A/B Testing Tools for Optimizing Product Marketing Materials: Improving Conversion Rates and Engagement.

Intuition and experience alone are no longer sufficient to guide product marketing decisions. As markets become more crowded and customer attention more fragmented, systematic optimization of marketing materials has evolved from a nice-to-have advantage to a fundamental necessity. A/B testing—the practice of comparing two versions of content to determine which performs better—has emerged as the cornerstone methodology for data-driven product marketing optimization.

Implementing effective A/B testing isn’t just about selecting the right tools—it’s about establishing a culture of continuous optimization that transforms guesswork into evidence-based decisions. Here’s how modern A/B testing platforms can help product marketing teams improve conversion rates and engagement across channels, with practical guidance on tool selection, implementation strategies, and organizational considerations.

The Evolution of A/B Testing in Product Marketing

A/B testing has undergone a remarkable transformation over the past decade, evolving from basic email subject line comparisons to sophisticated multi-variant experiments across the entire customer journey.

From Basic Comparisons to Experimentation Platforms

Early A/B testing focused primarily on simple comparisons—testing two email subject lines or landing page headlines to see which generated higher open rates or conversions. These tests typically involved manual implementation, basic measurement, and limited statistical validity.

As digital marketing matured, second-generation testing tools emerged with more robust capabilities. These platforms enabled marketers to test multiple elements simultaneously, implement more sophisticated statistical models, and integrate testing across multiple channels.

Today’s third-generation experimentation platforms represent a quantum leap forward. Modern tools leverage machine learning, behavioral analytics, and automated optimization to support comprehensive testing programs that continuously improve marketing performance. Rather than running occasional tests, leading organizations now maintain persistent experimentation programs that systematically optimize every aspect of the customer experience.

The Rise of Evidence-Based Product Marketing

This technical evolution coincides with a fundamental shift in how organizations approach product marketing—from opinion-driven decisions to evidence-based optimization. This shift reflects several key changes in the B2B marketing landscape:

• Increasing Competition: As technology categories mature, product differentiation often diminishes, making marketing effectiveness a critical competitive advantage.
• Rising Acquisition Costs: Escalating customer acquisition costs have intensified the focus on conversion optimization to maximize marketing ROI.
• Stakeholder Expectations: Investors and executives increasingly expect marketing decisions to be backed by data rather than intuition.
• Channel Proliferation: The expanding array of marketing channels has created greater complexity, requiring more systematic approaches to optimization.

Modern A/B testing platforms have evolved to address these changes, enabling product marketers to make evidence-based decisions about positioning, messaging, creative approaches, and channel strategy.

Core Capabilities of A/B Testing Platforms

For product marketers, several key capabilities distinguish truly effective A/B testing platforms.

Experiment Design and Management

The foundation of effective testing is thoughtful experiment design:

• Hypothesis Framework: Structured approaches for developing testable marketing hypotheses
• Experiment Templates: Pre-built designs for common testing scenarios
• Traffic Allocation: Flexible controls for directing user traffic to different variations
• Scheduling Capabilities: Tools for timing test launches and conclusions

These capabilities ensure that tests are scientifically sound and aligned with marketing objectives.

Case Study: When cloud monitoring platform Datadog implemented Optimizely’s experiment management capabilities, their product marketing team established a structured testing program for product page optimization. The framework allowed them to systematically evaluate different messaging approaches, visual presentations, and call-to-action strategies, resulting in a 28% increase in product demo requests over six months.

Variant Creation and Editing

Efficient testing requires streamlined processes for creating variations:

• Visual Editors: WYSIWYG interfaces for modifying web pages without coding
• Multi-Channel Variation: Capabilities for testing across websites, emails, ads, and other touchpoints
• Dynamic Content Testing: Tools for evaluating different content elements based on user attributes
• Creative Asset Management: Systems for organizing and deploying different visual and messaging variations

These capabilities accelerate the testing process and reduce the technical barriers to experimentation.

Audience Targeting and Segmentation

Effective testing requires precise control over who participates in experiments:

• Behavioral Segmentation: Targeting based on previous user actions and engagement patterns
• Attribute-Based Targeting: Selection of test participants based on demographics, firmographics, or other characteristics
• Contextual Targeting: Experiment delivery based on situational factors like device, location, or referral source
• Random Sampling: Capabilities for ensuring statistically valid test groups

These targeting capabilities ensure that tests deliver relevant insights for specific customer segments and use cases.

