you'll need to find a new tenant, if you can, who may not pay as much per square foot as we do. Or let's renegotiate our lease terms now. In this case, the average monthly rent per square foot for newly renegotiated leases would be a dynamic metric for the GAP. It's a place they could save money. This is why announcing all your bad news at once is often a good business strategy. How much impact a business change can have on the metric, is another important thing that we need to observe. If the metric is noisy, then lots of things are affecting it. If its twitchy, that means it's very specifically offended by what we're doing. Traditional metrics, like quarterly revenues, are impacted by dozen of different factors, many of which are completely outside the control of our business. The great new advertising campaign that we launched may not even show up in quarterly revenue metrics if, in fact, most of our customers are government agencies on long-term contracts with a very long sales cycle. Total revenues is always an aggregate number, and as they say on the Kissmetrics website, aggregate data is kind of worthless. Dynamic metrics are twitchy. Small changes in process, in our process, can lead to big impact. For example the percentage of people who will fill an online shopping cart and take their shopping cart all the way to purchase is extremely sensitive to average page load times. Studies have shown that pages that load in three or more seconds are much less likely to lead to sales than pages that load in less than three seconds. 40% of web users will abandon completely, a web page that does not load in three seconds. This is why there's an entire industry devoted to what's called edge caching. Content delivery networks, like Akamai, that retail stores pay to store copies of their websites locally, physically near their customers around the world, saving a precious second or two in load times. By the way, through a recent project my students at Duke did for a website performance monitoring company, we learned that on mobile devices many retails companies' web pages are taking 20 seconds or more to load. It appears that many global retail companies do not yet test their mobile based loading times from remote locations around the world. If your company does not already do load time performance testing for mobile devices in all global markets where it is active, It should start immediately.
