Whatever problem your product solves, part of your mission is to make sure your customers understand its value, stay engaged, and recommend it to others.
That’s why user-facing analytics is on your roadmap: It’s an opportunity to upgrade your customer experience, placing useful data right in the app where they need it most.
But what makes for remarkable customer-facing analytics? The first thing is flexibility and data freshness. Can users find exactly what they’re looking for to support their decision-making? Can they adjust the aggregations and see accurate data visualized in different ways?
That’s not all. The best embedded analytics solutions out there are totally cohesive with the brand and visual guidelines of the rest of the app—an extension of the existing fantastic user experience rather than a gap in it.
In this article, you’ll find use cases of user-facing analytics to give you an idea of what’s working for other businesses right now. Then, we’ll dig into real-life examples of impressive analytics solutions to give you inspiration.
If you enjoy these examples, you can also check out our 'Part 2' where we cover 5 more examples of excellent analytics experiences.
Do you want to build remarkable analytics experiences in your own app? Learn how.
Top uses for user-facing analytics
There are many benefits of user-facing analytics, but that doesn’t mean your decision-making team is completely sold on the idea of embedding them into your tool. Here are some top use cases to help you explain the value of analytics to your stakeholders.
Self-service data requests
Let’s face it, your data team is busy enough without handling every business intelligence query that comes in. One great benefit of building self-serve analytics into your app or website is that customers can find insights themselves, which reduces the number of data requests your internal team has to deal with and allows them to focus on more important tasks.
Let’s say one of your external stakeholders is a supply chain logistics partner. Giving them access to certain levels of self-service analytics means they can now access historical shipping data, inventory levels, and shipping schedules to make quick data-driven decisions when route planning—without adding to your data team’s workloads.
Customer activity summaries
It’s common for B2B SaaS apps to use data visualizations to summarize customer activity so their customers can see exactly what value they’re getting from the platform. This means customers are more informed and their processes are more efficient.
For example, if your product is a booking app for short-term rentals, you could show vendors stats like number of booked rooms, revenue, referrals, and repeat guests. Giving them access to this information shows them how much business they’re driving through your platform. It also empowers them to think of ways to increase their engagement and make informed business decisions—like where to focus their marketing efforts.
Embedded website statistics
Adding embedded analytics to your website to showcase real-time statistics about your product or service has two interesting benefits. One, your site becomes more dynamic and engaging thanks to increased sense of transparency and recency. Two, you get to communicate key things about your brand, such as performance stats or brand values.
Let’s take an ‘eco-friendly’ ecommerce business as an example: By showcasing stats like ‘liters of water saved’ or ‘carbon emissions reduced’, it bolsters its own reputation as a sustainable brand while showcasing the positive impact of the customer’s choices.
6 User-facing analytics examples to help you get inspired
It can be complex to build customer-facing analytics into your app. Pulling it off means prioritizing a slick user experience that fits seamlessly with the rest of your product—all while giving users flexible aggregations and useful data visualizations that help them do their jobs.
Next up are six examples of user-facing data analytics from companies we think have nailed it.
Stripe is a household name when it comes to payment processing. If you haven’t paid for something via Stripe yet, well—believe us: You have.
But, why is Stripe so successful? It understands what its users are looking for and delivers it in a remarkable way.
As a payments processor, analytics aren’t necessarily the core piece of Stripe’s offering, but the dashboard is right there on the homepage when a user logs in, which goes to show how valuable these insights are. Users can find out metrics like gross and net payments received over a particular period and see how they compare to previous periods—all crucial to understanding a business’s financial health.
Each element of the dashboard is intuitive, easy to navigate, and has a load of useful, custom subcomponents and logic within each component. So there’s a lot of flexibility in terms of what data users can access. But it’s the attention to detail we love the most. For example, every transition follows the protocols of the rest of the platform, meaning the brand experience is totally consistent throughout.
Squarespace changed the game with its no-code website builder and hosting platform. That said, analytics was something of an afterthought in the company’s early days. Most users turned to Google Analytics and other solutions to get an idea of how their website was actually performing. So to increase the perceived value of its service, Squarespace dramatically improved the quality of its data analytics.
