什么是数据驱动营销？完整指南和技巧

数据驱动营销是使用客户数据和分析来制定战略决策、个性化信息并根据实际证据优化活动的营销方法。

Marketing Fundamentals

Working on a hunch today means gambling with your marketing budget. McKinsey's 2021 research on the value of getting personalization right found that companies that grow faster drive 40% more of their revenue from personalization than slower-growing peers. That is an association between growth and personalization, not proof that personalization alone causes the growth.

For most companies, the challenge is not a lack of data but putting the data they already have to work. Invoca's data-driven marketing statistics collects industry statistics on how marketers use data. This guide shows what to do, what to avoid and how to get more from the data you already have.

Understanding Data-Driven Marketing Fundamentals

What is data-driven marketing exactly?

A data-driven approach to marketing moves away from gut feel toward fact-based planning. Instead of guessing what customers are interested in, you look at real behavior and demographic insights and feed them into predictive analytics to build campaigns grounded in evidence.

Traditional marketing can feel like fishing with a net in the open ocean and hoping to catch something valuable. Data-driven marketing is closer to fishing with sonar: it gives you a much better idea of where the fish are and when they are likely to bite, though never a guarantee.

SAP Emarsys describes data-driven marketing as "strategies built on insights pulled from the analysis of big data", used to form predictions about future behavior. In practice, that definition covers a lot of ground.

At its core, data-driven marketing means capturing data from relevant, permitted customer touchpoints, analyzing it to understand customer behavior and using it to anticipate what customers are likely to do next. Behavioral data shows what customers did; understanding why usually takes testing or research on top of it.

The building blocks of data-driven marketing

Collection of first-party data underpins every data-driven strategy. This might include website analytics, email activity, purchase history, customer service records and social media interactions. Privacy pressure makes it more important: HubSpot's guide to first-party data cites HubSpot's 2025 Industry Trends Report, in which 88% of marketers said data privacy changes were keeping them up at night.

Customer Data Platforms (CDPs) bring data from many systems together into usable customer profiles. Beyond storing information, they can give a more current view of each customer's journey across the touchpoints they capture, with freshness depending on how the data arrives.

Analytics and machine learning turn raw data into predictive insight: not just what happened last quarter, but what is likely to happen next. Measuring the payoff takes discipline. MIT Sloan Management Review's 2025 AI and data science trends notes that many organizations report large productivity gains from generative AI but few measure them carefully, and it recommends controlled experiments.

Marketing automation platforms let you run data-driven strategies at scale. Whether it's automated email journeys, dynamic website personalization or programmatic advertising, automation helps turn insights into action across thousands or millions of customer interactions.

Why Data Driven Marketing Matters More Than Ever

The landscape of competition has been radically altered

Let's talk about what data changes in practice. Measured's overview of data-driven marketing benefits points to faster decision-making as one of the benefits of using data well. Faster, better-informed decisions can be a real competitive advantage.

Customer expectations raise the stakes. According to McKinsey's Next in Personalization 2021 research, 71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when this doesn't happen. Personalization is no longer a nice-to-have.

Companies such as Netflix and Amazon built recommendation engines on customer data, and those engines are now central to how customers use their services. That is the result of treating data as a strategic asset, not a lucky accident.

Consumer behavior demands smarter approaches

Customer journeys are also more complex. Buyers may find you on social media, research on your website, compare prices on mobile, read reviews, visit a store and finally buy online. Without data to connect these interactions, you're flying blind.

Invoca's data-driven marketing trends cites a Google study estimating that consumer journeys can involve anywhere from 20 to 500 touchpoints, depending on how complex the purchase is. Data-driven marketing helps by building customer profiles from interactions across channels.

Three Companies: Data-Driven Marketing in Practice

Netflix: The personalization powerhouse

Netflix has made a science of data. It analyzes viewing behavior, including what members watch, pause, abandon and rewatch, and uses it to power personalized recommendations.

Markivis's Netflix case study describes how analytics feeds Netflix's recommendations and retention strategy. The aim is an experience personalized enough that leaving feels like losing a service that understands you.

