Your users are exporting data out of your product so they can analyze it somewhere else. Leadership wants to know whether to build analytics in-house or buy an embeddable platform, and the budget conversation is already tense. If you own that decision, The Product Manager's Guide to Embedded Analytics Users Love was written for you. It explains what it takes to put embedded analytics inside your app in a way that customers actually use, and how to show that the effort pays off.
Produced by Product-Led Alliance in partnership with ThoughtSpot, the guide combines survey findings from product managers across North America, Europe, South America and beyond with commentary from product leaders at AppDirect, Munetrix and Farfetch. Respondents range from heads of product to associate PMs, at companies of very different sizes.
Why embedded analytics is now a product decision
For years, analytics was something teams planned to add after the MVP, usually as a job for developers. The guide argues that this approach no longer works. Users expect to explore their data inside the apps they already use, without an analyst on hand to help. A data export button and a fully embedded analytics module produce very different results for engagement, retention and revenue.
You'll see what your peers expect embedded analytics to change in their products, what those who already offer it have measured over the past year or so, and the obstacles that slow teams down. Budget, time to market and the steady stream of feature requests all come up. The guide also lays out what a PM is actually responsible for here, from discovery and scoping through build, buy or partner decisions, launch and measurement.
Designing analytics UX for self-service users
A large part of the guide covers analytics UX, because adoption depends on it. It looks at how to get the right data to the right user at the right moment, and how to balance simple, consistent interfaces against flexible, free-form exploration. It also explains why letting users drill into a chart builds more trust than handing them a static report. You'll find practical advice from surveyed PMs on what matters most when optimizing an analytics experience, along with the baseline capabilities they believe users now expect.
The guide places all of this within the modern data stack. It explains each layer in plain terms (ingestion, storage, transformation, data science and analytics) and shows where product teams say they are investing now and next. If you need to explain to engineering or leadership why the experience layer deserves attention, this section gives you the context.
After reading, you'll be better prepared to:
- Assess embedded BI vendors against ten evaluation criteria, from extensibility and custom code to deployment style and scalability
- Choose metrics that show whether your in-app analytics is working, including early signals of user satisfaction
- Find ways to turn analytics into new revenue through packaging, add-ons and upsells
- Win internal buy-in with a business case your stakeholders will take seriously
Building the business case for in-app analytics
Getting approval is often harder than choosing a tool. The guide includes concrete advice for winning over executives and other stakeholders. It covers aligning the project with company strategy, addressing each stakeholder's goals and being honest about time to value. It also describes how PMs are monetizing analytics, presenting seven distinct ways to generate revenue from an analytics offering, and how teams use analytics to stand out from competitors that already have dashboards of their own.
Real voices make the case tangible. Survey respondents describe in their own words what embedded analytics has done for their products, and the expert contributors share how they would start if their company had barely considered data before. The guide closes with an overview of ThoughtSpot Embedded for readers who want to see how one platform approaches these problems.
Download the full guide to get the survey results, the vendor evaluation criteria, the monetization options and the stakeholder playbook in one place. It's a practical reference for any product manager deciding how to bring self-service analytics into their product and needing the evidence to back that decision.