Digital Clienteling Platform
Increased client lifetime value by 68% by transforming transactional e-commerce into relationship-driven digital concierge experiences
Client & Context
Heritage European Luxury House
A 150+-year European luxury house with ~320 retail locations across 45 countries and annual revenue > $4.2B, known for leather goods, ready-to-wear, and accessories — positioned among the top tier of global luxury.
Post-pandemic digital acceleration: e-commerce rose from ~12% to ~28% of revenue.
Increasing competition from digitally native luxury brands with superior online experiences.
Customers expect Amazon-level convenience and Netflix-level personalization while the brand must preserve heritage and craftsmanship.
Legacy technology limits personalized experiences at scale; internal friction between heritage preservation and digital transformation.
My Role — VP, Creative Strategy & Digital Experience
End-to-end strategic ownership from vision through launch and optimization.
Direct report to the Chief Digital Officer with dotted line to the Chief Marketing Officer.
P&L ownership for digital clienteling initiatives with $12M budget authority.
Team & Timeline
6 Core Strategy, 8 UX/UI Design, 7 Content & Editorial, 14 Technology & Engineering, 5 Data Science & Analytics, 12 Agency partners
Discovery & Strategy (3 months), Concept Development & Testing (4 months), Technical MVP Build (6 months), Pilot Launch (2 months), Global Rollout (3 months)
Business Challenge
The brand's digital commerce experience was transactional rather than relational. While in-store clients enjoyed white-glove service from dedicated client advisors who remembered their preferences, occasions, and style evolution, online clients experienced a generic e-commerce interface indistinguishable from mass-market retail. This disconnect was eroding the brand's core value proposition: intimate, personalized luxury service.
Technical Pain Points
Siloed data systems prevented unified client view across channels
Legacy e-commerce platform (Salesforce Commerce Cloud) lacked personalization capabilities
No integration between online behavior and in-store client books
CRM data quality issues: 40% of client records incomplete or duplicated
Content management system unable to support dynamic, personalized experiences
Mobile experience was responsive web, not native app, limiting engagement features
Constraints
Budget: $12M over 18 months (constrained vs. initial $18M request)
Timeline: Board mandate for pilot launch within 12 months
Technology: Must integrate with existing Salesforce ecosystem; no full replatform
Privacy: GDPR/CCPA compliance required; no third-party data purchasing
Brand: All experiences must pass brand creative committee review
Retail: Store associates must embrace, not resist, digital clienteling tools
Baseline Metric | Baseline Value |
|---|---|
Digital Revenue Share | 28% of total retail |
Online Repeat Purchase Rate | 34% (vs. 72% in-store) |
Average Order Value (Digital) | $1,240 |
Client Lifetime Value (Digital-Only) | $3,100 |
One-Time Purchaser Rate | 62% |
Digital NPS | 48 |
Email Open Rate | 18% |
App Downloads (existing app) | 340K total |
Strategic Objectives & KPIs
High-Level Goals
Transform digital from transaction channel to relationship platform
Achieve parity between digital and in-store client lifetime value within 3 years
Reduce dependency on new client acquisition by improving retention economics
Create competitive moat through proprietary clienteling technology
Creative Goals
Express brand heritage and craftsmanship values in every digital interaction
Create emotional resonance equivalent to in-store luxury experience
Develop visual and interaction language that elevates beyond e-commerce conventions
Enable storytelling at the individual client level, not just broadcast marketing
Technical Goals
Establish unified client data platform enabling real-time personalization
Integrate digital and physical client records into single view
Build recommendation engine combining algorithmic and human intelligence
Create scalable content architecture supporting 1:1 personalization
Deploy native mobile app with engagement capabilities beyond web
KPIs
Metric | Baseline | Year 1 Target |
|---|---|---|
Repeat Purchase Rate | 34% | 50% |
Average Order Value | $1,240 | $1,550 |
Client Lifetime Value | $3,100 | $4,800 |
One-Time Purchaser Rate | 62% | 45% |
Digital NPS | 48 | 65 |
Email Engagement Rate | 18% | 32% |
Platform-Engaged Client Revenue | N/A | +25% vs. non-engaged |
Research & Insight
Market / Competitor Audit
