⏱️ The Essentials in 3 Minutes |
🧠 Understand the Value of Measuring Impact
Measuring the impact of Didask AI is first and foremost about understanding whether it genuinely helps your collaborators learn better and work more effectively. Usage or satisfaction metrics alone are not enough: what matters is the effect on behaviors, the reduction of blockers, and real skill development.
A rigorous measurement approach also allows you to manage the deployment with clarity: identify what works, correct what does not, and demonstrate value to stakeholders to sustain the investment.
📊 Track Available Satisfaction Indicators
Real-time user feedback
Each AI response can be rated via a thumbs up or thumbs down system. These ratings are aggregated in a dashboard that measures the overall satisfaction rate and identifies the types of questions generating the most dissatisfaction.
Access: Didask AI section > Feedback tab.
Engagement analysis
Usage statistics provide signals about perceived value:
Recurring users: frequent return indicates perceived usefulness
Messages per conversation: longer exchanges suggest positive engagement
Usage growth: accelerating adoption reflects added value
Access: Didask AI section > Statistics tab.
📈 Observe Indirect Impact Indicators
Reduction in support load
Monitor changes in requests directed at trainers and support teams:
Decrease in repetitive questions
Reduction in interruptions during training
Progressive learner autonomy
Improvement in the learning experience
Observe improvement signals in your training data:
Increase in training completion rates
Reduction in mid-path drop-offs
Positive learner feedback on their overall experience
🧮 Estimate the ROI of Your Deployment
Time saved for trainers
Simple method: number of questions handled by the AI multiplied by the average trainer response time.
Example: 1,000 questions per month, at an average of 5 minutes per response = 83 hours saved per month.
Improvement in collaborator productivity
Factors to consider: reduced time spent searching for information, faster skill development, fewer errors thanks to better understanding of procedures.
✨ Measurement Tips
Baseline: document the situation before deployment (support time, learner satisfaction) to measure progress with reliable comparison points.
Qualitative feedback: complement quantitative data with interviews with users and trainers.
Regular monitoring: establish a monthly measurement cadence to detect trends quickly.
Communication: share early positive indicators to maintain team engagement around the deployment.
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