Deciding How to Represent Website Data - Part 1: Bar Graphs, Line Charts, Tables

Published February 20, 2015
Some people have really strong opinions about charts – or more specifically, about data visualization. Pie charts seem to get folks particularly riled up. In fact, Steve Fenton declared there are many, many reasons to despise pie charts. On the other hand, Bruce Gabrielle maintains those who cry for the death of pie charts are flat out wrong. He argues pie charts deserve your respect.
While I’m not going to weigh in on the great pie-chart debate, I do believe selecting the right data visualization tool for the job is important when creating digital marketing reports.
It is important because, for your analytics insights to have an impact, they must first be understood. Whether your audience is a client, your boss, or your internal marketing department, selecting the right chart will help you communicate the data more effectively.
This is the first post in a three-part series where we will break out common analytics reporting situations and identify the best approaches to representing the data (yes, sometimes it may even be a pie chart).
Part I of this series looks at how to visualize time series data. Part II examines segmenting data for a single time period. Part III looks at how to best present data to compare performance across time periods.

Blog Image Data Visualization Chart Types


Single Metric Time Series

With time series data, your audience is most often interested in understanding a trend over time or in looking at what happened at a specific point in time (e.g. a spike). In the simplest case, we are looking at a single metric and a basic line chart is most commonly used.

One problem with the basic line chart is that day-to-day fluctuations can make it difficult to discern trends. To solve this problem, it is useful to combine the daily time series data with a rolling average over a longer period of time. The rolling average helps to smooth out the daily fluctuations and reveal the trend more clearly.


Daily Sessions with Rolling Average Chart


These two time series charts show the same data – Daily Sessions – over the same period of time. The chart on top is taken from Google Analytics; the lower chart, which includes the 7-day rolling average (red line), was produced by Megalytic.

Both charts show that Sessions (or website visits) fluctuate on a weekly pattern (dropping off on weekends) and that the average is generally increasing as time goes on.

However, the key drawback of the top chart is that you cannot tell what the daily average is for Sessions or by how much it has improved. By including the 7-day rolling average, it gives you an average daily value for traffic and clearly shows how it is changing over time.

In the lower chart, we see that December started with around 500 Sessions per day, dropped way down during Christmas and New Year’s, and by early February was running close to 700 Sessions per day.

Of course, you can achieve some of the same benefits by graphing the data weekly and not including the rolling average, but you lose two important pieces of information. First, you no longer have a visual representation of the daily average. Second, you no longer communicate the weekly fluctuation in traffic for this website.

The examples below use the Megalytic reporting tool to generate data visualizations. To follow along and create the same charts with your own data, sign up for a free trial (no credit card required).

Single Metric Time Series – Segmented by Dimension

A key technique in web analytics involves segmenting data along different dimensions to gain insight into what is driving the changes. For example, you may want to segment Sessions by Acquisition Channel to determine if traffic growth is coming from Organic Search, Social or somewhere else.

But, what is the best way to visualize time series data segmented by dimension? In truth, there is no one best answer. It depends on your audience and what point(s) you want to emphasize.

Suppose you want to show the audience how much each segment has grown or shrunk over time? Then, a basic line chart like the one below may be best.


Sessions by Channel Time Series


Here, you can see clearly that Organic Search contributes the most traffic and that it has grown from about 1.4k to 2.6k weekly sessions. One difficulty with line charts like this is that once they have more than three series, they begin to look cluttered and confusing to the eye. In this case, we have six lines; that is about the maximum you can have and remain readable. As an alternative, you may opt to render the same data as a time series bar chart. As shown below, this can make it a bit easier to make sense of all six dimensions.


Session by Channel Bar Time Series


Whichever you choose -- the line series or the bar series -- what you cannot easily comprehend is the relative contribution of each channel to the whole and how it has changed over time.