(We conducted comprehensive analysis of 14 luxury competitors and 8 best-in-class digital experiences from adjacent categories—hospitality, private banking, premium automotive)
No luxury competitor had successfully translated in-store clienteling to digital at scale
Most luxury e-commerce sites were functionally identical to mass-market retail
Burberry's 'R Message' and Gucci's 'Live' showed early experiments but lacked depth
Best personalization examples came from outside luxury: Spotify, Netflix, Stitch Fix
Private banking digital platforms offered relevant models for high-touch relationships
Audience Research
Quantitative survey: 4,200 clients across 8 markets (existing clients and prospects)
Qualitative depth interviews: 48 high-value clients ($25K+ annual spend)
Ethnographic observation: 120 hours of in-store client advisor interactions
Digital behavior analysis: 18 months of clickstream data from 2.1M unique visitors
Associate interviews: 35 client advisors across 12 flagship locations
Audience Segment | Characteristics |
|---|---|
Heritage Loyalists (22%) | Long-term clients; value tradition and craftsmanship; prefer in-store |
Affluent Explorers (31%) | Discovery-oriented; respond to storytelling; omnichannel comfortable |
Digital Natives (28%) | Younger; expect digital excellence; less brand-loyal |
Occasion Purchasers (19%) | Gift-driven; high AOV but low frequency; need guidance |
Data Analysis
Clients who interacted with client advisors had 3.2x higher LTV than self-service
Personalized email outreach converted at 4.7x generic promotional campaigns
Mobile sessions were 2.1x longer than desktop but converted 60% less
Clients who purchased from 3+ categories had 89% retention vs. 41% single-category
Time between first and second purchase was strongest predictor of lifetime retention
Insight Statements
Luxury clients don't want efficiency; they want recognition. While mass-market e-commerce optimizes for speed and convenience, luxury clients value feeling known and remembered. The friction of human interaction isn't a bug; it's a feature that signals worthiness of attention.
Purchase history is the least interesting data about a client. What clients browsed, saved, and considered tells us about their aspirations. What occasions they're preparing for, how their style is evolving, what they almost bought; this context creates meaningful personalization.
Digital 'personalization' feels algorithmic; human curation feels personal. Clients can sense when recommendations come from machines vs. humans. The most effective model combines algorithmic efficiency with human judgment and the ability to surprise with unexpected relevance.
Loyalty isn't purchased with points; it's earned through relationship depth. Transactional loyalty programs train clients to wait for promotions. True loyalty comes from accumulated understanding that makes switching feel like starting over; emotional, not economic switching costs.
Ideation & Concept Development
Creative Brief
Challenge: Create digital experiences that feel as personal as the brand's legendary in-store service.
Target: Affluent clients who value recognition and relationship over convenience.
Insight: Digital personalization feels hollow because it lacks human judgment and relationship memory.
Proposition: Your Digital Atelier - A personal stylist who knows your story and evolves with you.
Tone: Warm but not familiar; knowledgeable but not presumptuous; anticipatory but not intrusive.
Mandatories: Brand heritage integration; privacy-first data approach; omnichannel continuity.
Concept Development
(We conducted a 3-week intensive ideation sprint with the following structure)
Week 1: Divergent exploration — 200+ concepts generated across 8 workshops
Week 2: Convergent selection — concepts clustered, evaluated, and reduced to 12 finalists
Week 3: Concept development — 4 concepts developed into detailed scenarios for testing
Ideation Methods
How Might We framing sessions with cross-functional teams
Analogous inspiration from hospitality, private aviation, and concierge medicine
Client journey 'stress testing' identifying moments of highest relationship opportunity
'Day in the Life' scenario mapping for target client segments
Reverse engineering competitor weaknesses into opportunity spaces
Constraint-driven ideation: 'What if we had no website, only messaging?'
Concept Themes
Digital Concierge: AI-first experience with chatbot as primary interface.
Style Story: Narrative-driven experience positioning purchases as chapters in personal journey.
Virtual Atelier: Appointment-based digital consultations with remote stylists.