Case Study: Enterprise security platform Tanium used VWO’s advanced segmentation capabilities to test different messaging approaches for distinct buyer personas. By creating separate experiments for technical decision-makers and executive buyers, they discovered that technical audiences responded best to capability-focused messaging while executive audiences prioritized risk reduction narratives. This insight led to a comprehensive revision of their persona-based communication strategy, increasing engagement rates by 34% across segments.

Statistical Analysis and Reporting

Converting raw test data into actionable insights requires sophisticated analysis:

• Statistical Significance Calculation: Automated evaluation of whether results represent genuine differences or random variation
• Confidence Intervals: A Clear indication of the reliability of observed differences
• Segment Comparison: Analysis of how different user groups respond to tested variations
• Visualization Tools: Graphical representations of test results for easier interpretation

These analytical capabilities help marketers distinguish meaningful insights from statistical noise, leading to more reliable decisions.

Multi-Variate Testing

Beyond simple A/B comparisons, advanced platforms support testing multiple variables simultaneously:

• Element Interaction Analysis: Evaluation of how different combinations of elements perform together
• Multivariate Models: Statistical approaches for understanding complex relationships between variables
• Factorial Design: Structured testing of multiple variables while controlling for interactions
• Impact Isolation: Methods for determining which specific elements drive performance differences

These capabilities help marketers understand not just what works better, but why it works better—critical intelligence for developing effective marketing strategies.

Types of A/B Testing Tools for Product Marketing

Several categories of A/B testing tools have emerged, each with distinct advantages for different product marketing use cases.

Web Experience Optimization Platforms

These platforms focus primarily on testing and optimizing website experiences. Leading examples include Optimizely, VWO, and Adobe Target.

Advantages for Product Marketing:

• Comprehensive website testing capabilities
• Visual editors for creating variations without coding
• Integration with web analytics platforms
• Robust segmentation and targeting

Limitations:

• May offer limited capabilities for non-web channels
• Often requires significant traffic to achieve statistical significance
• Can involve substantial implementation complexity
• May represent a significant investment for early-stage startups

Best Fit For: Companies with established web presence and sufficient traffic volumes who need sophisticated optimization capabilities for product pages, pricing pages, and conversion flows.

Email Testing Tools

These solutions specialize in optimizing email marketing performance. Examples include Litmus, Email on Acid, and the testing capabilities within email platforms like Mailchimp and HubSpot.

Advantages for Product Marketing:

• Specialized features for testing email-specific elements
• Preview capabilities across email clients and devices
• Integration with email delivery platforms
• Spam testing and deliverability optimization

Limitations:

• Focused primarily on the email channel
• May offer less sophisticated statistical analysis
• Limited integration with broader marketing optimization
• May duplicate capabilities of existing email platforms

Best Fit For: Companies with significant email marketing programs that need to optimize product announcements, nurture sequences, and other email-based marketing materials.

Case Study: When cloud security platform Lacework implemented Litmus’s email testing capabilities, they discovered that their product update newsletters rendered poorly on mobile devices, causing 62% of recipients to abandon reading. After optimizing their email templates based on testing insights, mobile engagement increased by 47%, significantly improving the reach of their product communications.

Landing Page Optimization Tools

These specialized platforms focus on creating and testing dedicated landing pages. Examples include Unbounce, Instapage, and Leadpages.

Advantages for Product Marketing:

• Simplified landing page creation without developer resources
• Built-in A/B testing capabilities
• Focus on conversion optimization
• Rapid implementation and iteration

Limitations:

• Typically limited to landing pages rather than entire websites
• May create disconnected experiences from the main website
• Can result in content management complexity
• May offer less sophisticated analytics than dedicated testing platforms

Best Fit For: Early-stage companies needing to quickly deploy and optimize campaign-specific landing pages without significant development resources.

All-in-One Marketing Platforms

These comprehensive platforms include A/B testing as part of broader marketing capabilities. Examples include HubSpot, Marketo, and Salesforce Marketing Cloud.

