Embedded Analytics Examples and Use Cases in 23 Different Industries

Embedded analytics is one of the most effective ways to increase product value. It drives adoption, unlocks new revenue, and makes your app harder to walk away from. 

That’s why SaaS companies and Independent software vendors (ISVs) are replacing external BI tools with embedded dashboards. It reduces churn, cuts support burden, and puts answers where users already work. 

But embedding analytics isn’t just about adding charts. It requires tradeoffs around UX, integration, scalability, and dev effort. Choosing the right path depends on how others have solved similar problems. 

This guide breaks down 26 embedded analytics examples across real products and industries. Use these use cases to benchmark your roadmap, evaluate priorities, and build analytics that users actually adopt. 

What is Your Industry?

1. ISV: Enhance User Experience & Higher Revenue 

For ISVs, analytics should live inside the product, not in a separate tool. If users have to export data or log into a portal, adoption drops and retention suffers. 

Embedded analytics changes that. When dashboards are built into the product experience, they feel native. This enhances user engagement, reduces churn, and provides ISVs with greater control over reporting as a feature. 

A common embedded analytics example is using tiered access. Customers unlock advanced dashboards, exports, or self-service reporting as part of a premium plan. This creates monetization opportunities without adding support burden or dev complexity. 

UI flexibility and integration control matter here.  

Typically, ISVs rush to Microsoft’s Power BI as a solution. However, many ISVs look for a Power BI Embedded alternative to avoid rigid iFrames, unpredictable costs, or limited white-labeling options. 

If you’re building a product and trying to improve adoption, embedded analytics is a proven lever. It adds value inside the app, where users already spend their time. 

2. SaaS: Maximize the adoption of tools and provide customers with the best ROI 

In SaaS, users don’t wait. If insights aren’t immediate, they abandon the feature, or worse, the product. 

That’s the risk with external BI tools. They add friction, slow time to value, and push customers back to spreadsheets. Embedded analytics addresses this by placing data directly within the app, where users make decisions. 

Strong SaaS teams use this to drive adoption. One common embedded analytics example is surfacing usage dashboards directly in the onboarding flow. Others tie in-app reporting to key actions, like campaign performance or customer health. 

It’s not just for users. Product teams utilize embedded analytics to track feature usage, identify retention triggers, and pinpoint workflow bottlenecks in real-time. That feedback loop helps prioritize the roadmap without waiting on analysts. 

There’s also revenue upside. Advanced filters, exports, and white-labeled dashboards often sit behind premium tiers, with the right architecture that doesn’t add support load or increase dev scope. 

Learn how other teams approach embedded analytics for SaaS from data models to UI decisions. 

3. Banking: Deliver great benefits to customers and employees alike

Banking platforms depend on real-time data. But legacy BI tools slow teams down and leave customers in the dark. 

Embedded analytics addresses this by integrating insights directly into the product itself. One of the most clear-cut examples of embedded analytics in this space is fraud monitoring. When suspicious transactions appear, customers receive instant alerts in their dashboard, not hours later through a third-party portal. 

Internally, credit teams use embedded dashboards to assess risk with live data. That includes income patterns, repayment history, and credit utilization trends. Instead of chasing exports, teams make faster, more accurate decisions in context. 

On the customer side, banking analytics tools are now a core feature. Personalized dashboards display spending trends, savings goals, and cash flow in real-time. This reduces support requests and makes digital banking more engaging, especially on mobile. 

4. CRM: Maximize customer lifetime value

CRM platforms lose value when insights lag behind activity. If sales, support, or success teams need exports to track key metrics, they fall behind. 

Embedded analytics solves this by integrating data directly into the CRM workflow. One of the clearest embedded analytics examples is pipeline visibility. Reps can see deal velocity, conversion rates, and health scores without leaving the dashboard. That makes it easier to prioritize leads and spot churn risks before they escalate. 

Support and success teams utilize CRM analytics in the same manner. Ticket volume, CSAT trends, and response time data are all live in context, allowing agents to personalize outreach without switching tabs or tools. 