Curated Collections: Highly personalized product selection refreshed weekly.
Concept testing with 180 clients revealed Style Story and Virtual Atelier resonated most strongly. The final concept merged these: a narrative-framed experience with human stylist touchpoints at key moments.
Merging Brand Narrative with Technical Feasibility
(The creative vision required technical capabilities that stretched but didn't break existing infrastructure)
'Your Style Story' narrative framing was implementable through content personalization on existing CMS with enhanced tagging
Human stylist integration required new appointment booking and video consultation tools but could leverage existing retail associate workforce
Unified client memory required Customer Data Platform investment but aligned with planned IT roadmap
Heritage content library existed but needed new metadata architecture to enable personalized surfacing
We established 'creative non-negotiables' that technology must serve, and 'creative adaptables' that could flex based on technical constraints. This framework prevented both over-promising and under-imagining.
Technical Architecture & Prototyping
High-Level Technology Stack
Layer | Technology |
Customer Data Platform | Segment CDP with custom identity resolution |
E-Commerce Platform | Salesforce Commerce Cloud (existing, enhanced) |
Content Management | Contentful (headless CMS migration) |
Personalization Engine | Dynamic Yield + custom ML models |
Mobile Application | React Native (iOS and Android) |
Video Consultation | Twilio integration with custom UI |
Analytics | Amplitude (product) + Looker (business) |
Recommendation Engine | Custom hybrid: collaborative filtering + stylist override |
Architecture Overview
(The platform architecture was designed around three core principles)
Unified Client Identity: Single customer view aggregating web, app, store, CRM, and clienteling data with real-time synchronization
Headless Content: Decoupled content repository enabling personalized assembly across all touchpoints
Human-in-the-Loop AI: Recommendation engine outputs reviewed and augmented by stylist team before client-facing deployment
Prototype Development
(We developed three prototype stages to de-risk the concept before full build)
Clickable Concept (Week 4-6)
Figma prototype demonstrating core user journeys
Tested with 24 clients in moderated sessions
Validated: narrative framing resonated; clients wanted more human touchpoints
Functional MVP (Week 10-16)
Limited functionality build on staging environment
Personalization logic with sample data set
Video consultation booking (not actual consultations)
Tested with 120 clients in unmoderated remote sessions
Validated: personalization accuracy acceptable; booking UX needed simplification
Pilot-Ready Beta (Week 20-24)
Full functionality for pilot scope
Integration with production client data (consenting pilot participants)
Live stylist consultations with trained associates
Tested with 500 clients in 3 pilot markets
Validated: ready for expanded pilot with defined optimization roadmap
Tools Used
Design: Figma, Principle (animation), Maze (unmoderated testing)
Development: React Native, Node.js, GraphQL, PostgreSQL
Data: Segment, Snowflake, dbt, Python (ML models)
Project Management: Jira, Confluence, Miro
Communication: Slack, Zoom, Loom (async updates)
Production & Execution
Development Workflow
(We operated in 2-week agile sprints with the following cadence)
Sprint Planning: Monday morning with full cross-functional team
Daily Standups: 15-minute sync; async updates in Slack for distributed team
Design Review: Wednesday afternoon; creative committee input on user-facing elements
Sprint Demo: Alternating Fridays; stakeholder visibility into progress
Retrospective: Following sprint demo; continuous process improvement
Release cycles operated on a monthly cadence for major features, with hotfixes deployed continuously.
Key Deliverables
Native Mobile App: iOS and Android apps with full clienteling functionality
Responsive Web Experience: Enhanced web platform with personalization and stylist access
Stylist Dashboard: Associate-facing tool for client management and outreach
Content Library: 2,400+ tagged content assets for personalized assembly
Recommendation Engine: Hybrid ML model with human override capability
Analytics Dashboard: Real-time performance monitoring for business and product teams
Training Program: 40-hour curriculum for 320 client advisors globally
Collaboration Highlights
Cross-Functional Integration
Success required unprecedented collaboration between historically siloed functions. We established a 'Platform Council' meeting bi-weekly with representatives from Digital, Retail, Marketing, IT, and Finance to ensure aligned priorities and rapid decision-making.Agency Partnership Model
Three agency partners (UX design, content production, technology implementation) were integrated into our sprint structure rather than operating on traditional brief-and-deliver cycles. Agency team members attended standups and had direct Slack access, reducing handoff friction and accelerating iteration.Retail Associate Involvement
We embedded 6 top-performing client advisors into the project team as 'Voice of Stylist' consultants. Their input shaped the stylist dashboard UX and ensured the platform enhanced rather than threatened their client relationships. This investment paid dividends in adoption: associates became advocates rather than resistors.
