How to make your dashboard more dashing with Google Data Studio

Google Data Studio dashboards are relatively easy to create, but how can we make them pop? What can we do for them to not only provide useful data but also look great.

In this post, I’ll show you how to make your Google Data Studio reports look great!

1. Build a funnel using the bar feature in the table in Google Data Studio

This is often speedy and simple. This permits the viewer of the dashboard to see how channels or campaigns are performing.

For the representation of metrics use the bar format. Click on Style -> Column ->Bar/Pub, and check the “Show number”. You can use this to build a visualization of a process that can be presented by steps, and not only through a conversion funnel.

When you have more than one goal assigned for one step in the funnel but also want to use more metrics to create a combination for each step of a funnel, the following idea might come in handy.

2. Create a calculated metric inside the Google Data Studio

Once you have numerous goals for one step in a funnel, you’ll need to add and calculate all the conversions for that phase. For instance, for sites centered on getting leads, the goal is accomplished when clients make contact by mails, forms, or phone calls. It’s one macro goal that is being measured in three different ways.

To calculate the collective goals of “leads”, you just have to add them.

But you’ll be able to do that only if goals are commonly exclusive. In this case, it’s sensible to accept that a client will contact you merely once, per session, so it actually works.

Here are some steps to make a calculated metric

Let’s include all the contact goals and get the “contact” metric;

  1. Choose the goals you wish to combine.
  2. If you need the dimension to be accessible all through the dataset, within the main menu go to Resource -> Manage added data sources -> Edit -> +Add a field. But in case you would like the dimension as it were at the table or chart level, click “+ create new field” right at the end of the Fields Picker. Or, within the chosen dimension zone, tap “+ Add dimension”.
  3. You can create the metric utilizing math operators (just as the Google Analytics metric feature that is calculated)
  4. Utilize the new metric

3. Create a personalized dimension in the Studio

In Google Data Studio, you are not able to treat your data as you’d in a spreadsheet, but you will be able to control your data the way you want to. You can create personalized dimensions through the conditional logic, the function CASE.

You can make dimensions based on exclusive sets of data. For instance, you can visualize visits from non-paid and paid traffic or non-branded and branded campaigns.

Let’s assume a company invests in SEO and also on Instagram content. Creating a dimension with values like; Google Organic, Insta Paid, Insta Organic, Rest Paid Social, Rest Organic, etc. will assist you to see, how your essential marketing channels are performing. You can utilize this new measurement as a filter within the dashboard or construct a table or chart to show metrics for those values.

Here are some steps to create a personalized dimension

To demonstrate the above, let’s make a straightforward dimension: non-paid and paid traffic.

  1. Define it: line out the conditions, which in this instance are pain channels in Google Analytics: Paid Search, Paid Social(custom), Display, and Affiliates. Non-paid channels will be the rest.
  2. In case you need the dimension to be accessible all through the dataset, within the main menu go to Resource -> Manage added data sources -> Edit -> +Add a field. But in case you would like the dimension as it were at the table or chart level, click “+ create new field” right at the end of the Fields Picker. Or, within the chosen dimension zone, tap “+ Add dimension”.
  3. You can use the CASE function to characterize it, solely on logic. This illustration can be composed in at slightest two ways, using or not using regular expressions.
  4. Create segments or filters or even tables using the dimensions newly created; “Paid & Non-Paid Traffic”.

4. Consolidate your source data

Google Analytics, for illustration, registers distinctive Facebook sources varying based on devices used to log in to Facebook and also registers the source of visit, if it’s from an app or a site. Well, that’s just a fact, and you don’t have to go to that extend!

The recommended practice is to have a filter that is unificatory in your Google Analytics View that replaces all Facebook sources with just one; well, that filter isn’t that easy to use to make historical comparisons.

If the filter isn’t set up in Google Analytics, you can utilize the function, CASE to do a similar operation in Google Data Studio and show simple and valuable information.

5. Combine your data sources

A huge advantage of Google Data Studio is that it enables you to present data from various sources in one visualization. You’ll be able to highlight not as it were data from Google Analytics but from Google Ads, Facebook, YouTube, Instagram, Google Sheets, and CRM, etc.

