Standard Analytics transforms closed-ended survey responses into clear, structured insights that drive smarter decisions across the organization. Survey analysts can quickly filter and interpret data to uncover actionable trends. Executive leaders benefit from concise, high-level summaries that support fast, strategic decision-making. For product managers and researchers, consistent feedback helps track satisfaction and fine-tune product strategies to better meet customer expectations. With its speed, clarity, and relevance, Standard Analytics empowers every team to act with confidence and precision.

Business Use Cases

• Ease of Analysis: Closed questions generate structured, quantifiable data, making it easier to perform statistical analysis, identify trends, and produce clear, consistent reports.

• Consistency: They ensure consistency in responses, allowing for reliable comparisons across different segments and periods, which enhances the accuracy and depth of analysis.

• Time Efficiency: Closed questions are quick and easy for respondents to answer, which helps boost response rates and accelerates the overall data collection process.

• Actionable Insights: They produce clear, actionable feedback that enables businesses to identify key issues and effectively prioritize improvements.

• Scalability: Closed questions enable businesses to efficiently process and analyze large volumes of survey data, making it easier to scale insights across the organization.

• Objective Feedback: They minimize ambiguity, delivering more reliable, objective responses that enhance the overall accuracy of survey results.

• Standardization: Closed questions offer standardized feedback, enabling comparisons across customer groups and regions.

Closed-ended questions offer a range of valuable benefits that enhance the effectiveness of survey programs. They provide clear, actionable data that supports informed decision-making and prioritization. By simplifying the survey experience, they encourage higher response rates and faster data collection. Their structured format allows for efficient analysis, making it easier to identify trends and patterns across large datasets. This scalability supports feedback collection from diverse customer bases, while the consistency of responses boosts confidence in decision-making. Closed questions also enable benchmarking over time or across segments, accelerate feedback loops for timely action, and support the tracking and customization of key performance metrics such as NPS, CSAT, and CES.

Prerequisites

In order to have access to Survey Analytics you need to have View permissions for both Response and Analytics of the survey.

• View: You can access the Survey Analytics and view existing surveys.

Setting Up Question Types and Widget Visualizations

For each question, we offer various visualizations that represent data distribution and highlight key points of interest. One such visualization is the response distribution, which shows how responses are spread across different categories. This visualization is available for all question types, except for conjoint and heatmap questions.

Let us go through each question type and understand the available widgets present:

Multiple Choice Questions

Response Distribution is represented in a column chart:

• X Axis: It represents options.

• Y Axis: It represents response count or percentage.

CSAT

Response distribution is represented in Pie Chart.

Gauge chart can be used (if statement is single) and Bar Chart in case of multiple statements.

Rating Scale

• Response distribution is represented by Stacked Bar chart.

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  X Axis: Represents Response Count.

  Y Axis: Represents statements.

  Each bar in the response distribution is color-coded, with each color representing a different choice.

  For example, the bar for a rating of 2 in green will indicate how many respondents rated Product Variety as a 2.

  Average rating: Gauge chart (if single statement)/ Bar chart (If multiple statements):

• Gauge chart is used incase of single statement.

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• Bar chart in case of multiple statements.

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Slider

Response distribution for a single statement is represented by Pie Chart.

Average Distribution for a single statement is represented by Gauge Chart.

Response Distribution for Multiple Statement is represented by Line Graph.

NPS

Response distribution is represented by Bar Chart.

The NPS (Net Promoter Score) is visualized using a gauge chart to clearly reflect customer loyalty and satisfaction. It is calculated by subtracting the percentage of detractors (respondents rating 0–6) from the percentage of promoters (respondents rating 9–10). The score ranges from -100 to 100, with higher values indicating stronger customer advocacy and satisfaction.

Rank Order

Response distribution is represted by Stacked column chart.

Average Rank is represented by Bar Chart. Average rank is calculated by sum of all ranks given to an item divided by the total number of ranks given

Matrix

Response Distribution

Response Concentration

Text/Media

Text and media questions are not included in reports, as they do not generate structured response inputs that can be quantified or analyzed statistically.

File Upload

No widget supported as of now.

Heatmap

Heatmap response distribution is represented by Column Chart.

Heatmap Insights: In this widget, red regions indicate areas with a higher number of clicks, helping to visually highlight the most engaged or frequently interacted parts of the interface.

Conjoint

Conjoint feature importance is represented by Bar Chart. This widget highlights how important each feature is in comparison to others when customers make purchase decisions. For example, when buying a phone, customers may consider several features, but most tend to prioritize camera specifications over charging speed. This means the camera holds greater relative importance. Improving high-importance features like the camera is more likely to influence customer demand than enhancing lower-priority ones such as charging speed.

In the chart below, relative importance is plotted for each feature on a scale from 0 to 1. A higher value indicates greater influence on customer decisions. In this case, price emerges as the most important feature, suggesting it has the strongest impact on purchase behavior compared to other factors.

Filters Supported in Standard Analytics Dashboards

You can implement different criteria to refine the responses in the Standard Analytics Report according to your particular requirements. The report accommodates the following range of filters:

Widget Level Filters

A similar set of filters is available at the widget level, which includes a date filter that enables you to narrow down metrics and dimensions based on a particular date range.

Add to Dashboard

You can add any widget or visualization from the Standard Analytics Dashboard directly to your Custom Dashboard.

Additionally, you can add visualizations created through the drill-down of any Standard Analytics widget directly to your Custom Dashboard using the "+ Add to Custom Dashboard" option available in the third pane.

Setting Up Custom Dashboard

1. Navigate to the Standard Analytics Dashboard.

2. Select the + Add to Custom Dashboard option from the Horizontal Ellipses (3 dots) actions menu.

   

3. Go to Add to Custom Dashboard window and fill in the details:

   

   1. Widget Name: You have the option to assign a name to the widget, which will subsequently be included in your Custom Dashboard.

   2. Type: You have the option to choose the type of dashboard. For instance: CFM Analytics, Listening Dashboard, Benchmarking Dashboard, and so on.

   3. Dashboard: You have the option to choose the dashboard and the particular section within it where you wish to place the widget.

   4. Create Dashboard: You have the option to name the new dashboard to which the widget will be added.