Their data strategy goes beyond recommendations: viewing data can also inform decisions about what to produce and how to release it.

Spotify: Using data for viral marketing

Spotify Wrapped turns a year of listening data into a personal, shareable story. NoGood's Spotify case study describes how Wrapped is designed for social sharing, so users effectively help market the service.

Spotify's use of data goes beyond year-end campaigns. Its recommendation systems use listening behavior to create personalized playlists and suggestions for each user.

Engaged Social's analysis describes how Spotify uses listening data and machine learning to personalize the experience. Once a service understands a listener's taste, leaving becomes less appealing.

American Express: The closed-loop advantage

American Express runs a closed-loop network: it issues its own cards and also acts as the acquirer for merchants, which gives it visibility into both sides of many transactions.

A 2017 student analysis on Harvard Business School's Digital Initiative platform credits American Express's analytics with a 40% rise in new customer acquisition via online interactions and with fraud models that identified about $2 billion in potential annual incremental fraud incidents. Transaction data also helps Amex offer value-added services that strengthen customer relationships.

Their approach to data also carries over to risk, customer service and product development. By looking at spending behavior, they can predict which customers might be interested in particular services, which might be a flight risk, and even which might be at risk for fraud or in financial distress.

Where data-driven marketing creates value

Precise targeting to reduce wasted ad spend

Untargeted advertising wastes budget. As Data Science Dojo's overview notes, data-driven marketing reduces wasted spending by identifying the prospects most likely to become customers before you invest heavily in acquisition.

Highly refined audience segmentation lets you create what marketers call "segments of one", treating each customer almost as a micro-market. As a simple illustration, not an observed result, a $10,000 ad budget could go toward 1,000 strong prospects instead of spraying a message at 100,000 random people who may never buy from you.

The effect can compound. McKinsey's explainer on personalization, published in 2023, says personalization most often drives revenue lifts of 5 to 15% and marketing ROI gains of 10 to 30%. These are reported ranges, not a guaranteed gain; results depend on your starting point and execution.

Maximizing Customer Lifetime Value shifts the Profit Story

A 2014 Harvard Business Review article reported that, depending on the industry, acquiring a new customer can cost five to 25 times more than retaining an existing one. Data-driven marketing helps you see which customers are likely to have the highest lifetime value (LTV) and allocate resources accordingly.

In McKinsey's 2013 DataMatics survey, intensive users of customer analytics were nine times more likely to surpass competitors in customer loyalty. That is an association from a survey, not proof that analytics caused the difference, but it shows why understanding which behaviors predict long-term value is worth the effort.

Retention matters just as much. By analyzing the behaviors that lead to churn, companies can spot high-risk customers and launch focused retention campaigns. The same HBR article cites research by Frederick Reichheld of Bain & Company in which increasing customer retention rates by 5% increased profits by 25% to 95%. Those figures come from specific historical studies and industries, so treat them as context rather than a forecast for your business.

Real-time optimization accelerates performance improvements

Traditional marketing campaigns have a quarterly rhythm into which all activities fall: plan, launch, wait for results, analyze, adjust. Real-time optimization lets you improve performance daily, or even hourly.

Programmatic advertising platforms are capable of testing dozens of creative variations at the same time, while adjusting budget on the fly based on top performers. Email campaigns can adapt send times on a subscriber by subscriber level. Sites can customize content based on visitors' behavior and preferences.

Google's overview of Analytics benefits highlights customer-centric measurement across sites and apps and machine learning that can surface predictive insights. The faster you can see what's working, the sooner you can optimize during a campaign rather than only after it ends.

Constructing Your Data-Driven Marketing Technology Stack

Analytics basics: Get to the measurement basics

Your analytics platform is the foundation for every other solution. It provides cross-platform tracking, machine learning insights and predictive metrics that help connect complex customer journeys. Execution is the secret: incomplete tracking undermines data quality. For a broader view of how analytics tools are changing, see Statology's review of 2024 analytics trends.

