Deloitte and Meta study reveals critical gaps in marketing personalization

Deloitte Digital released comprehensive research findings on August 6, 2025, revealing significant insights into consumer expectations for personalized marketing experiences and organizational challenges in implementation. According to the study commissioned by Meta, 80% of US consumers are more likely to make a purchase when brands offer personalized experiences, yet many organizations fail to deliver on these expectations due to siloed data management and inadequate cross-functional collaboration.

The research, titled "The Path to Personalization: Strategies for Marketers," examines three critical areas: consumer trust in personalization, technology adoption by leading brands, and the imperative for cross-functional collaboration in data strategy. Meta VP Derya Matras emphasized the strategic importance of personalization in a statement accompanying the release, noting that brands excelling in personalization within their industries often experience more frequent engagement and enhanced loyalty over time.

Consumer demand for personalized experiences has reached unprecedented levels, with 78% of US consumers wanting personalization that saves them money, according to the study. Additionally, 73% of EU consumers responded positively to seeing advertisements with useful information for products or services they intended to purchase. The research highlights a growing expectation gap where consumers increasingly demand tailored interactions but many brands struggle to meet these demands across convenience, customer service responsiveness, privacy, and transparency.

The compound annual growth rate of the AI market is projected to increase 42.2% from 2020 to 2027, reaching $733.7 billion by 2027, according to the research. This surge in investment is driven by recognition that artificial intelligence, machine learning and data analytics can significantly enhance personalization capabilities for brands. However, the study reveals that personalization maturity levels vary significantly across industry, organization and geolocation.

Deloitte's analysis identifies three distinct approaches to personalization implementation. Customer-based personalization focuses on delivering highly specific recommendations by tailoring experiences to individual user data and interactions. The foundation requires transparency, data security and privacy to foster trust while creating personalized journeys. Cohort-based personalization groups customers into segments based on shared interests or behaviors, allowing brands to craft targeted messaging for distinct groups. Aggregated data personalization leverages insights from large datasets to identify broader trends and patterns that inform strategies enhancing overall customer experience.

Technical implementation challenges represent a significant barrier to successful personalization programs. According to the research, organizations must evolve their data strategy to include collection, management and utilization processes for AI purposes. Ensuring data privacy and security, including compliance with relevant regulations such as GDPR and CCPA, is paramount. The study emphasizes that selecting appropriate technology stacks, deciding on deployment environments, and planning integration of AI systems with existing IT infrastructure require comprehensive strategic planning.

Cross-functional collaboration emerges as a critical success factor in the research findings. While marketing teams often spearhead personalization initiatives, successful implementation and execution requires engagement across multiple organizational groups. The study identifies specific roles for executive sponsors, operations teams, marketing departments, data and technology teams, sales and product divisions, and risk, compliance and security functions in creating effective personalization programs.

Marketers offering greater personalization see a 16-percentage-point increase in conversion rates compared to those with low personalization efforts, according to the study. Customer Lifetime Value can increase by 20 percentage points when brands successfully implement comprehensive personalization strategies. These metrics demonstrate tangible business impact when organizations address consumer concerns and provide tailored interactions effectively.

The research reveals significant differences in organizational maturity across personalization capabilities. Leading organizations demonstrate executive buy-in and cross-functional collaboration across all departments, while less mature organizations typically confine personalization efforts to marketing silos. Advanced implementations include unified 360-degree customer profiles, predictive modeling, recommendation engines, and real-time personalization capabilities powered by AI and machine learning technologies.

Data management practices represent another critical differentiator identified in the study. Organizations with low personalization maturity typically maintain siloed first-party data with limited integration capabilities. More advanced implementations feature single customer views enriched with third-party data, while champion-level organizations achieve unified, comprehensive customer profiles spanning all touchpoints and interactions.

Strategic integration of AI and Generative AI requires careful consideration of multiple factors, according to the research. Organizations must ensure AI initiatives align with strategic goals by defining clear objectives for implementation. This involves identifying specific business problems or opportunities that AI will address, such as improving operational efficiency, enhancing customer experiences, or driving innovation. Establishing key performance indicators and metrics to measure success and impact is essential for tracking progress and ensuring AI efforts deliver tangible value.

The study emphasizes that successful personalization requires transparency in data collection practices, user-friendly settings for data controls, and regular communication about data usage. Building consumer trust through clear privacy policies, easy-to-understand permissions, and regular updates on data practices enables organizations to leverage customer insights effectively while maintaining transparency and respecting privacy preferences.

