What Is Embedded Analytics? Why It Matters & How to Use It

As marketing teams manage increasingly complex data across platforms, the need for fast, actionable insights grows. Embedded analytics brings reporting directly into the tools and workflows that teams already use, eliminating context switching, improving decision-making, and unlocking new value for both internal stakeholders and end-users. 

This guide breaks down how embedded analytics works, why it matters, and how to implement it effectively. 

What Is Embedded Analytics?

Why does it matter?

At its core, embedded analytics allows users to access data in context and the flow of work.

Rather than exporting data from a platform and analyzing it elsewhere, users get answers where decisions are made, whether it’s budget planning, campaign optimization, or executive reporting.

This contextual visibility minimizes tool-hopping, enhances focus, and helps operational teams act faster on data.

How Does Embedded Analytics Work?

Let’s take a closer look at how embedded analytics works. 

Understanding the technical foundations will make it easier to see the benefits and to choose the integration method that best fits your use case.

Embedded analytics integration methods

There are four primary architectural approaches for integrating embedded analytics into applications, each suited to different levels of customization, scalability, and control.

1. iFrame and Widget Embedding

The most straightforward method is iframe or widget embedding, where a pre-built dashboard from a BI tool, such as Looker, Tableau, or Power BI, is displayed within another application.

This approach requires minimal development effort and allows non-technical teams to quickly deploy interactive visualizations within portals, CRMs, or customer-facing products.

While easy to implement, iframe-based solutions come with trade-offs: limited UI flexibility, dependency on the third-party BI tool’s authentication, and potential issues with responsiveness or cross-platform compatibility.

2. API-driven integration

More advanced teams often opt for API-based integration.

In this model, analytics components are custom-built using frontend frameworks and powered by data served through APIs. These APIs connect to a backend data store, such as a cloud data warehouse or a marketing data platform, and enable dynamic queries, metric calculations, and filtering in real-time.

However, it requires ongoing engineering effort and strong governance to manage data security, query optimization, and performance at scale.

3. SDKs and component libraries

Some BI vendors offer SDKs or prebuilt component libraries that accelerate development while still allowing for a high degree of customization. These SDKs typically include UI components (for example, charts, filters, tables) that can be configured programmatically and tied to real-time data queries.

This approach balances speed and flexibility. Developers can embed highly interactive visualizations with granular control over layout and user access, while skipping much of the boilerplate code required in pure API integrations.

4. Headless BI and composable architectures

In more advanced setups, companies may use what's called a headless BI approach, which separates the back-end analytics engine from the front-end dashboards and visualizations.

Instead of tying data logic directly to a specific dashboard tool, headless BI tools manage all core reporting functions, such as metric definitions, data calculations, and access rules behind the scenes.

This setup lets teams plug the same trusted data into different interfaces: websites, mobile apps, internal platforms, or partner tools.

This model is part of a larger trend toward composable architectures, where companies mix and match tools based on their specific needs.

What Are the Benefits of Embedded Analytics?

Embedded analytics isn’t just a tech convenience. It delivers tangible business benefits by fundamentally improving how people consume and act on data. 

1. Higher user adoption

Standalone BI tools often suffer from low adoption outside of technical teams—embedded analytics changes this by placing insights exactly where decisions are made.

Keeping analytics in-workflow dramatically reduces context-switching and makes it far more likely that insights will actually be used.

2. Data-driven decisions at scale

Because insights are available at the moment of need, users can make decisions based on up-to-date data rather than intuition or stale reports. They don’t waste time hunting for insights. 

In a marketing scenario, this might mean adjusting an ad campaign in real-time upon seeing underperforming metrics on the dashboard embedded in the marketing platform. 

Quicker access to relevant data = quicker reactions to opportunities or issues.

3. New revenue streams and products 

Embedded analytics can power client-facing dashboards, performance portals, or white-labeled analytics offerings. For agencies and martech providers, this opens up opportunities to monetize data as a service. 

You can package insights as part of a premium offering or create entirely new products around campaign performance, ROI tracking, or real-time benchmarking.

Case study

Signal Theory, a full-service advertising agency, utilizes the Improvado analytics solution. The platform has unlocked new business opportunities and empowered the Data Insights department to work closely with client teams.

