Vision Care Support
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ZEISS
Welcome to ZEISS Vision Care Support. 👋
I can help you with prescription lenses, XR Optical Inserts, orders, warranty, and more. How can I assist you?
Common questions
Powered by ZEISS Vision Care AI · Answers sourced from verified knowledge base
LEADBeyond × ZEISS Vision Care
Customer Support Operations Center
All Stages
12,847
Total Tickets
Pre-Purchase
2,156
16.8%
Purchase & Order
1,927
15.0%
Delivery & First Use
3,341
26.0%
Issue Resolution
3,854
30.0%
Returns & Warranty
1,184
9.2%
Stabilization
385
3.0%
18.2%
1.8 min
Avg. Response Time
Target: <3 min · Benchmark: 4.2 min
12.4%
94.7%
Resolution Quality Score
FCR 89.2% + CSAT 96.1% + No Reopen 98.8%
0.34
4.82 /5
CSAT Score
Top 5% industry · NPS: +67
6.2%
42%
AI Deflection Rate
Resolved without human agent
1.3 avg
1.4 avg
Escalation Depth
Avg. handoffs before resolution
Ticket Pipeline — Stage-Gate Velocity
i
So what?Bottlenecks become visible instantly. If "Pending Customer" grows, your follow-up SLA is at risk. If "Escalated" spikes, Tier 2 capacity needs rebalancing.
3,241
New /
Incoming
→
2,847
Triaged
↓ 4.2min avg
→
2,156
In
Progress
↓ 12min avg
→
1,203
Pending
Customer
↓ 2.1h avg
→
487
Escalated
↓ 6.2min avg
→
11,644
Resolved
↓ 18min avg
SLA Monitoring — Live Alerts
i
So what?Red indicators demand immediate action. The escalation handoff SLA breach (12.4 min vs 10 min target) signals a Tier 2 staffing gap during peak hours.
First Response Time
Threshold: <5 min
1.8 min
64% below target
Resolution Time (P1)
Threshold: <1 hour
38 min
37% below target
Resolution Time (P2)
Threshold: <4 hours
3.4 hrs
15% below target
Customer Follow-up
Threshold: <24 hours
6.2 hrs
74% below target
Escalation Handoff
Threshold: <10 min
12.4 min
24% above target
CSAT Survey Response
Threshold: >90%
96.1%
6.8% above target
Channel Performance Matrix
i
So what?AI Chatbot leads in every metric at the lowest cost. Social channel CSAT (4.41) and response time (18.6 min) flag a gap — consider dedicated social support agents or AI triage.
Channel Volume Resol. Quality T-to-Expert T-to-Knowledge CSAT Deflect %
AI Chatbot 5,394 97.2% 0.3s 0.8s 4.91 100%
Email 3,597 93.4% 8.2m 3.4m 4.76 —
Phone 2,312 91.1% 12.4m 4.1m 4.68 —
Self-Service 1,029 96.8% — 1.2m 4.83 100%
Social 515 88.7% 18.6m 5.2m 4.41 —
4.8 min
6.2 min
Time-to-Expert
From escalation to specialist engagement
balanced
78%
Skill-Based Utilization
Agents working within skill match
8.7%
73.4%
Knowledge Reuse Rate
Articles reused across 3+ resolutions
3.1%
2.1%
Repeat Contact Rate
Same issue within 7 days
23.1%
2.4 min
Time-to-Knowledge
Avg. time to find relevant KB article
Expert Capacity Utilization — Weekly Heatmap
i
So what?Lisa M. hits 94% on Tuesdays and Thomas S. 96% on Wednesdays — both above the 90% overload threshold. Redistribute mid-week load or add flex capacity to prevent burnout and SLA breaches.
Mon
Tue
Wed
Thu
Fri
Sat
Sun
AI Assistant
42%
48%
55%
47%
38%
22%
15%
Lisa M. (Optics)
87%
94%
82%
91%
76%
—
—
Thomas S. (Tech)
78%
81%
96%
73%
85%
—
—
Anna K. (Digital)
71%
79%
84%
68%
77%
—
—
Max W. (Warranty)
65%
69%
74%
82%
63%
—
—
Sophie B. (A11y)
58%
64%
67%
72%
61%
—
—
>90% (overloaded) 70–90% (optimal) 50–70% (available) <50% (AI-heavy)
Skill-Based Agent Utilization
i
So what?78% skill-match rate means 22% of tickets are handled by non-specialists. Increasing to 85% would cut resolution time by ~18% and improve FCR by ~5 points based on industry data.
AI
AI Assistant (Tier 0)
All categories · 24/7
94%
LM
