Customer Experience Personalization in 2019 - Beyond Basic Segmentation

Personalization has evolved from a marketing buzzword to a critical business strategy. As customer expectations continue to rise and competition intensifies across industries, organizations are moving beyond basic segmentation to deliver truly individualized experiences. This comprehensive guide explores the current state of customer experience personalization in 2019, highlighting advanced strategies, technologies, and implementation approaches that are helping brands create meaningful connections with their customers.

The Evolution of Personalization

How personalization has matured over time:

• From Segmentation to Individualization: Moving beyond broad customer groups
• From Rules to Intelligence: Leveraging AI and machine learning for dynamic personalization
• From Single Channel to Omnichannel: Creating consistent experiences across touchpoints
• From Marketing to Experience: Extending personalization beyond communications
• From Transactions to Relationships: Building ongoing connections with customers

These developments are shaping how organizations approach personalization in 2019 and beyond.

The Business Case for Advanced Personalization

Why organizations are investing in sophisticated personalization:

1. Measurable Business Impact

Quantifiable benefits of personalization:

• Conversion Rate Improvement: 10-30% increase in conversion rates
• Average Order Value Growth: 20% higher transaction values
• Customer Retention Enhancement: 10-15% reduction in customer churn
• Marketing Efficiency: 10-30% improvement in marketing ROI
• Revenue Acceleration: 5-15% increase in overall revenue

Example: Amazon attributes 35% of its revenue to its personalization engine, which creates individualized recommendations based on browsing history, purchase patterns, and similar customer behaviors.

2. Rising Customer Expectations

How consumer demands are evolving:

• Anticipatory Experiences: Expecting brands to predict needs
• Contextual Relevance: Demanding interactions appropriate to time, place, and situation
• Value Exchange Awareness: Willing to share data for meaningful benefits
• Seamless Recognition: Expecting consistent identity across touchpoints
• Emotional Connection: Seeking brands that understand personal preferences

Research Insight: According to Accenture, 91% of consumers are more likely to shop with brands that recognize them, remember them, and provide relevant offers and recommendations.

3. Competitive Differentiation

Using personalization as strategic advantage:

• Experience Innovation: Creating distinctive customer interactions
• Loyalty Enhancement: Building stronger emotional connections
• Premium Positioning: Justifying higher prices through superior experiences
• Barrier to Switching: Making it difficult for customers to leave
• Brand Perception: Strengthening reputation for customer-centricity

Example: Starbucks has differentiated itself through its mobile app's personalization capabilities, which remember favorite orders, suggest new products based on preferences, and deliver location-specific offers, contributing to a 7% increase in same-store sales.

Advanced Personalization Strategies

Sophisticated approaches moving beyond basic segmentation:

1. Predictive Personalization

Anticipating customer needs and behaviors:

Predictive Approaches

• Next Best Action: Determining optimal next steps for each customer
• Propensity Modeling: Predicting likelihood of specific behaviors
• Churn Prevention: Identifying and addressing at-risk customers
• Lifetime Value Forecasting: Predicting long-term customer worth
• Preference Anticipation: Inferring unspoken needs and desires

Implementation Example: Netflix's recommendation engine doesn't just suggest content based on past viewing—it predicts what you'll want to watch next based on viewing patterns, time of day, device used, and even how you browse the catalog, resulting in a system that drives 80% of content discovery.

2. Contextual Personalization

Adapting to customer circumstances:

Contextual Factors

• Location Awareness: Tailoring experiences based on physical position
• Time Sensitivity: Adjusting content based on time of day or season
• Device Adaptation: Optimizing for the specific device being used
• Weather Response: Modifying offers based on local conditions
• Behavioral State: Recognizing where customers are in their journey

Implementation Example: Uber uses contextual personalization to predict where riders want to go based on time of day, location, and past behavior, simplifying the booking process by suggesting likely destinations before users even begin typing.

3. Emotional Personalization

Connecting with customers on a deeper level:

Emotional Approaches

• Tone Adaptation: Adjusting communication style to match preferences
• Value Alignment: Highlighting shared beliefs and principles
• Life Event Recognition: Acknowledging significant personal milestones
• Empathetic Response: Showing understanding during difficult situations
• Celebration Participation: Joining in customer achievements and special moments

Implementation Example: Spotify creates emotionally personalized experiences through curated playlists that match moods and moments, with features like "Discover Weekly" and "Daily Mix" that understand not just what music you like, but how different songs make you feel.