Advantages for Product Marketing:

• Integration across multiple marketing functions
• Unified data model for customer interactions
• Consistent user experience for marketers
• Combined analytics across channels and campaigns

Limitations:

• Testing capabilities may be less sophisticated than specialized tools
• Typically requires commitment to the platform’s broader ecosystem
• Can represent a significant investment for early-stage companies
• May involve platform lock-in concerns

Best Fit For: Companies already using or considering these platforms for broader marketing automation who want integrated testing capabilities without managing additional tools.

In-Product Experimentation Tools

These platforms focus on testing within software products rather than marketing materials. Examples include LaunchDarkly, Split.io, and Firebase A/B Testing.

Advantages for Product Marketing:

• Connect product and marketing experiments
• Test onboarding flows and in-product messaging
• Evaluate feature adoption strategies
• Bridge the gap between product and marketing teams

Limitations:

• Often requires developer implementation
• May focus more on feature flagging than marketing optimization
• Typically designed for product management rather than marketing teams
• Can involve complex integration with marketing systems

Best Fit For: Product-led companies with tight integration between product and marketing functions that need to optimize the entire user journey from marketing through product experience.

Case Study: When workflow automation platform Zapier implemented Split.io for in-product experimentation, their product marketing team extended testing into the onboarding experience. By systematically testing different product introduction sequences, example use cases, and activation paths, they increased new user activation rates by 23% and improved feature adoption by 18% for key integration capabilities.

Key Marketing Materials to Test

Product marketers should prioritize testing efforts on materials that most directly impact business outcomes.

Product Messaging and Positioning

Testing different messaging approaches helps identify what truly resonates with target audiences:

• Value Proposition Statements: Different articulations of the core product value
• Feature Framing: Various ways of presenting product capabilities and benefits
• Pain Point Emphasis: Different prioritization of customer challenges addressed
• Competitive Positioning: Alternative approaches to differentiation

These tests help product marketers move beyond subjective opinions to determine which messaging approaches drive engagement and conversion.

Product Page Optimization

Website product pages represent critical conversion points deserving systematic optimization:

• Page Structure: Different arrangements of content elements and information hierarchy
• Visual Presentation: Alternative approaches to product imagery, videos, and demonstrations
• Technical Content: Various methods for presenting specifications and capabilities
• Social Proof Integration: Different approaches to featuring testimonials and case studies

These tests help identify the most effective ways to present product information for different audience segments.

Case Study: Enterprise software company HashiCorp used Optimizely to test different approaches for their product pages. Their experiments revealed that technical audiences engaged more deeply with pages that began with architecture diagrams rather than benefit statements, while business audiences showed the opposite preference. This insight led them to implement dynamic page structures that adapted based on visitor characteristics, increasing demo requests by 41% and technical documentation engagement by 37%.

Conversion Elements

The specific elements that drive action warrant particular testing focus:

• Call-to-Action Messaging: Different language and framing for action triggers
• Form Design: Various approaches to information collection and lead capture
• Pricing Presentation: Alternative methods for communicating pricing and packaging
• Trial/Demo Offers: Different value propositions for initial product engagement

These tests help optimize the critical transition points where interest converts to action.

Email Campaigns

Product announcements and nurture emails benefit from systematic optimization:

• Subject Lines and Previews: Different approaches to driving open rates
• Content Structure: Alternative arrangements of email content
• Visual Design: Various design approaches for email templates
• Call-to-Action Placement: Different positions and presentations of email CTAs

These tests help ensure product messages actually reach and engage their intended audiences.

Content Marketing Assets

Longer-form content used for thought leadership and education deserves testing attention:

• Content Formats: Comparing the effectiveness of different content types (e.g., whitepapers vs. interactive tools)
• Title and Description Testing: Alternative framing of content offers
• Gating Strategies: Testing different approaches to information collection for content access
• Distribution Methods: Comparing performance across different content delivery channels

These tests help maximize the reach and impact of product marketing thought leadership content.

Implementation Framework for Technology Startups

Implementing A/B testing requires a structured approach, particularly for resource-constrained startups.

Phase 1: Foundation Building (Weeks 1-4)

Start with establishing the essential elements of an effective testing program:

• Tool Selection: Choose appropriate testing platforms based on your specific needs and resources
• Implementation Planning: Develop a technical approach for deploying testing capabilities
• Key Metric Definition: Establish the core metrics that will drive testing decisions
• Hypothesis Framework: Create a structured approach for developing testable marketing concepts

This foundation ensures that your testing efforts align with business objectives and generate reliable insights.

