12 min read
‧ 12 min read
In plain terms, embedded analytics means you’re showing charts, metrics, or reports inside your product, rather than sending people off to a separate tool. It’s not new, but it’s becoming a must-have in SaaS and business apps. People expect their data to be there when they need it—ideally without asking anyone on your team for a custom report.
In this guide:
TL;DR: Embedded Analytics overview.
| Topic | Summary |
|---|---|
| What is embedded analytics? | Embedded analytics means integrating data visualizations, charts, and reports directly inside your product so users can analyze and act on data without leaving the app. It provides real-time, interactive analytics tightly coupled with the SaaS platform user experience. |
| Key features | Ranges from static dashboards with filters to fully interactive tools allowing users to drill down, build custom analyses, or ask data questions on demand. White-labeling ensures seamless integration with the app’s look and feel. |
| Embedded analytics benefits for users and business | Enables users to self-serve analytics for faster, better decisions right inside workflows—no need to wait for data teams. For businesses, it boosts customer engagement, reduces churn, differentiates products, creates new revenue streams, and streamlines dashboard management for scalability. |
| Use cases | Useful in SaaS where users need frequent data-driven decisions, e.g., healthcare patient outcomes, ecommerce sales and retention, fintech portfolio analysis, marketing campaign performance. Key themes: reduce ad hoc report requests, shorten time-to-value, feature adoption, customization at scale. |
| Differences from traditional BI | Embedded analytics delivers answers inside the product in real time, enabling users to self-serve without dependency on separate BI tools, which require data team involvement and slow down decision-making. |
| Choosing a solution | Evaluate time to value, integration mode, scalability, security features (row-level permissions, scoped filters), and pricing model (per user, query, or dashboard). Ideally, enable non-engineering teams to manage analytics content. |
| Summary | Embedded analytics is now a must-have in SaaS, providing users smart, in-app access to data and empowering both users and businesses. Proper planning around security, data governance, and scaling ensures a successful embedded analytics experience that drives retention, revenue, and engagement. |
Let’s get into it.
Say you’ve built a SaaS platform for managing marketing campaigns. Your customers are scheduling emails, tracking engagement, and managing segments. At some point, they will ask: are my campaigns working? They’ll want to see the data about the open rates, click-throughs, engagement trends, and make decisions based on that data.
Embedded analytics means giving your customers their data - and the tools to explore it - directly inside your product.
Embedded analytics let your users see their data right inside your product, and there’s a range of what that can mean in practice: from pre-defined static dashboards with some filters on top, to charts that let people drill down and explore, to interactive tools that allow people to ask their own questions and build their own analyses.
Interactivity is really the secret sauce here. If you enable people to explore their own data, it turns your analytics from a place that displays data into one that helps users work with their data: instead of showing a static metric like “CTR = 12%,” you’re enabling people to ask, “What did CTR look like last month? What about by campaign?”—and get the answer without leaving your app or asking for help.
And because you’re putting these tools inside your product, white-labeling is essential: your users shouldn’t ever know there’s a separate analytics tool under the hood. It should just feel like part of the app they’re already using, including fonts, colors, logos, maybe even interaction flow for a seamless experience.
If your product is part of someone’s workflow, they’re probably making decisions with it. Without embedded analytics, your users rely on someone else in their org—usually an analyst or ops person—to pull numbers, build dashboards, or answer basic questions.
By embedding analytics into your product, you let users:
This shift empowers the end user, especially in tools where decisions happen quickly, like CRMs, support platforms, or E-commerce tools, so people go from “I wonder if this is working” to “Yes, and here’s why” without leaving your app.
Embedded analytics can show up anywhere your users need access to data, but it’s especially useful in SaaS products that sit close to business processes, and people rely on data to make frequent, day-to-day decisions.
Some examples:
The specific use cases can vary, but a few common themes show up across all of them:
Learn how a digital learning platform implemented Metabase embedded analytics for educators and cut support requests by 87%.
Embedded analytics is the alternative to traditional BI (that’s “business intelligence”) where if someone wants to ask a data question — say, what was the CTR on that one email — they have to go through a few steps: ask a data person to set up the integration; wait for the data person to pull the numbers and build a dashboard; go to a separate BI tool to see the results - all with a few (or a lot) back-and-forth along the way. It slows down decision-making and ties up the data team with one-off requests. And at the end of the day, it’s someone else’s dashboard in someone else’s tool.
Embedded analytics, on the other hand, enables your users to get the data they need right when they need it — inside your product. It’s your users answering their own questions, in your product, in real time.
To add embedded analytics to your product, you’ll need to first decide what users should be able to do with it. What questions do you expect users to ask? Do they just need visibility into usage? Do they want to slice performance by team or campaign? This helps scope both the UI and data complexity.
Learn how an eCommerce platform went from from zero to a proof of concept for their in-app reporting in under 30 days with a single developer using the Metabase Embedded Analytics SDK.
Embedded analytics is a great idea in theory, but in practice, there are some real-world hurdles you’ll probably run into:
Sure, technically you can drop an iframe into your app in an afternoon. But getting your data to show up in the first place (and getting the right data shows up for the right user) and making UX feel like part of your product takes a more effort. You’ll need to wire up authentication, set up user-level permissions, and tweak styling.
What helps: Start small. Pick one report to embed first, and treat it like a scoped feature rollout. Use that to prove out the integration pattern before scaling.
You need to make sure users can’t access data they shouldn’t see, and that your system is logging, isolating, and scoping data correctly. Especially in industries like healthcare or finance, embedded analytics has to play nicely with the compliance frameworks that you’re working under.
What helps: Choose tools that support row-level security, scoped embeds, and audit logging out of the box.
Even if only a fraction of your users use reporting, the load can spike if you let people explore data on their own. A few badly written queries can bring things down fast.
What helps: Model your data with an eye towards analytics: use pre-aggregated tables where you can, set sensible limits on filters and date ranges, and monitor performance early. You don’t want analytics to be the thing slowing down your app.
Once you pick a vendor for embedded analytics, it’ll be hard to switch, especially if you’re using more advanced embedding approaches like SDKs, so you’ll want to minimize the risk that you’ll need to rebuild everything from scratch in the future.
What helps: Go with a product that’s stable, well-documented, and actively maintained.
If you’re not building everything yourself (and most teams shouldn’t), you’ll want to evaluate platforms built for embedded use cases. Here’s what to look for when evaluating your choices:
Metabase Embedded Analytics offers solutions for all use cases, whether you need a basic dashboard in your product before lunchtime, or fully customized and deeply integrated analytics in your app. Start with basic static embedding for free, or start a free trial for white-labeled interactive analytics.
