20240037586. INFLUENCE SCORING FOR SEGMENT ANALYSIS SYSTEMS AND METHODS simplified abstract (Verint Americas Inc.)
Contents
INFLUENCE SCORING FOR SEGMENT ANALYSIS SYSTEMS AND METHODS
Organization Name
Inventor(s)
Hamed Janani of Vancouver (CA)
Anirudh Challa of Burnaby (CA)
Hong Wang of San Mateo CA (US)
Mohamad Al-sharara of Westmount (CA)
[[:Category:José R. Benk� of Ann Arbor MI (US)|José R. Benk� of Ann Arbor MI (US)]][[Category:José R. Benk� of Ann Arbor MI (US)]]
Zealand Cooley of Albion MI (US)
Danielle Vesia of San Mateo CA (US)
INFLUENCE SCORING FOR SEGMENT ANALYSIS SYSTEMS AND METHODS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240037586 titled 'INFLUENCE SCORING FOR SEGMENT ANALYSIS SYSTEMS AND METHODS
Simplified Explanation
The segment analysis system described in the patent application analyzes survey data to determine the influence of each custom question/response combination (segment) on a given aggregate scored survey metric for a specific date/date range. The system filters out surveys that do not include a matching scored survey metric and date, as well as surveys that do not meet user-defined filtering criteria. Once the extraneous surveys are eliminated, the system calculates an influence score for each question/response combination across the pool of surveys. It identifies the segment with the greatest positive and negative influence on the aggregate scored survey metric for the given date/date range. The system generates reports for the segment analysis and stores all segment analyses for further comparative analysis.
- The system analyzes survey data to determine the influence of each question/response combination on an aggregate scored survey metric.
- Surveys without a matching scored survey metric and date are excluded from consideration.
- Surveys that do not meet user-defined filtering criteria are also removed.
- Influence scores are calculated for each question/response combination.
- The segment with the greatest positive and negative influence on the aggregate scored survey metric is identified.
- Reports are generated for the segment analysis.
- All segment analyses are stored for further comparative analysis.
Potential applications of this technology:
- Market research: The system can be used to analyze survey data and identify the key factors influencing customer satisfaction or product preferences.
- Customer feedback analysis: Companies can use the system to understand the impact of specific questions and responses on overall customer feedback scores.
- Quality improvement: The system can help identify areas for improvement based on customer feedback and prioritize actions accordingly.
Problems solved by this technology:
- Manual analysis: The system automates the analysis of survey data, saving time and effort compared to manual analysis.
- Data filtering: The system filters out irrelevant surveys, ensuring that only relevant data is considered for analysis.
- Identifying influential factors: The system identifies the segments that have the greatest positive and negative influence on the aggregate survey metric, providing insights into the key factors driving customer satisfaction or other metrics.
Benefits of this technology:
- Efficiency: The system automates the analysis process, reducing the time and effort required for manual analysis.
- Accuracy: By analyzing a large pool of surveys, the system provides more accurate insights into the factors influencing survey metrics.
- Actionable insights: The system helps companies identify specific areas for improvement based on the influence scores of different question/response combinations.
Original Abstract Submitted
the segment analysis system analyzes survey data to determine the influence each custom question/response combination (segment) has on a given aggregate scored survey metric for a given date/date range. the system removes from consideration all surveys that do not include a scored survey metric and date that matche the aggregate scored survey metric and given date/date range. the system further removes from consideration all surveys not pertaining received user-defined filtering. once the system has eliminated all extraneous surveys from consideration, the system segments each question/response combinations across the pool of surveys to generate an influence score for each question/response combination. the system identifies which segment has the greatest positive and negative influence on the aggregate scored survey metric for the given date/date range. the system generates reports for the segment analysis and stores all segment analyses for further comparative analysis.