In addition to fundamental web analytics, you require customer data integration between online and offline touches. This includes email opens, social reactions, phone calls, in-store visits, and customer service encounters housed within centralized customer profiles.

As customer paths to conversion get more complex, attribution modeling becomes more important. First-touch attribution credits the first touchpoint you observed, which may not be the customer's actual first contact; last-touch credits the observed touchpoint just before conversion. Data-driven attribution assigns credit to observed touchpoints under its model's assumptions. None of these models proves that a channel caused the sale.

Customer data platforms: bringing customer data together

CDPs address a major problem in modern marketing: data silos. Your email tool has open rates, your CRM has sales calls and your website analytics show who has been browsing, but without integration you are missing the full view.

CDPs such as Segment, Adobe Real-Time CDP or Salesforce's CDP unify the available, permitted data from multiple sources into customer profiles. How complete and current those profiles are depends on identity matching, connectors, your plan and whether data arrives by streaming or in batches. This makes triggered campaigns possible with combined behavioral signals, such as targeting users who visited your pricing page and opened your last email but have not purchased in 60 days.

Integrated data also supports better personalization. HubSpot's first-party data guide cites Twilio's State of Personalization Report, in which 56% of consumers said they were more likely to make a subsequent purchase after a personalized shopping experience. That is what consumers reported in a survey, not a guaranteed or proven causal effect.

Marketing automation: Scaling personalized experiences

Automation platforms help apply data-driven decisions at scale. Beyond basic email automation, current platforms offer dynamic content optimization, behavioral triggers and AI-assisted send-time optimization based on how each customer tends to engage with email.

Cross-channel orchestration, where customer activity in one channel triggers personalized activity in another, is a more advanced form of automation. A website visitor who viewed a certain product may see targeted social media ads, personalized email recommendations and tailored landing page experiences, all coordinated using recent customer data.

Efficiency gains depend on the quality of your data. First-party data is the fuel for this kind of automation, and Invoca's guide to first-, second- and third-party data explains how it differs from second- and third-party data. When routine personalization runs automatically, marketers can focus on strategy instead of execution.

Implementation Strategy: Your Step-by-Step Roadmap

Phase 1: Data foundation and infrastructure (for example, months 1-2)

Begin with a thorough data audit: you need to know what you already capture and what is missing. Map the customer touchpoints where you are permitted to collect data, from initial awareness to post-purchase engagement. Many teams find they have more information than they thought; it is just spread across various systems. The timings in this roadmap are illustrative and depend on your scope and data.

Set up proper tracking infrastructure across all online assets. This involves Google Analytics 4 configuration, Meta Pixel setup, email platform connectors and CRM syncing. Many tracking problems start with a poor initial setup, so take time to get the basics right.

Put data governance in place to support quality and compliance. This includes a data retention policy, privacy controls and documentation that helps team members know what data exists and where it is.

Phase 2: Integration and analysis (for example, months 2-3)

Select and deploy your customer data platform or integration solution. Begin with one critical integration, such as linking your email platform to your CRM or your website analytics. Concentrate on providing fast value, not on fully integrating.

Formulate simple segmentation plans with your combined data. Start with basic segments such as "customers who bought within 90 days" or "prospects who visited pricing page but didn't convert." These base segments will drive your first data-driven campaigns.

Educate your team on new tools and methods. The biggest hurdle for data-driven marketing is often adoption by people, not technology. Invest in workshops, training and documentation that help team members interpret and act on data.

Phase 3: Campaign optimization and scaling (for example, months 3-6)

Kick off your first data-driven campaigns using what you learned from that consolidated data. Begin with personalized email, customized web content or targeting your ads to interested audiences. Clearly define what success looks like and have testing protocols for continuous improvement.

Build an A/B testing infrastructure that lets you experiment scientifically. Test subject lines, landing pages, ad creative, send times and personalization tactics. A testing calendar helps you keep learning and optimizing.

