Text Widget
A widget for adding a title or brief description (within 50 characters) inside the dashboard without any data integration.
🧙 This is useful when:
Distinguishing specific areas of the dashboard or displaying section titles to improve readability.
Providing a brief background explanation or definition for the following group of widgets to aid data interpretation.
Image Widget
The image widget lets you directly upload image files to the dashboard. It supplements context that is difficult to convey through charts or numbers alone, allowing you to configure your dashboard more intuitively.
🧙 This is useful when:
When conducting concept evaluations, ideas are often presented in image form (concept boards) to help respondents easily visualize and evaluate them. When visualizing results in a dashboard after data has been collected, you can add the concept board using an image widget.
Scorecard Widget
An effective visualization method for highlighting a single numerical metric that is central to data analysis. Supports the most important single statistics such as frequency, average, and NPS (Net Promoter Score).
🧙 This is useful when:
Highlighting the core KPI that should be checked first upon entering the dashboard.
Quickly monitoring the current status of key metrics such as total response count and average satisfaction.
The variables available for the scorecard widget vary depending on the selected statistic. When the statistic is frequency or variable, rating or numeric variables can be used; when the statistic is NPS, NPS variables can be used.
Try configuring it like this!
💡 Widget purpose | I want to display the current NPS (Net Promoter Score) to immediately understand the level of customer loyalty and check performance against targets.
Click the [+ Add widget] button and select the 'Scorecard' widget.
Click the pencil icon to open 'Scorecard widget settings' and enter each item.
Title: Enter the widget's title, such as 'Current NPS Score.'
Statistic: Select NPS from among frequency, average, and NPS.
Variable: Select the variable from the survey where NPS was asked.
Click [Done] to create the scorecard widget and view the NPS score for the selected variable.
Bar Chart Widget
The most versatile visualization method for visualizing various types of categorical data such as single choice, multiple choice, and ranking. It can be used even when the total of all data exceeds 100%, and is suitable for comparing sizes between items or checking rankings.
🧙 This is useful when:
Identifying the ranking of items with a high share among those selected by respondents.
Using the cross-analysis feature to compare and analyze differences in responses across specific groups (e.g., by age group).
The bar chart widget can be created using 'choice,' 'multiple choice,' 'ranking,' 'short text,' and 'ranking text' variables.
Try configuring it like this!
💡 Widget purpose | I want to analyze "the factors considered when purchasing this product (multiple selections allowed)" to understand the purchase decision factors customers consider most important!
Click the [+ Add widget] button and select the 'Bar chart' widget.
Click the pencil icon to open 'Bar chart widget settings' and select each item.
Chart shape: Select your desired shape from horizontal, horizontal group, vertical, or vertical group.
Sort: Select the sorting criteria for bars — by response (most responses first) or by value (label order).
Axis range: Enter the minimum and maximum values for the axis, and the widget will be displayed according to your desired value range.
💡 Usage tip | When should you select Group?
Group is used when you want to compare data through cross-analysis.
For example, if you select a gender variable for Group and an age variable for Bar, you can see the composition ratio by age group within gender.
Clicking the [Done] button immediately creates the bar chart widget. Through this chart, you can check the ranking and proportion of factors that customers consider important in real time and derive clear insights for your next strategic action.
Donut Chart Widget
When the total of all data equals 100%, it visualizes the proportion each item occupies in donut form. Choices exceeding the set maximum number of slices are automatically grouped as 'Other' to improve readability.
🧙 This is useful when:
For data where the total equals 100%, such as single-choice responses, quickly comparing composition ratios at a glance and identifying major components.
The donut chart widget can be created using 'choice' and 'short text' variables.
Try configuring it like this!
💡 Widget purpose | I want to visualize the 'ratio by product purchase channel' to understand the proportion of major acquisition channels and analyze marketing contribution!
Click the [+ Add widget] button and select the 'Donut chart' widget.
Click the pencil icon to open 'Donut chart widget settings' and select each item.
Variable: Select the variable from the survey where the product purchase channel was asked.
Sort: Select the sorting method — by response or by value.
Number of donut slices: The number of donut slices appears automatically based on the number of choices in the selected variable.
Click [Done] to create the widget. Through this chart, you can visually see at a glance the proportion the selected items occupy in the total.