The enhanced reporting capabilities have also proven valuable in client negotiations. By showcasing the breadth and depth of their analytics, Signal Theory has successfully integrated these capabilities into their Statements of Work, reinforcing their value to existing clients.

"We’re starting to sell data into our clients’ other businesses. It’s growing and spreading across different teams and brands."

Shayna Tyler

Analyst at Signal Theory Inc.

4. Enhanced customer engagement and satisfaction

By delivering insights in context, embedded analytics creates a more satisfying user experience, whether those users are internal staff or external customers.

For internal users, having a dashboard at their fingertips means less frustration and more empowerment to do their jobs well.

For external customers, being provided analytics as part of a product adds significant value. They get a streamlined experience without needing additional tools, which improves satisfaction and trust.

5. Productivity and efficiency

For companies embedding analytics into their own products, this approach eliminates the overhead of building and maintaining a custom reporting infrastructure from scratch. 

Instead of allocating internal resources to build dashboards or client-facing analytics, teams can integrate proven solutions that scale with their business.

The result is faster time to market, lower development costs, and fewer support requests related to reporting or data access.

Case study

AdCellerant provides digital advertising services to a diverse range of clients, from small coffee shops seeking basic metrics to sophisticated car dealerships requiring granular analysis at the ad group level.

AdCellerant needed to expand its platform with more advertising integrations. However, in-house development took over 6 months per integration and approximately $120,000 in costs.

Instead, AdCellerant chose Improvado, which offers over 500 pre-built integrations. Improvado’s embedded iframe provided a seamless white-labeled experience, allowing end-users to connect accounts directly through the AdCellerant web application.

"It's very expensive for us to spend engineering time on these integrations. It’s not just the cost of paying engineers, but also the opportunity cost. Every hour spent building connectors is an hour we don’t spend deepening our data analysis or working on truly meaningful things in the market."

Jonathan Hemnes

EVP of Engineering at AdCellerant

What Are the Key Use Cases of Embedded Analytics?

Embedded analytics isn’t a one-size-fits-all feature; it’s a flexible capability that can be tailored to different contexts across internal operations, customer-facing products, and service delivery. 

Below are the most common applications relevant to marketing and analytics environments.

1. Internal business dashboards 

One common use case is embedding analytics into internal tools and portals to create real-time business dashboards. 

Rather than relying on static reports or logging into a separate BI app, companies integrate dashboards into the systems their teams use every day, such as CRMs, campaign managers, product dashboards, or custom internal tools. This real-time visibility into business metrics keeps everyone aligned and informed. 

2. Customer-facing applications

Another powerful use case is embedding analytics within customer-facing products or services.

The benefit is twofold: your customers gain valuable insights without leaving your application, and your product’s retention and value proposition increase significantly.

From a provider perspective, embedded analytics in customer apps can also be a competitive differentiator.

3. Marketing agency: client reporting

Marketing agencies often rely on embedded analytics to deliver campaign performance to clients in a streamlined, scalable way. 

Instead of creating static slide decks or manually exporting data, agencies build live dashboards that clients can access 24/7 through a secure portal. These dashboards are often tailored per client and white-labeled to reflect the agency's brand. 

Embedded analytics also supports layered access: clients can view only their own data, while account managers retain full visibility. This reduces reporting overhead, improves client transparency, and strengthens trust, all without additional engineering effort for every new account.

How to Implement Embedded Analytics?

So, you’ve recognized the value of embedded analytics. How do you go about implementing it? 

There are several considerations, from deciding whether to build or buy, to choosing the right platform, to executing the integration in a way that users will love. 

Here’s a guide through those decisions.

1. Build vs. Buy decision

The first step is deciding whether to build your own analytics infrastructure or partner with a third-party solution. 

Building in-house may offer maximum customization, but it requires significant resources, including skilled engineers, lengthy development timelines, and ongoing maintenance. This path can quickly become a cost center, especially when factoring in scalability, security, and data compliance requirements.

Buying or partnering with an embedded analytics provider offers a faster and often more cost-effective path to market. It allows teams to focus on core product features rather than maintaining dashboards, data pipelines, and user permission models. 

2. Choosing the right platform

Selecting the right embedded analytics platform depends on your product architecture, user requirements, and the complexity of your data ecosystem.

