survey-analysis

Survey Analysis

Overview

This skill provides structured analysis of survey and assessment responses in ServiceNow. It covers:

Aggregating survey scores from asmt_assessment_instance and asmt_assessment_instance_question
Analyzing individual response data from survey_response for detailed breakdowns
Identifying trends across time periods by comparing assessment results
Segmenting responses by department, location, group, or custom dimensions
Generating statistical summaries (mean, median, distribution) for each question
Producing actionable improvement recommendations based on score patterns

When to use: When analyzing customer satisfaction (CSAT), employee engagement, service quality surveys, or any assessment data collected through ServiceNow Performance Analytics assessments or surveys.

Value proposition: Transforms raw survey data into actionable insights, identifies areas needing attention before they become systemic issues, and provides data-driven recommendations for improvement initiatives.

Prerequisites

Plugins: com.snc.assessment (Assessment and Survey), com.snc.pa (Performance Analytics, optional)
Roles: assessment_admin, survey_admin, pa_admin, or admin
Access: Read access to asmt_assessment_instance, asmt_assessment_instance_question, survey_response, and related metric tables
Knowledge: Understanding of survey structure, scoring scales, and organizational hierarchy

Procedure

Step 1: Identify the Survey or Assessment

Find the survey definition and understand its structure.

Using MCP (Claude Code/Desktop):

Tool: SN-Query-Table
Parameters:
  table_name: asmt_metric_definition
  query: active=true^typeLIKEsurvey^ORtypeLIKEassessment
  fields: sys_id,name,description,type,category,state,evaluation_method,scale,number_of_responses
  limit: 20
  order_by: name

Using REST API:

GET /api/now/table/asmt_metric_definition?sysparm_query=active=true&sysparm_fields=sys_id,name,description,type,category,state,evaluation_method,scale,number_of_responses&sysparm_limit=20&sysparm_display_value=true

Get the survey questions/metrics:

Tool: SN-Query-Table
Parameters:
  table_name: asmt_metric
  query: metric_definition=[definition_sys_id]^active=true
  fields: sys_id,name,question,datatype,weight,scale,min_value,max_value,order,category
  limit: 50
  order_by: order

Step 2: Retrieve Assessment Instances

Pull completed survey instances for analysis.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: asmt_assessment_instance
  query: metric_type=[definition_sys_id]^state=complete^taken_on>=javascript:gs.monthsAgoStart(3)
  fields: sys_id,number,metric_type,user,taken_on,percent,state,group,department,trigger_id
  limit: 100
  order_by: -taken_on

Using REST API:

GET /api/now/table/asmt_assessment_instance?sysparm_query=metric_type=[definition_sys_id]^state=complete^taken_on>=javascript:gs.monthsAgoStart(3)&sysparm_fields=sys_id,number,metric_type,user,taken_on,percent,state,group,department&sysparm_limit=100&sysparm_display_value=true

Step 3: Aggregate Scores by Question

Analyze response distributions for each survey question.

Using MCP:

Tool: SN-Execute-Background-Script
Parameters:
  description: Aggregate survey scores by question
  script: |
    var definitionId = '[definition_sys_id]';
    var startDate = gs.monthsAgoStart(3);
    var results = { survey: '', period: 'Last 3 months', total_responses: 0, questions: [] };

    var def = new GlideRecord('asmt_metric_definition');
    def.get(definitionId);
    results.survey = def.name.toString();

    // Count total responses
    var countGa = new GlideAggregate('asmt_assessment_instance');
    countGa.addQuery('metric_type', definitionId);
    countGa.addQuery('state', 'complete');
    countGa.addQuery('taken_on', '>=', startDate);
    countGa.addAggregate('COUNT');
    countGa.query();
    if (countGa.next()) results.total_responses = parseInt(countGa.getAggregate('COUNT'));

