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An analytics dashboard is a visual tool to display metrics, insights, and key performance indicators (KPIs). It's designed answer questions by identifying trends from historical data sets, predicting outcomes, and shaping future decisions. For example, such a dashboard might display Google Analytics, ad spend per channel, revenue generated per channel, and total ad spend vs. ad sales.
Analytics dashboards aren't open-ended; they're designed to express an opinion about their subject, especially in customer-facing products. They enable customers to efficiently track data insights and performance, then share the results with your team. However, you must ensure your dashboard is designed properly so that it provides the insights and value you need in a way that all team members can easily understand.
This article will examine some best practices to follow when designing your product analytics dashboard, as well as some common antipattern practices to avoid.
With the vast and ever-increasing amounts of data available to users today, choosing what data to prioritize can be very challenging. That’s where user analytics data and dashboards come in. An analytics dashboard helps your users choose optimal options by providing them with relevant information.
An analytics dashboard should be able to:
Effective analytics dashboard designs help your users become more engaged with your product. This greatly contributes to the success of your product since the end users are the final consumers and the more engaging your product is the more likely they'll continue to use it. Obtaining relevant metrics that are useful to your users is the end goal; it’s a continual process that should keep improving over time.
Your analytics dashboard should be able to build a narrative around the data, answering the key questions your customers have, and delivering the key message in a clear and compelling way through insights and analysis. The following sections highlight some common best practices that can help you achieve these goals.
First and foremost, know your audience and build with your audience in mind. Some points to consider might involve asking the following questions to guide your dashboard development:
When deciding which features to include in a dashboard, there are many factors that need to be considered. Every industry has a different way of operating, so providing a standard set of guidelines that will be suitable for every dashboard is difficult. Regardless, you want to use your dashboard to show your customers what's most beneficial to them. So, you should start by considering the question you want to answer for your customer. For instance, if it’s a fitness app, you may want to consider answering questions like what are the users’ number of daily steps, calories consumed, calories burned etc. For banking apps, you may consider answering questions like what are the users’ total transactions over a time period, total amount debited, total amount credited etc.
Before you start building your dashboard, you should have a sketch, either hand-drawn or software-generated, to provide a complete picture of the design pattern. Your sketch will ensure that you follow these principles:
You shouldn't use multiple colors on each page. Not only is that poor design, but it can distract users. Each color you use should have a specific meaning. This way, users will automatically associate that color with the same or similar information at other places in the dashboard. Such a strategy offers better visibility, simpler navigation, and a more striking design.
The fonts you choose should be easily readable and displayed in a large enough size that users can clearly read the text.
Choose a background theme that offers an appealing first impression to users. It should offer an eye-catching visual without overwhelming the text.
To give proper context to the data analysis, be sure to display the data in the most appropriate way for your use case. For example, bar charts can compare multiple categories or values that change over time:
Line charts can present a graphical representation of data or display trends:
To ensure a uniform look, use one font everywhere and maintain two standard font sizes across the dashboard page.
Keep your layout and charts well aligned so that they're centered on the page and not distorted or scattered.
Icons and images will also catch the user's eye and help explain the analytics. In the diagram below, the metric in the top right for the quantity of trucks used can be seen as more relatable when the truck icon is used to explain context:
The following images created in Bootcamp demonstrate good dashboard design practices:
As you can see, these dashboards follow several good design principles:
If you don't follow best practices for design, you could end up with an analytics dashboard that is unappealing and lacks coherence. Design antipatterns can reflect badly on your organization and increase confusion among your users. The examples below demonstrate dashboards created with bad design practices:
You should be able to identify a number of bad design practices and antipatterns:
These dashboards could be improved by applying the following design principles:
A customer-facing analytics dashboard can provide key data insights for more informed decision-making, which better positions your organization for success. To maximize the effectiveness of your dashboard, though, you need to ensure that it's designed well.
By applying the best practices listed in this article, you'll help ensure that users will be able to quickly identify and act on vital information. This will enable you to find ways to further grow the user-base of your product.
To empower your customers to analyze and use your data most effectively, try Propel Data. Propel is an Analytics API that for product companies to build customer-facing analytics in record time. Propel works well with your existing data stack without having to build new data pipelines or aggregations. Join the waitlist to learn more.