Phase 2: Initial Testing Program (Weeks 5-12)

Begin with focused tests that can deliver quick wins and build momentum:

• High-Impact Page Testing: Start with optimizing your most important conversion pages
• Email Campaign Optimization: Implement testing within product announcement emails
• Call-to-Action Refinement: Test variations of your primary conversion triggers
• Simple Segmentation: Begin exploring how different audience segments respond to variations

This initial program delivers tangible improvements while building testing capabilities and organizational buy-in.

Case Study: B2B payments platform Modern Treasury implemented this phased approach to A/B testing, beginning with focused tests on their product pages and signup flow. Their initial experiments revealed that emphasizing security compliance features increased conversion rates for financial services prospects by 42%, while emphasizing API flexibility drove higher engagement from technology companies. These early wins built executive support for a more comprehensive testing program.

Phase 3: Expanded Testing (Months 4-6)

With initial success established, expand to more sophisticated testing approaches:

• Multi-Variate Testing: Experiment with testing multiple variables simultaneously
• Personalization Testing: Evaluate different content experiences for specific segments
• Customer Journey Testing: Expand beyond isolated touchpoints to optimize sequences
• Cross-Channel Coordination: Implement consistent testing across multiple channels

This expansion moves beyond optimizing individual elements to improving the entire customer experience.

Phase 4: Advanced Optimization (Months 7+)

Build toward a mature, ongoing optimization program:

• Automated Optimization: Implement algorithmic approaches to continuous improvement
• Predictive Testing: Use previous results to forecast the performance of new variations
• Advanced Analytics Integration: Connect testing data with broader marketing and business metrics
• Testing Center of Excellence: Establish cross-functional expertise and governance

This mature approach transforms testing from a series of isolated experiments to a continuous optimization engine.

Testing Methodology Best Practices

Effective A/B testing requires rigorous methodology to generate reliable insights.

Hypothesis Development

Begin with clear hypotheses that connect specific changes to expected outcomes:

• Structured Format: Use consistent “If we [change X], then [metric Y] will [increase/decrease] because [rationale Z]” format
• Evidence-Based Development: Ground hypotheses in user research, analytics data, or previous test results
• Prioritization Framework: Systematically evaluate hypotheses based on potential impact, implementation effort, and strategic alignment
• Documentation Practices: Maintain a central repository of hypotheses, tests, and outcomes

This structured approach ensures tests address meaningful business questions rather than subjective preferences.

Statistical Validity

Design tests to deliver statistically valid results:

• Sample Size Calculation: Determine appropriate test duration based on traffic levels and expected effect size
• Significance Thresholds: Establish clear standards for when results can be considered conclusive
• Control Group Management: Maintain consistent baseline experiences for valid comparisons
• Test Isolation: Prevent multiple simultaneous tests from confounding results

These practices help distinguish genuine insights from random variation, preventing false conclusions.

Case Study: When data analytics company Amplitude implemented VWO’s statistical analysis framework for their product marketing tests, they discovered that many of their previous “winning” tests had actually been inconclusive due to insufficient sample sizes. By implementing proper sample size calculation and significance thresholds, they reduced false positives by 68% and increased their confidence in testing outcomes.

Segment Analysis

Look beyond aggregate results to understand segment-specific responses:

• Pre-Test Segmentation: Define relevant audience segments before launching tests
• Post-Test Discovery: Analyze results to identify unexpected segment patterns
• Segment Size Considerations: Ensure segments are large enough for valid analysis
• Segment Action Planning: Develop specific strategies based on segment-level insights

This segmented approach reveals how different audience groups respond to various marketing approaches, enabling more targeted optimization.

Test Duration and Timing

Establish appropriate timeframes for conclusive results:

• Minimum Duration Standards: Set baseline test periods based on traffic levels and conversion rates
• Business Cycle Consideration: Account for weekly, monthly, or seasonal variations in behavior
• External Factor Monitoring: Track market events or campaigns that might influence results
• Stopping Rules: Define clear criteria for concluding tests early or extending their duration

These timing considerations ensure that test results represent genuine performance differences rather than temporal anomalies.

Analyzing Results and Making Decisions

Converting test data into action requires thoughtful analysis and decision-making processes.

