Today, in our high-tech, data driven world, companies love to say they are “evidence based” or “data driven”; but what does this really mean? Collecting data doesn’t help businesses achieve success unless they can use the data to guide and support the decision-making process. It is not enough for companies to simply collect data…They must collect data AND THEN apply good metrics. What exactly are “good metrics”, you ask?
ARE EASY TO UNDERSTAND – the metric should be relatable and easy to remember and discuss
ARE EXPRESSED AS A RATIO OR RATE – Example: You can collect data regarding distance traveled only or you can collect data regarding distance traveled per hour – While the former data point does provide a certain level of information, the latter helps you determine, not only how far you’ve gone, but whether or not you should speed up or slow down
COMPARE A DAILY MEASURE TO THE SAME MEASURE OVER A PARTICULAR PERIOD OF TIME – Example: Speed now vs. Average Speed/hour
ARE BASED ON FUNDAMENTAL METRICS LIKE REVENUE, CASH FLOW, GROSS SALES, ETC.
CHANGE BEHAVIOR – Ask: What will the business do differently based on changes in the metric?
While establishing good metrics, based on the above criteria, may sound easy, business analysts and decision makers must be wary of applying “false metrics”. False metrics are metrics that are not tied to true business goals; revenue tied to “deals in the pipeline” (vs. revenue tied to “deals closed”) is a good example of a false metric. This is a “false” metric because, while acquiring leads is important, 'lead acquisition' is not a true business goal. This example of a false metric results in a focus on all leads, rather than a focus on closing deals with qualified clients and actually earning revenue, which is a true business goal.
There are two types of metrics:
Accounting Metrics (sales, revenue, etc.)
Experimental Metrics or test results that will help optimize products, pricing or market share
When attempting to choose metrics businesses must consider:
Qualitative vs. Quantitative Metrics -Qualitative metrics answer questions regarding “what” and “how much” while quantitative metrics tend to provide more information regarding “why”
Exploratory vs. Reporting Metrics -Exploratory metrics try to find unknown insights while reporting metrics keep you informed regarding day to day operations
Correlated vs Causal Metrics -Correlated metrics = two metrics that change together; causal metrics = one metric causes another to change
Leading vs. Lagging Metrics -Leading metrics provide a predictive understanding of the future, providing time to act, while lagging metrics explain the past
Good metrics are important regardless of industry type; specific metrics –metrics that drive the business- exist in all industries. Restaurant owners look at number of table turned in one night, while investors focus on ROI and media websites look at ad clicks. No matter what your business, data collection and good metrics are essential to learning what’s working, determining the right product and/or targeting the right market.
In short, companies looking to be "data driven" should be a) collecting the data AND THEN b) applying actionable metrics tied to actionable business goals. If your business would like help determining the best data collection methods and associated metrics to help you build, measure, and learn, please feel free to reach out via phone at 719-640-3348 or by email at firstname.lastname@example.org.