In this post, we’re going to see how to setup a Google Sheets and Mailchimp integration, using Apps Script to access the Mailchimp API.
The end goal is to import campaign and list data into Google Sheets so we can analyze our Mailchimp data and create visualizations, like this one:
Mailchimp is a popular email service provider for small businesses. Google Sheets is popular with small businesses, digital marketers and other online folks. So let’s connect the two to build a Mailchimp data analysis tool in Google Sheets!
Once you have the data from Mailchimp in a Google Sheet, you can do all sorts of customized reporting, thereby saving you time in the long run.
I use Mailchimp myself to manage my own email list and send out campaigns, such as this beginner API guide (Interested?), so I was keen to create this Mailchimp integration so I can include Mailchimp KPI’s and visualizations in my business dashboards.
For this tutorial I collaborated with another data-obsessed marketer, Julian from Measure School, to create a video lesson. High quality video tutorials are hard to create but thankfully Julian is a master, so I hope you enjoy this one:
(Be sure to check out Julian’s YouTube channel for lots more data-driven marketing videos.)
Regular readers will know of my enthusiasm for building dashboards, especially using Google apps (like this one or this how-to article).
So I was super excited in May of this year (2016) when Google launched Data Studio, a free data visualization and dashboard tool to compete against incumbent dashboard vendors Microsoft PowerBI, Tableau and Qlickview.
Here, I’m excited to share my initial impressions and show you some of the basics steps to build dashboard reports using this tool.
I’ve been using it these past few days to build several different test dashboards.
First impressions: I love it. It’s simple and intuitive. Impressive.
Of course, since it’s a beta launch and it’s a nascent product, there are still areas where it’s lacking sufficient depth or flexibility and it’s a little buggy in places (see discussion below) but overall it’s a significant entrance into the Business Intelligence world for Small and Medium Businesses.
For Enterprise businesses, Google has Data Studio 360 – the full-fat, paid platform.
This article focusses on the free Data Studio version.
Data Studio: The smart way to build dashboards and reports with your Google data
So, another data analysis tool? What’s the value proposition here then?
The premise is that you can connect all of your disparate business data sources (only Google services at the moment but there are other non-Google data connections coming soon) and easily build beautiful, interactive dashboard reports to display that information. And all atop Google’s super-reliable, powerful, scalable architecture. Plus, as with so many Google products, it’s free.
It’s a simple drag-and-drop process to add charts and build reports, and it doesn’t require any knowledge of coding. That makes it super quick to create and modify reports. There’s a boat-load of data and formatting options so you can customize reports to match your needs.
Example: Social Media Reporting Dashboard with Google Data Studio
Want to see what this tool is really about? Heck yeah, I thought so!
Well there’s no better way to do this than seeing a real dashboard with real data.
So I’ll show you a dashboard for reporting social media referral traffic of a mid-size website (~500k pageviews a month). I’ll show some of the steps below and discuss how easy it is.
Great, well here’s our dashboard:
I’m not going to go through every step in detail, instead I’ll mention a few of the main steps and key points to keep in mind.
There are three steps to using Data Studio: 1) connecting to a data source, 2) creating visualizations in a dashboard, and 3) sharing your finished creation.
Step 1: Setting up Data Studio and connecting to a data source
You’ll be taken to your home page, showing a bunch of example Google templates (well worth a look) and any of your own dashboards:
Clicking the big blue plus in the bottom right of the home page creates a new dashboard from where you can connect to different Google data sources:
I’ve chosen to connect to Google Analytics and then to a specific web property, as shown in the following image:
This imports all of the data into my report and makes it available for charting or displaying on my dashboard. I discuss these steps in more detail in the video at the beginning of this post.
Step 2: Creating visualizations and creating a dashboard
In the control bar along the top of the window, there are 9 chart types available (line, bar, pie, table, geographic, scorecard, scatter plot, bullet chart and area chart) and the ability to add images, text, rectangles and circle shapes. Plus there are two filters available: date and a general “type” filter.
