The Personalization Revenue Gap: Why 50% of Brands Leave Money on the Table (And How Leaders Capture 40% More Revenue)

Here's what actually happens when you ask customers about their experiences with personalization: only 43% recognize their interactions as personalized. Now ask the brands delivering those experiences what percentage they've personalized, and they'll tell you 61%. This gap isn't just a perception problem; it's a revenue problem costing the industry billions.

I've spent the past decade optimizing personalization campaigns, and I can tell you that the difference between brands that excel and those that struggle isn't usually about technology or budget. It's about understanding what customers actually value from personalization and building systems that consistently deliver it. According to recent research from Deloitte, companies that get personalization right generate 40% more revenue than their competitors. More telling: their customers spend 37% more per transaction and show 78% higher repeat purchase rates.

The research is clear. The business case is overwhelming. Yet according to a Mastercard study of enterprise brands, 50% lack dedicated resources to scale personalization effectively. They're leaving massive revenue opportunities on the table while their competitors pull ahead. This article breaks down what separates personalization leaders from laggards, using data from seven major research studies and real performance metrics from companies that have cracked the code.

The Reality Check: Customers and Brands See Different Worlds

Let's start with an uncomfortable truth. When BCG surveyed over 23,000 consumers globally about their personalization experiences, two-thirds reported having at least one interaction that felt inaccurate or invasive. The consequences? Customers unsubscribed, disengaged, or simply never returned. Your sophisticated personalization engine means nothing if customers perceive it as creepy rather than helpful.

The perception gap runs deeper than isolated bad experiences. Brands report personalizing 61% of customer interactions, yet customers only recognize 43% as personalized. This disconnect reveals a fundamental misunderstanding of what personalization means to customers versus what marketing teams think they're delivering.

Consider the typical approach: a customer browses hiking boots on Tuesday, and every interaction for the next month features hiking gear. The brand congratulates itself on personalization. The customer feels stalked and annoyed. Real personalization requires understanding context, timing, and customer intent beyond simple behavioral tracking.

Research from multiple sources confirms that 71% of consumers expect personalized interactions, while 76% experience frustration when brands fail to deliver. The expectations are high, but so are the rewards for getting it right. Companies delivering genuinely valuable personalized experiences see measurably better business outcomes across every metric that matters.

What Customers Actually Want: Show Them the Money

Here's where most personalization strategies go wrong: they focus on what's technically possible rather than what customers actually value. According to research analyzing consumer priorities, 78% want personalization that helps them save money. When asked about purchase influences, 84% said personalized discount offers or special bundles had medium to high impact on their decisions.

This isn't subtle. Customers are telling us explicitly what they want from personalization: tangible financial benefits. Yet many brands remain focused on recommendation engines and content customization without connecting these capabilities to customer outcomes that matter.

The three reasons customers appreciate personalization, according to BCG's comprehensive research, break down into clear priorities. Value comes first—customers consistently cite finding the best price as their primary reason for appreciating personalized experiences. Restaurants like McDonald's and Sweetgreen, grocery chains including Woolworth's in Australia and Sobey's in Canada, mass retailers like Target, and beauty retailers including Sephora, Boots, and Ulta Beauty have successfully launched personalized offer programs that deliver clear value to customers.

The business case for value-focused personalization is compelling. BCG's research found that personalized offers consistently generate three times higher return on investment compared to mass promotions. When you shift investment from broad discounting to targeted offers based on individual customer data, you protect margins while increasing conversion rates.

Convenience represents the second pillar of valuable personalization. Amazon Prime pioneered frictionless one-click shopping by leveraging existing customer data. Today, companies across industries apply similar approaches to make every interaction seamless. Voya, a health insurance and asset management provider, launched myVoyage, allowing customers to link financial and health information for tailored, timely recommendations about paying down student debt or optimizing retirement benefits.

The impact of convenience-focused personalization shows up in conversion metrics. According to multiple research sources, personalization that reduces customer effort can increase conversion and cross-sell rates by 30% to 40%. When you eliminate friction at key decision points, customers complete more transactions and add more items to each order.

Enjoyment, the third pillar, often gets overlooked in ROI-focused discussions, but it drives long-term customer lifetime value. Marriott recognized that travel is inherently emotional and relaunched its personalized Ambassador service to deliver "surprise and delight" moments. Starbucks brought back baristas writing customer names on cups, restoring a customized touch to the morning coffee routine. Macy's is testing Red Carpet, a membership service offering dedicated concierges, priority stylist bookings, and exclusive benefits.