Embedded dashboards also support account expansion. Usage trends and engagement signals identify accounts ready for upselling or requiring intervention. When analytics live inside the workflow, teams act faster and stay focused.  

5. Crypto: Leverage the most suitable market conditions and invest intelligently

Crypto platforms run on real-time data. But when analytics live outside the product, users miss critical signals and teams lose visibility. 

Embedded analytics changes that. One of the most relevant embedded analytics examples in crypto is real-time trade tracking. Dashboards show portfolio performance, market shifts, and volatility without delay. Traders don’t have to refresh or rely on external tools. 

On the backend, exchange platforms use embedded dashboards to monitor liquidity, detect anomalies, and flag risky accounts. Metrics like user activity spikes, wash trading patterns, and wallet clustering help surface threats early. 

Crypto analytics features also support power users. Tools such as historical comparisons, real-time order books, and sentiment indicators are integrated directly into the app, rather than being accessed through a separate portal. That improves engagement, retention, and response time in a volatile market. 

6. Education: Improve learning outcomes effectively

Education platforms generate a lot of data, but most of it goes unused. Teachers and administrators need real-time insight inside the systems they already use. Static exports and outdated reports slow them down. 

One of the clearest embedded analytics examples in this space is student performance tracking. Dashboards show attendance, assignments, and participation as it happens. That gives educators the visibility they need to adjust support quickly. 

For example, Casebook, a platform used across education and human services, built embedded dashboards to help caseworkers and staff track student progress, filter reports, and act on data without relying on technical teams. 

Education analytics also supports classroom decisions. Teachers use dashboards to monitor engagement trends and tailor instruction. Embedded reporting gives administrators a live view of curriculum performance, resource allocation, and school-wide outcomes. 

When analytics live inside the product, data becomes part of the workflow. That’s how schools and platforms move from reporting to action. 

7. Mining: Drive Sustainable Growth in Today’s Rapidly Changing Mining Industry 

Mining platforms collect data from equipment, logistics, sensors, and safety systems. But without embedded analytics, most of that information stays locked in exports or siloed tools. 

One of the most common embedded analytics examples in mining is equipment monitoring. Dashboards track machine health, fuel use, and downtime. Field teams catch early failure signs and schedule maintenance before breakdowns stall production. 

Safety is another key use case. Real-time dashboards highlight risk zones, exposure levels, and compliance gaps while teams are still on-site. That’s a major improvement over static, end-of-day reports. 

Mining analytics tools also support production planning. When dashboards are embedded into logistics and scheduling platforms, managers adjust load distribution, material flow, and resource allocation in real time. 

With embedded analytics, mining teams make operational decisions where the data lives. That improves response time, reduces reporting overhead, and helps maximize the return on every ton moved. 

8. Finance: Shape future strategies and improve the decision-making process in real-time

Traditional BI workflows slow finance teams down. Exporting reports, switching tools, or waiting on analysts create friction that breaks decision cycles. 

Embedded analytics solves that by putting real-time insight inside the platform. One of the most useful embedded analytics examples is portfolio tracking. Wealth platforms show asset allocation, exposure, and performance in the same interface advisors use with clients. 

Banks and lenders rely on finance analytics dashboards to manage budget cycles, model forecasts, and evaluate loan risk. Metrics like acquisition cost, gross margin, and approval rates are available in context without leaving the platform. 

Predictable cost models also matter in this space. Teams building financial software often opt for embedded analytics pricing models that scale with contracts, rather than usage spikes. That keeps reporting costs stable even as customer volume grows. 

Embedded analytics isn’t just a planning tool. It provides finance teams with live, actionable insights inside the tools they use to allocate capital, assess risk, and improve performance.  

9. Healthcare: Provide the right healthcare to patients at the right time

Healthcare platforms depend on real-time insight. But most analytics live outside the workflow, delayed by exports or buried in EHR backends. That slows decision-making and limits impact. 

One of the clearest embedded analytics examples in healthcare is patient risk monitoring. Dashboards display early signs of deterioration, utilizing live vitals, lab results, and patient history. This gives clinical teams the data they need to act quickly and reduce readmissions. 