For organizations beginning personalization initiatives, the research recommends four essential steps. Creating a compelling business case requires defining clear objectives, emphasizing competitive advantages, highlighting market trends, and providing detailed return on investment analysis. Analyzing organizational maturity involves evaluating current capabilities across people, processes, and technology dimensions. Developing strategic action plans should outline progress dependencies and establish clear implementation roadmaps. Collaborating with AI committees ensures technical expertise, risk management, and strategic alignment throughout implementation processes.

This research demonstrates the growing importance of personalization as a competitive strategy while highlighting implementation challenges that organizations must address. As consumer expectations continue to evolve and technology capabilities advance, brands that successfully integrate cross-functional collaboration with robust data management practices will achieve sustainable advantages in customer engagement and business outcomes.

Timeline

• August 6, 2025: Deloitte and Meta release comprehensive personalization study revealing 80% of consumers expect personalized experiences
• May 21-22, 2025: Google Marketing Live 2025 showcases AI-powered advertising innovations including enhanced personalization capabilities
• January 1, 2025: Perplexity AI founder outlines visionwhere AI agents replace human attention in advertising models
• December 8, 2024: Perplexity AI reveals implementation of sponsored questions and side media advertising formats
• November 18, 2024: Perplexity launches AI-powered shopping assistant with one-click checkout feature for personalized commerce
• October 19, 2024: Treasure Data unveils AI framework for Customer Data Platforms enabling personalized marketing campaigns
• July 2025: McKinsey analysis reveals 13 frontier technologies driving transformation in marketing personalization

PPC Land explains

Personalization: The practice of tailoring products, services and experiences to meet individual customer preferences and behaviors. In digital marketing, personalization encompasses everything from customized product recommendations to dynamic website content that adapts based on user history. Modern personalization leverages artificial intelligence and machine learning to analyze vast amounts of customer data, enabling brands to deliver relevant experiences at scale while building stronger customer relationships and driving business outcomes.

Cross-functional collaboration: The coordinated effort between different departments within an organization to achieve shared objectives in personalization initiatives. This approach breaks down traditional silos between marketing, IT, sales, customer service, and data teams to create unified strategies. Successful cross-functional collaboration requires clear role definitions, shared goals, open communication channels, and executive sponsorship to ensure all departments contribute their unique expertise toward comprehensive personalization programs.

Artificial intelligence: Advanced computer systems capable of performing tasks that typically require human intelligence, including learning, reasoning, and decision-making. In marketing personalization, AI analyzes customer behavior patterns, predicts preferences, and automates content delivery across multiple touchpoints. The technology enables real-time optimization of campaigns, dynamic pricing strategies, and predictive analytics that help brands anticipate customer needs before they are explicitly expressed.

Data strategy: A comprehensive framework for collecting, managing, storing, and utilizing customer information to drive business decisions and personalization efforts. Effective data strategies encompass first-party data collection from direct customer interactions, third-party data integration from external sources, and robust governance policies ensuring privacy compliance. Organizations must balance data accessibility for personalization with security requirements and regulatory obligations such as GDPR and CCPA.

Customer Lifetime Value: A metric measuring the total revenue a business can expect from a single customer throughout their entire relationship with the brand. In personalization contexts, CLV helps organizations prioritize high-value customers for targeted experiences and measure the long-term impact of tailored marketing investments. According to the research, effective personalization strategies can increase CLV by 20 percentage points through improved customer satisfaction and retention rates.

Machine learning: A subset of artificial intelligence that enables computer systems to automatically learn and improve from experience without explicit programming. In marketing applications, machine learning algorithms identify patterns in customer behavior, predict future actions, and optimize campaign performance in real-time. These systems continuously refine their accuracy as more data becomes available, making personalization efforts increasingly sophisticated and effective over time.

Customer Data Platform: A unified database that combines customer information from multiple sources to create comprehensive, real-time customer profiles accessible across an organization. CDPs integrate data from websites, mobile apps, email campaigns, social media, and offline interactions to provide a single view of each customer. This consolidated approach enables consistent personalization across all touchpoints while maintaining data quality and compliance with privacy regulations.

First-party data: Information collected directly from customers through a company's own channels, including website interactions, purchase history, email subscriptions, and customer service contacts. This data type is considered the most valuable for personalization because it represents actual customer behavior and preferences rather than inferred characteristics. First-party data also provides greater privacy compliance and control compared to third-party alternatives.

Conversion rates: The percentage of users who complete a desired action, such as making a purchase, signing up for a newsletter, or downloading content. In personalization contexts, conversion rates measure the effectiveness of tailored experiences in driving customer actions. The research demonstrates that marketers offering greater personalization achieve 16-percentage-point increases in conversion rates compared to those with limited personalization efforts.

