Reading Time: 3 minutes

Marketers are flooded with metrics: campaign performance, customer data, A/B test results, and more.

But what happens when you dig beyond averages?

The five-number summary is a statistical tool that gives you a deeper view of your data’s distribution—allowing you to spot outliers, skew, and hidden patterns that averages alone miss. I covered having these five metrics on a box plot chart if interested.

In this post, we’ll break down the five-number summary, walk through a marketing-focused example, and show you exactly how to use it to improve decision-making and communication with stakeholders.

What Is the Five-Number Summary?

The five-number summary consists of:

• Minimum – The smallest data point
• Q1 (First Quartile) – 25% of the data falls below this point
• Median (Q2) – The midpoint (50th percentile)
• Q3 (Third Quartile) – 75% of the data falls below this point
• Maximum – The largest data point

This summary is the basis for box plots, a visualization method that marketers can use to compare campaign performance, user behavior, or segment-specific metrics.

Why Marketers Should Care

1. Averages Lie

If you report an average email open rate of 23%, is that good? Not necessarily.

• What if 80% of your emails perform at 15% or lower, but one high-performing campaign skews the average?
• The five-number summary exposes this variability by showing how spread out the data really is.

2. Spotting Segmentation Opportunities

Let’s say you’re analyzing customer lifetime value (CLV) across a dataset of 1,000 customers. A five-number summary might look like:

• Min: $35
• Q1: $120
• Median: $180
• Q3: $290
• Max: $1,850

That range between Q3 and Max suggests a high-value segment that’s well outside the norm. These aren’t just “top spenders”—they may require their own marketing strategy, loyalty program, or targeted messaging.

3. Diagnosing Performance Spread in A/B Tests

Suppose you ran 20 variations of a Facebook ad. Instead of reporting the average cost per click (CPC), you calculate a five-number summary:

• Min: $0.42
• Q1: $0.63
• Median: $0.78
• Q3: $0.94
• Max: $1.53

Insight:

• Ads in the Q1 range ($0.63 or below) could be scaled.
• Ads above Q3 may need optimization or retirement.
• Outliers (Max = $1.53) deserve investigation—was the targeting wrong? Did the creative flop?

This approach helps you move beyond binary “winner/loser” language and think in gradients of performance.

How to Calculate It

If you’re dealing with a spreadsheet:

1. Sort your data in ascending order.
2. Identify:
   • Minimum = first value
   • Maximum = last value
   • Median = middle value
   • Q1 = median of the lower half
   • Q3 = median of the upper half

Using tools:

• Python (pandas): df['metric'].describe()
• Excel: Use MIN(), QUARTILE.INC(range,1), MEDIAN(), etc.

Example: Email Campaign Click Rates

Campaign	Click Rate (%)
A	1.8
B	2.1
C	2.4
D	3.2
E	3.6
F	3.9
G	4.0
H	4.1
I	4.3
J	4.7

Sorted Click Rates: 1.8, 2.1, 2.4, 3.2, 3.6, 3.9, 4.0, 4.1, 4.3, 4.7

• Min = 1.8
• Q1 = (2.4 + 3.2)/2 = 2.8 (Q1 is the median of the lower half of data: 1.8, 2.1, 2.4, 3.2, 3.6)
• Median = (3.6 + 3.9)/2 = 3.75
• Q3 = (4.0 + 4.1)/2 = 4.05 (Q3 is the median of the upper half of data: 3.9, 4.0, 4.1, 4.3, 4.7)
• Max = 4.7

Insight: Most campaigns cluster between 2.8% and 4.05%. Campaign A is clearly underperforming. Campaign J might be worth studying to replicate success.

Going Further: Box Plots

You can visualize the five-number summary using a box plot, a compact chart that quickly communicates the spread and shape of your data.

• The box spans from Q1 to Q3, covering the middle 50% of the data (the interquartile range).
• A line inside the box shows the median, highlighting the central tendency.
• Whiskers extend to the minimum and maximum values within 1.5x the interquartile range.
• Outliers beyond the whiskers are plotted as individual points, making them easy to spot.

