Forbes published an article on meaningful metrics, which provides examples and delves into some common quotes you might have heard from time to time. "You can't manage what you don't measure" and "If you can't measure it, you can't improve it" are explored more in depth in the article. In short, it is explained that just because you can measure something doesn't mean that you should. One example provided to justify this claim was from the early days of social media. An easy data point to measure was profile views or followers. Does this mean that high profile views implies that someone is an influencer or has far reach? Perhaps not - the author notes that there are paid services to artificially boost (or purchase) viewership. This makes measuring such data less useful. An example of meaningful data, from the same Forbes article, is from a financial services organization. They found that there was a low number of new accounts being opened and instead of measuring new accounts being opened alone, they focused on smaller intervals throughout the entire process. This allowed the team to identify different areas needing addressed to boost the account signups. Lessons learned from this are that measuring strictly to get an end result may not be as effective as measuring the means to get to the end goal. Veronica Head recommends that you answer three questions to determine if data is meaningful. They are:
- Can I tie this back to real performance? Does the data actually mean anything? In the article, Head compares this to running. Tracking the number of miles ran is great, but were those miles ran over the course of a day, 30 minutes, or 10 minutes? The more specific you can make the data, the more helpful it is and more easily it can impact performance.
- Does this tell a story of what's really happening? The tricky thing about data is that it can be used in many ways. Based on how the data is gathered, analyzed, and portrayed, different stories can be told. Sometimes, the same data can be used to tell more than one story simply by manipulating the graphics used when conveying the information. Always be careful in determining whether the data and means used to convey the data are representative of what is actually happening.
- Can you do something with this information? The final question that Head poses is whether there is something actionable that can come out of the data. In the article, the example given is related to a webpage receiving 7,000 views in a month. That might be a great number, but what can you do with it? If that number cannot be tied to sales, consumer demographics, or specific influencers (maybe you are paying someone to promote your brand), then those 7,000 views are only traffic to the site.