Build out attribution modeling that connects marketing to business results. Seeing which observed touchpoints precede conversions supports smarter budget planning and campaign refinement, though attribution alone does not prove what caused a sale. Begin with simple models and add complexity as your data and understanding mature.

Navigating Privacy and Compliance Challenges

The privacy-first marketing approach

Privacy laws such as the GDPR and CCPA need not block data-driven marketing; they can push you toward relationships built on transparency and value exchange. They also carry real penalties: as CookieYes's GDPR marketing checklist notes, fines for the most serious GDPR infringements can reach €20 million or 4% of total worldwide annual turnover for the preceding financial year, whichever is higher.

Use consent management platforms to give people clear, friendly privacy controls. Straightforward opt-in procedures, granular preference settings and easy opt-out options help build trust and support compliance. When consumers feel in control of their data, they are more likely to engage positively.

Value exchange is paramount in privacy-first marketing. Instead of gathering data because you can, gather it because it powers experiences customers actually want: Netflix members get better recommendations from their viewing data, and Spotify users get personalized playlists from their listening habits. Value exchange does not replace a lawful basis, though. Collect only the data you need from permitted touchpoints, obtain valid consent where it is required and make opting out easy.

First-party data strategy as tracking tightens

Browsers and platforms keep tightening limits on third-party tracking, so collecting first-party data is more important than ever. Organizations that build direct customer relationships are better placed as privacy rules tighten.

Instead of forcing information from customers, offer clear value so they share information willingly. That could be personalized recommendations for products, access to exclusive content, benefits in a loyalty program, or an individually tailored shopping experience. When customers see real value in sharing data, they become willing participants.

You can collect data gradually, without overwhelming customers, using progressive profiling. Don't ask for everything at the first step; gather more information over later interactions. Each interaction lets you learn more and deliver more value.

Future Trends Shaping Data-Driven Marketing

AI and ML incorporation

AI-powered marketing platforms are moving from experimental to essential. WordStream's AI marketing trends describes 2024 as a big year for the mass adoption and integration of AI among marketing professionals. Algorithms can help predict customer behavior, create personalized content and build campaigns faster.

Companies of every size now have access to predictive analytics. Rather than only diagnosing what happened last quarter, you can estimate which customers are likely to churn, which prospects are ready to buy and which campaigns are likely to drive the best ROI. That marks a shift from reactive to proactive marketing.

By applying machine learning to real-time personalization, you can offer experiences that respond quickly to how an individual customer behaves. Website content, email campaigns and advertising creative can adapt automatically to individual preferences and predicted intent, so more customer contact can feel tailored to the individual.

The ascent of zero-party data collection

First-party data, and specifically zero-party data (data that customers intentionally provide to you through surveys, preference centers, and interactive content) is growing in importance. As opposed to inferred behavioral data, zero-party data is explicit customer preferences and intentions.

Interactive content methods such as quizzes, polls and preference tests make collecting customer feedback more engaging and add immediate value. A beauty brand, for example, might use a skin-type quiz to recommend products while learning preferences that can inform future marketing, with clear consent.

Social places and participation spaces facilitate the continuous conversation between brands and clients. When customers feel listened to and appreciated, they are more willing to share preferences, feedback, and engage in co-creation efforts that result in marketing insight that's actually useful.

Common Implementation Challenges and Solutions

Data quality and integration obstacles

Poor data quality undermines even the best analytics. Northbeam's guide to data-driven marketing recommends starting with clean first-party data and warns that not every data trend signals cause and effect. Establish validation and audit routines and automated quality checks so information stays trustworthy.

Integration problems often slow marketing teams down when they try to connect many platforms. Begin with simple integrations that offer a clear return, instead of trying to connect everything at once. Choose tools with pre-built connectors for leading marketing platforms.

Adoption can be hindered due to resistance toward change management from those comfortable with conventional methodologies. Alleviate fears with training, clear communication of benefits and some quick wins to show value. Put doubting Thomases in a pilot to create believers internally.