The site gives you multiple views depending on what the user is looking for (e.g. Geography, Traffic Sources, and Site Content). They can view preset or custom date ranges, which will persist even when they toggle to a different view.
Its analytics page is fast-loading, which gives users access to a range of metrics such as time on page, bounce rate, and exit rate within seconds. Users can also see which devices, search engines, and even search terms have brought visitors to the site—and what page they then visited based on their search.
Rather than compete with Google Analytics, Squarespace has introduced a pretty seamless integration that allows users to combine data from the two sources right within Squarespace itself.
Additionally, Squarespace analytics work really well on mobile devices too, showing the level of consideration the product team has undertaken to make sure this solution serves its users everywhere.
If anyone knows how to leverage data, it’s Spotify. From the listener’s perspective that means new music recommendations powered by clever algorithms and experimental features like ever-changing playlists. But artists, podcasters, and publishers that use Spotify also get access to all kinds of useful data that drives business decisions.
The analytics found on Spotify can be used to help artists plan venue sizes, locations, and dates of tours, as well as tailor their marketing campaigns according to their listener demographics.
The platform takes a unique design approach on its analytics pages, emphasizing text-based stats at the top of each tab rather than showcasing a load of charts and graphs. You can toggle between time periods to see listeners, streams, and followers, and even compare these rates to other artists, but it all looks really non-threatening for users without a tech background.
For those with more advanced goals, there are still options to explore the data in more depth, using bar charts and graphs lower down on the page. Of course, it’s Spotify, so their visual branding is absolutely on point too.
Mailchimp is an email and marketing platform that gives businesses the option to design, automate, and analyze marketing campaigns. Users get comprehensive audience analytics to track the growth of their contact lists over time. The ‘Contacts by status’ section shows the number of subscribed, unsubscribed, and non-subscribed contacts with a date picker for analyzing specific time periods.
One thing Mailchimp excels at is embedding analytics right on a campaign level. Users can analyze real-time results of A/B tests as well as more general stats like open, bounce, and click rates (they can even check these metrics in relation to industry benchmarks—a nice touch).
However, the platform does sacrifice easy navigation: You can dig deep into a single campaign, but seeing data from multiple campaigns in a report or chart requires a lot more clicking around. We’d love to see Mailchimp up its game by adding even more customizable visualizations and a more intuitive user experience for those looking up the numbers.
Intercom is a SaaS tool for customer service that aims to make messaging-based customer experiences more satisfying. It also comes with an analytics platform that makes it easier for managers to track customer support metrics while setting team KPIs.
Intercom’s real-time analytics dashboard gives an overview of the organization’s inboxes and customer support teams, showing at-a-glance metrics like total number of messages waiting for a reply.
What’s good about it? One key feature is that managers can add their own charts, choosing from charting components like counts, line graphs, or table charts. Teammates can group or filter customer charts according to different attributes, and there’s no limit to the number of charts you can add.
Intercom’s charts are very user-friendly, using date pickers to help team members dig deep into the data. It also uses custom subcomponents to show relevant info like the team’s progress since the previous period. Metrics like SLA and customer satisfaction score (CSAT) update every 15 mins, but the rest are set to auto-refresh every 60 seconds while the dashboard is active, so it’s good and fresh too.
There’s also a dedicated tab for monitoring team inboxes. Not only can you see metrics like active teammates, conversations waiting for a first reply, and so on, but you can check the team’s availability, capacity, and performance. Managers can hover over a team’s name in the inbox to see which teammates are assigned to that team, who’s active, and the number of open conversations and tickets. Best of all, everything is easy to navigate and fits Intercom’s branding perfectly.
Another big name in its industry, Shopify is an ecommerce platform for starting or growing an online business. Its analytics dashboard gives users the opportunity to review the store’s activity, get visitor insights, and analyze transactions. But really that’s just the tip of the iceberg of Shopify’s analytics.
Our favorite feature is the Live View, because it visualizes data in a completely unique and engaging way — while sticking to the overall look and feel of Shopify branding.
