Setting Up Custom Dashboard Using Dataset
Updated
This module allows you to design personalized reporting dashboards for analyzing survey outcomes based on their unique requirements. It offers more flexibility than the conventional per-question analytics feed, which presents a static, non-customizable perspective. With this feature, you can choose pertinent metrics, implement filters, and arrange data in a manner that suits your needs. This enables you to concentrate on particular trends, assess performance across various segments, and efficiently derive actionable insights.
Business Use Cases
Designing personalized analytics dashboards empowers you to customize their data analysis experience, allowing them to examine and interpret survey results in ways that align with their unique business requirements.
Using Feedback Trends to Improve Operational Efficiency: Analyzing customer feedback over specific time periods can help identify recurring issues, such as long wait times. These insights enable timely operational adjustments, like increasing staff during peak hours, to improve overall customer experience and satisfaction.
Efficient Data Analysis: Using custom reports to segment post-transaction feedback by location allows businesses to quickly identify trends, such as low staff helpfulness scores in specific regions. These insights support targeted actions, like focused training programs, to enhance the customer experience across locations.
Consistent Performance Tracking: Comparing customer feedback across different locations helps identify performance gaps, such as long wait times during peak hours. Sharing best practices from high-performing sites enables more consistent service and improves overall operational efficiency.
Custom dashboards enable targeted problem-solving by allowing users to drill down into specific issues, such as long wait times or low staff helpfulness, and implement precise, data-driven solutions. They also support improved resource allocation by identifying trends and underperforming segments, making it easier to schedule additional staff during peak periods or roll out targeted training programs. Additionally, these tools enhance performance monitoring through comparative analysis across locations or time frames, enabling organizations to benchmark performance, share best practices, and address inefficiencies in a consistent and informed manner.
Prerequisites
You must have permission to create reporting dashboards in order to access the custom dashboards option. Currently, survey dimensions and metrics are available to dashboard users. Custom analytics permissions for each survey will be introduced in a future release.
Setting Up Custom Dashboards
Go to Sprinklr Insights and navigate to Customer Feedback Management.
Click Datasets in the left pane. The data pipeline feature has been enhanced to enable you to JOIN two or more survey dimensions, creating new unified dimensions with combined data from these surveys.
Click Create Normal Pipeline, to start creating a new data pipeline. You can see the list of all data pipelines previously created.
Click Add Datasource to begin setting up Data Pipelines, add all the surveys you wish to join as sources through Add Datasource.
There are 3 ways in which data sources can be added:
From File
From Sprinklr
From Generic Source
Click From Sprinklr to add survey dimensions, which can be accessed through this option which is the most commonly used.
Go to Add Datasource from Sprinklr and start filling in the details:
Go to the Basic Information section:
Type: Select the type of data source as CFM Analytics.
Datasource Name: Enter the name of the datasource.
Survey: Select the name of the survey you wish to set up as the source.
Select columns to create the datasource: You need to select atleast one metric and dimension .You will see a list of all standard survey dimensions, as well as survey-agnostic dimensions like "survey response ID." Select the metrics and dimensions you want to join and include in the new dataset, one at a time.
Option Time zone and Data range: Can be used to only bring in the data for a particular time range.
Click Date Filters and you can also setup option data filters which will result in the datasource having those data points that match the set criterion.
Click + Add Filters to add more filters.
Click Add Datasources to setup the datasource in the canvas.
You can add any number of datasources, which you wish to finally join.
After setting up all the desired data sources for joining, the user can start the JOIN operation setup by clicking the “+” icon next to a data source.
Note: Only two data sources can be connected using the JOIN function,the datasource next to the one which initiated the JOIN function is considered automatically.At once, multiple data sources can be used to create a dataset through nested JOINs.
In the Join page fill in the details:
Join Name: Enter the name of the JOIN.
Select Join Type: Select the type of JOIN you wish to use. "Outer JOIN" is the most commonly used type for surveys.