While the immediate business impact of enjoyment-focused personalization is harder to measure, it compounds over time through improved brand health and customer lifetime value metrics. According to BCG, brands should balance investments that drive immediate returns (personalized offers and recommendations) with subtler touches that enhance enjoyment, recognize loyalty, and help customers discover products when needed.

The Performance Gap Between Leaders and Laggards

Research from Mastercard analyzing enterprise brands reveals stark performance differences between companies with mature personalization capabilities and those still building foundational systems. The gap isn't incremental; it's transformational across every meaningful business metric.

Personalization leaders were 48% more likely to exceed revenue goals in 2023, outperforming targets by 9.9% on average. They reported 71% higher improvements in customer loyalty and 67% higher increases in purchase frequency compared to brands with low personalization maturity. These aren't marginal gains; they represent fundamental differences in customer behavior and business outcomes.

Breaking down the specific metrics, leaders showed 54% improvement in conversion rates versus 38% for low-maturity brands. Customer engagement increased 61% for leaders compared to 37% for laggards. Customer satisfaction improved 57% versus 29%, average order value increased 61% versus 44%, and lifetime value grew 54% compared to 34%.

The critical differentiator isn't technology or budget alone. According to the research, only 50% of brands have dedicated personalization support, while the other half relies on ad hoc resources or operates without dedicated teams entirely. Without centralized expertise, personalization efforts fragment across separate functions like product management, merchandising, and IT, making it impossible to drive consistent, effective implementation.

Budget allocation patterns reveal the commitment gap clearly. Personalization leaders devote 66% of their total marketing budget to personalization initiatives, compared to just 38% among low-maturity brands. That 28 percentage-point difference has widened from 18 percentage points in earlier research, suggesting leaders are doubling down on personalization while laggards fall further behind.

Perhaps most telling: leaders plan to increase personalization spending by 46% in 2024, while low-maturity companies expect only 16% increases. This divergence signals that companies experiencing strong returns from personalization are accelerating investment, creating a widening performance gap that will be increasingly difficult for laggards to close.

Case Studies: What Works in Practice

Let's examine specific companies that have achieved measurable results from personalization, using exact metrics from published research. These aren't marketing claims; they're documented performance outcomes from real implementations.

Yves Rocher, a cosmetics and beauty brand, implemented Bloomreach's personalization platform and achieved extraordinary results. The company delivered product recommendations within 0.1 seconds of customer action, essentially eliminating the latency that typically creates disjointed experiences. This real-time capability drove a 17.5-fold increase in clicks on recommended items and an 11-fold increase in purchase rates. The speed and relevance of personalization transformed browser behavior into buying behavior at scale.

Netflix provides perhaps the most well-known personalization success story, with 75% of content watched coming from its recommendation system. In 2024, Netflix generated $39 billion in revenue, representing 15.7% year-over-year growth. The company maintains over 1,300 recommendation clusters built on viewer preference data, creating hyper-personalized content discovery experiences that keep subscribers engaged month after month.

Sephora's implementation of AI-powered personalization demonstrates the long-term compounding value of getting customer experience right. The beauty retailer launched Virtual Artist in 2016, enabling customers to virtually try products before purchasing. This AI and augmented reality integration made online shopping more compelling and reduced purchase uncertainty. The results speak for themselves: Sephora's e-commerce net sales grew from $580 million in 2016 to over $3 billion in 2022, more than quintupling in six years.

Amazon's recommendation engine, processing 35% of total e-commerce sales through personalized product suggestions, sets the industry standard. According to McKinsey research, the sophistication of Amazon's machine learning algorithms analyzing browsing and purchase history creates such relevant recommendations that customers regularly discover and buy products they weren't actively seeking.

Jenson USA, an online bike retailer, focused personalization on understanding differences between customer segments. By implementing sophisticated behavioral segmentation through Bloomreach that updated based on in-session user behavior, Jenson achieved an 8.5% improvement in revenue per visitor overall and a 26% increase in revenue per visitor on mobile devices. The mobile results particularly demonstrate the value of personalization in environments where screen space limitations make relevance essential.