AI Committee: A cross-functional governance body comprising data scientists, AI specialists, risk and compliance professionals, and executive leaders responsible for overseeing artificial intelligence adoption within an organization. These committees establish AI strategies, manage implementation risks, ensure ethical AI use, and align technological capabilities with business objectives. For personalization initiatives, AI committees provide technical expertise and strategic guidance while maintaining security and compliance standards.

Key Terms and Definitions

Personalization: The practice of tailoring products, services and experiences to meet individual customer preferences and behaviors. In digital marketing, personalization encompasses everything from customized product recommendations to dynamic website content that adapts based on user history. Modern personalization leverages artificial intelligence and machine learning to analyze vast amounts of customer data, enabling brands to deliver relevant experiences at scale while building stronger customer relationships and driving business outcomes.

Cross-functional collaboration: The coordinated effort between different departments within an organization to achieve shared objectives in personalization initiatives. This approach breaks down traditional silos between marketing, IT, sales, customer service, and data teams to create unified strategies. Successful cross-functional collaboration requires clear role definitions, shared goals, open communication channels, and executive sponsorship to ensure all departments contribute their unique expertise toward comprehensive personalization programs.

Artificial intelligence: Advanced computer systems capable of performing tasks that typically require human intelligence, including learning, reasoning, and decision-making. In marketing personalization, AI analyzes customer behavior patterns, predicts preferences, and automates content delivery across multiple touchpoints. The technology enables real-time optimization of campaigns, dynamic pricing strategies, and predictive analytics that help brands anticipate customer needs before they are explicitly expressed.

Data strategy: A comprehensive framework for collecting, managing, storing, and utilizing customer information to drive business decisions and personalization efforts. Effective data strategies encompass first-party data collection from direct customer interactions, third-party data integration from external sources, and robust governance policies ensuring privacy compliance. Organizations must balance data accessibility for personalization with security requirements and regulatory obligations such as GDPR and CCPA.

Customer Lifetime Value: A metric measuring the total revenue a business can expect from a single customer throughout their entire relationship with the brand. In personalization contexts, CLV helps organizations prioritize high-value customers for targeted experiences and measure the long-term impact of tailored marketing investments. According to the research, effective personalization strategies can increase CLV by 20 percentage points through improved customer satisfaction and retention rates.

Machine learning: A subset of artificial intelligence that enables computer systems to automatically learn and improve from experience without explicit programming. In marketing applications, machine learning algorithms identify patterns in customer behavior, predict future actions, and optimize campaign performance in real-time. These systems continuously refine their accuracy as more data becomes available, making personalization efforts increasingly sophisticated and effective over time.

Customer Data Platform: A unified database that combines customer information from multiple sources to create comprehensive, real-time customer profiles accessible across an organization. CDPs integrate data from websites, mobile apps, email campaigns, social media, and offline interactions to provide a single view of each customer. This consolidated approach enables consistent personalization across all touchpoints while maintaining data quality and compliance with privacy regulations.

First-party data: Information collected directly from customers through a company's own channels, including website interactions, purchase history, email subscriptions, and customer service contacts. This data type is considered the most valuable for personalization because it represents actual customer behavior and preferences rather than inferred characteristics. First-party data also provides greater privacy compliance and control compared to third-party alternatives.

Conversion rates: The percentage of users who complete a desired action, such as making a purchase, signing up for a newsletter, or downloading content. In personalization contexts, conversion rates measure the effectiveness of tailored experiences in driving customer actions. The research demonstrates that marketers offering greater personalization achieve 16-percentage-point increases in conversion rates compared to those with limited personalization efforts.

AI Committee: A cross-functional governance body comprising data scientists, AI specialists, risk and compliance professionals, and executive leaders responsible for overseeing artificial intelligence adoption within an organization. These committees establish AI strategies, manage implementation risks, ensure ethical AI use, and align technological capabilities with business objectives. For personalization initiatives, AI committees provide technical expertise and strategic guidance while maintaining security and compliance standards.

Summary

Who: Deloitte Digital conducted research commissioned by Meta examining marketing personalization strategies across organizations and consumer expectations.

What: A comprehensive study revealing that 80% of US consumers are more likely to purchase when brands offer personalized experiences, while identifying critical gaps in organizational implementation including siloed data management and inadequate cross-functional collaboration.

When: The research findings were released on August 6, 2025, as part of Meta's broader initiative to understand personalization effectiveness in digital marketing.

Where: The study examined US and EU markets, with specific focus on organizational capabilities across various industries and geographic regions implementing personalization strategies.

Why: Rising consumer expectations for tailored experiences, combined with AI market growth projected to reach $733.7 billion by 2027, necessitate comprehensive understanding of personalization implementation challenges and opportunities for competitive advantage.