    // Get questions
    var metrics = new GlideRecord('asmt_metric');
    metrics.addQuery('metric_definition', definitionId);
    metrics.addQuery('active', true);
    metrics.orderBy('order');
    metrics.query();

    while (metrics.next()) {
      var question = {
        name: metrics.name.toString(),
        question_text: metrics.question.toString(),
        responses: 0,
        avg_score: 0,
        min_score: 999,
        max_score: 0,
        distribution: {}
      };

      var qa = new GlideAggregate('asmt_assessment_instance_question');
      qa.addQuery('metric', metrics.sys_id);
      qa.addQuery('instance.state', 'complete');
      qa.addQuery('instance.taken_on', '>=', startDate);
      qa.addAggregate('AVG', 'value');
      qa.addAggregate('MIN', 'value');
      qa.addAggregate('MAX', 'value');
      qa.addAggregate('COUNT');
      qa.query();

      if (qa.next()) {
        question.responses = parseInt(qa.getAggregate('COUNT'));
        question.avg_score = parseFloat(qa.getAggregate('AVG', 'value')).toFixed(2);
        question.min_score = parseFloat(qa.getAggregate('MIN', 'value'));
        question.max_score = parseFloat(qa.getAggregate('MAX', 'value'));
      }

      // Score distribution
      var dist = new GlideAggregate('asmt_assessment_instance_question');
      dist.addQuery('metric', metrics.sys_id);
      dist.addQuery('instance.state', 'complete');
      dist.addQuery('instance.taken_on', '>=', startDate);
      dist.addAggregate('COUNT');
      dist.groupBy('value');
      dist.query();

      while (dist.next()) {
        question.distribution[dist.value.toString()] = parseInt(dist.getAggregate('COUNT'));
      }

      results.questions.push(question);
    }

    gs.info(JSON.stringify(results, null, 2));

Step 4: Identify Trends Across Periods

Compare survey results across time periods to detect improvements or degradation.

Using MCP:

Tool: SN-Execute-Background-Script
Parameters:
  description: Compare survey scores across monthly periods
  script: |
    var definitionId = '[definition_sys_id]';
    var trends = { survey: '', monthly_scores: [] };

    var def = new GlideRecord('asmt_metric_definition');
    def.get(definitionId);
    trends.survey = def.name.toString();

    for (var i = 5; i >= 0; i--) {
      var monthStart = gs.monthsAgoStart(i);
      var monthEnd = gs.monthsAgoEnd(i);

      var ga = new GlideAggregate('asmt_assessment_instance');
      ga.addQuery('metric_type', definitionId);
      ga.addQuery('state', 'complete');
      ga.addQuery('taken_on', '>=', monthStart);
      ga.addQuery('taken_on', '<=', monthEnd);
      ga.addAggregate('AVG', 'percent');
      ga.addAggregate('COUNT');
      ga.query();

      var monthData = {
        period: new GlideDateTime(monthStart).getMonthLocalTime() + '/' + new GlideDateTime(monthStart).getYearLocalTime(),
        avg_score: 0,
        response_count: 0
      };

      if (ga.next()) {
        monthData.avg_score = parseFloat(ga.getAggregate('AVG', 'percent')).toFixed(1);
        monthData.response_count = parseInt(ga.getAggregate('COUNT'));
      }

      trends.monthly_scores.push(monthData);
    }

    // Calculate trend direction
    var scores = trends.monthly_scores.filter(function(m) { return m.response_count > 0; });
    if (scores.length >= 2) {
      var latest = parseFloat(scores[scores.length - 1].avg_score);
      var previous = parseFloat(scores[scores.length - 2].avg_score);
      trends.trend_direction = latest > previous ? 'Improving' : latest < previous ? 'Declining' : 'Stable';
      trends.change_percentage = ((latest - previous) / previous * 100).toFixed(1) + '%';
    }

    gs.info(JSON.stringify(trends, null, 2));

Step 5: Segment by Department or Group

Break down results by organizational dimension for targeted insights.