Lisa Müller
Optical Inserts · Prescriptions
87%
TS
Thomas Schmidt
Vision Pro · Technical
82%
AK
Anna Krüger
MyZEISS · Digital Services
79%
MW
Max Weber
Warranty · Returns
71%
SB
Sophie Braun
Accessibility · Eye Tracking
68%
78% skill-match rate — agents routed to their specialty domain. Target: 85%.
Resolution Quality Breakdown
i
So what?A composite quality score replaces single-metric FCR for holistic view. At 94.7%, quality is strong — but the 89.2% FCR signals room for improvement in first-touch resolution training.
94.7% composite
First Contact Resolution
89.2%
CSAT ≥ 4.5
96.1%
No Reopen (7d)
98.8%
SLA Compliance
95.3%
Resolution Quality is a composite score weighting FCR (35%), CSAT (25%), No-Reopen (25%), and SLA (15%) — replacing single-metric FCR for a holistic quality view.
Escalation Depth Funnel
i
So what?82% of tickets resolved at Tier 0 + Tier 1 (vs. 68% industry avg). The 4% reaching Tier 3 Engineering are mainly display hardware issues — potential product feedback loop.
Tier 0 — AI Resolution
5,394 (42.0%)
Tier 1 — Generalist Agent
5,138 (40.0%)
Tier 2 — Product Specialist
1,798 (14.0%)
Tier 3 — Engineering / R&D
517 (4.0%)
Avg. Escalation Depth: 1.4 handoffs — 82% resolved within Tier 0 + Tier 1, vs. 68% industry avg.
Root-Cause Clusters (Top 8)
i
So what?Pairing code issues (#1, 1,847 tickets) are the single largest driver of support volume. A proactive in-box QR guide and updated AI training would cut this cluster by an estimated 40%.
1
Pairing Code Issues
Vision Pro · Delivery & First Use
1,847
2
MyZEISS Registration Errors
Digital Services · Issue Resolution
1,523
3
Prescription Verification
Optical Inserts · Pre-Purchase
1,241
4
Display Blurriness / Spots
Vision Pro · Issue Resolution
1,089
5
Warranty Claims Processing
After-Sales · Returns
892
6
Contact Lens Compatibility
Vision Pro · Pre-Purchase
764
7
Reader Diopter Selection
Optical Inserts · Purchase
612
8
Accessibility / Eye Tracking
Vision Pro · Issue Resolution
438
AI-Powered Strategic Insights
i
So what?These are machine-generated recommendations based on pattern analysis across all support data. Each insight links a finding to a concrete action with estimated ROI.
AI deflection saves €380K annually
42% of inquiries resolved by AI without human intervention — up from 31% last quarter. Primary driver: pairing code FAQ resolution now at 94% accuracy.
Vision Pro 2 launch: +67% pairing tickets
Root-cause cluster #1 surged following the Apple Vision Pro 2 launch. Recommend: video guide KB article, proactive in-box QR code guide, and AI prompt refinement for new device model.
Knowledge gap: Progressive lens customization
14 unanswered queries in 7d about progressive lens customization. Time-to-Knowledge averages 8.2 min for this topic (3.4x overall avg). Action: create dedicated FAQ category with 8 articles.
Skill-routing optimization opportunity
22% of Tier 2 escalations could be handled at Tier 1 with targeted training on "Display Blurriness" and "Prescription Verification" clusters. Estimated saving: 340 agent-hours/month.
Knowledge Base Health & Metrics
i
So what?68.2% article freshness means nearly 1 in 3 articles is stale (>30 days). Stale articles correlate with 2.1x longer Time-to-Knowledge. Prioritize review of high-traffic articles first.
247
Total Articles
94%
Coverage Score
6
Languages
18
Categories
12
Pending Reviews
3.2d
Avg. Article Age
Knowledge Reuse Rate73.4%
Auto-Resolution Rate87.3%
Article Freshness (updated <30d)68.2%
Skill-Match Routing Accuracy78.0%
Prepared exclusively for ZEISS Vision Care