Adding charts is as simple as selecting the one you want in the toolbar, then creating a container for it somewhere on your dashboard canvas. Google will then build the chart, with some default data (which you can easily modify). The following GIF shows this step, along with changing the data being displayed and formatting the chart:
I’d encourage you to play around with the tool for a while at first, adding and deleting different charts to get a feel for what they look like and what sort of data they can display. There’s more info in the video at the start of this post.
For the Social Media dashboard above, I’ve made use of line charts, tables, scorecards and a geographic map. I’ve added a filter to the data source to restrict the data to show Social visits only:
Once you’ve finished building, switch to view mode to lock everything down and remove the gridlines. The filters still work in this view mode.
Switching between editing and viewing mode is done by toggling the blue toggle switch in the top R corner of the window, as shown in the following GIF:
Step 3: Sharing your dashboard
As a final step, you’ll probably want to share your dashboard with colleagues, clients or friends. Click the big Share button in the top R corner of the screen to bring up the share menu pane.
Here, you can enter email addresses of people you want to share the dashboard with or, as shown in the following image, copy the shareable link and send that to people, share on social media or your website. In both cases you can control whether others will have editing access or see the dashboard as view-only.
What I really liked about Data Studio
It’s flexible and can connect to multiple different data sources.
It’s super easy to create charts. Without too much effort you can produce really professional-looking, slick dashboards.
It’s really quick to create reports.
There’s a huge level of customization, especially with the Google Analytics data connection.
Multiple pages allow you to create a hierarchy of dashboards of increasing levels of detail/complexity, to tell a story.
Best of all, the data range and filter range controls are very easy to implement but incredibly powerful in how they work.
And of course, like many Google products, it’s free!
On a similar note, snapping to a grid to help line up objects would be helpful. Provided it could be toggled on/off of course!
More data connections, for example to SQL databases. This is coming soon…
I’d love to see more granular control over the chart details, for example formatting of the axes and working with data labels. The range of options is a great step in that direction and you can already make beautiful looking charts but this would take it to the next level.
I could not select multiple items by holding down the Ctrl (or Cmd) key and selecting, which struck me as odd. You can select multiple objects by dragging and holding the cursor across them however.
When I tried sharing, I was prompted to log into Google despite choosing the option to share with anyone. So, it seems that non-Google users can not view these dashboards at the moment.
When in Full Screen display mode, the page buttons are hidden and barely accessible, making it difficult to navigate through the dashboard.
If you’re invested in the Google ecosystem, work with data, especially Google Analytics, then I’d encourage you to give it a try. I’m sure Google will invest in this product and build out the features and data connection options. It’ll be interesting to see where it goes and how it competes with Microsoft’s PowerBI and other vendor offerings.
In this tutorial you’ll learn how to make a histogram in Google Sheets with a normal distribution curve overlaid, as shown in the above image, using Google Sheets.
It’s a really useful visual technique for determining if your data is normally distributed, skewed or just all over the place.
What is a Histogram?
A histogram is a graphical representation of the distribution of a dataset.
In this example, I have 1,000 exam scores between 0 and 100, and I want to see what the distribution of those scores are. What’s the average score? Did more students score high or low? How clustered around the average are the student scores? Are the scores normally distributed or skewed?
What is a Normal Distribution Curve?
The normal distribution curve is a graphical representation of the normal distribution theorem stating that “…the averages of random variables independently drawn from independent distributions converge in distribution to the normal, that is, become normally distributed when the number of random variables is sufficiently large”.
Bit of a mouthful, but in essence, the data converges around the mean (average) with no skew to the left or right. It means we know the probability of how many values occurred close to the mean.
We expect 68% of values to fall within one standard deviation of the mean, and 95% to fall within two standard deviations. Values outside two standard deviations are considered outliers.
We expect our exam scores will be pretty close to the normal distribution, but let’s confirm that graphically (it’s difficult to see from the data alone!).
Let’s see how to make a Histogram in Google Sheets and how to overlay a Normal Distribution Curve, as shown in the first image above.
