Implementing web analytics is a WHAO process. I invented this funny acronyme to summarize its 4 phases:
- Tracking what matters for your business (the “What shall I track?” phase)
- Monitoring data being able to tell whether your business is doing well or not (the “How am I doing?” phase)
- Analyzing negative KPIs in an actionable way (the “And now?” phase)
- Taking the right actions to optimize your business based on the analysis outcomes (the “Optimize” phase)
The W-Phase implies going through the process of defining a measurement plan, which I explained in my previous article.
Step 3 and 4 are not in the scope of today’s article, so I will focus on giving you a profi-tip for the How am I doing phase by presenting a graph that condenses multiple KPIs in a single plot and can be applied to a number of use cases!
To help you understand how this way of plotting data could serve your business purposes, I have built a fictive example and will explain you how to interpret the outcomes.
The company Acme Inc. uses a Blog to promote its consulting services. Being a company that sells analytics services, they know very well what needs to be done to track blog analytics. After having gone through the process of creating a measurement plan, they have identified 5 engagement metrics:
- User has read an article from the top to the bottom
- User has taken enough time to read an article (opposed to a “scanner”, he is a “reader”)
- User has commented an article
- User has shared an article
- User has liked an article
The main business objective of Acme Inc. is clearly to engage their visitors to consume their content.
In order to be able to optimize contents, they would like to evaluate the topics about which they write according to the 5 defined engagement metrics (their KPIs).
For this purpose we will use a bubble graph. Each colored bubble will represent a topic and the 3 dimensions (X = horizontal, Y = vertical, Z = bubble size) will be
- X-Axis: % of readers (vs. scanners)
- Y-Axis: average % of article completion (how deep do users go when scrolling articles? 100% = complete article)
- Bubble Size: Engagement Index (a weighted average of # comments, # likes, # shares)
How to interpret bubble position on the axis?
How to interpret the bubble size?
How to compute the Engagement Index?
The engagement index is the normalized weighted sum of your engagement metrics.
- Define a weight for each of your metrics (give high points if you judge that conversions on that metric represent high engagement)
- Take the monthly total of each metric and multiply by their weight
- Divide by the sum of the weights
- [Optional] Normalize to the highest engagement (to get a better idea of the relative engagement across topics)
Acme Inc. gave 4 points to “comments”, 3 points to “shares”, 2 points to “likes”, 1 point for “unique pageview”. Here is how to compute the engagement for each topic.
And the Engagement Index is simply a normalized engagement based on the campaign with highest engagement, so that you can get a number between 0 and 1.
By applying the engagement formula to the Google Analytics data in a Google Spreadsheet, here is the outcome of the engagement by topic compared between the current month and the previous one.
Note that these graphs are interactive and data comes from Google Spreadsheet (check out this article to learn how to implement them)
Isn’t that powerful? In one single plot you have an answer about your “How am I doing” phase with respect to your main business objective.
Even if I already gave you a cheat sheet about how you should interpret this graph I will provide you the solution to the “And now?” question 😉
First of all, the plot will just tell you how things look, but not why they look like this! For this you need business insights, which are a capital asset when analyzing data.
In this particular case it’s all about knowing what was posted this month. The fact that Trends has such a low engagement might be simply due to the fact that there were actually no new posts about it. If that’s not the case, Acme Inc. should try to understand if the posts were just not interesting, or not adapted to their blog audience.
The plot is just a trigger for your data analysis. Drilling down the data, segmenting and analyzing the possible causes is not something that you should do on a dashboard. Furthermore it requires context.
Knowing the context, analyzing the data against your goals and optimizing your efforts according to the results is the essence of analytics.
Techy Goodie: in the next post you will learn how to implement that with
- Google Analytics
- Google Spreadsheets
- A static HTML page …only copy-paste skills are required 😉
Read all our Web Analytics post
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I have over 8 years of experience in consulting and hands-on experience in Web Analytics, especially with Google Analytics (10+ years), Google Tag Manager (3+ years), Google Data Studio (1+ years) and Microsoft Power BI (2+ years).
I am also a proud dad, a passionate football fan (AC Milan) and I like to spend all my free time writing on my food-blog (www.cucina.li). What can I cook for you? 🙂
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