Gauge Chart Widget
Visualizes NPS (Net Promoter Score) data as a semicircular gauge chart to show scores intuitively. A target value can be set, and the chart's color changes depending on whether the target is achieved to indicate status.
🧙 This is useful when:
Quickly checking the level of customer loyalty (NPS) toward a brand or product with a single chart.
Managing the status of customer satisfaction goal achievement by checking the current NPS position relative to the configured target.
The gauge chart widget can be created by selecting 'NPS' variables.
Try configuring it like this!
💡 Widget purpose | I want to set an NPS target (e.g., +20 points) and, if the current score falls short of the target, prompt immediate corrective action through a color change!
Click the [+ Add widget] button and select the 'Gauge chart' widget.
Click the pencil icon to open 'Gauge chart settings' and select each item.
Variable: Select the variable from the survey where NPS was asked.
Target value: Enter the target value for value A, and enter a value 20 points higher than the NPS target for value B.
Clicking [Done] immediately creates a gauge chart reflecting the configured target value and current score. Through this chart, you can intuitively see where the current score stands relative to the target.
Radar Chart Widget
Places multiple evaluation items (axes) on a single chart to summarize and compare multi-dimensional data. It allows you to understand the overall balance of data and the relative strengths and weaknesses of each item, and is effective for overlapping and comparing multiple subjects.
🧙 This is useful when:
Visually identifying your own relative strengths and weaknesses through comparison of attribute evaluations by brand or competitor.
Getting a clear picture of the score distribution across multiple attributes of a single subject at a glance to check balance.
The radar chart widget can be created by selecting 'numeric' or 'rating' variables.
Try configuring it like this!
💡 Widget purpose | I want to compare 'the evaluation of Brand A vs. Brand B's image attributes (innovation, trustworthiness, etc.)' to develop a brand positioning strategy!
Click the [+ Add widget] button and select the 'Radar chart' widget.
Click the pencil icon to open 'Radar chart widget settings' and select each item.
Comparison target: Enter Brand A in Comparison target 1, then click the + button to enter Brand B in Comparison target 2. (※ Up to 3 comparison targets can be set.)
Axis: Enter the evaluation items (innovation, trustworthiness, etc.) on each axis and select the variable from the survey that assessed that item.
Axis range: Enter the minimum and maximum values for the axis, and the widget will be displayed according to your desired value range.
Once the widget is created by clicking [Done], you can visually compare the degree of advantage per attribute for the two brands on the radar chart and develop a positioning strategy.
Text Representative Response Widget
A widget that uses the MMR algorithm to select and display the key responses that can represent all open-ended responses.
🧙 This is useful when:
Quickly identifying the most important customer voice (VOC) from large-scale open-ended data without missing it.
Rapidly identifying key topics that require follow-up qualitative analysis.
The text representative response widget can be created by selecting 'text' variables.
Try configuring it like this!
💡 Widget purpose | I want to extract representative responses for the specific reasons customers felt 'satisfied' or 'dissatisfied' during CS consultations (e.g., "quick resolution," "unfriendly attitude," "lack of clear explanation") to use in CS training and script improvement!
Click the [+ Add widget] button and select the 'Text representative response' widget.
Click the pencil icon to open 'Text representative response widget settings' and select each item.
Variable: Select the variable from the survey asking about reasons for feeling satisfied or dissatisfied during CS consultations.
Number of representative responses: Select the number of representative responses you want to view through the widget. (Up to 20 can be selected.)
Click [Done] to complete the widget. Now share the dashboard with team members with 'view permission' to transparently share CS satisfaction-related data and begin collaborating.
PSM (Price Sensitivity Meter) Widget
Visualizes PSM survey results that evaluate the optimal or appropriate price for a product, and automatically derives the key metrics needed for price decision-making. The 4 price question variables ('Too cheap' ~ 'Too expensive') must be mapped accurately.
🧙 This is useful when:
Scientifically determining the minimum, optimal, and maximum price range that customers can accept when launching a new product or repricing an existing one.
The PSM widget can be created by selecting 'numeric' variables.
Try configuring it like this!
💡 Widget purpose | I want to derive the 'psychological threshold price of a premium product' based on customer data to establish an objective basis for pricing!