A basic chart from any supporting data source can be supportive. For illustration, you’ll layer data from Google Search Console into any report.

That assists you to prove analytics data, uncover regular patterns, or exhibit accomplishments that haven’t however recognized as traffic.

Here are some bright ideas to get started with that dashing dashboards!

The excellence of Google Data Studio is that you just can construct actually any kind of marketing report or dashboard using it. Be that as it may if you’re dealing with blank-page-disorder (you heard that right!) ….here are some ideas.

1. Paid channel comparisons

Since advertising platforms like Google Ads, Facebook Ads, and LinkedIn Ads stores their performance data on their siloed UIs, Data Studio is extraordinary for bringing all this data into a single view.

2. Paid Social comparisons

When you’ve got ahold of the paid channels that work great for the audience and of course your purposes, you’ll need to begin reporting on the performance of campaign-level inside each channel.

3. SEO Reporting

whereas most SEO’s we know, lean toward to crunch their data on Google Sheets, it makes perfect sense to make client’s reports in Data Studio – essentially since the reports will see more pleasant.

4. Content & Email Marketing

Data Studio can be utilized for measuring and tracking the performance of your email marketing and content practices.

Exploring Google Data Studio?… Tag along. Here is something for you to start with;

Google Data Studio is a user-friendly tool that can be used by anybody with a computer that has an internet connection but is particularly valuable for Data Analysts, Data Scientists, and Business Analysts. The objective of this article is to grant you an insight into making a dashboard using Google Data Studio. This tool is useful for depicting information visually through interactive charts and tables.

The root cause for most of the issues in a business is ineffective communication, this includes, from top engineers to product managers to business analysts. Distribution of results, discoveries, and findings to others could be difficult. This tool can offer assistance to overcome those issues by showing information in a simple viewing arrangement. Below, I will explain a few straightforward steps to assist you to make a basic, and compelling dashboard.

Firstly, connect your data set

The primary step is to get your data set. If you need to follow along but don’t have a data set on your own, however, you’ll be able to make some randomly generated data in Google Sheets or by rapidly naming a few columns and relegating a few values in columns. The screenshot given underneath presents the sources you can connect your data, and I choose to show Google Sheets because it is a very simple tool for compiling information.

The first thing that will pop up to you is the editing form of your dashboard that will consequently show a table of your information. I have a few fields, where a few are numeric and a few are text (indicated by a 123, and ABC, separately).

Let’s visualize!

You will be presented with the data set you choose; change the type of the table to “Table with heatmap” using the chart type. It clearly differentiates the metrics you choose with colors. This add-on is valuable for effortlessly displaying the content of a column’s value with not only numbers but with color as well. Here you are, indeed in seconds with a dashing and color-coded chart!

Add more impressions!

Let’s step it up a score, and visualize the data, using a more unique chart. One of the best options in the chart options is the map options. Choosing the “Geo chart” implies that you got to make sure that your data features a geo highlight component. For instance, if your data sets have a region included, you may need to form a chart that accurately identifies the areas of the regions.

Baking all together!

Once you have got two or more charts wrapped up, you’ll be able to click on “view” mode, which will take you to an alternate mode. You can share your dashboard with others, similar to how you would share Google sheets or Docs.

Another highlight is, once you are in the dashboard view, you can hover over your charts, alter their sizes effortlessly and put together meaningful visualizations to attain your idealized dashboard format, just the way you want to!

This dashboard was simple to form, I say this because you didn’t have to do any downloading or anything complicated, and most importantly it’s free! Even though we created a dashboard rapidly, you can create much more complex dashboards with the help of particular metrics and goals in your business.

Creating a dashboard can be a bit intimidating at first, but with a few exertion and guides, like this one, you will be well on your way to making an effective dashboard for endless purposes. If you are in the analytical field you can easily make charts or visualizations using Google Data Studio, which is quicker, less demanding, and ultimately gives intelligent and interactive dashboards than creating from Python programming dialect or visualizing through complex machine learning calculations!

Leave a Comment