May 9, 2017 / Keri Hettel

Targets, benchmarks, and baselines, oh my

As we present data to our clients, there is one question we can always count on – Is that good?

As we present data to our clients, there is one question we can always count on – Is that good? I recently found myself asking a similar question. I’ve been training for a race and tracking my runs. Initially I just wanted to make sure I was running the right mileage so I’d know what to expect on race day, but my app kindly shares my pace and mile split time at every mile marker. As I listened to my app talking (telling me I was running more slowly than I thought :/), I thought about where I should be. Where did I expect to be? What did I think was “good”?

As I contemplated my own race day goals, it reminded me of the importance of being able to answer that same “Is that good?” question for our clients. As we share data, we must set expectations so that when we look at results, we are setting ourselves up to drive what to do next – to know if we achieved our goals, or if not, how to improve so we can get there.

In order to set proper expectations, we need to think about 3 distinct types of numbers: targets, benchmarks, and baselines. Many times, these numbers are used interchangeably, or not at all. Yet, it is important to understand their distinction and what role each plays in providing perspective about the data at hand. You can think about them this way:

  • Your target is your gold medal: it’s an achievable goal, but something that you will have to work really hard for
  • Your benchmark is looking to the runners to your left and to your right: it tells you what you need to do to be aligned with the performance of your competition
  • Your baseline is your time from your last race: it’s your starting point to ensure you improve over time

As I struggle to decide what I want my goal time to be, I recognize it can be difficult to establish these numbers. You may need to gather past performance data from disparate data sources. You may need to consider adjacent industry performance if data in your industry is limited. Finally, you may even need to apply custom adjustments to align with a unique element of the program you are measuring. Ultimately, it is not important that these numbers are perfect, but it IS important that they are a part of your everyday data conversations.

Keri Hettel / SVP

With nearly 15 years of analytical experience and 10 years in digital healthcare marketing analytics, Keri leads the analytics vision of intelligence-driven design and development providing data solutions, measurement programs, and advanced analytics techniques to help inform and optimize business decisions throughout each client engagement.