Data Segmentation and Filtering
Data segmentation and filtering are foundational elements of effective survey analysis and are key to deeper data exploration. These capabilities allow analysts to narrow the scope of results to specific respondent groups and compare their feedback across different contexts. On the YourCX platform, every variable—whether sourced from survey responses, technical data, or external systems—can be used both as a filter and a segmentation criterion.
Filtering Data to Analyze Specific Respondent Groups
Filtering enables you to focus only on responses that meet defined conditions, helping eliminate noise and zero in on specific cases—such as mobile users, dissatisfied customers, or visitors who completed their goal.
Example:
A financial services provider wants to identify the main pain points among customers who rated their experience poorly (NPS 0–6). By applying a filter to isolate those responses, the company discovers recurring issues like long wait times on the helpline and an unintuitive mobile app interface.
Segmenting Responses to Compare Results Across Groups
Segmentation allows you to display results for multiple groups simultaneously, making it easier to spot similarities and differences—e.g., across regions, contact channels, age groups, or traffic sources.
Example:
An online learning platform segments satisfaction scores by age group: 18–24, 25–34, and 35+. The analysis shows that younger users are more satisfied with the mobile experience, while older users report more navigation issues. This insight enables the company to adapt the interface to meet the needs of less tech-savvy users.
Combining Filtering and Segmentation in a Single Analysis
Combining these two mechanisms allows for highly targeted analysis. You can first filter the dataset to a specific group, then segment that group using another criterion.
Example:
An online store filters responses to include only customers who made a purchase in the past month. Then, it segments them based on their traffic source (e.g., email campaign, Google Ads, direct visit). The analysis reveals that customers who arrived via the newsletter report the highest satisfaction—prompting the company to allocate more budget to that channel.
Comparing Results Across Time Periods
Filtering by date or time range makes it possible to observe how results evolve. This supports trend identification and helps assess the impact of improvement efforts or marketing campaigns.
Example:
After launching a new payment system, the customer service team analyzes responses from two timeframes—before and after implementation. The results show a clear increase in satisfaction and a decrease in complaints about checkout issues.
Analyzing Results by Location
Segmenting by location (e.g., region, store, or service channel) helps organizations with distributed operations better manage service quality by addressing location-specific challenges.
Example:
A retail chain analyzes CSAT scores by physical store. One branch shows significantly lower satisfaction compared to the network average. Comments reveal the issue stems from unprofessional staff behavior. The head office takes local corrective action before the issue impacts the overall brand reputation.
Applying segmentation and filtering not only increases the precision of your analysis, but more importantly, enables your organization to make decisions based on the real needs and behaviors of specific customer groups. This approach improves the accuracy of operational and marketing actions while also strengthening customer relationships through better understanding of their expectations.