Using MCP:

Tool: SN-Execute-Background-Script
Parameters:
  description: Segment survey results by department
  script: |
    var definitionId = '[definition_sys_id]';
    var segments = [];

    var ga = new GlideAggregate('asmt_assessment_instance');
    ga.addQuery('metric_type', definitionId);
    ga.addQuery('state', 'complete');
    ga.addQuery('taken_on', '>=', gs.monthsAgoStart(3));
    ga.addAggregate('AVG', 'percent');
    ga.addAggregate('COUNT');
    ga.groupBy('user.department');
    ga.orderByAggregate('AVG', 'percent');
    ga.query();

    while (ga.next()) {
      segments.push({
        department: ga.user.department.getDisplayValue() || 'Unknown',
        avg_score: parseFloat(ga.getAggregate('AVG', 'percent')).toFixed(1),
        response_count: parseInt(ga.getAggregate('COUNT'))
      });
    }

    gs.info(JSON.stringify(segments, null, 2));

Step 6: Analyze Free-Text Responses

Extract themes from open-ended survey responses.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: asmt_assessment_instance_question
  query: instance.metric_type=[definition_sys_id]^metric.datatype=string^string_valueISNOTEMPTY^instance.taken_on>=javascript:gs.monthsAgoStart(3)
  fields: string_value,metric.name,instance.taken_on,instance.user.department
  limit: 100
  order_by: -instance.taken_on

Using REST API:

GET /api/now/table/asmt_assessment_instance_question?sysparm_query=instance.metric_type=[definition_sys_id]^metric.datatype=string^string_valueISNOTEMPTY^instance.taken_on>=javascript:gs.monthsAgoStart(3)&sysparm_fields=string_value,metric.name,instance.taken_on,instance.user.department&sysparm_limit=100&sysparm_display_value=true

Step 7: Generate Recommendations Report

Compile findings into an actionable report.

Using MCP:

Tool: SN-Add-Work-Notes
Parameters:
  table_name: asmt_metric_definition
  sys_id: [definition_sys_id]
  work_notes: |
    === SURVEY ANALYSIS REPORT ===
    Survey: IT Service Satisfaction Q1 2026
    Period: January - March 2026
    Total Responses: 342

    OVERALL SCORE: 78.4% (Previous quarter: 74.2%, +5.7%)

    QUESTION BREAKDOWN:
    1. Response Time Satisfaction: 72.3% (Low - needs attention)
    2. Resolution Quality: 85.1% (Good)
    3. Communication Clarity: 81.7% (Good)
    4. Overall Experience: 74.5% (Below target of 80%)

    DEPARTMENT SEGMENTS:
    - Engineering: 82.1% (Highest)
    - Sales: 71.3% (Lowest - declined 8% from Q4)
    - Marketing: 78.9% (Stable)
    - Finance: 76.2% (Improved +4%)

    KEY THEMES FROM COMMENTS:
    - Positive: Knowledge base improvements noted (23 mentions)
    - Negative: Long wait times for priority 3 tickets (31 mentions)
    - Negative: Inconsistent updates during resolution (18 mentions)

    RECOMMENDATIONS:
    1. Focus on P3 response times for Sales department (dedicated queue recommended)
    2. Implement automated status updates every 4 hours for open tickets
    3. Continue knowledge base investment (showing positive ROI)
    4. Schedule targeted follow-up survey for Sales department

    TREND: Improving (+5.7% QoQ)
    RISK: Sales department declining - escalate to VP Sales

Tool Usage

MCP Tools Reference

Tool	When to Use
`SN-Query-Table`	Query survey definitions, instances, questions, and responses
`SN-Get-Record`	Retrieve specific survey or assessment details
`SN-Natural-Language-Search`	Find surveys by keyword or description
`SN-Execute-Background-Script`	Complex aggregations, trend analysis, segmentation
`SN-Add-Work-Notes`	Post analysis reports to survey or assessment records