Teams that use predictive analytics in healthcare can identify high-risk patients based on age, comorbidities, or medication patterns. That insight supports earlier interventions and better triage. 

Scriptly, a pharmacy SaaS platform, uses Reveal to embed dashboards directly into its product. Pharmacists can now track medication trends, refill behavior, and patient engagement in real-time. They no longer need to switch tools or rely on static reports. 

Operational teams use healthcare analytics to manage staffing, equipment usage, and demand across departments. With the right embedded analytics platforms, they get full visibility while keeping clinical systems simple. 

10. Insurance: Simultaneously increase customer satisfaction and profits

Insurance teams make high-stakes decisions fast. But most analytics live outside the workflow, trapped in BI tools that don’t keep up with fraud risk or underwriting demands. 

Embedded analytics changes that. One of the clearest embedded analytics examples in insurance is fraud detection. Dashboards flag inconsistencies by comparing real-time claim data to known fraud patterns and policy history. That shortens response time and reduces false payouts. 

Underwriters use embedded dashboards to evaluate risk directly in the workflow. Inputs like profile data, claim history, and location-based risk are scored in real time, so decisions stay fast and consistent. 

At the portfolio level, insurance analytics tracks policy retention, lifetime value, and churn signals. These insights help teams optimize pricing, marketing, and service for each product line. 

When analytics is delivered in context, it moves from reporting to real-time risk management. That’s what modern insurance platforms require. 

11. IT: Make your IT services and offerings optimal to all users and clients

IT teams don’t just manage systems. They’re responsible for performance, uptime, and efficiency across the entire organization. But without embedded analytics, key metrics stay buried in siloed tools or delayed reports. 

One of the clearest embedded analytics examples in IT is system monitoring. Dashboards track latency, error spikes, and infrastructure usage in real time. That helps teams detect issues early and resolve them before they cascade. 

Datacom built an internal portal that gives engineers self-service access to system data. No more waiting on reports or pulling devs off roadmap work to answer ops questions. 

Teams use IT analytics to forecast capacity, monitor SLA compliance, and plan budgets based on actual workload trends. Dashboards are embedded into admin tools, support consoles, and internal portals. 

For teams that need full control, platforms with an embedded SDK allow devs to integrate analytics directly into existing apps. That means consistent UI, secure access control, and no need to rebuild workflows from scratch. 

12. Energy: Mitigate Risks & Enhance Sustainability Efforts 

Energy platforms process massive data flows from smart meters, sensors, and industrial control systems. But without embedded analytics, that data stays locked in silos or delayed in batch reports. 

One of the most common embedded analytics examples in energy is predictive maintenance. Dashboards track real-time signals, such as temperature, pressure, and vibration, to flag equipment risk before failure. That prevents downtime and avoids costly repairs. 

Operations teams use energy analytics to monitor output, consumption, and grid load. Embedded dashboards support fast decisions on resource allocation and demand balancing across sites. 

As environmental regulations tighten, teams embed analytics into their sustainability workflows. Dashboards show carbon output, energy loss, and efficiency metrics without waiting for manual rollups or monthly audits. 

Embedded analytics gives energy teams control over performance, safety, and compliance from inside the tools they already use. That’s how they operate faster and meet regulatory targets without adding process overhead. 

13. Manufacturing: Bring efficiency to the manufacturing chaos

Manufacturing systems run on timing and precision. However, without embedded analytics, teams waste time searching for data across production lines, ERP platforms, and spreadsheets. 

One of the most impactful embedded analytics examples in manufacturing is live production monitoring. Dashboards track cycle time, equipment utilization, and bottlenecks in real time. That helps shift managers respond faster and optimize throughput. 

Forecasting is another core use case. Teams utilize manufacturing analytics to forecast delays, material shortages, and capacity constraints, leveraging demand and lead time data. Embedded dashboards help planners adjust before issues cascade across the supply chain. 

Many platforms integrate directly with ERP or MES backends using stored procedures in embedded analytics. This allows them to query operational data securely and avoid syncing across multiple disconnected systems. 