Click the [+ Add widget] button and select the 'PSM' widget.
Click the pencil icon to open 'PSM widget settings' and select each item.
Price unit: Enter the price unit to display, such as KRW, USD, JPY, or CNY.
Variable: Refer to the table below to select variables.
PSM widget setting item | Meaning of the survey question | Variable to select |
Too cheap | A question asking for the price at which the respondent would feel suspicious about quality if the price were any lower | (Example question) "At what price range for this product/service would you feel it is too cheap and have doubts about the quality?" |
Cheap | A question asking for the price at which the respondent feels the price is inexpensive | (Example question) "At what price range would you feel this product/service is inexpensive?" |
Expensive | A question asking for the price at which the respondent feels the price is expensive | (Example question) "At what price range would you feel this product/service is expensive?" |
Too expensive | A question asking for the price at which the respondent would absolutely not purchase | (Example question) "At what price range for this product/service would you feel it is too expensive and would not purchase it?" |
View AI insights: AI interprets the data of the widget.
3. Once the widget is created by clicking [Done], you can check the Acceptable price range through the graph.
Scatter Chart Widget
Visualizes the relationship (correlation) between two variables or the distribution of data in the form of a scatter chart.
It allows you to check a large amount of data densely on a single screen, making it effective for grasping a lot of information at once. You can also add average values, median values, or specific constant values to the chart as reference lines to divide and interpret the data into quadrants.
🧙 This is useful when:
Identifying priorities or understanding the correlation between two metrics.
Using reference lines to distinguish areas of strength/weakness for specific items and draw strategic implications.
The scatter chart widget can be created using 'choice,' 'multiple choice,' 'ranking,' 'numeric,' 'rating,' and 'NPS' variables.
Try configuring it like this!
💡 Widget purpose | I want to compare 'feature satisfaction' and 'usage frequency' to discover features that customers consider important (high satisfaction) but don't use often (low frequency) and improve usability!
Click the [+ Add widget] button and select the 'Scatter chart' widget.
Click the pencil icon to open 'Scatter chart widget settings' and select each item.
◾ Axis settings
Axis label: Enter 'Feature satisfaction' and 'Usage frequency' in the respective X and Y axis labels.
Variable type: Select the variable type to represent the value of the dot.
Statistic: Statistics applicable based on the selected variable type are displayed. Select the reference value to display on the chart.
◾ Dot settings
Label: Enter the label to display on the dot.
X axis / Y axis: Select the variable matching the label set for each axis.
◾ Reference line settings
A reference line is a line that visually marks a reference value for interpreting data in the widget. By displaying lines based on average, median, or constant values (a specific value set by the user), it helps you easily see at a glance whether data is above or below the reference.
For example, setting reference lines on both the X and Y axes allows you to divide the scatter chart into 4 areas (quadrants), making it easy to intuitively interpret data — such as areas above or below average, or areas with high frequency but low satisfaction.
Funnel Widget
Identifies at a glance where drop-offs occur in multi-stage processes such as stage-by-stage conversion rates from brand awareness to purchase, or service usage flows. You can compare and analyze the brand funnel status of your own brand and competitors.
This is useful when:
Understanding your own strengths and weaknesses at each stage through brand funnel comparison.
Visually identifying stages in a specific service usage process where customer drop-off occurs to derive improvement points.
The funnel widget can be created using 'choice' variables.
🧙 Try configuring it like this!
💡 Widget purpose | I have confirmed that the drop-off rate at the 'consideration' stage of our brand's 'awareness–consideration–purchase' funnel is high, and I want to deploy intensive marketing activities focused on that stage.
Click the [+ Add widget] button and select the 'Funnel' widget.
Click the pencil icon to open 'Funnel widget settings' and select each item.
◾ Funnel settings
Enter the name of the brand to be analyzed in the funnel in the label, and select the variable that distinguishes the brand from the variable list.
Funnel analysis can be used for data collected in a staged structure such as awareness → experience → conversion, or for questions where respondents select only the single closest response for a specific brand.
The number of funnel stages is set to one fewer than the number of choices in the selected variable.
When comparing multiple brands, all brands must use variables with the same number of choices.
◾ Own brand settings
You can set your own brand from among the registered labels.
When your own brand is set, AI insights are focused on reading your own brand.
