To harness the full power of Zurvey.io's text analysis capabilities, it is crucial to not only analyze pure textual data, but to also enrich it with metadata whenever possible.
Zurvey.io’s processing capabilities extend to non-textual dimensions, which are accessible for every dataset and datastream input. During the dataset creation process, it is imperative to set up these dimension types accurately to unlock the related dimension-specific functionalities, such as filtering or visualization.
3. Customer Experience Metric Dimensions – NPS®, CSAT, CES
1. Number
Incorporate numeric data seamlessly into your analysis by labeling it as numbers.
This dimension can accommodate metadata such as annual customer spending, contract durations, and more. Visualizations for this data extend to column charts, line charts, and scatter plots on the dashboard. Filtering options are available with selectors such as equals, not equals, greater than, greater than or equals, less than, less than or equals.
2. Category
Label your data as a category when it shares a common characteristic or attribute across records, allowing for effective classification and grouping. In a customer voices database, for example, individual responses can be categorized based on demographics (e.g., age group, gender, location) or purchasing behavior (e.g., frequent buyers, occasional buyers).
It is essential to note that this data should not exceed 1000 unique values to ensure optimal filtering and visualization functionality, preventing potential limitations and maintaining dashboard loading performance.
You can filter category data using selectors such as is, is not, is one of, is none of. Visualizations for category data include options such as column charts, bar charts, and pie charts.
3. Customer Experience Metric Dimensions – NPS®, CSAT, CES
Zurvey.io specializes in the analysis of the Voice of the Customer, making it highly likely that your data will contain Customer Experience (CX) metrics as metadata. It's crucial to label this data correctly during the dataset creation process to leverage data-specific chart visualizations and calculations on the dashboard. The following CX metric dimension types are available.
Net Promoter Score® (NPS)
The Net Promoter Score® (NPS) is a widely used metric for measuring customer satisfaction and loyalty on a scale of 0-10 (or sometimes 1-10). Businesses use NPS® to assess the likelihood of customers recommending their products or services to others. The metric is typically based on a single question: "On a scale of 0 to 10, how likely are you to recommend our product/service to a friend or colleague?"
Respondents are categorized into three segments based on their scores:
- Promoters (score 9-10): Highly satisfied customers likely to recommend.
- Passives (score 7-8): Satisfied customers but less enthusiastic.
- Detractors (score 0-6): Dissatisfied customers likely to spread negative feedback.
The Net Promoter Score® is calculated by subtracting the percentage of detractors from the percentage of promoters, resulting in a score that can range from -100 to +100.
Read more about how NPS® data is visualized on our dashboards here.
Customer Satisfaction Score (CSAT)
The CSAT, or Customer Satisfaction Score, measures the level of satisfaction customers have with a product, service, or overall experience. Expressed as a percentage, CSAT typically ranges from 0% to 100%. The data should be on a numeric scale from 1-5.
The CSAT score is calculated by taking the sum of positive responses (4 and 5) divided by the total number of responses, multiplied by 100 to get the percentage:
CSAT Score = (Number of Positive Responses / Total Responses) * 100
Read more about how CSAT data is visualized on our dashboards here.
Customer Effort Score (CES)
The CES, or Customer Effort Score, assesses the ease with which customers can complete a specific task or interaction with a company. Data should be on a numeric scale, ranging from 1 to 5 or 1 to 7 (both versions are available in Zurvey.io, choose according to your data), where higher scores indicate lower perceived effort.
The CES is calculated based on the following formula: CES% = Easy% (5-7 or 4-5) - Difficult% (1-3 or 1-2).
Read more about how CES data is visualized on our dashboards here.
4. URL Dimension
Label your data as a URL when it represents a link. This allows for specific filtering options on the dashboard, such as selectors for contains, starts with, exact match. While this data is not visualized on the dashboard, in verbatim settings, you can choose to display this data on text units.
5. Unique ID Dimension
Label your data as a Unique ID when identifiers are assigned to each record within the dataset. The primary purpose of a unique ID is to distinguish one specific record from all others in a collection. It's crucial to ensure that no two records within the dataset share the same identifier.
While this data will only be included in static outputs, won't be visualized on the dashboard in any form, and filtering options won't be available, it is of utmost importance to set up this data correctly. Mislabeling, such as categorizing it as a number or category, can negatively impact dashboard performance and analysis speed.
6. Date Dimension
Label your data as a Date if it conforms to one of the most frequently used formats below:
- 2024.01.02 (13:17(:24))
- 2024-01-02 (13:17(:24))
- 2024/01/02 (13:17(:24))
- 02.01.2024 (13:17(:24))
- 02-01-2024 (13:17(:24))
- 02/01/2024 (13:17(:24))
- 01.02.2024 (13:17(:24))
- 01-02-2024 (13:17(:24))
- 01/02/2024 (13:17(:24))
In addition to the listed formats, there are more niche formats that can be parsed; learn more about these here. It's important to note that if there is no timezone included in your data, it will be processed as GMT.
Date dimension is crucial in order to benefit from filtering on the dashboard (calendar view) and special time series charts like Opinion distribution based on time or Most frequent topics by time.
Please keep in mind that in case of continuous datastreams like API, NMI Integration, Email connector or surveys, there is a timestamp (submitted_at) automatically assigned to the records to properly track the time of incoming data. This date is also filterable on the dashboard and included in all exports.
7. Individual Marker
The Individual Marker dimension is a special dimension type in Zurvey.io, designed to bridge the gap between traditional categories and unique identifiers. It serves as a valuable tool when dealing with datasets containing identifiers that are not necessarily unique but occur frequently. This dimension type is particularly useful in scenarios such as call center discussions or email transcriptions, where a single discussion involves multiple records within a larger dataset. In this case, call IDs or discussion IDs within call transcriptions or email discussions can be marked as Individual Markers.
You can leverage the Individual Marker dimension to filter data on the dashboard using various selectors. These selectors include:
- Contains: Enables searching within the entire string of the marker.
- Starts With: Allows filtering based on markers that begin with a specific string.
- Is One Of: Permits the inclusion of multiple markers in the input, similar to tagging.
Additionally, this dimension type can be displayed on verbatims in the Feed view and in the Drill down view. You only need to set it up in Verbatim Settings. This dimension also has a dedicated tab and charts on the dashboard.