Copy the raw data scores from here into your own blank Google Sheet. It’s a list of 1,000 exam scores between 0 and 100, and we’re going to look at the distribution of those scores.
Step 2: Name that range
Create a named range from these raw data scores, called scores, to make our life easier. Highlight all the data in column A, i.e. cells A1:A1000, then click on the menu Data > Named ranges… and call the range scores:
Set up the frequency bins, from 0 through to 100 with intervals of 5. Put 0 into cell F2 and then you can use this formula to quickly fill out the remaining bins:
=F4 + 5
(it adds 5 to the cell above). Name this range bins.
Step 5: Normal distribution calculation
Let’s set up the normal distribution curve values.
Google Sheets has a formula NORMDIST which calculates the value of the normal distribution function for a given value, mean and standard deviation. We calculated the mean and standard deviation in step 3, and we’ll use the bin values from step 4 in the formula.
In G2, put the formula:
Drag it all the way down to G22 to fill the whole Normdist formula column:
Step 6: Normal distribution curve
Let’s see what the normal distribution curve looks like with this data.
Select the bins column and the Normdist column then Insert > Chart and select line chart, and make it smooth:
You’ll have an output like this:
That’s a normal distribution curve, around our mean of 56.9. Great work!
We now need to calculate the distribution of the 1,000 exam scores for our histogram chart.
As we’re going to create a totally new chart with the histogram and normal curve overlaid (easier than modifying this one), you can put this normal distribution chart to one side now, or delete it.
Step 7: Frequency formula
Leave column H blank for now (we’ll fill this in shortly).
In column I, let’s use the FREQUENCY formula to assign our 1000 scores to the frequency bins. Type the following formula into cell I2 and press Ctrl + Shift + Enter (on a PC) or Cmd + Shift + Enter (on a Mac), to create the Array Formula. It’ll fill in the whole column and assign all the scores into the correct bins:
Copy this column of frequency values into the adjacent column J (we need this for our chart).
Pro tip: you can just copy I1:I2 into J1:J2, it’ll fill out the whole column with values.
Step 9: Scale the normal distribution curve
We need to scale our normal distribution curve so that it’ll show on the same scale as the histogram. Since we have 1,000 values in bins of 5, our scale factor is 5,000. Meaning, when I multiply the normal distribution values by 5,000, they’ll be comparable to the histogram values on the same axis. Also, they’ll sum to 1,000 matching the number of values in our population.
So in the blank column H, add the following formula and drag down to H22:
= G2 * 5000
Our completed data table now looks like:
Step 10: Create the chart
This is where we see how to make a histogram in Google Sheets finally!
Note: the screenshots shared below show the old chart editor. The new chart editor opens in a side pane, but the steps and options are essentially the same.
Hold down Ctrl (PC) or Cmd (Mac) to highlight the bins data column, the Normal distribution and two histogram columns, but omit the Normdist formula column, as follows:
Then Insert > Chart, and select Combo chart:
Select the option to use column F as labels:
In the Customization tab, remove the title and legend. Select the Smooth option:
Select the vertical axis. Delete the axis name. Set to have a range of 0 to 150, and set the major gridlines to 4.
In the series section of the customization menu, choose the Normal Distribution series, and change from columns to line, so your chart looks like this:
Next, choose the Histogram series and change the type from line to columns:
Select the Histogram 2 series and change the type from line to stepped area:
Then change the color to red, the line thickness to 1px and the opacity to 70%, to make our chart look like a histogram (this is why we needed two copies of the frequency column):
Final tidy up: set the axes labels font size to 10, then click in the chart area to move and resize the it by dragging the edges outwards, so it fills out the whole of our chart canvas:
Voila! You’ve now learnt how to make a histogram in Google Sheets, overlaid with a normal distribution curve:
To conclude, we can see our exam score data is very close to the normal distribution. Hooray!
If we look closely, it’s skewed very, very slightly to the left, i.e. it has a longer tail on the left, more spread on the left. See how there is space between the red bars and the blue line on the left side, but the red bars overlap the blue curve on the right side. It’s subtle though.