Time Series Widget
Used when checking data changes (trends) over time. Supports various aggregation criteria such as year, month, week, day, and day of week, and provides trend lines and reference lines as options.
This is useful when:
Running a single survey over a long period and continuously tracking metric changes.
Analyzing changes in metrics before and after specific events or campaigns to verify their effectiveness.
The time series widget can be created using 'choice,' 'multiple choice,' 'ranking,' 'numeric,' 'rating,' and 'NPS' variables.
🧙 Try configuring it like this!
💡 Widget purpose | I want to visualize 'quarterly NPS score trend changes' and compare NPS changes during a specific customer event period against a reference line to measure effectiveness!
Click the [+ Add widget] button and select the 'Time series' widget.
Click the pencil icon to open 'Time series widget settings' and select each item.
◾ Variable type and data settings
Variable type: Select NPS as the variable type to view in the time series chart.
Statistic: Depending on the variable type, you can select what to display from NPS score or Promoters / Detractors ratio.
X-axis variable: Set a date and time variable as the X-axis reference variable. To check quarterly NPS score trends, select the date when responses to the NPS survey were submitted as the variable.
Trend line: You can select a linear trend line or polynomial trend line to see the data flow, or choose not to display a trend line if not needed.
💡 Usage tip | Understanding terminology
Promoters: Respondents who gave 9–10 points in the NPS survey — customers highly likely to actively recommend the brand or service.
Detractors: Respondents who gave 0–6 points in the NPS survey — customers who are dissatisfied or at risk of churning.
Linear trend line: A trend line that expresses the overall increase or decrease flow of data as a straight line. Suitable when you want to simply understand the direction of change.
Polynomial trend line: A trend line that expresses curves or fluctuation patterns in the data. Useful when changes are complex at different points in time or when you want to see changes in flow in detail.
◾ Reference line settings
A reference line is a line marking a reference value for interpreting data in the widget.
In particular, the Y-axis reference line helps you easily check whether values are above or below the reference by comparing changes in time series data against the reference value.
This allows you to interpret data centered on how the change flow compares to the reference, rather than just the magnitude of change.
◾ Other settings
Y-axis range: Enter the minimum and maximum values for the Y-axis, and the widget will be displayed according to your desired value range.
Default view: You can configure the most appropriate method for viewing the dashboard.
Multi-Rating Widget
Visualizes up to 10 evaluation items with the same scale (score) in a single horizontal stacked bar graph for comprehensive comparison. Statistical figures suited to your analysis purpose — such as average, standard deviation, and Top/Bottom % (proportion of top/bottom scale responses) — can be displayed together.
🧙 This is useful when:
Comparing response distributions and patterns across multiple items such as satisfaction, importance, and image evaluations at a glance.
Quickly identifying items with the highest proportion of positive/negative responses among multiple evaluation items.
The multi-rating widget can be created using 'rating' variables.
Try configuring it like this!
💡 Widget purpose | I want to compare 'customer satisfaction (5-point scale) for three product attributes A, B, and C' side by side to see which attribute has the highest 'very satisfied (Top %)' ratio and reflect it in product development direction!
Click the [+ Add widget] button and select the 'Multi-rating' widget.
Click the pencil icon to open 'Multi-rating widget settings' and select each item.
Scale: Select the scale used to evaluate satisfaction.
Data settings: Enter the item to analyze evaluation scores in the label, and select the variable that distinguishes that item from the variable list. The selected variables must have the same scale.
Statistic: Select statistics such as average, standard deviation, Top1, Top2, Bottom1, Bottom2, etc.
💡 Usage tip | Understanding terminology
Top1(%): The item that recorded the highest value among all items.
Top2(%): The combined value of the top 2 items among all items.
Top3(%): The combined value of the top 3 items among all items.
Bottom1(%): The item that recorded the lowest value among all items.
Bottom2(%): The combined value of the bottom 2 items among all items.
Bottom3(%): The combined value of the bottom 3 items among all items.
Have you got a good understanding?
By selecting a widget that matches the type of data you want to view or your analysis purpose, you can track data more easily and efficiently.
If you have any other questions that weren't resolved by this guide, please click the [Customer Support icon] in the bottom right corner of your screen to contact us. Our team will do its best to help you resolve any difficulties