How to Build Customer-Facing Analytics for B2B SaaS

Useful takeaways from this story.

Usage metrics, billing summaries, conversion funnels, performance dashboards.

Customers increasingly want to ask questions about their data inside Claude or ChatGPT and get a chart back.

They embed a BI tool (Metabase, Looker, Power BI) and fight with multi-tenancy, iframe styling, and paid embedding licenses.

Building the complete brief

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The useful part

Usage metrics, billing summaries, conversion funnels, performance dashboards. They embed a BI tool (Metabase, Looker, Power BI) and fight with multi-tenancy, iframe styling, and paid embedding licenses. Customers increasingly want to ask questions about their data inside Claude or ChatGPT and get a chart back.

How it works

You end up writing middleware to filter queries by tenant, which is exactly the custom infrastructure you were trying to avoid.
Multi-tenancy is an afterthought Most BI tools were built for internal teams, not B2B products serving hundreds of tenants.
Multi-tenancy is either missing, manual, or gated behind an enterprise license.
Metabase requires the Enterprise license ($500+/month) for row-level permissions and sandboxed embedding.

What to take from it

Both approaches burn engineering time on the wrong problem.

Example or evidence

You end up building analytics infrastructure instead of your product.

Details worth keeping

Every customer expects analytics inside your product. They shouldn't have to ask your support team for a CSV export. The question isn't whether to ship customer-facing analytics.

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