The area time series chart can help show how the total breaks down across the dimensions and how the breakdown has changed over time. This chart is similar to the line chart, except that each line has the area underneath filled in and the lines are stacked on top of each other, so that the top line is showing the total.


Session by Channel Time Series Area Chart


With this area chart, you can see that Organic Search is not only the largest, but that it is significantly larger than the other channels. You can also see that the total traffic has increased from just less than 3k sessions to slightly more than 4.5k sessions.

This helps us to see how the individual channels stack up to produce the total traffic, but it is tough to see how the shares are changing. For example, Paid Search seems to represent a smaller percentage of the total traffic than it used to, but by how much has it changed? It is not visually apparent here.

We can change that by normalizing the Y-axis to show the Percentage of Total Sessions instead of the actual sessions.


Sessions by Channel as Percentage Time Series


With the data normalized, we can more clearly see that the contribution of Paid Search has shrunk relative to the total number of Sessions. In late November through early December, the purple band is much thicker than in early February.


As the examples above illustrate, how you visualize data is nearly as critical as what data to show when communicating information to your team. When deciding upon the best way to represent your website data, keep in mind the point(s) you are trying to get convey. For example, if you are working with segments and want to emphasize the size of each individual segment, a line chart will help you see growth over time. If you want to show the actual size of each segment within the context of how it contributes to the total, use the area time series chart. And if it is most important to show how the percentage contribution of the segments has changed over time, use the area time series together with a Percentage on the Y-axis.

Thankfully, Megalytic provides a variety of charts and tables to help marketers represent data in a clear and impactful way.

Next in our series about data visualization, we’ll cover aggregate data for fixed time periods and delve into dimensions and segmentation.



This is the first post in a three-part series where we break out common analytics reporting situations and identify the best approaches to representing the data. Here, we have looked at how to visualize time series data. Part II examines segmenting data for a single time period. Part III looks at how to best present data to compare performance across time periods.


When the client first came to you, you talked up the value of Google Analytics. You emphasized the importance of seeing where your traffic was coming from. You went on and on about how Google Analytics can show traffic sources to pinpoint whether people came from search, social media or a specific site referral, and how valuable this data was. You sold them on it, so much so that your client looked forward to receiving that first report, the magical day when they would finally understand where visitors were coming from.
But then the report came, and it looked like this:



It showed that 10% of your client’s traffic came from “(direct)/(none)”. What does this label mean? How do you explain Direct traffic to your client? Better yet, how do you explain “none”?
Let’s take a closer look at understanding Direct traffic in Google Analytics and how we can address it with clients.
Digital marketers spend a lot of time focused on PPC and SEO campaigns in order to drive desirable traffic to a website. The phrases we’re ranking for and bidding on get meticulous attention, so much so that we often forget about some of the other ways that visitors find us.

We put a tremendous amount of the effort we put into reviewing organic search data and PPC campaign performance in analytics. But how closely do we monitor referral reports?

If that’s not a channel you review regularly, you may be missing out on seeing traffic that is coming directly from links you’ve obtained around the web, local business listings, news mentions, and more. Many times, links are only considered as a means to an end, a metric that Google uses in determining how to rank sites in the SERPs (search engine results pages). But the fact is, many of a site’s links may be directly contributing to its traffic.

In this article, we’ll review how to look at referral reports in Google Analytics, and some of the many ways to use that data to better inform your web marketing decisions.


Remember how your mom told you not to stand too close to the television because it might hurt your eyes?

The same rules can apply to data. If you’re too close, you may miss the patterns and trends that are crucial to understanding your website’s performance. You can’t judge a site’s performance looking at data in the bubble of a single day, you must consider any day’s traffic compared to the days before and after.

Google Analytics makes it fairly easy to analyze trends over long periods of time. But it also allows you to stand right in front of that TV, to look at more granular levels of time, right down to the hour.
There’s a better way to get that close to the data, without burning your retinas. We’ll cover how to analyze traffic effectively in today’s post.