Alibaba deployed AI-powered personalization across its Tmall and Taobao e-commerce platforms, resulting in a 20% sales increase during the fourth quarter of 2020. The scale of Alibaba's implementation, serving hundreds of millions of customers across diverse product categories, demonstrates that personalization effectiveness doesn't diminish as you grow; it becomes more powerful with more data and more sophisticated algorithms.

RoadLords, a truck navigation app company, used in-app messaging for personalized content and net promoter score campaigns, engaging 11% of its user base. While this example shows smaller absolute numbers, it demonstrates that personalization works across different business models and customer bases, not just for retail giants.

Sofology, a furniture retailer, connected offline and online commerce data to personalize the purchasing experience. By integrating data from store visits and call center interactions with digital behavior, Sofology created comprehensive customer profiles that informed personalization across all touchpoints. This omnichannel approach recognizes that customer journeys rarely follow linear digital paths, especially for considered purchases like furniture.

Generational Dynamics in Personalization Expectations

Understanding how different generations perceive and respond to personalization helps allocate resources effectively. Research from Deloitte and Mastercard reveals significant variations in stated preferences versus actual behavior across age groups.

Among Gen Z and millennial consumers, approximately 50% consider personalized experiences very or extremely important. This preference has intensified since 2022 as these digital-native generations gain purchasing power and influence market dynamics. Their expectations for seamless, relevant experiences stem from growing up with recommendation algorithms and customized content feeds.

Gen X consumers show more moderate enthusiasm, with 25% rating personalized experiences as very or extremely important. Baby boomers express the least interest, with only 16% considering personalization highly important. However, these lower percentages don't mean older consumers are immune to personalization's influence.

The key insight: all generations respond positively to personalization that delivers clear value, even if they don't articulate "personalization" as a priority. Gen Xers and boomers were more influenced by one-stop online stores with multiple brands, while Gen Z responded strongly to social media and influencer recommendations. Millennials showed highest responsiveness to search engines, social media, and customer reviews.

Channel preferences vary significantly by generation as well. More than one-third of Gen Z consumers prefer receiving personalized marketing in physical stores, while nearly a third of baby boomers named direct mail as their top preference. These differences demand segmented approaches rather than one-size-fits-all personalization strategies.

For customer support, younger consumers overwhelmingly prefer mobile apps and web chat—channels they control and can access on their schedule. Gen Z and millennials are nearly twice as likely as older consumers to want customer support through social media interactions. In contrast, phone calls with service representatives and interactions with sales associates (channels where timing isn't under customer control) were preferred by fewer than a third of consumers across all age groups.

The strategic implication: personalization strategies must account for generational differences in both stated preferences and actual behavior patterns. Younger consumers expect explicit personalization and sophisticated recommendation engines. Older consumers respond to personalized value propositions and convenience but may not think of these experiences as "personalization" per se.

The Technology and Data Foundation Leaders Build

Let's cut through the vendor marketing and examine what actually matters in personalization technology infrastructure. According to research from the CDP Institute and Gartner, the foundation starts with customer data platforms that unify information from multiple sources into single customer views.

Only 55% of surveyed brands have implemented CDPs, despite these systems being widely considered essential for personalization at scale. Without unified customer data, personalization efforts remain limited to individual channel optimizations that can't deliver consistent experiences as customers move across touchpoints.

The personalization engine market grew 21.1% in 2023 to reach $908 million, with projected compound annual growth rate of 23.8% through 2027. This expansion reflects both new market entrants and existing players significantly increasing their investments. According to Gartner's analysis, 51% of chief marketing officers plan to increase digital commerce personalization investment over the next 12 months.

Three data types fuel effective personalization: zero-party data (information customers directly share), first-party data (compiled from transactions and engagement across channels), and third-party data (enrichment from external sources). Research from the CDP Institute found that when one company tested different consent language formats, opt-in rates increased 20%—demonstrating that how you ask for data significantly impacts what customers share.

However, third-party data quality varies dramatically. Testing by the CDP Institute found that gender data from one commonly used third-party source was accurate only 60% of the time. This inaccuracy can undermine personalization effectiveness, making customers feel misunderstood rather than seen.

Identity resolution—accurately connecting customer interactions across devices, channels, and sessions—represents one of the most challenging technical requirements. You need to identify registered users versus anonymous visitors, determine which identifiers take priority when multiple are available, and establish clear policies about when to merge known and anonymous profiles.