Key Challenges & Decisions
Challenge 1: Data Quality Crisis
Problem: Six weeks into development, data audit revealed 43% of client records had quality issues preventing reliable personalization. Duplicate records, missing fields, and inconsistent formatting made unified client view impossible.
Decision Point: Delay launch 3 months for data remediation, or launch with limited personalization accuracy?
Resolution: We chose a hybrid approach. Implemented real-time data enrichment for new interactions while running parallel remediation on historical records. Launched on schedule with 'progressive personalization' that improved as clients engaged, rather than waiting for perfect data.
Outcome: Personalization accuracy improved from 62% at launch to 89% within 6 months as enriched data accumulated.
Challenge 2: Stylist Capacity Constraints
Problem: Initial concept assumed 1:1 video consultations for all high-intent moments. Capacity modeling revealed this would require 3x existing associate headcount or create unsustainable wait times.
Decision Point: Hire significantly more stylists, limit consultation access, or redesign the model?
Resolution: We redesigned around 'asynchronous styling.' Clients could request personalized recommendations at any time; stylists would respond within 4 hours via curated digital lookbooks rather than live video. Video consultations were reserved for highest-value moments (first-time engagement, major purchases, special occasions).
Outcome: Model achieved 94% of client satisfaction scores of synchronous consultations at 35% of the labor cost.
Challenge 3: Brand Committee Resistance
Problem: The brand's creative committee initially rejected personalized interface elements, arguing that varying the experience violated brand consistency guidelines developed for print and advertising.
Decision Point: Comply with existing guidelines (limiting personalization), or advocate for guideline evolution?
Resolution: We developed a 'Personalization Brand Standards' framework demonstrating how personalization enhanced rather than diluted brand expression. Presented case studies from hospitality brands showing personalization as luxury signifier. Committee approved updated guidelines establishing parameters for personalized elements.
Outcome: Framework adopted globally; became template for brand's broader digital transformation guidelines.
Challenge 4: Privacy Regulation Uncertainty
Problem: Mid-project, new data privacy regulations in key European markets created uncertainty about consent requirements for personalization features.
Decision Point: Build to anticipated strict standards (limiting features), or build flexibility to adapt?
Resolution: Implemented 'privacy-by-design' architecture with granular consent management. Created three personalization tiers based on consent level, ensuring meaningful experience even with minimal consent while rewarding fuller data sharing with richer personalization.
Outcome: When regulations clarified, we were already compliant. Tiered approach became competitive advantage as competitors scrambled to retrofit privacy controls.
Final Solution
Product Description
The Digital Clienteling Platform is a native mobile app and responsive web experience that transforms luxury e-commerce into relationship-driven personal styling. The platform remembers each client's preferences, occasions, and style evolution, delivering personalized product curation, proactive outreach, and human stylist access that makes digital shopping feel as personal as the brand's legendary in-store service.
Core Features
Your Style Story: Personalized home experience framing product recommendations as chapters in the client's ongoing style narrative. Content adapts based on browsing behavior, purchase history, saved items, and stylist notes. Clients see 'Your Story Continues' rather than 'Recommended Products.'
Personal Stylist Access: On-demand access to human stylists via asynchronous messaging, curated lookbook delivery, and scheduled video consultations. Stylists have full visibility into client history and preferences, enabling personalized guidance that builds on relationship context.
Occasion Wardrobe: Clients can share upcoming occasions (weddings, galas, travel, professional milestones) and receive curated recommendations tailored to each event. Platform proactively reaches out as occasions approach with styling suggestions.
