Use Data Or Be Used By Data!

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The June 19 issue of Seotistics is here for you!

Have you ever looked at a dashboard and thought "This stuff doesn't make any sense".

You are not the only one.

Web Analytics and Marketing are infested by vanity metrics and useless dashboards.

This issue will show you some basic mistakes to avoid.

N.B. Last week I had problems sending you issue #7, so for those who missed it, you can read it here.

🔑 Key Concepts

Let's start with some basic definitions:

• Data Visualization (DataViz): Graphical representation of data and information. You can say it's a conversion to a visual format.
• Pareidolia: detecting patterns when there are none.
• Cognitive Load: the effort to process information.
• Cluttering: excessive cognitive load caused by too many elements on a visualization.

After reading this issue, you'll have a clear overview of what to avoid.

You won't become the master of DataViz but you'll be above the average, which is also good.

If you've always despised many Looker Studio dashboards, you are in the right place.

🧮 Actionable SEO Tip - Do The Right Thing

You have two choices in life: do wrong but cool things or do boring but correct things.

Effective DataViz may be boring because it doesn't have to be glamorous.

You can get the best of both worlds but again, effectiveness first.

Consider these factors first:

• Who is your audience?
• What do you want to achieve?
• Do you have the right data?

In most cases, simple plots do the trick. You don't have to spend hours on it if your audience is "unsophisticated".

Many Looker Studio dashboards want to be cool but not practical.

Showing a lot of scorecards and line plots may sound logical but doesn't make any sense in practice.

If I have to make a decision, I'd prefer an uncluttered view instead.

This is why I don't use the Looker Studio dashboards you find online...

I create them myself based on the requirements (if any).

❌ What To Avoid

These are the DataViz capital sins:

• Using 3D visualizations - confusing
• Pie charts - terrible for proportions
• Axes not starting at 0 - misleading
• Bad use of color - problematic
• Wrong use of bars - misleading

All of them are easy to avoid, do yourself a favor and learn them!

The worst offenders are not starting the axes at 0 and misusing pie charts.

How many dashboards have you seen with something like 10 pie charts!?

Those are terrible to use...

Think about what your audience may need to assess the situation or make a decision.

P.S. Most of this stuff is covered in detail in the book I linked in Recommended Reads (and also mentioned below).

✅ What Works

This is what works according to Knaflic’s Storytelling with Data (picture below adapted by Andrea d'Agostino in his article)

The heatmap is my favorite example as I keep bugging you all with this example below:

Marco Giordano @GiordMarco96 You must admit my table drips 😎 This is my usual heatmap with a twist, more formatting! Presenting data is mandatory nowadays. I would only change the palette to have red as high instead. 12:0 PM • Apr 14, 2023 1 Retweets 20 Likes

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Effective plots reduce cognitive load, making sure your audience can quickly understand what's happening.

In practice, a lot of executives want tables with conditional formatting and simple text.

Barplots are another powerful tool because you can use them for pretty much anything.

🧠 Your Brain Is Playing Tricks On You

Our mind is flawed as we suffer from several biases.

One of them is pareidolia, the capacity to spot a pattern when there is none.

This is everpresent in DataViz and you have to think twice before you jump to conclusions.

I recommend you actually inspect different subsets as well to go into depth.

But that isn't all, there are multiple biases that affect our online behavior:

• Cognitive
• Anchoring
• Negativity
• Confirmation

In plain English, proper DataViz should take into account some common issues we face as humans.

Highlighting the correct elements and trying to be as neutral as possible are good solutions.

Some of our findings can contrast our views and that can lead us to manipulate our data or ignore important insights.

If you want the best overview of Neuroscience in Search and learn more about biases, you must consult Giulia Panozzo's website. 👈

❌ My Personal Pet Peeve

I really hate when SEOs post their unpolished plots because anyone can produce them.

If you can tell where they come from, it's not something I would sell or present for serious reporting.

Don't get me wrong, it's fine to use basic stuff for demonstrative purposes.

But for commercial uses, I'd seriously consider spending 30 minutes on improving it.

The "wow effect" is one of the most sought-out things by clients, for some reason.

BUT and I say once again BUT...

... your plots should communicate something.

Communication is the #1 requisite of DataViz, else you are simply producing cool stuff for the sake of it.

We don't need to be super good, but making some small adjustments can make our industry better.

I learn every day because DataViz isn't even my first specialization but trust me, it's worth it!

💡 The SEO Insights

Now that you've read some DataViz theory, let's see how you can use all of this with SEO data:

👉 Avoid paying/using huge Looker Studio dashboards, focus on key metrics

👉 Think how a dashboard can give you actionable SEO tasks

👉 Be sure it doesn't require excessive maintenance

Visualizing data is powerful because we are visual animals, after all.

This brings other issues, such as being easily fooled by colors, patterns and shapes.

If you want to speed up your SEO execution, you need to be extremely careful.

This stuff is also powerful if you want to get backlinks by publishing original research.

Create good plots/infographics and it'll rain backlinks!

This is one of those cases where you need to be cool and effective at the same time.

🧵 My Selection Of Twitter Threads

A quick recap for those who haven't read them all or need a refresher:

• ​Interpret GSC metrics and avoid pitfalls​
• ​Why I've chosen to pursue Analytics​
• ​SEO and AI: some considerations​
• ​Fatal Content Websites Mistakes​

👥 Launching a Community (Join The Waitlist)

We have launched our Discord community and I will contact every person who hasn't joined yet (starting from tomorrow).

Our goal is to encourage actual SEO testing and building meaningful connections.

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I am also considering using Twitter for communities... why not!?

🔎 Analytics For SEO Ebook (v3)

This ebook is aimed at SEOs or Business Owners who want to explore the combination of SEO and Analytics.

If you want to start building your data empire, this is the (initial) path to take.

It will teach you or your employees to:

👉 Avoid common pitfalls that cost you money 💸

👉 Create meaningful analyses that add value 💯

👉 Shorten the learning time of Analytics ⏳

This comes with monthly updates because I want to create the Ultimate Guide out there.

The May update includes the following new information:

✅ Detecting Keyword Cannibalization (The Actual One) - Use Case 6

✅ Statistics 101 (Small Update)

✅ Updates For Use Cases 2 and 5

✅ Introduction to R

✅ Brief Introduction to Joins (More in v4)

✅ N-grams and How You Can Use Them

v4 (End of June) will feature:

• Data Simulation (How to Generate Synthetic Data for Testing)
• Updates to Use Cases 2, 5 and possibly creating Use Case 7
• Going Deeper With Analysis (Google Analytics, Screaming Frog, etc.)

v5 (End of July) and will feature:

• Financial Analysis (Costs/Benefits)
• Updates to all the Use Cases
• Decision Theory and Concepts for Agencies/Portfolio Owners
• Information Analysis (maybe... to be decided)

📚 Recommended Reads

This topic requires dedication and a lot of time. I show you the best resources to get started now:

• ​Presenting SEO Data the Right Way​
• ​Storytelling with Data​
• ​How Users Make Decisions​
• ​Techniques of Data Visualization: from Charts to Storytelling​

❗️ Feedback and Recommendations

If you have ideas/recommendations for the next issues of Seotistics, you can simply reply to this email.

Marco Giordano
​SEO Specialist & Data Analyst

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