4. Behavioral Personalization

Responding to specific customer actions:

Behavioral Triggers

• Browse Abandonment: Re-engaging customers who viewed products
• Cart Recovery: Encouraging completion of abandoned purchases
• Post-Purchase Engagement: Following up after transactions
• Inactivity Response: Re-activating dormant customers
• Milestone Recognition: Acknowledging loyalty and engagement thresholds

Implementation Example: Sephora's personalization engine tracks products browsed, time spent viewing items, and purchase history to create highly targeted follow-up communications with relevant product recommendations and educational content, achieving email open rates 2-3x higher than industry averages.

Personalization Technology Ecosystem

The tools and platforms enabling advanced personalization:

1. Customer Data Platforms (CDPs)

Unifying customer information:

CDP Capabilities

• Identity Resolution: Creating single customer views across channels
• Data Unification: Combining information from multiple sources
• Profile Management: Maintaining comprehensive customer records
• Segmentation Tools: Creating dynamic customer groupings
• Activation Interfaces: Connecting data to execution systems

Key Platforms: Segment, Tealium, Adobe Experience Platform, and Treasure Data are leading the CDP space with increasingly sophisticated capabilities for managing customer data.

2. Decisioning Engines

Determining optimal experiences:

Decisioning Capabilities

• Real-Time Processing: Making instant personalization decisions
• Multi-Variate Testing: Comparing multiple experience variations
• Machine Learning Integration: Improving decisions over time
• Business Rule Management: Combining AI with explicit guidelines
• Optimization Algorithms: Maximizing specific business outcomes

Key Platforms: Evergage, Dynamic Yield, Monetate, and Adobe Target provide sophisticated decisioning capabilities that determine the best content, offers, and experiences for each customer.

3. Delivery Technologies

Executing personalized experiences:

Delivery Mechanisms

• Web Personalization: Customizing site content and functionality
• Email Personalization: Creating individualized messages at scale
• Mobile Experience Optimization: Tailoring app interactions
• Ad Customization: Delivering relevant advertising content
• In-Store Technology: Connecting digital insights to physical experiences

Key Platforms: Optimizely, Salesforce Marketing Cloud, Braze, and Movable Ink enable the delivery of personalized content across channels, with increasingly sophisticated capabilities for real-time adaptation.

4. Analytics and Optimization

Measuring and improving personalization:

Analytics Approaches

• Attribution Modeling: Understanding personalization impact
• A/B and Multivariate Testing: Comparing experience variations
• Customer Journey Analytics: Tracking cross-channel experiences
• Predictive Analytics: Forecasting future behaviors and outcomes
• ROI Measurement: Quantifying business impact of personalization

Key Platforms: Google Analytics 360, Adobe Analytics, Amplitude, and Mixpanel provide the measurement capabilities needed to understand personalization effectiveness and identify improvement opportunities.

Implementation Approaches

Strategies for successful personalization initiatives:

1. Data Foundation Development

Building the information base for personalization:

Data Strategy Elements

• First-Party Data Focus: Prioritizing owned customer information
• Progressive Profiling: Gradually building customer understanding
• Data Quality Processes: Ensuring accurate, complete information
• Identity Resolution: Connecting data across touchpoints
• Governance Framework: Managing data ethically and legally

Best Practice: Start with a comprehensive audit of existing customer data, identifying gaps and quality issues before implementing advanced personalization to avoid making decisions based on flawed information.

2. Use Case Prioritization

Focusing on high-value opportunities:

Prioritization Criteria

• Business Impact: Revenue and profit potential
• Implementation Complexity: Technical and operational requirements
• Data Readiness: Availability of necessary information
• Customer Value: Benefit to the customer experience
• Organizational Alignment: Fit with strategic priorities

Best Practice: Create a personalization roadmap that balances quick wins with strategic initiatives, starting with use cases that have high business impact and relatively low implementation complexity to build momentum.

3. Cross-Functional Collaboration

Aligning teams for personalization success:

Collaboration Approaches

• Personalization Centers of Excellence: Dedicated cross-functional teams
• Shared Metrics: Aligned goals across departments
• Joint Ownership: Distributed responsibility for outcomes
• Skill Development: Building capabilities across the organization
• Executive Sponsorship: Leadership support for initiatives

Best Practice: Establish clear roles and responsibilities across marketing, IT, analytics, and customer service teams, with shared KPIs that encourage collaboration rather than departmental optimization.