Skills gaps and resource constraints

Many marketing departments lack analytical talent. In 2023, HubSpot's AI adoption research found that only 27% of marketers using AI felt extremely confident they could identify faulty results, a reminder that analytical judgment matters. Training capable generalists can be more practical than hiring only expensive specialists.

Budgeting can be a challenge when data-driven projects need technology investment up front. Build business cases that tie data capabilities to revenue results rather than tool features. Start with small pilots that prove results and ROI before asking for a bigger budget.

Organizational divisions between marketing, sales and customer support staff can splinter data driven initiatives. Form cross-functional teams, set up shared measures and employ technologies that promote collaboration rather than competition across functions.

Defining What Success Looks Like: Measuring What Matters

Revenue and profitability metrics

Customer Lifetime Value (CLV) is a core measure of data-driven marketing success. Track CLV by acquisition channel, customer segment and campaign type, using a consistent revenue or margin basis, to see which strategies drive the most value.

Return on Marketing Investment (ROMI) compares the net incremental contribution your marketing generates, after subtracting marketing costs, with those costs, as the American Marketing Association describes it. Calculate it on a consistent profit or margin basis; gross revenue divided by spend is ROAS, a different measure. Analyze ROMI by campaign, channel and customer segment to find your best performers.

Revenue attribution shows how credit for sales is assigned to the touchpoints you can observe, under each model's assumptions; it does not prove incrementality. Multi-touch models can give a fuller picture than first-touch or last-touch models, but privacy limits and offline gaps leave holes in the data. Validate major budget changes with controlled experiments where possible.

Operational efficiency indicators

Cost per acquisition (CPA) often falls as targeting improves. Track CPA trends by channel and customer segment to find optimization opportunities.

Campaign optimization speed is how quickly you improve performance through testing and iteration. Teams that run more well-designed tests learn faster.

Data quality scores help keep your insights accurate as data grows and gets more complex. Apply completeness, accuracy, consistency and timeliness metrics across all data sources. Advanced analytics tools are of little use with poor data quality.

Building Your Data-Driven Marketing Team

Essential roles and skill sets

Marketing analysts are the conduits between data and strategy, interpreting complex numbers into actionable recommendations. Seek talent with a background in statistics, marketing experience, and the ability to communicate and explain findings to non-technical colleagues.

Customer data experts handle integrations, quality checks and privacy compliance. Look for experience with technical data platforms, knowledge of privacy regulations and the ability to work across teams to improve how data is collected and used.

Marketing technologists work to set up and manage the systems that support data-driven strategies. Good candidates bring together a love of marketing with technical skill in analytics platforms, automation and integration tech.

Organizational structure considerations

Collaboration across departments becomes crucial because data-driven marketing spans channels and teams. Establish clear lines of communication, shared success measures and ongoing review processes that keep teams focused on customer-centric goals.

Central vs. distributed analytics: Both models have benefits, depending on your organization's size and complexity. Smaller companies often do well with one central analytics team under a strong leader. Larger organizations may embed specialists in business units so their skills match each function's needs.

Learning culture means you and your team are always up-to-date with the latest technologies and methodologies. Promote conference attendance, digital education and experimenting with new tools and techniques. In a fast-paced data driven marketing world those who stop learning stagnate quickly.

Frequently Asked Questions

How much money should I spend on data-driven marketing tools and training?

There is no universal figure. Budget for analytics tools and data infrastructure based on your goals and data maturity, and plan for training and implementation support as well as software. The tools are only as useful as the people using them.

You've probably heard about data-driven and data-informed marketing, but what's the difference, really?

Data-driven marketing lets quantitative findings make the decision: if the data points one way, that becomes the strategy. Data-informed marketing weighs quantitative data alongside qualitative factors such as brand values, customer feedback and strategic goals. Many successful companies take a data-informed approach.

How long before you can expect to see results from data-driven marketing efforts?

Early gains from simple segmentation and targeting can appear fairly quickly, while larger ROI improvements usually take longer, as data quality improves and optimization cycles speed up. Timelines depend on your sales cycle and starting point.

How do I balance my data privacy obligations with marketing effectiveness?

