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For most SaaS companies, analytics starts as a cost center. You build dashboards because users expect them. You invest engineering time because "reporting" shows up on every feature request list. And somewhere along the way, the analytics layer quietly becomes one of the most expensive and least monetized parts of your product.
But a growing number of product teams are flipping this equation — turning their embedded analytics from a checkbox feature into a genuine revenue driver. Not by slapping a "Premium" label on a tab full of charts, but by understanding what users actually value enough to pay for, and structuring their pricing around that.
The embedded analytics market is projected to grow from roughly $20 billion in 2024 to $75 billion by 2032, according to Fortune Business Insights. That growth isn't driven by more dashboards. It's driven by SaaS companies figuring out how to make analytics a product worth paying for — through premium tiers, add-ons, and entirely new revenue streams.
Here's how they're doing it, and what separates the analytics monetization strategies that work from the ones that don't.
Five years ago, embedded analytics was a differentiator. Having dashboards in your product put you ahead of competitors who didn't. Analytics has become table stakes.
But here's the interesting part: table stakes doesn't mean commoditized. It means the baseline has risen. When everybody has dashboards, the revenue opportunity shifts from "having analytics" to "having analytics that are meaningfully better." And users are willing to pay for that difference.
McKinsey's research has consistently found that data-driven companies are significantly more likely to acquire customers and drive profitability. PwC found that customers are willing to pay up to 16% more for a better experience — and in B2B SaaS, the analytics layer is a major part of the experience. When your product helps a customer's team make faster, more confident decisions, that's not a feature. That's a competitive advantage they'll pay to keep.
The question for product teams isn't whether analytics can generate revenue. It's how to structure the offering so that it does.
The monetization models that work in practice fall into a few proven patterns. The right one depends on your product, your user base, and how central analytics is to your core value proposition.
This is the most common and most straightforward model: basic reporting included in all plans, with deeper analytics reserved for higher-priced tiers. It works well when your lower-tier users need simple visibility (am I on track?) but your power users need the ability to dig deeper, compare, benchmark, and make strategic decisions.
You see this pattern across SaaS. HubSpot gates its advanced reporting and custom dashboards behind its Professional and Enterprise plans. Mixpanel prices based on event volume and reserves cohort analysis, advanced segmentation, and deeper behavioral analytics for paid tiers. Zendesk includes basic performance metrics in its entry plans but reserves customer analytics and AI-powered insights for higher tiers.
The principle is the same everywhere: give users enough data at the basic level that they see the value of analytics, then make the path to "I need more" natural enough that upgrading feels like a logical next step rather than a forced upsell.
Some products don't fit neatly into a tier structure for analytics. Maybe your core product is strong enough that most users don't need advanced data capabilities, but a meaningful subset would pay specifically for them.
Strobbo, an HR and workforce management platform, took this approach. Their basic package covers planning, time-tracking, and payroll — the core job. Analytics is offered as a premium add-on at a fixed price that scales with organization size.
The result: roughly 40% of their larger clients have purchased the analytics add-on, with power users checking dashboards up to five times a day. As Nick Geys, Strobbo's co-founder, put it: they consider analytics a hygiene factor that their platform can't do without, but by making it a premium module, they've turned it into a revenue stream that differentiates them from competitors offering only bare-minimum reporting.
Read more: https://www.luzmo.com/blog/how-to-monetize-embedded-analytics
Timewax, a project resource planning tool, initially bundled analytics into their most expensive plan. But they noticed a mismatch: some users on that plan didn't use analytics at all, while users on lower plans desperately wanted it but couldn't justify the full upgrade just for data insights. So they restructured to a mix-and-match model where customers choose from a menu of add-ons, with analytics as one option. Since the restructuring, 11% of customers have chosen analytics as their preferred add-on, and the flexibility has made upselling between plans significantly easier.
Read more: https://www.luzmo.com/resources/case-study-timewax
This is a clever variation that Timewax also deployed. When they updated their pricing and packaging, legacy customers on old plans had no incentive to switch. So Timewax made one feature exclusive to new plans: an embedded dashboard editor that let users create and customize their own reports. Legacy users could see the feature existed, but couldn't access it without migrating. Analytics became the carrot that pulled customers onto more profitable plans before their annual renewal.
Some SaaS companies go further — they don't just monetize their analytics layer, they turn their aggregate data into an entirely new product. This is particularly effective when your platform processes data that becomes more valuable in aggregate: industry benchmarks, market trends, performance comparisons.
Enersee, an energy management platform, built their entire business model around this concept. Their core offering is an API-based data product that analyzes real-time utility consumption data and presents it through intuitive dashboards powered by Luzmo. For their customers — energy managers responsible for large building portfolios — the value isn't in raw data. It's in the anomaly detection, the visual alerts when something spikes, the ability to spot a malfunctioning ventilation system before the electricity bill arrives. Their analytics layer isn't an add-on. It is the product, and it directly drives the sales conversation because non-technical buyers can immediately see what they'd be getting.
Read more: https://www.luzmo.com/resources/case-study-enersee
Not every analytics feature justifies a price tag. The difference between "nice dashboard" and "analytics I'd pay for" usually comes down to a few specific capabilities.
The most monetizable analytics features are the ones that produce a measurable outcome. Fleet Perfection, a fleet management company, embedded Luzmo's analytics to provide real-time dashboards with predictive insights on vehicle valuations. The result: their team sped up vehicle sales by 30%. That's a number a customer can put in a spreadsheet and show to their CFO. It's not "better reporting" — it's a direct impact on revenue velocity.