Consider the following key factors and requirements:

• Integration and embedding capabilities: Ensure the platform can integrate smoothly with your application’s tech stack. It should support the embedding method you need. Verify that it has well-documented APIs or connectors for embedding and that it supports the environment in which your app operates.
• Data connectivity: Look for broad connectivity to the data sources you use. A good embedded analytics platform should easily connect to your marketing databases, data warehouses, or third-party SaaS data as needed. Avoid solutions that force you to move or duplicate data into their proprietary storage unless that fits your strategy; many modern tools can operate in your data warehouse.
• Security and access control: Evaluate the security features the platform provides, including robust user authentication integration, row-level or client-level security filters, encryption of data in transit and at rest, and compliance with relevant regulations (such as GDPR, HIPAA, or others, as applicable). Particularly in multi-client scenarios, such as an agency dashboard, you need strong isolation between data for different clients.
• Scalability and performance: Consider the scale at which you’ll be operating. How much data will you be analyzing, and how many concurrent users might be viewing dashboards? The chosen platform should be able to handle your data volume and user load with responsive performance.
• Customization and white-labeling: Since you want the embedded analytics to feel like a natural part of your application or brand, the platform should allow a high degree of customization. This includes UI theming (colors, fonts, logos), the ability to remove or modify any vendor branding (white-label), and the flexibility to customize interactions or embed components in various layouts.
• Pricing model: Finally, the solution must fit your budget and usage model. Embedded analytics pricing can vary widely – some charge by end-user, others by server or data volume, and some offer flat rates or usage-based pricing. 

3. Integration and user adoption

Embedding analytics is not just a technical task; it’s also a product and UX decision. 

A successful implementation requires clean user interface integration, thoughtful placement of insights within workflows, and responsive performance across devices.

Plan for phased rollouts, starting with internal users or beta clients. Monitor how users engage with the analytics features and gather feedback early. Clear onboarding, contextual tooltips, and alignment with user goals are essential for driving adoption. 

Even the most powerful analytics features will fall flat if they interrupt workflows or fail to deliver actionable value.

Embedded Analytics Best Practices

Even when an embedded analytics solution is integrated, the journey doesn't stop there. To ensure long-term success, consider the following best practices:

1. Standardize metrics and definitions: Inconsistent definitions of KPIs like ROAS or engagement rate can erode trust. Use a centralized metrics layer or governance framework to ensure all users see consistent, audit-ready calculations across views, clients, and use cases.
2. Prioritize role-based access and permissions: Not all users need the same level of visibility. Implement granular permissioning to control access by role, region, or client. This ensures compliance and keeps the experience focused and relevant for each audience segment.
3. Plan for embedded governance: Embedding analytics doesn’t eliminate the need for governance; it makes it more urgent. Build in usage monitoring, audit logs, and update versioning to maintain trust and meet regulatory or contractual obligations.
4. Invest in UX, not just data: Data that’s hard to interpret is a missed opportunity. Use layout, color, and contextual cues to make insights intuitive. Include filters, drilldowns, and in-chart guidance to support both novice and expert users.
5. Start small, then iterate and expand: It’s tempting to deliver a comprehensive analytics solution all at once, but often a phased approach works better. Begin with a set of core dashboards or analytics features that address the most important needs identified. 

Bringing Analytics to Where Decisions Happen

Whether you’re building internal dashboards, launching a white-labeled client portal, or integrating insights into a larger marketing solution, success depends on selecting the right architecture, aligning it with user needs, and ensuring scalability. The right embedded analytics approach can help turn static data into dynamic, business-driving action.

Improvado offers an enterprise-grade embedded analytics solution built specifically for marketing environments. With flexible API access, transformation logic, and white-label capabilities, it allows teams to deliver branded, data-rich experiences faster without building from scratch. 

Book a demo to see how Improvado can help bring embedded analytics into your ecosystem.

FAQ

What is embedded analytics?

Embedded analytics is the integration of data analysis and visualization capabilities directly into business applications, tools, or workflows. Instead of switching to separate BI platforms, users can access interactive dashboards, reports, and insights within the software they already use, such as CRMs, campaign managers, or marketing platforms.