On Datasources: Datasources will be automatically selected.
Add the columns to match in both datasources: You can map the metrics and dimensions selected in the previous step to the second data source. All mapped dimensions/metrics will be combined into new consolidated metrics/dimensions, while any unmapped fields will remain as independent metrics/dimensions in the final dataset.
Click + after you have a single JOIN node after combining all the data sources. You can create JOINs for each pair of data sources. You can also JOIN a JOINed dataset with a single datasource, thus creating a nested structure for connecting multiple surveys.
Finally, after you have a single JOIN node at the end after JOINING all the datasources, you needs to create a “Sprinklr Report” which shall create new dimensions and metrics powered by this dataset that can be used in custom reporting dashboards.
Click Sprinklr Report to generate new dimensions and metrics based on the dataset, which you can then use in custom reporting dashboards. A Sprinklr Report can be created via the “+” icon from the JOIN dataset node.
Go to Output Report Dataset page and in the Report Name section enter a meaningful name of the report.
Select the column of newly created dataset for which they wish create dimensions.
Toggle Unique column which is used to handle updates in the dataset as new responses come in for the surveys.
Click Save to complete the setup.
Toggle Pipeline status option and select Activate in the popup to activate the pipeline.
Click Manual Run at the botton to manually run the pipeline.
Set Frequency: Set the frequecy of the pipeline with which the pipeline can refresh. This will result in updating the dataset with the latest responses.
Start Date: Define a start date when the dataset can start fetching responses.
End Date: Define an end date when the dataset can stop fetching responses.
How to use it?
Once the data pipeline is set up and activated, the JOINed metrics and dimensions become available for use in custom reporting dashboards.
These dimensions and metrics can be accessed through the CFM Analytics Widget Builder.
All pipeline-generated metrics and dimensions are grouped under a separate category called “Dataset.”
You can filter the list of datasets by the name specified during the creation of the Sprinklr Report.
For example, if the dataset was named “Survey A1 B2,” you can locate it by searching for that name.
Once the filter is applied, we see the list of dimensions and metrics created for our dataset.
These can be used for creating widgets in similar way of single survey dimensions.
These can be used for creating widgets in similar way of single survey dimensions.
To build a robust and validated reporting workflow in Sprinklr, you should start by creating two sample surveys and filling in responses. Next, create a dataset by joining all the dimensions and metrics from both surveys, along with a few standard fields. Once your dataset is ready, create the Sprinklr Report based on it. After that, run the pipeline and set up the refresh time to ensure the data remains up to date. To validate your dataset structure, verify the list of dimensions and metrics by applying filters through the widget builder using the dataset. Then, create a sample dashboard and plot widgets using all the metrics and dimensions from your Sprinklr Report. To ensure consistency, also create similar widgets using individual dimensions and metrics from each survey used in the dataset. Finally, verify that the data from the combined dataset matches the data from the individual surveys. For example, plot a Counter widget using the joined NPS metric, then plot separate counters for each survey’s NPS metric, and confirm whether the average NPS from the dataset equals the average of the individual NPS scores. This process ensures data integrity and accurate cross-survey analysis.
Key points to note:
If the pipeline is inactive and the appropriate refresh frequency is not configured, you may only see outdated data.
Every item that the user requires in the combined dataset must be selected and included in the Sprinklr Report. At present, two or more surveys cannot be directly merged.
Manage Datasets
You can click on Vertical Ellipses(3 dots) against each data set and manage the datasets:
View: Helps to view the dataset.
Run: Helps to run the dataset manually.
Deactivate and Edit: Helps to deactivate or edit datasets.
Run History: Helps to provide the details of run history.
Share: Helps to share the to other workspaces or to specific users and usergroups.
Clone: Helps to clone a dataset.
Delete: Helps to delete a dataset.
Best Practices
You must always include the dimensions "Survey," "Survey Response ID," and "Survey Response Date" in every datasource.