When analytics lives inside production tools, teams act faster and make better decisions on quality, timing, and cost without adding reporting overhead. 

14. Real Estate: Appraise homes easier than ever before

Real estate platforms must deliver fast and accurate decisions based on constantly changing market data. Without embedded analytics, teams rely on static reports or external tools that slow down deal flow. 

One of the most practical embedded analytics examples in this space is pricing analysis. Dashboards show real-time supply, sales velocity, demand trends, and property details in one place. That helps teams adjust pricing based on current conditions, not last quarter’s data. 

Real estate analytics also supports investment planning, asset performance tracking, and risk modeling. With historical trends and predictive models embedded inside deal management tools, analysts can forecast returns, compare markets, and flag outliers before closing. 

When analytics lives inside internal platforms or investor portals, decisions happen faster. Teams spend less time gathering data and more time acting on it. 

15. Retail: Forecast future sales and calculate efficient inventory policies to optimize stock levels

Retail moves fast. But without embedded analytics, teams rely on delayed exports and siloed data that slow down decision-making and increase risk. 

One of the most clear-cut examples of embedded analytics in retail is inventory optimization. Dashboards show live stock levels, sales velocity, and reorder thresholds. That helps prevent overstocking and out-of-stock situations, which erode margins and customer trust. 

Customer-facing embedded dashboards enable shoppers and sales representatives to view order status, product performance, and loyalty data within the same interface they already use. No redirects. No data gaps. 

On the business side, retail analytics supports campaign targeting, demand forecasting, and buyer segmentation. When insights are embedded into the tools marketers already use, teams act faster and miss fewer revenue moments.  

16. Supply Chain: Predict future demand accurately

Supply chains fail when teams react too late. Without embedded analytics, planners rely on outdated forecasts, siloed ERP data, and manual reports that delay action. 

One of the most valuable embedded analytics examples in supply chain operations is demand forecasting. Teams use historical orders, supplier behavior, and live market signals to project needs in real time, not at the end of a reporting cycle. 

Lanxing embedded Reveal dashboards into its SCATLAS platform. Logistics teams now manage capacity, optimize routes, and track shipments from one interface. No spreadsheets. No separate reporting tools. 

Supply chain analytics also helps cut cost and risk. Dashboards highlight delays, inventory mismatches, and inefficiencies before they hit margins or service levels. 

Embedded analytics makes supply chain data actionable. That’s how teams stay ahead of disruptions and keep operations moving. 

17. Telecommunications: Personalize deals, solve customers’ problems, and sell more

Telecom platforms move massive volumes of data, but most teams still rely on batch reporting and disconnected systems that delay action. Embedded analytics puts usage, performance, and behavior data where it matters—in the tools teams already use. 

One of the most valuable embedded analytics examples in telecom is customer segmentation. Dashboards show usage trends, churn indicators, and plan affinity in real time. That lets teams deliver targeted offers, price by usage tier, and retain high-value customers before they drop. 

Telecommunications analytics also powers network performance monitoring. Dashboards track latency, signal health, and error events live, helping ops teams fix issues before users are affected. 

Fraud and security teams use embedded analytics to flag abnormal patterns and prevent account compromise. When insights are embedded into internal tools, response times shrink and customer trust improves. 

18. Security: Identify suspicious activities, and detect threats and anomalies in real-time

Security teams face constant pressure to detect threats fast. But without embedded analytics, they rely on disjointed dashboards, delayed logs, and siloed platforms that slow response. 

One of the most relevant embedded analytics examples in security is anomaly detection. Dashboards track login behavior, IP access, and network traffic in real time. That helps teams catch credential abuse or lateral movement before damage spreads. 

Teams use embedded analytics tools to monitor endpoints, correlate alerts across systems, and drill into user behavior all without leaving the platform they already use. That shortens investigation time and reduces alert fatigue. 

Some analytics integration examples include machine learning models trained on live user data. These models spot abnormal activity patterns faster than rule-based systems and adapt as threats evolve. 

Strong embedded analytics security provides security and IT teams with full visibility and faster control within the same tools they already manage. That’s how you move from alert to action without switching systems.   