Why it matters: Box plots convey spread, skew, and outliers far more efficiently than bar or line charts. For marketers, they’re ideal for comparing campaign results, ad performance, or audience segments across multiple datasets.

Tools to create them:

• Excel (with custom chart types)
• Tableau
• Python (matplotlib or seaborn)
• Google Sheets (with add-ons or workarounds)

Who Came Up With It?

The five-number summary was introduced by statistician John Tukey in the 1970s as part of his foundational work in exploratory data analysis (EDA).

Final Thoughts

The five-number summary isn’t just for statisticians. It’s a practical, underused tool for marketers who want to:

• Evaluate performance distributions
• Identify segmentation insights
• Optimize creative or media buys
• Communicate data nuance to non-technical stakeholders

Don’t just look at averages—look at the spread. You’ll find better strategies hiding in your data.

Reading Time: 3 minutes

The Data Display Events page within the Settings > Property Settings of Google Analytics is a powerful feature often overlooked. It’s essential to review it regularly, ideally every six months, to ensure your data is accurate and comprehensive.

Below is an example of the events setup on my Google Analytics account, which are largely default events. I’ve used a 90-day comparison, sorting them in descending order by event count for clarity. It’s important to note that Google Analytics does not allow you to rename or delete events once they’ve been created, so setting them up correctly from the beginning is critical.

Why Event Tracking Setup is Crucial

Since Google Analytics event tracking is not retroactive, the sooner you get it right, the better. While BigQuery offers more control over data modification, it’s not ideal to be repeatedly answering management’s questions about misspelled events or improperly tracked data. You want a clean, well-organized event setup right from the start.

5 Questions to Ask When Reviewing Your Events Page

Here are the key questions to ask when reviewing the events page in Google Analytics:

1. Are all event names following the proper naming convention?

Event names should be lowercase with underscores separating words for readability. Consistency is crucial—although dashes are also used by some, underscores are generally preferred for compatibility.

Tip: Use a naming convention checklist and stick to it across your entire event tracking setup. For example, you can establish a format and progression tracking for custom events using vip_insiders_ask, vip_insiders_consider, and vip_insiders_transact.

2. Are there any duplicate events that should be removed?

It’s easy to end up with duplicate events if naming conventions aren’t followed. For example, you might have both a custom “Click” event and the default “click” event, leading to confusion and data inconsistencies.

Tip: Regularly audit your event list for duplicates and remove or merge them as needed to prevent clutter and confusion in your reports.

3. Are there events not getting any data?

Events that aren’t receiving data could be a sign of broken tracking or that the event is no longer relevant (e.g., tied to an expired marketing campaign). Removing outdated or broken events helps keep your setup clean and efficient.

Tip: Set up periodic checks to ensure events are firing correctly. Google Tag Assistant and GA Debugger are helpful tools for testing.

4. What are we not tracking?

As your website evolves, your analytics setup may lag behind. Review the customer journey and consider new interactions that need tracking. Whether it’s a new form submission, video play, or specific CTA, ensure your tracking is aligned with the most important actions on your site.

Tip: List key actions users take on your site and make sure you’re tracking them. New product launches or feature updates are great opportunities to reassess your tracking strategy.

5. What are our trends telling us?

Not all events are of equal value. Look at trends in event data, such as spikes or drops, and investigate any outliers. These trends can provide valuable insights into user behavior, helping you identify areas for optimization.

Tip: Analyze the relationship between event trends and conversion data. For example, if increased video views correlate with higher conversions, consider optimizing video content.

Additional Considerations:

• Event Categorization: Group your events logically (e.g., “Clicks,” “Form Submissions,” “Video Plays”) to make reporting easier.
• Custom Dimensions and Metrics: If you need deeper insights, use custom dimensions and metrics in your event tracking to capture additional data points.
• Custom Alerts: Set up custom alerts in Google Analytics to notify you when specific events trigger, especially for critical actions.

Conclusion

Regularly auditing and optimizing your Google Analytics events is key to ensuring your tracking is accurate and aligned with your business goals. By addressing these questions, you can streamline your event setup, avoid common pitfalls, and make sure your data tells a meaningful story.