Six Data Visualizations That Rock!

Regular readers of this blog have often been treated to my undying love to data visualization (Death & Taxes, Make Analysis Connectable, Analytics V2).

Analytics of any sort is hard enough for most, except perhaps the gifted, and a solid visualization of even the most complex data has an amazing power to transcend complexity and make life for the every day Marketer, Analyst, Ms. Somebody or Mr. Nobody just a tiny bit easier.

But more than anything good visualization of data promotes data democracy. They ensure that the data presented will be understood, often with little explanation and the recipient will do something with the data. Case in point Jesse's Death & Taxes graph example.

Yet we life in a world (especially Web Analytics) where it is hard to find lots of examples of great data visualizations. This post covers seven interesting and every day examples of illustrating data. Each does something unique and something different and helps make the world a better place:

#1: FeedBurner City Tag Cloud.
#2: FeedBurner Subscribers Pie Chart.
#3: "Heat Map" Tables in ClickTracks.
#4: TitleZ "Lifetime Table".
#5: Amazon.Com Customer Ratings Distribution.
#6: Google Analytics Comparison To Site Average.

I hope that it inspires you to present your data in more meaningful ways (and if you are a Vendor / Solution Provider then here is your to do list).

I use these data visualizations every day (well almost)………

#1: FeedBurner City Tag Cloud.

City data (or referring URL's or search keywords etc) have a horrendously long tails and usually have lots of rows of data. This is the most common way of presenting the data:

I get very little data, sure I know which city is up or down but the total amount of data is quite little and not very helpful.

Now switch to a tag cloud view and we are cooking:

Suddenly I have so much more data to look and, and it is in context. I know the high and low cities, I can see other cities that I would normally not have a chance to look at (Pune, East Ham, Pompano Beach etc).

Why it works?

1) Surfaces a lot of data very quickly and easily.

2) The text size help communicates volume of visitors in context effectively, if can easily see big cities I need not target in my marketing campaigns and small cities that just could use some love. Nice.

#2: FeedBurner Subscribers Pie Chart.

Haven't pie charts been done to death? There are certainly lots of terrible pie charts, especially in Excel (gets much better in Excel 2007, more on that below).

But I love this pie chart in FeedBurner:

Why it works?

1) Raw numbers and percents, you need both to get the gravity of the situation. :)

2) In most pie charts the pie is the hero, in this case it is, at best, a stage prop. The numbers are the heros.

3) The raw number on top, big and bold, is a nice touch. Everyone can actually do math, but not really. It is good to have that number up there.

#3: "Heat Map" Tables in ClickTracks.

People in the office are sick and tired of me saying: No I don't like it because it is not "instantly useful". That's my bar. If I look at something, a graph or table or data presentation, it has to to be instantly useful, i.e. it should communicate the first blush of humble insights as soon as you lay your eyes on it.

Welcome my best friend: heat maps in tables! Why not?

Why it works?

1) Even though there is lots of data here it is pretty easy for your eye to gravitate towards what is most important, darker cells.

2) Look how easy it is to notice the trend in your numbers, see the outliers? You don't have to think, that's what's cool about it. I say this from experience that it is really hard for people to pick this out from a simple non heat-map table.

Here is another example of a "heat map table", this one from the Google Search History tool.

Notice the same benefits as the ClickTracks example. (How come this is not standard in all analytics tools?)

#4: TitleZ "Lifetime Table".

Like every first time author I obsess about my book's amazon ranking. But Amazon's ranking changes too fast (here today, gone tomorrow!). Willem turned me on to TitleZ, a wonderful site that allows me to track history.

Now I can track the trend over time and measure real success.

(Click on the image for a higher resolution version.)

But that is not all, notice that along with giving you a longer term trend you also get some very cool metrics that are chock full of insights. Best and worst ranks. 7-day, 30-day, 90-day and Lifetime Averages.

Those numbers are especially helpful when you want to compare your performance with that of others (and check if you are just a flash in the pan!):

(One instant insight: Don't pre-launch a book on amazon, it messes up your Worst Rank and Lifetime Average!)

Why it works?

1) Your performance this very instant (or today or last week) is not truly insightful. TitleZ shows how you can show lots of great data in a tiny table and communicate so much. Nothing sexy about it, just good old fashioned actionable insights.

2) Lifetime Average. I absolutely adore this number. It is so rare to find it in any web analytics report / package but it is so easy to see how it gives you key context to your current and historical performance. (What's your best and worst conversion rate? Do you know your 7-day, 30-day, 90-day average bounce rates?)

#5: Amazon.Com Customer Ratings Distribution.

Sometimes you want a quick summary of the data, at other times you want more than the summary. Why not both?

Recently I noticed that Amazon had a little "down arrow thingy" next to the ratings of the books. If you hover the mouse pointer over the "down arrow thingy" it expands to give you a distribution pattern of the customer ratings! Sweet!

Admit it, until you saw the distribution of the ratings you did not really know that you needed it so badly, especially in cases where there might be many data points.

Why it works?

1) Insights are often hidden in the nuances, and in this case the distribution provides a awesome picture that can help you decide if you should buy my friend Gradiva's book (you should!).

2) It is very easy to understand, includes nice bars as well as raw numbers, and has a helpful link to more info.

#6: Google Analytics Comparison To Site Average.

In this table can you figure out which keywords are performing well and which ones are not when it comes to the critical metric of bounce rate?

How about now?

I'll admit it is a slightly imprecise comparison because the first table also includes two additional metrics. Still you can easily imagine how looking at just a whole column of bounce rate raw numbers is kind of useless unless you can compare it to something. You press two buttons in Google Analytics and it will compare the bounce rates to the Site Average and bam (!!) you have actionable insights.

Why it works?

1) It is almost self explanatory (and even if you are not a average junkie you can see how this acts as a wonderful initial filter). You instantly know which keywords are working and which are not. You don't have to think (you can if you want to, but initially you don't have to).

2) It puts data in context, in this case Visits (where does most of my traffic come from and how bad is it!). In the drop down you can change Visits to Time on Site or Goals etc.

These are all diverse examples, all very everyday life and all around you, of how different companies and tools make our lives easier. Many of them are not necessarily pretty, yet every single one of them is very well thought out and instantly useful.

What do you all think? Much ado about nothing? Have better examples? Disagree with something above? Please share your feedback and critique via comments.

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