Data Benchmarks: Look at Your Own Data First
A benchmark is defined as a standard or point of reference against which things may be compared or assessed. When it comes to managing and analyzing your data, comparing it to larger data benchmarks created from your own historical data is the best place to start. No matter the phase of a project, whether it’s months or years in, the same questions always seem to come up as soon as you mention data or analytics: “But really, how are we doing?” and, “could you share some benchmarks so we know if this is any good?” While it might not always be the most satisfying immediate response, our team usually replies with some additional questions to consider:
- What kind of historical data do you have access to?
- Who in the industry would you consider a comparator/peer in your work?
- Has your team developed a measurement plan or outlined the goals for this particular platform or strategy?
- Your own historical data, which you collect over time
- Industry data, often collected by vendors like Google Analytics or Ad Platforms
- Competitor data, which is usually accessed using a data software/tool (i.e., Sprout Social, Brandwatch, SocialTables, RivalIQ, etc)
Internal benchmarks: your own dataAs a first step, benchmark your performance against itself with historical data. To do so, establish which metrics matter the most to your organization’s measure of success. Once you’ve established those metrics, take a look back at the last year of data you have on those particular metrics. If you happen to have more than a year’s worth of data, even better; however, organizations often undercover that something went awry or changed with data collection along the way, and there is only have about 6 months of data to work with — and that’s ok. The important part of developing benchmarks is that you have some historical data to look at to see what the trends have been month over month, or week over week. A simple way to move from historical data and trends to benchmarks is to look at monthly averages and use those to project a realistic range for growth over the next 6 months to a year. For example, if you have decided that email open rates are one of your KPIs for 2020, look at your average monthly open rate for your main newsletter throughout 2019. Let’s say that in 2019 you saw the average fluctuate between 17% and 23%. Averaging those data points out and setting a goal of increasing that average open rate is a perfect example of setting a benchmark for next year.
External benchmarks: industry and competitor dataInternal benchmarks are important because without them you don’t have a compass against which to check your progress or have a target to reach. From there, industry data benchmarks will help you see how your organization stacks up against the rest of your industry and provide you with a more complete picture. For example, if you are a mid-sized nonprofit focused on environmental issues, you could look in the benchmarking section of your Google Analytics reports comparing your average monthly site traffic to other similarly-sized organizations that also focus on environmental issues. And finally, competitor data benchmarks measure your numbers against the ones that matter most. For competitor data, you can look into a direct competitor’s Twitter account through a social media monitoring tool (e.g., Sprout Social or Hootsuite) and compare their total followers or engagement rates to your own. All three benchmarking categories provide you with valuable context, help you set purposeful targets, gain insights into industry trends, and let you see how you’re doing compared to your competition. We’ll dive into these two external benchmarks and how to better distinguish between them in an upcoming blog post – so stay tuned!
Need help developing your own data benchmarks?
If you are still having trouble wrapping your head around your analytics data, give us a shout. We’d be happy to talk through your pain points and help you get clarity.