Heritage Connections: Product pages include 'Heritage Moments' connecting items to brand history, artisan stories, and craftsmanship traditions. Personalization surfaces heritage content aligned with demonstrated interests.
Seamless Omnichannel: Complete continuity between digital and physical. In-store associates see digital engagement history; digital stylists can book in-store appointments; purchases made anywhere appear in unified client profile.
Tech Specs
Specification | Detail |
|---|---|
Platforms | iOS 14+, Android 10+, Web (Chrome, Safari, Edge) |
Personalization Latency | <200ms for content assembly |
Recommendation Accuracy | 89% relevance score (human-evaluated) |
Uptime SLA | 99.9% |
Data Refresh | Real-time for behavioral; hourly for transactional |
Languages | 12 languages at launch |
Markets | 45 countries |
Results & Impact
Before/After Metrics
Metric | Before | After (Year 1) |
|---|---|---|
Repeat Purchase Rate | 34% | 51% (+50%) |
Average Order Value | $1,240 | $1,637 (+32%) |
Client Lifetime Value | $3,100 | $5,208 (+68%) |
One-Time Purchaser Rate | 62% | 38% (-39%) |
Digital NPS | 48 | 78 (+63%) |
Email Engagement Rate | 18% | 41% (+128%) |
Digital Revenue Share | 28% | 48% (+71%) |
Engagement Metrics
Metric | Result |
|---|---|
App Downloads (Year 1) | 2.1M (+518% vs. prior app) |
Monthly Active Users | 840K |
Stylist Consultation Requests | 127K (Year 1) |
Consultation-to-Purchase Rate | 67% |
Average Session Duration | 8.4 minutes (+3.2x vs. web baseline) |
Push Notification Opt-In | 72% |
Qualitative Results
Client feedback: 'Finally, a luxury brand that knows me online like they know me in the store'
Associate feedback: 'This makes my job easier, not harder. I can help more clients better.'
Press coverage in Vogue Business, Business of Fashion, WWD, and Luxury Daily
Recognized as 'Best Luxury Digital Experience' at Digital Fashion Innovation Awards
Platform approach presented at NRF, Shoptalk, and Luxury Interactive conferences
ROI Summary
Investment | Amount |
|---|---|
Total Project Investment | $12M |
Incremental Digital Revenue (Year 1) | $94M |
Incremental Margin Contribution | $38M |
ROI | 317% |
Payback Period | 4.2 months |
Learnings & Next Steps
What I Would Iterate
Earlier Associate Involvement: Bringing retail associates into the process at month 3 was valuable; bringing them in at month 1 would have avoided several UX dead-ends and accelerated adoption planning.
More Aggressive Data Quality Investment: The mid-project data crisis was predictable. I would advocate more strongly for pre-project data remediation, even if it delayed timeline, to avoid the progressive-personalization workaround.
Pilot Market Selection: Pilot markets were chosen for operational convenience. In retrospect, selecting markets with higher digital maturity would have generated stronger proof points faster.
Content Velocity: Initial content production capacity was undersized for personalization needs. Would build content team earlier and larger to avoid launch-period content gaps.
New Skills & Tools Mastered
Customer Data Platform architecture and implementation (Segment ecosystem)
Hybrid human-AI personalization system design
Privacy-by-design frameworks for GDPR/CCPA compliance
Change management for retail associate adoption of digital tools
Luxury-appropriate agile methodology balancing speed with craft
Influence on Future Work
(This project established frameworks and principles I now apply to every engagement)
Human-in-the-loop as default for high-consideration personalization
Narrative framing for experience design, not just product presentation
Relationship metrics over transaction metrics as north star
Privacy as brand asset, not compliance burden
Cross-functional integration as prerequisite, not aspiration
The platform itself continues to evolve. Phase 2 (in progress) extends clienteling to post-purchase care, styling services, and client community.
Translate luxury service into relationship-driven commerce
I help translate high-touch service models into digital experiences that blend human expertise with AI personalization, creating deeper customer relationships and measurable growth. If you’re tackling luxury-to-digital transformation, relationship-first commerce, or personalized AI experiences, let’s discuss how my experience could be useful — let’s talk.