Beyond Surface Metrics

Look deeper than headline conversion rates to understand true performance:

• Downstream Impact Analysis: Evaluate how variations affect later-stage metrics like qualification rates and customer lifetime value
• Engagement Quality Assessment: Examine not just click rates but engagement depth and subsequent behaviors
• Segment Performance Variation: Analyze how different user groups respond to tested variations
• Interaction Effect Identification: Understand how multiple elements work together in driving performance

This comprehensive analysis prevents optimization for superficial metrics that might not translate to business outcomes.

From Data to Insights

Transform raw test results into actionable marketing insights:

• Pattern Recognition: Identify consistent themes across multiple tests
• Principle Development: Derive generalizable guidelines from specific test outcomes
• Insight Documentation: Capture learnings in accessible formats for broader organizational application
• Knowledge Distribution: Share insights across teams to inform broader marketing strategies

This knowledge management approach ensures that testing creates lasting organizational intelligence rather than just incremental improvements.

Case Study: Cloud communications platform Twilio established a sophisticated testing insight management process that transformed individual test results into a comprehensive messaging playbook. By systematically analyzing patterns across dozens of tests, they identified six core messaging principles that consistently drove higher engagement across segments. These principles now guide all new product marketing material development, accelerating effectiveness and reducing the need for extensive testing of fundamentals.

Implementation Planning

Develop systematic approaches for implementing successful variations:

• Deployment Processes: Establish clear workflows for implementing winning variants
• Segment-Specific Rollouts: Plan for tailored experiences based on segment-level results
• Performance Monitoring: Track ongoing performance after implementation
• Iteration Planning: Identify follow-up tests to further refine successful approaches

This implementation discipline ensures that testing actually translates to improved marketing performance rather than remaining theoretical.

Organizational Considerations for Testing Success

Technical implementation represents only half the challenge—organizational alignment is equally critical for testing success.

Testing Culture Development

Foster an organizational mindset that values evidence over opinion:

• Leadership Modeling: Executive demonstration of data-driven decision-making
• Success Celebration: Recognition of both positive and negative test results as valuable learning
• Failure Acceptance: Creating a safe space for tests that disprove popular hypotheses
• Curiosity Cultivation: Encouraging exploration and questioning of marketing assumptions

This cultural foundation ensures testing becomes a core practice rather than a peripheral activity.

Cross-Functional Collaboration

Establish workflows that connect product marketing with adjacent functions:

• Product Team Integration: Coordination between product marketing and product management testing
• Sales Feedback Loops: Mechanisms for sales to contribute hypotheses and receive insights
• Creative Collaboration: Processes for marketing creative teams to participate in test design
• Technical Partnership: Relationships with development teams for implementation support

These collaborative approaches ensure testing efforts align with broader organizational priorities and capabilities.

Case Study: Enterprise software company Atlassian implemented a cross-functional “optimization guild” that brought together product marketing, demand generation, product management, and design teams for collaborative testing. This approach identified critical disconnects between product marketing messages and actual product experiences, leading to a comprehensive realignment that improved trial conversion rates by 28% and reduced early-stage customer churn by 17%.

Skills and Resources

Invest in the capabilities needed for effective testing:

• Testing Expertise: Development of specialized knowledge in experiment design and analysis
• Creative Resources: Access to design and copywriting capabilities for creating variants
• Technical Support: Implementation resources for more complex testing scenarios
• Analytics Capabilities: Data analysis skills for extracting meaningful insights

These resource investments ensure testing programs deliver their full potential value rather than being constrained by capability gaps.

Integration with the Marketing Technology Stack

A/B testing delivers maximum value when integrated with other elements of the marketing technology ecosystem.

Analytics Platform Connection

Link testing tools with broader analytics systems:

• Data Consolidation: Unified view of testing results alongside other performance metrics
• Attribution Integration: Connection between test variations and downstream conversion events
• Audience Synchronization: Consistent segmentation across testing and analytics platforms
• Custom Dimension Tracking: Passing test variation data to analytics for longitudinal analysis

These connections provide context for test results and help identify broader implications of specific optimizations.

CRM and Marketing Automation Integration

Connect testing with customer data and communication systems:

• Prospect Journey Tracking: Following test participants through marketing and sales processes
• Personalization Coordination: Aligning testing and broader personalization initiatives
• Campaign Performance Analysis: Understanding how tested elements perform within campaigns
• Lead Quality Impact: Evaluating how different variations influence lead quality and conversion

These integrations help understand how marketing material optimizations affect the entire customer acquisition process.

