Google Data Studio is a free reporting tool you can use to create highly customizable dashboards and visualizations from different data sources.

It’s important to track metrics across your website and sales funnels. After all, not keeping an eye on your analytics could mean missing out on key business insights. But what if you’re making decisions based on wrong data without even knowing it?

First part of the Customer Overview data studio template

I’ve been working on creating some Data Studio reports when I needed insights to analyze different business sections and some days ago I crafted a simple dashboard for a better analyzing the customer base through Google Analytics connection.

In this report, you can understand correlations between device categories and conversion rates, interests on CR, channels on average prices and AOV (average order value) and some other interesting metrics.

In the first part of the report, you can see the most important KPI such as Users, Sessions, New Users, Bounce Rate and Average Session Duration in the period selected in the top right corner of the dashboard and its comparison with the previous period in percentage.

In the section below there the demographic dimensions with the revenue metric. It could be interesting too if you consider putting these dimensions with the Unique Purchases metric…

Device Categories, Channels and Time.

The report is structured to show important information based on the conversion rate and three other dimensions that let me know where they’re coming from, what are their interests and usually readings and in what time they usually feel more comfortable to purchase.

1. Channels

The channel of origin is one of the most important dimensions to keep eye on to understand where are your customers and how to uplift the marketing campaigns ROAS.

Different metrics on Channel dimension

What is the most profitable channel based on the conversion rate?

These questions should sound pretty interesting for most of the marketers around out there and as you can see in the screenshot below, the report shows the result aggregated information where you should go deep inside the data and exploding and activating them through some other Google Cloud products.

Combining different dimensions and metrics

2. Device categories dimensions

Each of these 4 charts shows a specific answer on the customers’ profiles: this following screenshot different insights by different angles enlightening the interests, channels, location, and age on device categories and E-commerce conversion rate metrics.

Device category charts

Mixing it up these charts you can create detailed information about your customers succeeding to understand how to uplift your marketing performance bidding and conversion rates.

What is the highest conversion rate of customers’ interests, based on the device category?

This following chart shows you the answer to this questions and it could be really interesting to understand how to optimize your bidding information or perhaps understand better how to aggregate information about your customer segments; you could be able to drive a better communication or some specific offers related to their hobbies too.

Interest chart

3. Time

Google Analytics sample all data to analyze and uncover meaningful information in the larger data set. In the following screenshot, you can see a matrix with Sessions, Ecommerce Conversion Rate and AOV on Hours of the day which show you the most hours of the week where you should concentrate your attention.

Time Matrix

What is the highest time and day of the week where the customers are focussing to buy?

For each day I considered combining the sessions, conversion rate percentage and the revenue in a matrix with the sampling of the week and hours. In this way, it shows me the most important “gain points” related to an economics value and not only the conversion rate percentage metric which could be high for a particular time/day/session but not really relevant in business terms.

Download the dashboard by clicking this following link:–4992–8298-e0b09944b94e