Why most data analyses lead nowhere

I once helped a colleague on the customer success team, Lou, analyze our retention data.

Lou asked me: “Can you help me get a report on the number of inbound service requests filed in the past quarter for each of our customers?” Easy enough I thought. I pulled the data from our help desk, created the necessary report and sent it to Lou. I thought that was the end of it.

A few days later, Lou came back and said: “Thanks for your help last time. Can you also get me a report on the amount of time that we spent responding to requests in the past quarter for each of our customers?”

The report wasn’t complicated to create, but we lacked the data. We did not track time spent servicing customers. After speaking with Lou, we decided to have the team start tracking their time. We recorded over a month worth of data before we created a first version of Lou’s desired report. Lou looked happy with the results, so I thought this was again the end of that project.

Wrong. Over the following weeks, Lou requested a half-dozen more reports, and we initiated the tracking of many new data points. A good amount of time and energy went into this retention analysis.

After Lou’s requests died down, I curiously asked: “So how did all the reports help you in the end? Did you find what you were looking for?”

Turns out, Lou didn’t really have a goal in mind… Lou was at first curious about how much resources we were spending per client, which led to follow up questions along the way. Based on the data, Lou eventually suggested to the customer success leadership to start setting limits to the amount of service time each client could access per month. Yet because of other priorities and constraints, the suggestion was never implemented. So nothing came out of the analysis project.

The good news is that the new data points we tracked provided us a ton of useful information that eventually led to other changes that helped improve our retention goals. However, that took another few weeks. And fact is, the whole project could have been a complete waste of time.

Having worked on hundreds of analyses with dozens of data-driven companies, I can confidently say that teams without an analytical process in place have an extremely high chance of wasting time performing data analysis. Start-up companies today have at their disposal an unprecedented amount of data, but having data doesn’t guarantee good decisions. Similarly, it doesn’t matter what BI tools we use, they are all useless if we don’t know what questions need to be answered.

To avoid wasting time and energy while pursuing analytics projects, this blog post will showcase an analysis process and framework to leverage before any analytical work begins, ensuring that any time investment is justified.


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