19. Logistics and Delivery: Turn your logistics data into actionable insights to improve your business performance

Logistics teams depend on speed and precision. But when analytics live outside the core system, response time suffers and visibility breaks down. Embedded analytics brings operational data into the platforms teams already use. 

One of the clearest embedded analytics examples in logistics is order tracking. Dashboards show real-time fulfillment progress, delivery status, and exception alerts. That gives managers a chance to intervene early and avoid missed handoffs. 

Warehouse leaders use embedded analytics tools to monitor pick rates, labor efficiency, and packing times. With live data in the workflow, they adjust staffing, spot delays, and improve on-time performance. 

Synergy Logistics, the company behind SnapFulfil, embedded analytics into its WMS. Customers gained real-time insight into inventory, shipping activity, and operational trends inside a single interface. That reduced dependence on external reporting tools and improved day-to-day decision speed. 

These analytics integration examples show how logistics teams move from delayed reaction to live response. That’s how you stay ahead of disruptions and scale efficiently. 

20. Marketing: Enhance campaigns and understand customers better

Marketing teams need to act fast. But without embedded analytics, they rely on stale reports, siloed data, and slow analyst cycles that delay action and cost performance. 

One of the most impactful embedded analytics examples in marketing is campaign performance tracking. Dashboards show real-time KPIs across platforms, so marketers can shift spend, adjust messaging, or pause underperforming assets without waiting for manual reports. 

Embedded charts show engagement, attribution paths, and ROI across audience segments. Teams get a clear view of what’s working across email, paid, and social in one place. 

Predictive models help identify high-LTV cohorts, churn risks, and response trends. Embedded analytics tools make this data accessible inside campaign tools, turning complex behavior patterns into fast tactical decisions. 

Data Analytics capabilities also help teams connect spend to revenue, experiment faster, and optimize offers across multiple channels. These examples of embedded analytics show how marketing platforms can move faster, with less dependency on external reporting or manual segmentation. 

21. Airlines: Drill down into flight statistics by route, airport performance, and aircraft maintenance needs

Airline operations depend on real-time decisions. But with data scattered across booking systems, crew scheduling tools, and aircraft logs, response time suffers. Embedded analytics tools unify that data in the systems teams already use. 

One of the most practical embedded analytics examples in aviation is dynamic pricing. Dashboards surface demand shifts, booking trends, and route performance, enabling pricing teams to adjust fares before inventory fills or stalls. 

Ops teams use embedded analytics to monitor on-time performance, rebalance loads, and optimize gate assignments. These dashboards live inside dispatch tools and planning systems, giving crews immediate visibility when delays or reassignments are needed. 

Predictive maintenance is another critical use case. By combining telemetry data, flight logs, and service history, teams can identify components at risk of failure and schedule replacements in advance. That reduces unexpected groundings and protects revenue. 

These analytics integration examples show how aviation teams use embedded analytics tools to optimize operations, lower costs, and keep schedules on track without switching between disconnected systems. 

22. Hospitality: Boost your hotel’s revenue, occupancy, and profitability

Hotel platforms handle massive data flows from bookings, point-of-sale systems, guest interactions, and third-party sites. But without embedded analytics, key insights get buried across disconnected tools. 

One of the most useful embedded analytics examples in hospitality is occupancy forecasting. Dashboards combine past booking patterns, seasonal curves, and event calendars to predict demand by channel, day, or room type. That supports dynamic pricing and better inventory control. 

Hotels also use embedded analytics tools to manage revenue, track campaign performance, and monitor competitor rates. With these insights embedded into the PMS or CRM, teams can adjust offers, reprice in real time, and personalize promotions based on live booking behavior. 

On the operations side, analytics integration examples include connecting property management, guest profiles, and upsell modules. Staff see service history, preferences, and upgrade triggers in one interface instead of toggling between systems. 

Embedded analytics enable hotel teams to make faster decisions at every touchpoint, from rate strategy to guest engagement, without relying on offline reports or separate tools. 

