> For the complete documentation index, see [llms.txt](https://docs.questera.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.questera.ai/questera-ai-sdks/all-components/embedded-analytics.md).

# Embedded Analytics

Harness the power of personalized analytics to optimize usage and discover untapped features, while setting and tracking performance benchmarks for continuous improvement and engagement.

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### Line Chart

* **Optimization of User Engagement:** Track user activity trends over time to identify peak performance periods and areas for improvement. Harness personalized analytics for deeper insights into user behavior.
* **Discovery of Untapped Features:** Visualize the adoption rates of different features within your platform. Identify underused tools that could enhance user productivity and satisfaction.
* P**erformance Benchmarks Tracking:** Monitor progress against key performance indicators. Set benchmarks to achieve and surpass through continuous operational enhancements.

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### Scatter Chart

* **Correlation Between Feature Usage and User Success:** Analyze the relationship between the frequency of feature usage and user performance metrics. Identify patterns that signify high engagement and success.
* **Impact of Analytics on User Performance:** Scatter plot showcasing the impact of personalized analytics on improving user outcomes. Spot which analytics features correlate with higher efficiency and satisfaction.
* **Benchmark Achievement Scatter Analysis:** Display the distribution of user performance against set benchmarks. Use data points to determine where users are exceeding or falling short in their goals.

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### Line Styling

* **Custom Line Colors to Highlight Trends:** Utilize distinct colors to differentiate between various data sets. Enhance visibility and understanding of key trends that influence user success.
* **Dash Patterns for Data Differentiation:** Implement dash patterns to represent different types of analytics data. This helps in quickly identifying and comparing feature usage and performance metrics.
* **Line Thickness for Emphasis:** Adjust line thickness to emphasize significant data, such as peak usage times or major performance improvements. Thicker lines draw attention to critical insights and achievements.

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### Combo Bar/Line

* **Feature Adoption and Usage Trends**: The bar chart shows the adoption rates of new features; the line graph tracks overall user activity over the same period. Analyze how new tools impact user engagement.
* **User Success Metrics Over Time:** Combination chart depicting growth in key performance metrics alongside user engagement levels. Benchmark success and identify areas for strategic improvements.
* **Efficiency Gains Through Analytics:** Visualize efficiency improvements as bars, with user satisfaction trends overlaid as a line. Understand the correlation between analytics use and user success.

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### Stacked Bar Chart&#x20;

* **User Engagement by Feature**: Examine how different user segments interact with various platform features. Highlight areas with potential for increased engagement and optimization.
* **Feature Adoption Over Time:** Track the growth in usage of new features across different user groups. Identify trends and opportunities for promoting lesser-used capabilities.
* **Benchmark Achievement Levels:** Display the progress of different departments or teams in meeting their performance benchmarks. Use data to drive strategies for continuous improvement.

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### Horizontal Bar Chart&#x20;

* **Feature Utilization Efficiency**: Compare the effectiveness of different platform features in enhancing user performance. Highlight areas where embedded analytics have maximized efficiency.
* **Incremental Performance Improvements:** Showcase performance improvements over time with analytics interventions. Track how embedded analytics contribute to user success and platform growth.
* **Engagement and Retention Metrics:** Illustrate user engagement levels across various segments. Use data to pinpoint where personalized analytics have bolstered retention rates.

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### Stepped Line Chart

* **Step-by-Step User Engagement Analysis**: Observe step changes in user engagement levels over time. Use data-driven insights to implement targeted strategies for boosting activity and retention.
* **Feature Adoption and Exploration:** Highlight the stepwise increase in the adoption of new features. Pinpoint opportunities to promote lesser-used functionalities for enhanced user experiences.
* **Benchmark Achievement Tracking:** Monitor performance through distinct stepped intervals. Evaluate effectiveness of interventions to meet and exceed established benchmarks for success.

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### Pie chart

* **Feature Utilization Breakdown**: Explore the distribution of feature usage within your application. Pinpoint which tools are most and least utilized to guide future enhancements.
* **User Engagement Levels:** Analyze the segments of user engagement across your platform. Identify which sections attract the most attention and which need reevaluation.
* **Benchmark Achievement Overview:** Display the proportion of performance goals met versus those still pending. Utilize this data to motivate continuous improvement and user success.

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### Doughnut Chart

* **Feature Usage Distribution**: Explore the proportion of feature utilization across your platform. Gain insights into high and low usage trends to drive focus on personalized user training.
* &#x20;**Engagement Levels by User Segment:** Analyze engagement across different user segments. Use this data to tailor experiences and boost satisfaction and productivity for each group.
* **Benchmark Achievement Overview:** Visual representation of performance against set benchmarks. Identify areas exceeding expectations and those needing improvement for strategic action.

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### Multi Series Pie

* **User Engagement by Feature**: Examine how different user segments interact with specific features. Personalized analytics spotlight the most and least used aspects of your platform.
* **Feature Adoption Over Time:** Chart the growth in feature usage across various user cohorts. Highlight trends that point to successful integration or potential areas for enhancement.
* **Performance Benchmarks by User Group:** Compare key user groups against performance benchmarks. Use data-driven insights to tailor improvements and boost overall engagement.

<figure><img src="/files/T21IrRDe7xJEgiO9I6C8" alt="" width="563"><figcaption></figcaption></figure>

### Bubble Chart

* **Feature Utilization Analysis**: Compare the usage intensity of different features across various user segments. Larger bubbles indicate higher engagement, highlighting key areas of user interaction.
* **User Success Hotspots:** Identify which analytics tools are driving the most significant performance improvements. Bubble size reflects the impact on user success and operational efficiency.
* **Benchmark Achievement Insights:** Track and visualize progress toward performance benchmarks over time. Bubbles represent the degree of goal attainment, facilitating targeted strategy adjustments.

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### Floating Bar Chart

* **User Engagement Optimization Levels**: Illustrate the range of engagement scores before and after implementing analytics. Highlight the shifts in user engagement to pinpoint the effectiveness of personalized insights.
* **Feature Adoption Spectrum:** Display the spread of feature usage across different user segments. Identify which features are widely adopted and which remain largely untapped, guiding targeted improvements.
* **Progress Towards Performance Benchmarks:** Showcase initial and current performance metrics in a comparative format. This visual tracking aids in understanding how close users are to reaching established success benchmarks.

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