Testing capabilities separate sophisticated personalization platforms from basic ones. Leaders implement extensive A/B and multivariate testing across web, email, and mobile applications. They support AI-enabled targeting that learns from results and automatically optimizes experiences. According to Gartner's research, the most advanced implementations can conduct thousands of concurrent tests, using machine learning to identify winning variations faster than traditional statistical approaches.

Building Your Personalization Strategy: A Practical Framework

Implementation begins with defining your customer data strategy before selecting technology. According to the CDP Institute's research on successful implementations, start by identifying all sources of customer data—apps, websites, internal databases, and tools in your stack. Document integration capabilities for each source, as some support client-side SDKs while others require server-side integration.

Determine your identity resolution architecture by cataloging available identifiers from each data source. Define which identifiers accurately identify registered versus anonymous users, and which should take priority when multiple identifiers exist. Establish profile merge strategies: when customers sign up and link to known profiles, will you unify their pre-signup browsing activity or keep it separate?

Create a comprehensive data plan defining data points you'll collect and naming conventions across platforms and properties. The biggest delays to realizing value from personalization typically stem from poor data quality. Invest time upfront aligning data engineers and business users on what data to expect and in what format. Best-in-class implementations maintain single dictionaries or catalogs for customer data so all teams understand what the data represents.

Identify customer data activation systems—the downstream tools you'll feed with high-quality customer data through your personalization platform. These could be tools you're using today or tools you'll start using once your CDP is operational. Verify that your selected personalization vendors support integrations with these systems, both internal and external to your organization.

Account for privacy regulations and considerations by reviewing rules that apply to your company. Design your customer data architecture to reflect both regulatory requirements like CCPA and GDPR, as well as your company's values around data usage. Personalization platforms help you integrate data securely, but your collection and federation practices must reflect these requirements from the start.

Assemble cross-functional teams with clear roles. Successful implementations require coordination across marketing, product, analytics, and engineering. Identify key decision makers who have final authority on architectural choices. Assign project managers to coordinate stakeholders and set deadlines. Define development teams responsible for implementing code that powers your infrastructure. Designate data consumers who will use the platform to connect customer data to their tools and build audience segments.

Budget allocation patterns from personalization leaders provide guidance for resource planning. Leaders allocate 66% of their marketing budgets to personalization compared to 38% for low-maturity brands. They plan 46% spending increases in 2024 versus 16% for laggards. These investments span customer data platforms, decisioning engines, analytics and journey management resources, plus operational tools supporting creative content, segmentation, and experimentation.

The Implementation Reality: Process Without Resources Stalls

Here's what the research on implementation failures reveals: process alone doesn't drive results. You can document sophisticated personalization workflows and create detailed data governance frameworks, but without adequate resources to execute and iterate, progress stalls.

The Mastercard research finding that 50% of brands lack dedicated personalization support explains why so many companies struggle despite having solid strategies on paper. When personalization responsibilities fragment across separate functions like product management, merchandising, and IT, no single team has the bandwidth or authority to drive consistent progress.

Implementation requires dedicated roles: decision makers who have final authority on architectural choices, project managers coordinating cross-functional stakeholders, development teams implementing the code, data consumers using the platform for daily work, and subject matter experts from privacy, engineering, and business functions who can identify requirements other teams might miss.

Beyond initial implementation, ongoing operations demand similar dedicated focus. Personalization platforms require continuous optimization, testing, and refinement. Customer behaviors shift, market conditions change, and competitive dynamics evolve. Static personalization strategies decay in effectiveness over time.

Training represents another commonly overlooked requirement. Once implementation completes, the biggest blocker often becomes lack of knowledge among end users. Marketing teams need training on audience building, data forwarding, and profile lookups to extract value from their new capabilities. Without this enablement, expensive platforms sit underutilized while teams revert to familiar but less effective approaches.

According to research on barriers to personalization success, 30% of marketing technology leaders identify critical gaps in orchestrating personalized journeys across multiple channels and touchpoints. Additionally, 35% cite gaps in creating, storing, and distributing content across touchpoints. These operational challenges often matter more than technology selection, yet receive far less attention during planning phases.

Looking Forward: Where Personalization Is Heading

The convergence of AI capabilities, particularly generative AI, with established personalization platforms will dramatically expand what's possible in the next 24 months. According to Gartner research, 80% of e-commerce will use AI by 2025, with companies rapidly implementing these capabilities to remain competitive.