Case Study: When the marketing automation platform HubSpot integrated its Optimizely testing program with its own CRM system, it discovered that prospects who engaged with certain messaging variations converted to customers at 2.3x the rate of control groups, even though initial conversion rates were similar. This insight led them to prioritize long-term customer quality over immediate conversion optimization in their testing program.

Content Management System Alignment

Coordinate testing with content production systems:

• Template-Level Testing: Building test capabilities into core content templates
• Dynamic Content Framework: Creating flexible content structures that facilitate testing
• Publishing Workflow Integration: Embedding testing into content development processes
• Content Performance Feedback: Connecting test results to content strategy decisions

These alignments ensure testing becomes an integral part of content development rather than an afterthought.

Future Trends in A/B Testing

Several emerging trends are reshaping how product marketers approach testing and optimization.

AI-Powered Optimization

Artificial intelligence is transforming testing through:

• Automated Variant Generation: AI systems that create multiple test variations based on guidelines
• Predictive Testing: Algorithms that forecast variant performance before full testing
• Dynamic Traffic Allocation: Automated systems that adjust traffic flow to winning variations in real-time
• Pattern Recognition: AI-powered analysis that identifies success patterns across multiple tests

These capabilities help marketers scale their testing programs beyond what would be possible with manual approaches alone.

Unified Experimentation Platforms

The lines between different types of testing are blurring:

• Cross-Functional Testing: Platforms that support marketing, product, and engineering experiments
• Full-Journey Optimization: Unified testing across the entire customer lifecycle
• Comprehensive Analytics: Integrated measurement across all testing initiatives
• Shared Knowledge Repositories: Centralized insights from all organizational experiments

This convergence helps organizations optimize the entire customer experience rather than isolated touchpoints.

Privacy-First Testing Approaches

As privacy regulations and expectations evolve, testing approaches are adapting:

• Server-Side Testing: Moving test implementation from browsers to servers for greater privacy
• Consent-Based Participation: Explicit permission frameworks for experimentation
• Aggregate Analysis: Evaluation approaches that don’t rely on individual-level tracking
• First-Party Data Focus: Testing built on owned customer data rather than third-party information

These approaches help marketers maintain optimization capabilities while respecting privacy boundaries.

Experimentation as a Product Differentiator

Leading organizations are recognizing testing capabilities as strategic advantages:

• Testing Infrastructure as a Core Asset: Investment in proprietary testing capabilities
• Experimentation Velocity as KPI: Measuring testing capacity as a performance indicator
• Test-Driven Culture as Talent Attractor: Using data-driven approaches to recruit marketing talent
• Insight Generation as Competitive Edge: Leveraging superior customer understanding as a market advantage

This strategic perspective elevates testing from a tactical marketing activity to a fundamental business capability.

For product marketers in B2B technology companies, A/B testing has evolved from a nice-to-have capability to an essential practice for maintaining competitive advantage. By systematically testing different approaches to messaging, design, and user experience, product marketers can move beyond opinion-based decisions to evidence-driven optimization that demonstrably improves conversion rates and engagement.

The most successful product marketing teams approach testing not as a series of isolated experiments but as a continuous program of optimization that informs their entire marketing strategy. They invest in appropriate tools, rigorous methodologies, and supportive organizational practices that transform testing from a peripheral activity to a core discipline.

As you develop your A/B testing strategy, focus on:

1. Starting with clear business objectives rather than testing for its own sake. The most valuable testing programs address specific marketing challenges rather than generating interesting but ultimately inconsequential data.
2. Building a methodologically sound approach that delivers reliable insights. Superficial testing often creates misleading results that can actually harm marketing performance rather than improve it.
3. Balancing immediate optimization with strategic learning. While performance improvements are important, the most valuable outcome of testing is often the deeper understanding of customer preferences and behaviors that informs broader marketing strategy.
4. Investing in both technology and organizational capabilities. Even the most sophisticated testing tools deliver little value without the skills, processes, and cultural elements necessary to translate testing capabilities into business results.

By approaching A/B testing as a strategic discipline rather than just a tactical activity, you can develop deeper customer understanding, create more effective marketing materials, and ultimately build stronger connections between your products and the customers they serve.