For marketing teams, embedded analytics streamlines access to performance data, supports faster decision-making, and allows companies to deliver data-rich experiences to clients or internal users without building analytics infrastructure from scratch.

Why is embedded analytics important? 

Embedded analytics is important because it brings data insights directly into the tools and workflows where decisions are made. 

This eliminates context switching, speeds up access to critical information, and enables faster, more informed actions. 

For marketing teams, it enhances campaign performance, improves client reporting, and supports scalable, data-driven decision-making without building a full BI stack.

How does embedded analytics work? 

Embedded analytics works by integrating reporting and data visualization capabilities directly into existing software or workflows through APIs, SDKs, or iFrames. 

It connects to backend data sources, processes data through analytics engines, and delivers insights within the user interface, allowing users to access dashboards and metrics without needing to switch tools.

How is embedded analytics different from traditional BI?

Embedded analytics differs from traditional business intelligence (BI) in how and where users interact with data.

Traditional BI requires users to leave their primary tools and log into a separate platform to access reports and dashboards. Embedded analytics, on the other hand, integrates insights directly into the applications users already use.

Here’s a brief comparison.

What are some examples of embedded analytics? 

Here are some examples of embedded analytics in practice:

• Client reporting for agencies: Agencies embed branded dashboards directly into client portals to deliver real-time campaign performance, reducing manual report generation and improving transparency.
• Sales intelligence in CRMs: CRMs embed analytics to help sales teams visualize deal velocity, pipeline health, and forecast accuracy, without leaving the CRM interface.
• Product usage dashboards in SaaS apps: SaaS companies embed analytics into user dashboards to surface feature adoption trends, usage frequency, and customer health scores, supporting both CS teams and clients.
• Partner reporting in white-labeled platforms: Marketing platforms embed analytics into white-labeled environments to deliver tailored reporting for resellers, franchisees, or affiliates, enabling scalable, branded performance tracking.
• Campaign QA in creative workflows: Marketing operations teams integrate embedded analytics into asset management or creative review platforms to show campaign readiness metrics, like UTM completeness, metadata coverage, or tag validation.

How can marketing teams use embedded analytics?

Marketing teams use embedded analytics in several targeted ways:

• Client reporting: Agencies can embed white-labeled dashboards into client portals, automating cross-channel performance reporting and reducing manual work.
• Campaign optimization: Teams can embed real-time dashboards into their ad management tools, making it easier to adjust spend or creative mid-flight.
• Budget pacing: Embedded budget trackers in internal tools help teams monitor daily and weekly spend vs. forecast, flagging overspending or under-delivery early.
• Attribution monitoring: By embedding multi-touch attribution visualizations into CRM or analytics platforms, marketers can track source-to-sale journeys without toggling between tools.
• Leadership visibility: Executives can access high-level performance summaries directly within their planning or collaboration tools, keeping strategic alignment tight.

What are the top embedded analytics tools? 

Here are some of the top embedded analytics tools used by marketing and analytics teams:

• Improvado: Purpose-built for marketing data. Offers embedded solution, API access, white-labeling, and automated data transformation for scalable client or internal reporting.
• Looker (Google Cloud): Powerful for custom data modeling and embedded visualizations, often used in product-facing analytics.
• Tableau Embedded Analytics: Offers extensive visualization and dashboarding capabilities for teams embedding reports in customer portals or SaaS platforms.
• Power BI Embedded: A good option for Microsoft-centric organizations looking to integrate analytics into internal or client-facing tools.
• Qlik Sense: Known for associative data modeling and interactive embedded dashboards across complex datasets.

How do you implement embedded analytics in an application? 

To implement embedded analytics in an app, organizations typically choose one of the following integration methods based on control, scalability, and use case complexity:

• iFrame embedding: Quick to deploy; dashboards or reports are embedded via secure URLs. Limited customization and interaction.
• SDKs: Offers greater control over layout and interactivity, allowing tighter integration into front-end applications.
• APIs: Enables full customization of user experience and logic. Ideal for dynamic data querying, user-level filtering, and mobile app integration.
• Headless BI / composable architecture: Separates the analytics engine from the UI layer, allowing teams to pipe governed data into any interface.
• Custom-built front end with embedded data access: Best for organizations building a fully proprietary experience using embedded data connectors and API logic.