One-third of brands surveyed have already invested in generative AI to support personalization, while another 51% have concrete plans for spending in 2024. This rapid adoption reflects growing confidence in the technology's potential after initial experimentation phases proved out use cases.

Generative AI applications in personalization currently focus on content creation at scale, generating personalized email copy, product descriptions, and marketing messages tailored to individual customer segments. The efficiency gains let small teams produce content volumes previously requiring large creative departments.

More sophisticated applications emerging include AI-powered customer journey orchestration that automatically identifies optimal next actions based on real-time customer signals. Predictive analytics identify customers at risk of churn and trigger retention campaigns before problems escalate. Conversational AI creates personalized shopping assistants that guide customers through complex purchase decisions.

The challenge ahead isn't technological; it's organizational. As personalization capabilities expand rapidly, the gap between leaders and laggards will widen. Companies that have built strong data foundations, dedicated resources, and cross-functional processes will leverage new AI capabilities to pull further ahead. Those still struggling with basic personalization infrastructure will find it increasingly difficult to compete.

Investment patterns suggest this divergence is already accelerating. Leaders planning 46% budget increases for personalization in 2024 while laggards plan 16% increases means the performance gap will compound. When leaders capture 40% more revenue from personalization today, and they're investing at rates nearly three times higher than laggards, catching up becomes progressively harder.

The market opportunity remains massive. With personalization software revenue projected to maintain 23.8% compound annual growth through 2027, and 67% of brand leaders reporting they exceeded ROI expectations from personalization investments in 2023, the business case for aggressive investment continues strengthening. Companies that treat personalization as optional risk finding themselves unable to compete effectively within just a few years.

The Bottom Line

The research across seven major studies and dozens of case studies delivers a clear message: personalization done right generates measurably better business outcomes across every metric that matters. Companies that excel at personalization capture 40% more revenue, see customers spend 37% more per transaction, and achieve 78% higher repeat purchase rates.

The gap between leaders and laggards continues widening. Leaders allocate 66% of marketing budgets to personalization and plan 46% increases in 2024. They dedicate centralized resources to personalization rather than relying on ad hoc support. They focus investments on delivering tangible customer value—particularly money-saving offers—rather than personalization for its own sake.

Half of brands still lack the dedicated support required to scale personalization effectively. They're leaving massive revenue opportunities on the table while competitors build increasingly sophisticated capabilities. The technology and methodologies for effective personalization at scale are proven and accessible. What separates success from mediocrity comes down to commitment, resources, and focus on customer outcomes that actually matter.

Implementation requires more than technology purchases. It demands comprehensive customer data strategies, cross-functional coordination, ongoing optimization, and sustained investment over multiple years. Companies that approach personalization as a transformation initiative rather than a marketing tactic position themselves to capture the full value.

The question isn't whether to invest in personalization; the research settles that debate conclusively. The question is whether you'll commit the resources required to implement it effectively or continue fragmented efforts that leave value unrealized. The performance data from leaders proves what's possible. Now the choice is yours.

Frequently Asked Questions

Q: What ROI can I realistically expect from personalization investments?

A: According to BCG research, personalized offers generate three times higher ROI than mass promotions. Companies that excel at personalization generate 40% more revenue than competitors, with customers spending 37% more per transaction. However, these results require dedicated resources and proper implementation; brands with ad hoc personalization support show significantly lower returns. Leaders allocate 66% of marketing budgets to personalization and maintain centralized teams driving consistent execution. The timeline for achieving strong ROI typically spans 12-18 months from initial implementation, with results improving as systems learn from more customer interactions.

Q: How much should we budget for personalization technology and resources?

A: Personalization leaders allocate 66% of their total marketing budgets to personalization initiatives, compared to 38% among low-maturity brands. Leaders plan 46% budget increases in 2024 versus 16% for laggards. Beyond technology costs for customer data platforms, personalization engines, and analytics tools, budget for dedicated team members including data engineers, campaign managers, content creators, and testing specialists. According to Mastercard research, 50% of brands fail at personalization not because of technology limitations but due to lack of dedicated resources. Organizations should expect technology costs representing 30-40% of total personalization investment, with remaining budget supporting people, process, and content creation.

Q: What metrics should we track to measure personalization effectiveness?