Live View shows ecommerce managers what’s happening on their online store right now. It includes a two-dimensional world map as well as a three-dimensional globe, so they can see exactly where visits are coming from in real time. This comes in useful during high-traffic periods such as Black Friday, when ecommerce businesses want to see the immediate effects of their campaigns.
Live View isn’t just a visual gimmick, either. It’s highly interactive, allowing users to:
- Toggle between views of the map vs. the globe
- Rotate the globe to see visits and orders happening live
- Hover over visits or orders to see more information
- Zoom into a geographic area and search by city, region, or country
In comparison, the rest of Shopify’s data visualizations are pretty basic, presented mostly as line graphs. But as a result, it’s very clear and easy to navigate. Plus, hovering over different elements brings up a popover describing the information, which is especially valuable for team members who don’t have data backgrounds.
How Embeddable can help you build remarkable customer-facing analytics
We’d be willing to bet that the analytics solutions above were built from scratch. You don’t get Stripe-levels of customization from embedding a dashboard from your existing BI tool or buying out-of-the-box embedded analytics tools.
That seems to be the catch-22: You either need to spend multiple sprints to get your analytics solution ready to launch or settle for something inflexible that loads slowly and doesn’t match the rest of your in-app experience.
With Embeddable, you choose which data models to make available to your customers on your product. This makes your data always updated, fast-loading, interactive, and engaging. So, if you didn’t think you could prioritize building your own customer-facing analytics just yet, we can help you get there faster.
Flexible, custom, and consistent: The hallmarks of great customer-facing analytics
If you’ve been pushing user-facing analytics further down the backlog for months, you know how valuable this feature could be. However, you’ve yet to find a solution to deliver it in a way that meets the customer’s needs and doesn’t delay everything else you want to build for them.
The best examples of customer-facing analytics we’ve seen have two things in common:
- They’re easy to use and highly flexible: Rather than a few static charts and metrics, these solutions give users the option to filter and search data in different formats to get the information they need.
- They look great and reflect the app’s branding: These solutions deliver a consistent brand experience with simple user interfaces and engaging visualizations. Overall an awesome user experience.
Usually, delivering this level of quality really means building the analytics components into your app yourself. Embedding a third-party solution just doesn’t give you the same user experience. But you could cut development time and deliver something remarkable with Embeddable: A front-end toolkit, backend engine, and no-code builder for full-custom user-facing analytics. Try Embeddable to take user-facing analytics out of your backlog and move it into production without much hassle.
Want to learn more about building engaging, useful customer-facing analytics experiences? We've written an article about the design principles to consider for charts and dashboards here.
Want more examples? You can also check out our 'Part 2' where we cover 5 more examples of excellent analytics experiences.
Frequently asked questions about user-facing analytics examples
What is user-facing analytics?
User-facing analytics organize and visualize complex data using charts, graphs, and other components. Typically, user-facing analytics are accessed through a standalone platform or found embedded into an app the customer already uses for a different purpose. For example, you might find embedded analytics in a learning management system (LMS) that helps administrators track their students’ progress.
Despite the nearly identical names, user-facing analytics are different from user analytics, which is an internal method of analyzing data about your user.
What are the three approaches to embedding user-facing analytics?
There are three approaches to embedding user-facing analytics in your app:
- Building it yourself. Some companies choose to code their own user-facing analytics solution. This is best suited to projects with larger budgets and in-house engineering talent. It results in very custom solutions but it can eat up a lot of dev time and resources.
- Using a third-party BI tool. If building it yourself sounds too complex or expensive, you can either embed your existing BI dashboard or go for an out-of-the-box user-facing analytics solution. Typically, the drawbacks are poor loading performance and limited customization: The components are pretty basic and it won’t deliver the same brand experience as the rest of your app.
- Embeddable. For the best of both worlds, you can try Embeddable, which combines a front-end toolkit, back-end engine, and no-code builder for user-facing analytics. That means fully custom-embedded analytics without the launch times or the price tag of building everything from scratch. Learn more.