Service Level (SL)
82% → 97.5%
+8.5 pp
Case Study · Technical Support
Technical Support for a Global Electronics Brand
We implemented KPIs, quality control, and request analytics to improve support efficiency and increase first-contact resolution.
FCR
78% → 92%
+14 pp
AHT
10:36 → 06:34
−4:02
NPS (AZ)
84 → 94
+10
Project Overview
This project was delivered for a major international manufacturer of electronics and home appliances operating in 100+ countries. The goal was to improve support quality and increase first-contact resolution while optimizing operational costs.
Goals
- Increase FCR and reduce repeat contacts.
- Improve SL and consistency in meeting service standards.
- Enhance customer experience (NPS) and reduce handling time (AHT).
Approach
- Unified KPIs and ongoing QA checks using a standardized monitoring form.
- Analysis of repeat-contact drivers and operational bottlenecks.
- Motivation mechanics tied to quality and NPS outcomes.
Challenges at the Start
We identified three key improvement areas: service performance, customer experience, and system stability.
Service Level
Low SL
- Not all agents were consistently meeting targets.
- Frequent deviations from service standards.
- Insufficient quality control.
NPS
Low NPS
- Customer dissatisfaction with resolution quality.
- Data-entry mistakes and technical obstacles.
- Inconsistent communication skills across the team.
Tech
Technical Issues
- NPS survey outages and reporting issues.
- Unstable client CRM performance.
- Slow incident resolution.
What We Did
We focused on three tracks: quality, motivation, and technical optimization.
1
Training & Quality Control
- Weekly QA reviews with individualized feedback and coaching.
- Scripts, checklists, and training built around common scenarios.
- Bonuses/penalties linked to KPI compliance and quality.
2
Motivation Programs
- Incentives tied to KPI achievement (SL, FCR, AHT).
- NPS mechanics: promoters/detractors tracking + root-cause review.
- Weekly quality rankings and "progress awards".
3
Technical Optimization & Analytics
- Collected repeat-contact reasons and reported operational bottlenecks.
- Stabilized the NPS survey process and reporting.
- Aligned CRM improvements with priorities, SLA, and implementation tracking.
Results
Final "before/after" metrics and a visual comparison of key KPIs.
Service Level (SL)
82% → 97.5%
+8.5 pp
FCR
78% → 92%
+14 pp
AHT
10:36 → 06:34
−4:02
FRT (chat)
00:15 → 00:05
−00:06
KPI Comparison Chart
Before After
Note: the chart visually compares "before/after" for each KPI.
Metric
Before
After
Δ
Model entry errors
15%
2%
−13 pp
Service Level (SL)
82%
97.5%
+8.5 pp
NPS (AZ)
84
94
+10
AHT
10:36
06:34
−4:02
FCR
78%
92%
+14 pp
FRT (chat)
00:15
00:05
−00:10
How We Measured Success
Service Level (SL)
% of calls answered within the target time.
First Contact Resolution (FCR)
% of cases resolved without repeat contact.
Average Handle Time (AHT)
Average time to handle one request.
Net Promoter Score (NPS)
Likelihood to recommend: 9–10 promoters, 7–8 passives, 0–6 detractors.
First Response Time (FRT)
Time to the first response in chat.
Average Speed of Answer (ASA)
Average waiting time until an agent answers.
Response Rate (RR)
Share of customers who completed the NPS survey.
Conclusion
A quick summary of what worked — and what others can replicate.
Project outcome
KPI + QA + analytics deliver measurable improvement
A structured approach to training, motivation, and technical optimization improved support performance: service metrics and customer satisfaction increased, while operational costs became more controllable.
- Unified KPIs and regular QA quickly improve service consistency.
- Incentives tied to quality and NPS help sustain results.
- Repeat-contact analytics reduces load and accelerates resolutions.