When Lansweeper integrated Luzmo to replace their in-house analytics, the effect was similar. Their CPO, Maarten Saeys, described the outcome in stark terms: they skipped what would have been three years of internal development and immediately reached a level of analytics capability that drove their NPS scores significantly higher. For Lansweeper's customers, the analytics layer went from a frustration point to a competitive advantage — the kind of improvement that justifies premium pricing.
Read more: https://www.luzmo.com/resources/case-study-fleetperfection
Luzmo's State of Dashboards research found that 51% of users say dashboards lack meaningful interactivity, and 42% specifically want better filtering, sorting, and drill-down capabilities. Users don't just want to see data — they want to see their data, sliced the way they need it.
This is exactly where the monetization opportunity lies. Basic reporting shows everyone the same pre-built views. Premium analytics lets users customize, filter, build their own reports, and save views tailored to their workflow. The jump from "here's your data" to "here's your data, your way" is consistently one of the highest-value upgrades a SaaS product can offer.
Kenjo, an HR tech platform, is a good example. Their users were struggling with clunky Excel exports and manual data manipulation. After integrating Luzmo's interactive dashboards, they hit a 90% weekly adoption rate. When users can explore and manipulate data inside the product instead of exporting it, the product becomes stickier — and stickier products are worth more.
Read more: https://www.luzmo.com/resources/case-study-kenjo
A standalone number is information. A number compared to an industry benchmark, a peer average, or a historical trend is an insight. And insights are what people pay for.
This is one of the most underused monetization levers in SaaS analytics. If your platform aggregates data across hundreds or thousands of customers, you're sitting on benchmarking data that individual users can't get anywhere else. Surfacing it — "your email open rate is 23%, which is 4 points below the industry average for your segment" — turns analytics from a mirror into a coach.
Luzmo's research found that 78% of respondents say AI has already improved their work, and 70% believe AI will differentiate how products deliver insights. This isn't future speculation — it's current expectation.
AI features like natural language queries (ask your data a question in plain English), automated anomaly detection (get alerted when something unusual happens), and narrative summaries (understand what changed and why without interpreting a chart) are rapidly becoming the highest-value analytics capabilities. They're also natural premium features because they deliver disproportionate value to the users willing to pay — the ones who need answers fast but don't have time or skill to dig through dashboards.
Here's where the revenue case for embedded analytics either strengthens or collapses: the economics of getting it built.
Luzmo's research found that 41% of SaaS companies spend over four months building dashboards. And that's just the initial build — it doesn't account for ongoing maintenance, feature requests (nearly 40% of teams receive 10+ user requests per month for changes), or the opportunity cost of engineers building charts instead of building your core product.
When analytics is built in-house, the cost structure typically looks like this: 2-4 engineers for 4-6 months for a V1, ongoing maintenance consuming 1-2 engineers permanently, and a backlog of user requests that grows faster than the team can deliver. At a loaded engineering cost of $150-200K per year, you're looking at $500K+ before a single user sees a dashboard, and $300K+ annually to keep it running.
That math makes it hard to monetize analytics profitably. The internal cost is so high that the revenue from an analytics premium needs to be substantial just to break even — and because the internal build often delivers a mediocre experience (67% of SaaS teams have low confidence in their in-app analytics, per Luzmo's research), the willingness to pay stays low.
An embedded analytics platform changes the equation fundamentally. Instead of months of development, you're looking at weeks. Instead of a permanent engineering team, you're looking at a product team that can iterate without engineering dependencies. The time-to-revenue drops dramatically, and the quality of the experience typically goes up — because you're using a tool that's been purpose-built for exactly this use case, across hundreds of implementations.
Lansweeper's experience illustrates this perfectly. Their CPO spent very little time on the business case for Luzmo because the math was obvious: the cost of continuing to build internally — in engineers, opportunity cost, and customer satisfaction — dwarfed the cost of adopting a purpose-built platform. The result was a leap forward in capability that would have taken years to achieve internally.
If you're considering monetizing your analytics layer, here's a practical framework based on what we've seen work across hundreds of SaaS implementations.
The fundamental principle behind analytics monetization is simple: users pay for outcomes, not features. They don't pay for "advanced dashboards." They pay for faster decisions, clearer insights, less time wasted in spreadsheets, and more confidence in their strategy.
The SaaS companies that have cracked this — from workforce platforms like Strobbo turning reporting into a premium module, to fleet management companies accelerating sales by 30% through embedded insights, to technology platforms like Lansweeper leapfrogging three years of development — share one thing in common: they focused on what their users needed to do better, then built analytics that demonstrably helped them do it.
The embedded analytics market is growing into the tens of billions for a reason. But the revenue opportunity isn't in the market size. It's in the gap between what users expect from analytics and what most SaaS products currently deliver. Luzmo's research shows that gap clearly: 40% of users don't think their dashboards help them make decisions. That's not just a product problem. It's a pricing problem — because if your analytics doesn't deliver enough value, you can't charge for it.
Close that gap, and analytics stops being a cost center. It becomes one of your most profitable features.
Luzmo is an embedded analytics platform purpose-built for software products. Build customer-facing dashboards in days, not months — and turn your analytics layer into a revenue driver, not a cost center. From premium-ready visualizations to Luzmo IQ for AI-powered insights, every feature is designed to deliver the kind of analytics experience users will pay for. Start your free trial →
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