4. Testing and Optimization

Continuously improving personalization effectiveness:

Testing Approaches

• A/B Testing: Comparing specific experience variations
• Multivariate Testing: Evaluating multiple variables simultaneously
• Bandit Testing: Dynamically allocating traffic to better-performing experiences
• Holdout Testing: Measuring impact against non-personalized control groups
• Sequential Optimization: Iteratively improving based on results

Best Practice: Implement a structured testing program that moves beyond simple A/B tests to understand the complex interactions between different personalization elements and their combined impact on customer behavior.

Industry-Specific Personalization Applications

How different sectors are leveraging personalization:

1. Retail and E-commerce

Transforming shopping experiences:

• Product Recommendations: Suggesting relevant items based on preferences
• Personalized Search: Tailoring results to individual interests
• Dynamic Pricing: Optimizing offers based on customer value
• Customized Navigation: Adapting site structure to user behavior
• Individualized Promotions: Targeting offers to specific needs

Example: The North Face created a digital personal shopper that asks customers questions about their preferences and planned activities, then combines this information with local weather data to recommend appropriate products, increasing conversion rates by 35%.

2. Financial Services

Enhancing banking and investment experiences:

• Financial Health Insights: Providing personalized financial guidance
• Life Stage Adaptation: Adjusting services to changing needs
• Risk Profile Alignment: Matching products to comfort level
• Spending Pattern Recognition: Offering relevant services based on habits
• Goal-Based Recommendations: Supporting specific financial objectives

Example: Bank of America's virtual assistant Erica provides personalized financial guidance based on individual spending patterns, account balances, and upcoming bills, helping customers make better financial decisions and increasing mobile banking engagement by 19%.

3. Travel and Hospitality

Improving traveler experiences:

• Trip Recommendations: Suggesting destinations based on preferences
• Personalized Itineraries: Customizing travel schedules
• Loyalty Recognition: Acknowledging and rewarding repeat customers
• In-Stay Customization: Adapting services during visits
• Post-Trip Engagement: Maintaining connection after travel

Example: Hilton uses its Honors app to deliver highly personalized hotel experiences, allowing guests to select specific rooms, customize amenities, and use their phones as room keys, resulting in a 20% increase in customer satisfaction scores.

4. Healthcare and Wellness

Tailoring health experiences:

• Care Plan Personalization: Customizing treatment approaches
• Preventive Health Guidance: Providing individualized wellness advice
• Communication Preferences: Adapting outreach to patient needs
• Medication Management: Supporting adherence based on habits
• Health Journey Recognition: Acknowledging progress and milestones

Example: Kaiser Permanente personalizes digital health experiences by combining medical records, appointment history, and communication preferences to deliver relevant content, reminders, and guidance, improving patient engagement and health outcomes.

Overcoming Personalization Challenges

Addressing common implementation obstacles:

1. Data Silos and Quality Issues

Managing information challenges:

• Challenge: Fragmented customer data across systems
• Solutions:
  • Implement customer data platforms to unify information
  • Establish data quality processes and governance
  • Create unique customer identifiers across systems
  • Develop progressive profiling to fill information gaps
  • Prioritize first-party data collection

Example: Walgreens overcame data silos by implementing a unified customer data platform that connects in-store purchases, online activity, and loyalty program information, creating a comprehensive view that powers personalization across channels.

2. Privacy and Regulatory Compliance

Navigating legal and ethical considerations:

• Challenge: Balancing personalization with privacy requirements
• Solutions:
  • Implement transparent data collection practices
  • Provide clear value exchange for information sharing
  • Develop granular consent management
  • Create ethical guidelines for personalization
  • Design for privacy from the beginning

Example: Microsoft has developed a "privacy by design" approach to personalization that gives users granular control over their data while still delivering personalized experiences, building trust while complying with regulations like GDPR.

3. Organizational Alignment

Overcoming internal barriers:

• Challenge: Siloed departments and competing priorities
• Solutions:
  • Create cross-functional personalization teams
  • Establish shared metrics and incentives
  • Develop executive sponsorship
  • Implement change management programs
  • Build internal education about personalization value

Example: Coca-Cola established a digital transformation office that brings together marketing, IT, and analytics teams to collaborate on personalization initiatives, breaking down traditional silos and accelerating implementation.

4. Technology Integration

Connecting disparate systems:

• Challenge: Complex martech ecosystems with limited integration
• Solutions:
  • Develop API-first integration strategies
  • Implement customer data platforms as central hubs
  • Create standardized data models across systems
  • Prioritize vendors with strong integration capabilities
  • Build middleware solutions where necessary

Example: Home Depot integrated their e-commerce platform, in-store systems, mobile app, and marketing technologies to create a unified view of customers and inventory, enabling personalized experiences across all channels.

