Coaching Intervention Impact

Description

Purpose of Analysis

Coaching programs are often evaluated based on anecdotal feedback or short-term outcomes. However, without measuring behavioral change before and after coaching interventions, it is difficult to determine whether coaching efforts are effective.

The TrueAI platform captures time-based behavioral signals that allow coaching interventions to be evaluated using observable changes rather than subjective assessment.

This analysis answers:
Do coaching interventions lead to measurable changes in rep behavior?

---

Query

Query Intent

This query compares behavioral signals before and after a coaching intervention window. By aggregating activity and skill-related metrics across defined pre- and post-coaching periods, it highlights whether behavior changes are sustained over time.

The query is designed to evaluate impact at the behavioral level rather than focusing solely on revenue outcomes.

SELECT
  rep_id,
  rep_name,
  coaching_event_id,
  period,

  AVG(trueai_behavior_score) AS avg_behavior_score,
  COUNT(*) AS activity_count

FROM rep_behavior_signals

WHERE coaching_event_id IS NOT NULL
  AND period IN ('pre', 'post')

GROUP BY
  rep_id,
  rep_name,
  coaching_event_id,
  period

ORDER BY
  rep_id,
  coaching_event_id,
  period;

Sample Output

In this example, several reps show improved behavior scores following coaching interventions, while others show minimal or no change. This helps distinguish effective interventions from those requiring adjustment.

rep_name	coaching_event_id	period	avg_behavior_score	activity_count
Rep A	C-102	pre	0.54	38
Rep A	C-102	post	0.69	44
Rep B	C-118	pre	0.61	41
Rep B	C-118	post	0.63	39
Rep C	C-121	pre	0.49	35
Rep C	C-121	post	0.48	37

How to Interpret the Results

• avg_behavior_score reflects aggregated behavioral signals before and after coaching
• Meaningful increases post-coaching suggest effective intervention
• Minimal change may indicate insufficient coaching intensity or misaligned focus
• Activity counts help contextualize changes in behavior signals

---

Coaching Application

This analysis supports coaching by:

• validating whether interventions drive measurable behavior change,
• identifying which coaching approaches are most effective,
• and enabling iterative improvement of enablement programs.

Evaluating coaching impact using behavioral data helps move coaching from intuition-driven to evidence-based.