REST API Reference

Endpoint	Method	Purpose
`/api/now/table/asmt_metric_definition`	GET	Query survey/assessment definitions
`/api/now/table/asmt_metric`	GET	Retrieve survey questions and metrics
`/api/now/table/asmt_assessment_instance`	GET	Query completed survey instances
`/api/now/table/asmt_assessment_instance_question`	GET	Get individual question responses
`/api/now/table/survey_response`	GET	Access legacy survey response data
`/api/now/table/asmt_metric_result`	GET	Query pre-aggregated metric results

Best Practices

Set minimum response thresholds: Do not draw conclusions from segments with fewer than 10 responses
Use percentage scores for comparison: Raw scores vary by scale; normalize to percentages for cross-survey comparison
Include response rates: Low response rates may indicate survey fatigue or distribution issues
Segment meaningfully: Break down by dimensions that align with organizational accountability (department, region, support group)
Track trends over 3+ periods: Single-period analysis is less reliable than multi-period trend data
Weight questions appropriately: Not all questions are equally important; consider weighted averages for composite scores
Act on results: Every analysis should produce at least one actionable recommendation with a clear owner
Close the loop: Follow up on previous recommendations in subsequent survey analyses to track improvement

Troubleshooting

No Assessment Instances Found

Cause: Assessment definition sys_id is incorrect, or no instances are in "complete" state Solution: Query asmt_assessment_instance without the state filter to see all instances. Check if the definition uses a different state value (e.g., "closed" vs "complete").

Question Scores Showing Zero

Cause: The value field in asmt_assessment_instance_question may not be populated; some assessments store results in string_value or scaled_value Solution: Check the question datatype field and query the appropriate value column. For scaled questions, use scaled_value instead of value.

Department Segmentation Returns "Unknown"

Cause: Survey respondents' user records do not have the department field populated Solution: Try alternative grouping fields such as user.company, user.location, or user.cost_center.

Historical Data Missing for Trend Analysis

Cause: Assessment instances may be archived or the survey was recently created Solution: Extend the date range or check if the instance uses table rotation. For new surveys, note that trends require at least 2-3 periods of data.

Examples

Example 1: CSAT Score Dashboard

Input: "Analyze customer satisfaction survey results for Q1 2026"

Process: Query assessment instances for the CSAT survey, aggregate by question, segment by support group, compare with Q4 2025, and generate a dashboard-ready summary.

Example 2: Employee Engagement Trend

Input: "Show me the trend for employee engagement scores over the last 6 months"

# Run the trend analysis from Step 4 with definitionId for employee engagement survey
# Output: Monthly scores with trend direction and percentage change

Example 3: Low-Score Investigation

Input: "Why did satisfaction scores drop in the Sales department?"

# 1. Segment by department (Step 5) to confirm the drop
# 2. Drill into question-level scores for Sales respondents
# 3. Extract free-text responses from Sales (Step 6)
# 4. Cross-reference with incident/request data for Sales during the period
# 5. Generate targeted recommendations

Related Skills

reporting/trend-analysis - General trend analysis across ServiceNow data
reporting/executive-dashboard - Build executive summary dashboards
reporting/sla-analysis - Correlate survey scores with SLA performance
csm/sentiment-analysis - Sentiment analysis for customer feedback
hrsd/sentiment-analysis - Employee sentiment analysis

About this skill

Overview

Prerequisites

Procedure

Step 1: Identify the Survey or Assessment

Step 2: Retrieve Assessment Instances

Step 3: Aggregate Scores by Question

Step 4: Identify Trends Across Periods

Step 5: Segment by Department or Group

Step 6: Analyze Free-Text Responses

Step 7: Generate Recommendations Report

Tool Usage

MCP Tools Reference

REST API Reference

Best Practices

Troubleshooting

No Assessment Instances Found

Question Scores Showing Zero

Department Segmentation Returns "Unknown"

Historical Data Missing for Trend Analysis

Examples

Example 1: CSAT Score Dashboard

Example 2: Employee Engagement Trend

Example 3: Low-Score Investigation

Related Skills