Building a world-class support organization for the era of Vision Care — including XR optical inserts

A structured senior advisory engagement — two integrated workstreams, one team with proven impact, a fully operational support model designed for launch.

Incl.  Workstream A · Operating Model & TOM
Incl.  Workstream B · Knowledge & Training
Modular · Start with one or both
~16 wks
Advisory engagement
2
Senior partners — continuous involvement (ex-McKinsey, ex-Roland Berger)
Selected clients who trusted us to build and scale their service operations
Context
A once-in-a-decade product launch demands a purpose-built support org
01
New product category
XR Optical Inserts represent a fundamentally new product archetype — bridging vision correction and mixed reality. Existing support playbooks don't apply.
02
Building from scratch
No legacy support org to retrofit. This is a greenfield build — a rare opportunity to design roles, processes, and knowledge systems right the first time.
03
Speed to market matters
Support quality at launch shapes brand perception in a highly visible category. A reactive, patched-together support org is not an option.
✓ Proven Track Record

We built this before — and the results speak for themselves

At reneo (LIVINGS brand), we led the end-to-end advisory engagement to design a full customer support operating model from scratch — delivering measurable outcomes within 12 months. The same structured approach we bring to ZEISS.

CLIENT REFERENCE · reneo / LIVINGS · Available on request
+52
NPS improvement
−75%
Escalation rate
−37d
Avg. handling time
>90%
Processes digitized
Our Approach
Our approach to workstreams — one integrated engagement
A
Workstream A
Operating Model & TOM Design
Structure, governance, and capabilities for launch-ready support
M1
Current-State Assessment
Stakeholder mapping, as-is process documentation, gap & opportunity analysis
↳ As-Is Assessment Report
M2
Target Operating Model Design
Operating model canvas, channel strategy, insource/outsource assessment
↳ TOM Blueprint
M3
Service Model & Tiering
Tier definitions (L0–L3), service catalogue design, escalation & routing logic
↳ Service Catalogue
M4
Workforce Design & Roles
Role profiles, competency matrix, staffing model & capacity planning
↳ Workforce Blueprint
M5
Quality Framework & KPIs
KPI dashboard blueprint, quality monitoring program, voice of customer
↳ KPI Dashboard Design
M6
Validation & Go-Live Readiness
Setup review & stress test, risk assessment, go-live readiness scorecard
↳ Go-Live Scorecard
B
Workstream B
Knowledge Mgmt & Training Design
Capability building so agents are ready from day zero
M1
Current-State Assessment
Knowledge taxonomy design, documentation framework, content governance model
↳ KM Architecture Doc
M2
Onboarding & Ramp-Up Program
Week-by-week ramp plan, certification milestones, buddy & mentoring system
↳ Onboarding Journey
M3
Continuous Learning & Skills
Skill matrix per role, micro-learning modules, quarterly skill assessments
M4
Tooling & Technology
KB platform evaluation (Guru/Zendesk), AI-assisted search, LMS recommendation
↳ Tool Landscape Evaluation
M5
Sustainability & Quality Assurance
Content freshness scoring, KM health dashboard, annual maturity assessment
↳ KM Quality Model
Delivery Timeline
Indicative 16-week advisory engagement — modular, decision-driven
1
Weeks 1–3
Discover & Align
Stakeholder interviews
Current-state assessment
Gap & opportunity analysis
2
Weeks 4–8
Design & Build
Target operating model design
Service model & tiering
KM architecture & content framework
3
Weeks 9–11
Validate & Refine
Workforce & role design
Quality framework & KPIs
Stakeholder alignment & iteration
4
Weeks 12–16
Launch Support
Go-live readiness assessment
Hypercare advisory window
Knowledge & model handover
Already built for you

AI-assisted self-service and performance management are table stakes — so we built them before day one

Modern support organizations can't scale without deflection intelligence and real-time performance visibility. We've already delivered a working prototype of both — demonstrating speed, commitment, and exactly the kind of hands-on approach we bring to every engagement.

AI-Assisted Self-Service Chat
Functional prototype · Ready for ZEISS Vision Care
● Live prototype
AI Support Assistant — live prototype

A keyword-aware AI chat with contextual action buttons, designed to deflect tier-1 queries before they reach an agent. Built on the knowledge architecture from Workstream B — expandable to full LLM-backed responses as content matures.

→ Open AI Chat tab
Analytics Dashboard
Operations · Capacity · Intelligence
⬡ First cut
Analytics Dashboard — live prototype

A live three-tab analytics dashboard covering operational KPIs, capacity and expertise planning, and continuous improvement signals — structured around the KPI framework from Workstream A, module M5.

→ Open Analytics tab
Who You Will Work With
Senior partners leading every session — fully customer-facing

Both partners lead all client-facing sessions personally. Selective specialist support may operate in the background where required — but every interaction, deliverable, and decision is owned at partner level.

Frederic Rupprecht
Frederic Rupprecht
Co-Founder · Strategy & Analytics
Former McKinsey consultant with deep experience in strategy, value creation, and executive development in PE-backed and corporate settings. Grounded in real consulting methodology — the same frameworks used to assess leadership in top-tier strategy engagements. Led the reneo/LIVINGS customer operations transformation end-to-end.
linkedin.com/in/fredericrupprecht
Jonathan Günak
Jonathan Günak
Co-Founder · Operating Model & Enablement
Former Roland Berger strategist with a focus on buy-and-build, integration, and C-level capability building. Strategy and operations practitioner trained at the London School of Economics. Specialist in knowledge management architecture, organizational learning, and building training programs that stick — from day one.
linkedin.com/in/jonathan-günak
Why LEADBeyond
What makes us different from the alternatives
Senior-only delivery
Both partners personally on your account. No bait-and-switch with junior analysts. Every deliverable reviewed and owned at partner level.
Advisory built on real experience
We've done this before — in comparable organizational settings. Our frameworks are derived from practice, not theory, and adapted to your specific context from day one.
€
Fraction of Big 4 cost
Comparable outcomes without the overhead of large firm structures or brand premiums built into every invoice.
Commitment before contract
This AI assistant and analytics dashboard were built before the engagement started. It's the clearest signal of how seriously we take your challenge.

Ready to align on the next steps?

Download our complete proposal — including detailed workstream plans, advisory methodology, the reneo reference case, and full team credentials.

Download Full Proposal
PDF · Prepared exclusively for ZEISS Vision Care · Confidential