Rep Analytics

Rep Analytics

Rep Analytics provides detailed performance data for individual sales representatives. Managers can drill into each team member's practice history, evaluation scores, and progress trends.

Accessing Rep Analytics

1. Navigate to Dashboard or Team (under the Team section in the sidebar).
2. Select a specific team member.
3. View their individual performance dashboard.

Individual Performance Metrics

For each rep, you can see:

Practice Session Summary

• Total sessions -- Number of practice calls completed
• Recent sessions -- Sessions completed in the current period
• Average duration -- Mean length of their practice calls
• Session frequency -- How often they practice (daily, weekly, etc.)

Evaluation Scores

• Overall average score -- Aggregate score across all evaluations
• Score by behaviour -- Breakdown for each evaluated behaviour (objection handling, discovery, de-escalation, etc.)
• Score trend -- How scores have changed over time
• Highest and lowest scores -- Best and worst performance areas

Call Type Distribution

See which types of practice sessions the rep focuses on:

• Cold calls
• Discovery calls
• Product demos
• Closing calls
• Custom scenarios

This helps identify whether a rep is practicing a balanced range of scenarios or focusing too narrowly.

Viewing Call History

The rep's call history shows all their practice sessions:

Column	Description
Date	When the session occurred
Product	Which product was discussed
Call Type	The scenario type
Duration	Length of the session
Score	Evaluation score (if completed)
Customer	AI persona name

Click any call to view the full transcript, summary, and detailed evaluation.

Score Trend Analysis

The trend view shows how a rep's performance changes over time:

• Line chart -- Scores plotted chronologically
• Behaviour comparison -- Multiple behaviours shown on the same chart
• Moving average -- Smoothed trend line to show general direction
• Milestone markers -- When certifications were earned

Comparing Reps

Managers can compare performance across team members:

1. Select multiple reps or view the team-level comparison.
2. Side-by-side metrics show relative performance.
3. Identify who is improving fastest and who may need extra support.

Using Rep Analytics for Coaching

Identify Skill Gaps

Look for consistently low scores in specific behaviours. If a rep scores 2 on discovery across multiple sessions, that is a clear coaching priority.

Track Improvement

After coaching on a specific behaviour, monitor the rep's scores to see if the coaching had an impact. A rising trend confirms the intervention is working.

Set Targeted Goals

Use analytics data to set precise goals. Instead of "practice more," you can say "complete 5 discovery calls this week and achieve an average score of 3.5 or higher."

Review Session Quality

Check whether the rep's sessions are long enough to be meaningful. Very short sessions (under 2 minutes) may indicate the rep is not engaging fully with the AI customer.

SPICED Discovery Analytics

If a rep has completed SPICED-evaluated practice sessions, their dashboard includes a SPICED Discovery Skills section with:

• Radar chart -- A five-point radar showing average scores for Situation, Pain, Impact, Critical Event, and Decision. Each dimension includes a trend arrow indicating improvement, decline, or flat performance.
• Trend line -- A weekly chart showing overall SPICED score over time, with a period selector for 7, 30, 90 days, or all time.

SPICED data complements the behaviour-based evaluation scores by providing a framework-specific view of discovery conversation quality. Use SPICED analytics to identify which discovery dimensions a rep consistently struggles with and set targeted coaching priorities.

For details on SPICED scoring, see SPICED Discovery Scorecard.

Product-Level Analytics

Filter analytics by product to see how a rep performs across different offerings:

• Some reps may excel at selling one product but struggle with another.
• Product-specific coaching can address these gaps.
• Cross-reference with product knowledge to identify training needs.

Location Context

For multi-store organizations, rep analytics include location context:

• Which location the rep belongs to
• How their performance compares to location averages
• Location-specific metrics if the rep has worked at multiple locations

Note: Rep analytics data updates as new practice sessions and evaluations are completed. Recent sessions may take a few minutes to appear in the analytics due to caching.