23. Weather Forecast: Accuracy matters

Weather platforms pull in massive data streams from satellites, sensors, radar, and forecast models. Without embedded analytics, teams rely on slow reports or switch between tools to translate raw data into decisions. 

One of the most relevant embedded analytics examples here is real-time condition tracking. Dashboards display localized temperature, wind, humidity, and precipitation data in formats that facilitate rapid planning and operational decision-making. 

These analytics integration examples are critical in logistics, agriculture, and travel. Farmers use live forecasts to shift planting windows. Delivery teams adjust routes to avoid storms. Travelers pick safer departure windows based on current and predicted conditions. 

On consumer platforms, embedded analytics tools power real-time alerts, interactive weather layers, and personalized dashboards. Instead of sending users to third-party apps, teams keep decisions inside the interface and improve engagement during high-impact events. 

24. Agriculture: Identify the critical factors from large data sets that can impact your farm productivity

Modern agriculture runs on live data from sensors, drones, tractors, and connected irrigation systems. But without embedded analytics, that data stays trapped in dashboards farmers don’t use or reports that arrive too late. 

One of the most useful embedded analytics examples in agriculture is yield monitoring. Dashboards combine soil health, weather forecasts, and crop data to help growers improve yield and reduce waste during harvest. 

Other embedded analytics use cases include optimizing water use, adjusting fertilizer rates, and detecting pest activity with fewer field inspections. When that insight is embedded into the systems already used for planning or machinery control, farmers act on data without switching tools or losing time. 

These analytics integration examples show how connected equipment and embedded analytics tools turn field-level data into real decisions. That means fewer missed windows, better resource allocation, and smarter food production at scale. 

22. Government: Intelligently cut costs, optimize performance, and deliver reform in the public sector

Government platforms handle massive data flows from public safety, health, infrastructure, and finance systems. But without embedded analytics, most decisions are slowed down by legacy tools, siloed systems, and batch reports. 

One of the most relevant embedded analytics examples in government is emergency response planning. Dashboards combine inputs from dispatch systems, geographic data, and public health feeds to guide real-time decisions during critical events. 

Embedded analytics use cases in the public sector also include budget visibility, fraud detection, and service monitoring. These tools help leaders spot anomalies, track spending, and respond faster to community needs without waiting for centralized reports. 

As pressure grows for transparency and speed, embedded analytics tools are replacing outdated reporting systems. These analytics integration examples show how governments can deliver better services, reduce delays, and act on live data instead of static reports. 

23. Sports: The future of sports lies in the hands of sports analytics

Sports teams now depend on live data to guide decisions on performance, recruitment, and fan engagement. Embedded analytics tools give coaches, analysts, and front offices access to real-time insight without leaving the platforms they already use. 

One of the most valuable embedded analytics examples in sports is player performance tracking. Dashboards show speed, fatigue, shot efficiency, and movement data during training and matches. That allows coaches to adjust rotations, shift tactics, and manage workloads on the fly. 

Front offices use embedded analytics to improve scouting, model injury risk, and refine game strategies. Data from wearables, video analysis, and scouting reports feed into dashboards that streamline hiring, roster decisions, and player development. 

On the business side, marketing teams rely on embedded analytics tools to track merchandise trends, campaign results, and ticket sales in real time. These examples of embedded analytics demonstrate how sports organizations utilize data to compete more effectively on the field and in the front office. 

One Platform – Universal Solution 

Embedded analytics shapes how users interact with your product. It influences adoption, engagement, and how quickly users get value from your data. Done right, it becomes part of your product, not an add-on. 

Reveal helps teams ship analytics faster without giving up control. Its flexible API, front-end SDK, and scalable backend are built for in-product performance. There are no user caps and no usage penalties. 

End users get fast, usable features like interactive data visualizations, drilldowns, filtering, and drag and drop analytics that don’t require training. Designers get white label analytics and layout freedom. Engineers stay in control of data, auth, and versioning. 

Reveal supports machine learning outputs, predictive scoring, and team collaboration, all embedded where users already work. You can also create beautiful yet informative dashboards that feel native on any device. 

You can learn more about Reveal and how it works by downloading our SDK or by scheduling a quick product tour.