Measuring Personalization Success

Approaches to evaluating effectiveness:

1. Customer Impact Metrics

Assessing experience improvements:

• Engagement Metrics: Interaction depth and frequency
• Satisfaction Scores: NPS, CSAT, and other feedback measures
• Retention Indicators: Repeat purchase and churn reduction
• Loyalty Measures: Program participation and tier advancement
• Share of Wallet: Percentage of category spending captured

Best Practice: Implement voice of customer programs that specifically measure the perceived value of personalization efforts, connecting objective metrics with subjective customer feedback.

2. Business Performance Metrics

Measuring commercial outcomes:

• Conversion Rate Impact: Improvement in purchase completion
• Average Order Value: Increase in transaction size
• Customer Lifetime Value: Long-term revenue enhancement
• Marketing Efficiency: Improved return on marketing investment
• Revenue Acceleration: Overall business growth contribution

Best Practice: Use holdout testing to create control groups that don't receive personalized experiences, allowing for accurate measurement of personalization's incremental impact on business metrics.

3. Operational Efficiency Metrics

Evaluating internal improvements:

• Time to Market: Speed of implementing new experiences
• Resource Utilization: Efficiency of personalization efforts
• Automation Benefits: Reduction in manual processes
• Scale Achievements: Volume of personalized interactions
• Cost per Personalized Experience: Efficiency of delivery

Best Practice: Track both the costs and benefits of personalization initiatives to calculate true ROI, including technology investments, content creation expenses, and team resources.

The Future of Personalization: 2019 and Beyond

Emerging trends that will shape personalization evolution:

1. Hyper-Personalization

Moving to even more individualized experiences:

• Real-Time Adaptation: Instantly responding to behavioral signals
• Micro-Moment Optimization: Personalizing brief interaction opportunities
• Psychological Personalization: Adapting to individual decision-making styles
• Continuous Learning: Experiences that improve with each interaction
• Predictive Experience Design: Anticipating needs before they're expressed

Strategic Implication: Organizations should begin developing the data infrastructure and decisioning capabilities needed for real-time personalization, even if current execution focuses on simpler approaches.

2. Voice and Conversational Personalization

Adapting to new interaction models:

• Voice Assistant Integration: Personalizing voice-based experiences
• Conversational Commerce: Individualized shopping through dialogue
• Tone and Language Adaptation: Matching communication style to preferences
• Voice Recognition: Identifying customers through speech patterns
• Multimodal Experiences: Combining voice with visual personalization

Strategic Implication: Brands should begin experimenting with conversational interfaces that can deliver personalized experiences through natural language, preparing for the growing importance of voice-based interactions.

3. Augmented and Virtual Reality Personalization

Creating immersive individualized experiences:

• Virtual Try-On: Personalized product visualization
• Customized Immersive Environments: Tailored virtual spaces
• AR Information Layers: Individualized augmented information
• Spatial Computing Personalization: Adapting 3D experiences to preferences
• Mixed Reality Commerce: Personalized shopping in blended environments

Strategic Implication: Organizations in industries where visual products are important should begin exploring how AR and VR can deliver personalized experiences that help customers visualize products in their own context.

4. Ethical and Transparent Personalization

Building trust through responsible practices:

• Explainable Personalization: Helping customers understand recommendations
• Preference Control: Giving users influence over their experiences
• Bias Prevention: Ensuring fair and inclusive personalization
• Value-Based Design: Creating clear benefits for customers
• Transparency Mechanisms: Making personalization visible and understandable

Strategic Implication: As personalization becomes more sophisticated, organizations should develop ethical frameworks that ensure these capabilities are used responsibly and transparently to maintain customer trust.

Conclusion: Personalization as Competitive Necessity

As we progress through 2019, personalization has evolved from a nice-to-have marketing tactic to a fundamental business strategy that impacts the entire customer experience. The most successful organizations will be those that:

1. Build robust data foundations that unify customer information across touchpoints
2. Implement intelligent decisioning that goes beyond rules to true learning
3. Create seamless experiences that recognize customers across channels
4. Balance personalization with privacy through transparent, value-driven approaches
5. Measure and optimize based on both customer and business outcomes

By approaching personalization as a comprehensive experience strategy rather than merely a marketing technique, organizations can create meaningful connections with customers that drive loyalty, advocacy, and sustainable growth.

Remember that personalization is not a destination but a journey of continuous evolution—one that requires ongoing adaptation, learning, and refinement as both technologies and customer expectations change.

This article was written by Nguyen Tuan Si, a customer experience strategist with experience helping organizations develop and implement effective personalization strategies across various industries.