A: Start with conversion rate, which personalization leaders improve by 54% compared to 38% for low-maturity brands. Track customer engagement (leaders show 61% improvement versus 37%), customer satisfaction (57% versus 29%), average order value (61% versus 44%), and customer lifetime value (54% versus 34%). For specific campaigns, measure click-through rates on personalized recommendations—Yves Rocher achieved 17.5x increases. Track revenue per visitor improvements; Jenson USA saw 8.5% overall gains and 26% on mobile. Monitor repeat purchase rates, which reach 78% for customers experiencing effective personalization. Finally, measure recognition rates; currently customers recognize only 43% of experiences as personalized despite brands believing they've personalized 61%, indicating an effectiveness gap worth tracking.

Q: How do personalization expectations differ across generations, and how should that influence our strategy?

A: Gen Z and millennial consumers (approximately 50% rating personalization as very or extremely important) expect explicit personalization and sophisticated recommendations. Gen X (25%) and baby boomers (16%) show lower stated interest but still respond to personalized value and convenience. Channel preferences vary significantly: over one-third of Gen Z prefers personalized marketing in physical stores, while nearly one-third of baby boomers prefer direct mail. For digital channels, younger consumers overwhelmingly favor mobile apps and web chat. Gen Z and millennials are twice as likely as older consumers to want customer support through social media. The strategic approach: implement personalization that delivers tangible value (money-saving offers, convenience, relevant recommendations) across all segments, but vary channel selection and messaging style by generation.

Q: What are the most common personalization implementation mistakes to avoid?

A: The single biggest mistake: fragmenting personalization across multiple teams without dedicated centralized resources. Fifty percent of brands operate this way and consistently underperform. Second, focusing on technical capability rather than customer value; 84% of consumers are influenced by personalized discounts, yet many brands emphasize recommendation engines without connecting them to tangible benefits. Third, poor data quality undermines effectiveness; research found third-party gender data accurate only 60% of the time. Fourth, lack of proper identity resolution means you can't track customers across devices and channels, fragmenting their experiences. Fifth, insufficient testing and optimization; set-it-and-forget-it personalization decays in effectiveness as customer behaviors shift. Finally, inadequate training leaves platforms underutilized; marketing teams need enablement on audience building, data forwarding, and profile lookups.

Q: How long does it typically take to implement effective personalization at scale?

A: According to CDP Institute research, comprehensive implementation spans 6-12 months for the technical foundation (customer data platform, identity resolution, initial integrations) plus 6-12 months for operational maturity (testing, optimization, team training, process refinement). Organizations should follow a crawl-walk-run approach, starting with high-value use cases that can demonstrate ROI while building capability. Initial quick wins might come within 3-4 months for simple email personalization or product recommendations. However, sophisticated omnichannel personalization with AI-driven decisioning typically requires 18-24 months to reach full maturity. Companies reporting the strongest results, like Sephora (growing from $580 million to $3 billion in e-commerce sales over six years), invested consistently over extended periods rather than expecting immediate transformation.

Q: Is personalization worth it for B2B companies, or is this primarily a B2C strategy?

A: While most research examples focus on B2C (retail, streaming, e-commerce), personalization principles apply equally to B2B with adaptations for longer sales cycles and multiple decision-makers. The core insight—that customers expect relevant, valuable experiences and reward brands that deliver them—holds true regardless of business model. B2B personalization requires account-based data structures supporting complex relationships, content personalization addressing different stakeholder roles, and journey orchestration spanning months rather than days. According to research on personalization platforms, vendors increasingly support B2B use cases with account-level targeting, multi-stakeholder journey mapping, and integration with sales systems. The ROI fundamentals—higher conversion rates, increased order values, improved retention—deliver similar business impact in B2B contexts, just measured over longer timeframes with higher absolute transaction values.

References Section

BCG Global Consumer Radar Survey - "What Consumers Want from Personalization" - Boston Consulting Group (December 2024)

Bloomreach - "A Marketer's Guide to Personalization at Scale" (April 2024)

RBM Software - "Personalization in E-Commerce Using AI and Big Data" (June 2025)

CDP Institute - "How to Prepare for Your Customer Data Platform Implementation" (January 2022)

Mastercard - "Half of Ecommerce Brands Lack the Support to Scale Personalization Effectively" (April 2025)

Deloitte Digital - "Personalization: It's a Value Exchange Between Brands and Customers" (June 2024)

Gartner Magic Quadrant - "Personalization Engines" (February 2025)