What is Embedded Analytics? Definition for B2B SaaS

Embedded analytics is the integration of data visualizations and reporting directly into a software product. Instead of sending users to a separate BI tool, charts and metrics appear inside the application they already use. For B2B SaaS, this means your customers see their data without leaving your product. Common examples include usage dashboards in developer tools, spend breakdowns in procurement platforms, and campaign performance views in marketing software. The alternative is exporting CSVs or linking out to Looker, which fragments the user experience and makes your product feel incomplete.

Why do B2B products embed analytics?

Customer retention. Users who rely on your analytics to make decisions are harder to churn. The data becomes part of their workflow, not an afterthought they check in a separate tool. Products with embedded analytics report 20-30% higher retention rates because the switching cost includes losing visibility into their own data.

Upsell opportunity. Analytics features are a natural premium tier. Basic plans get summary metrics. Enterprise plans get full dashboards, custom reports, and API access. The data is already there; the packaging drives revenue. Many B2B SaaS companies price analytics as an add-on module, generating incremental ARR from data they already store.

Competitive differentiation. Two products with identical core features? The one with built-in analytics wins. Customers prefer products that show them their data without requiring a separate BI subscription and ETL pipeline.

What's needed for embedded analytics?

Multi-tenancy. Each customer sees only their data. This requires row-level security that filters every query by tenant, not application-level filtering bolted on as an afterthought.

Access control. Different users within the same tenant need different visibility. An admin sees everything. A team lead sees their department. RBAC at the data layer handles this without custom code per role.

Governed metrics. Every chart in your product should show the same number as your API response and your AI agent's answer. A semantic layer ensures this by defining metrics once and serving them to every surface.

Chart components. You need React components (or equivalent) that render charts from your governed data. Building from scratch with D3 or Chart.js means maintaining rendering logic alongside data logic.

How Bonnard powers embedded analytics

The React SDK (@bonnard/react) provides pre-built components that query the semantic layer directly: BarChart, LineChart, BigValue, and useBonnardQuery for custom visualizations. Each component accepts a publishable key that scopes the query to the current tenant.

import { BarChart } from "@bonnard/react";

<BarChart
  publishableKey={tenantKey}
  measures={["orders.total_revenue"]}
  dimensions={["orders.status"]}
/>

No separate BI tool. No iframe embedding. Your charts query governed metrics with tenant isolation and RBAC built in. Define the metrics in YAML, deploy with bon deploy, and the components render live data. The pre-aggregation cache serves frequent queries in single-digit milliseconds, so charts load as fast as your customers expect. When you update a metric definition, every chart across every tenant reflects the change on the next query, with no cache invalidation logic in your application code.

Read more about the full stack at /agentic-analytics.

Related terms

• Semantic Layer
• RBAC