4 Rules for Building a Great Web Analytics Metric

analytics metric

Let me begin by telling you a story about shark attacks. The summer of 2001 was christened the “summer of the shark” by Time Magazine because the focus was on shark attacks that year. This story was then picked up and further propagated by other news channels. Before we knew it, the story of the summer was about an unexpected increase in shark attacks. Stipulating that I’m not minimizing the gravity of shark attacks on anyone during that time or any other time – we all know that the shark is an apex predator and behaves as such – the point I want to make is this: The media used a useless metric (number of shark attacks) to drum up drama during an otherwise ho-hum, pre-9/11 summer. If they had contextualized that metric by relating it to another data point such as the number of shark attacks in the previous year, they probably would have opted to focus on another story instead. Even the lifecycle of the ant would have been more interesting.

This brings me to the first rule of creating a great metric. That’s what you are here to read, right?

Rule #1

Numbers without context are notoriously poor measures of performance. Performance is best understood in relation to another quantity. The next time someone throws out a metric “number of blah blah blah,” your first question should be “In relation to what?”


Let’s work through a few examples:

  • There were 11,000 visitors to in Q1 2023.
  • Microsoft made $18.3 billion in income in Q1 2023.
  • AAPL stock was trading at $143 on Jan 30, 2023.

Do you know what action you should take based on these statements? None. Zip. Nada. Here’s why – There is no comparison for any of the numbers listed above. Bay Leaf Digital’s visitors were 11,000 in Q1 2023, but compared to what? Is that figure rising or declining? Do I need to panic or celebrate??? Apple’s stock was at $143. Does that represent growth because of the new iPhone release? Or was that pretty much a flat day compared to the day before?

Metrics only make sense in relation to other metrics. That relative measure can be the same metric for a different time or a directly related metric for the same time. For example (using Bay Leaf Digital’s 11,000 visitors in Q1 2023):

Root Measurement Interesting Metric Type of Comparison 
11,000 Visitors Q1 2023 vs
10,000 Visitors Q4 2022
1000 more visitors Quarter over QuarterTime series for the same metric
11,000 Visitors of which 10,500 were New Users21x New Visitors compared to Returning VisitorsRatio Comparison
11,000 Visitors of which 8,030 saw more than 1 page or converted73% Visitors are EngagedPercent based comparison to a directly related metric

The relative measures above provide contexts to these metrics. Now we know the visitor count in January changed for the better. Or that we are getting more new visitors than repeats. Or that there is a small and interesting segment of highly engaged visitors. Now we have something to chew on. In web analytics, relative measures are way more useful than absolute measures.

Rule #2

Of the above relative measures, the percentage-based metric feels the most intuitive because of its use in everyday life. From announcing sales (50% off!) to discussing interest rates (11% APR), the use of a percent-based metric is widespread. That brings us to the second rule of a great metric.


This rule sounds easy enough, but there’s a lot more to it than you may imagine. A great metric should have the following characteristics:

1. A great metric should be easy to communicate. In a conversation, you should not have to pause to explain what it means. In the example above, if we said “60% of our visitors saw more than 1 page on the site,” people will get it. There is little room for misinterpretation.

2. It should be linear. When a change occurs, the size of the change is directly related to the impact on the business goal. In other words, the metric should not have a complex exponential or a logarithmic relationship to the goal.

3. It should be directionally intuitive. An increase/up is good. A decrease/down is bad. This is how we have grown up to understand life. Keep it that way.

Rule #3

The third key characteristic of a great metric is broad applicability. These could be time, marketing channels, visitor type, content, etc. The greater the ability of the metric to be universally applicable, the better the metric is.


We are going to continue using our earlier example of the percent-based engagement rate metric to explain further:

Context – 
Interesting Metric – 
Engagement Rate 
Derived Metric – Rate of Change Context – 
Visitor Type 
Interesting Metric –  
Engagement Rate 
Derived Metric – Percent (%) Difference
Q3 202255%First Time80% 
Q4 202270%15%Repeat66%-17.5%
Q1 202373%3%   

In the above two tables, we have applied the context of time and visitor type to the engagement metric. In both cases, the reports make a lot of sense. We see that the engagement rate was higher in Q1 2023 compared to both Q3 and Q4 of 2022, and that repeat visitors have a lower engagement rate compared to first time visitors. Because we were able to apply these contexts, we have even more information about the factors affecting engagement rate.

So here’s the third rule about building a great metric – It should have universal applicability.

A great metric can transcend the silos of teams in an organization. It can be used as a universal language across marketing, operations, design, and many other teams. Think about a designer looking at the engagement rate of the home page before and after an upgrade. Now think about the SEO expert analyzing engagement rates of her highest performing pages compared to the engagement rate of her channel as a whole. Across all these teams, the definition of the metric remains unchanged, yet it is widely accepted and applicable.

Rule #4

The most important characteristic differentiating great metrics apart from poor metrics is actionability. The report you generate should drive action from its recipients. The simple question to ask yourself or the person requesting the report is “What action are you going to take when there is a change in this metric?” If the answer is vague, then you know this is not a great metric. It might not even be a good metric.


By actionable, I mean a change in this metric should spur further segmentation and analysis to identify possibly causation. One more thing about being actionable, a great metric is closely tied to an organization’s business goal. So when it is time to action, the actions have a direct impact on the organization’s ability to achieve its business goal.

This fourth rule wraps up the key rules to be used when building great metrics. Remember, all four rules are needed to build a great metric. Satisfy three and you have a good metric. Satisfy four and you’ll get a great metric.

web analytics

A Last Word

To recap, here are the four rules to create a great web analytics metric:

1. It must be relative.

2. It must be simple.

3. It must be universally applicable.

4. It must be actionable.

Remember, not every metric you build will be a great metric and not every report will be a great report. However, if you work toward one standard report that becomes the reference point for departments organization-wide and that universal report contains all your great metrics, you’ll support the data-driven decisions that lead to success at any company.

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