Monthly Archives: December 2012

Much of marketing is about measurement: be it in determining the success of that recent promotional campaign, determining how to divvy up ad spending, or making the case for your share of next year’s budget. The inherent problem is one that executives often cite: the difficulty in tying specific marketing activities to revenue generation. While “big data” analytics and bulky, expensive CRM and / or ERP software can sometimes be used to get a better handle on overall marketing ROI, such solutions still do a poor job of teasing out contributions of individual activities and are most often beyond the capabilities of small companies to meaningfully manage or to afford. We must therefore pick and choose how to measure success in life science marketing, and meaningful measurement means choosing the right metrics.

Quick note: There was an excellent article in October’s Harvard Business Review on the topic, albeit from the perspective of measuring overall corporate financial performance perspective rather than marketing performance (subscribers can read it here).

There are three common reasons why you may be using the wrong metrics. The first is overconfidence. Perhaps you’ve been seen a metric be strongly predictive in the past or have been told of its importance by a respected peer. If you get it in your head that the metric is important then it’s easy for that thought to stick, regardless of whether or not there’s a basis in fact. The second is availability. Quite simply, we tend to use those metrics that are easily obtained, that we frequently encounter, or that simply come to mind quickly. The last is because use of a particular metric is the status quo: it’s either what you’ve been doing or what you know everyone else does.

In order for a metric to be valuable, it needs to be predictive (there is a causal relationship; a change in A causes change in B) and persistent (the causal relationship is reliably repetitive over time). In marketing, you often will not have troves of various companys’ data to sift through; you merely have your own company’s data. You may be able to use historical data to determine if a metric is persistently predictive of the desired outcome, but for young companies or those who have not been measuring marketing metrics, there may not be enough data to reliably determine which metric is the best to use. Even then, however, you can still take steps to ensure you use the right metrics.

First, you need to specify what your goals are. What are you trying to change? In marketing, this may be sales, it may be leads, etc. Secondly, using either past data or, barring the availability of sufficient data, a subjective best guess, create a theory of what metric(s) will drive the desired change. Third, identify the specific activities that you can undertake to improve your metric in order to create that desired change. Lastly, evaluate your decision. Did the metric perform as expected? Was it both predictive and persistent? Were you able to control (read: “improve”) it by undertaking specific actions?

In order to reliably improve marketing performance, you first need to know what to improve. By using metrics that are predictive and persistent, you’ll be able to set a clear path to achieving your marketing objectives.