Trust usually comes more easily through transparency than through doing the legal minimum. Explain in plain language what data you collect, why and how customers benefit, and offer preference centers that let customers manage how their data is used. Legal bases also need care: TrustArc's analysis of analytics under GDPR warns that broad consent may not provide a sufficient legal basis for data analytics under the GDPR, so review your approach with qualified advisers.

What are companies doing wrong when they try to use data to drive their marketing?

The three most frequent failures are: 1) Data for data's sake, data collection without a clear intent, more data is not better in-and-of itself if you can't act on insights. 2) They emphasized vanity metrics over business results, the ones that really move the needle on revenue and profitability. 3) Considering data-driven marketing as a project instead of a constantly evolving institutional competence that needs to be reinforced and developed constantly.

How can I demonstrate ROI to my executives for data-driven marketing investments?

Tie data initiatives to revenue results, not technical details. Before-and-after comparisons of customer acquisition cost, lifetime value and marketing efficiency are a start, but seasonality and other changes can confound them; where possible, use holdout groups or controlled tests to show incremental impact. Begin with pilot projects that can prove value quickly before seeking larger investments.

What are the key skills I should look for when hiring for data-driven marketing positions?

Analytical skills are more important than technical abilities in many marketing positions. Look for candidates who can find patterns, ask good questions and translate insights into actionable strategies. Statistical literacy, some SQL and experience with analytics platforms help but are not strictly necessary, since they can be taught. Critical thinking and communication skills are harder to develop and more critical to success.

How do I maintain a balance between automation and human creativity in data-driven marketing?

Creativity is guided by data; not replaced by it. Leverage insights to identify what makes an impact for your audience, and harness the power of creative thinking in finding interesting ways to communicate these messages powerfully. Automation takes care of the mundane optimization stuff, enabling people to concentrate on strategy, creative and relationship building. The best teams are the combination of data science and human insight, of inspiration and creativity.

What's the minimum viable data infrastructure to get started?

Begin with the basics: the introduction of sound tracking across digital touchpoints, basic CRM data capture and email marketing automation. This base allows segmentation, rudimentary personalization and campaign optimization to occur without massive upfront spend. Layer in complexity as you demonstrate value and develop your organizational capabilities.

Related Terms

Marketing Automation - Technology automating repetitive marketing tasks that agencies use to scale client campaigns efficiently
Customer Data Platform (CDP) - Technology platform unifying customer data from multiple sources for comprehensive customer insights
Predictive Analytics - Using data to forecast future marketing outcomes and customer behaviors
Return on Investment (ROI) - Key performance metric agencies use to measure and demonstrate campaign profitability for clients

Expert Tips for Advanced Implementation

Start with high-impact, low-complexity initiatives

Personalized email is a relatively low-effort place to start. Begin by personalizing subject lines with purchase history, region or browsing behavior, and test the results against a control group. For more examples, see SAP Emarsys's guide to data-driven marketing.

A testing tool such as Optimizely lets you test dynamic website content once its snippet or integration is implemented on your site, which usually takes some technical setup. Test home pages customized by traffic source, returning visitors or previous page views, and measure the effect on conversions.

Some ad platforms can build lookalike audiences from customer data you are permitted to use. Meta offers lookalike audiences, and Google Ads offers lookalike segments for Demand Gen campaigns, which Google is moving to a suggestion mode in phases during 2026; check each platform's current requirements and limits. Then compare cost per conversion with demographic-only targeting.

Avoid common implementation pitfalls

Don't try to boil the ocean by attempting total personalization from the start. Begin with one channel, demonstrate value and then scale methodically. Teams that try to do everything at once tend to get bogged down and lose sight of ROI.

From day one, keep data clean instead of cleaning dirty data later. Define validation rules, standardize naming conventions and run routine audits. Poor-quality data is the main culprit: it undermines advanced analytics and leads to wrong strategic guidance.

Focus not on flashy dashboards but on insights you can act on. Every metric should map to a specific decision or optimization opportunity. Visualizations that don't drive action waste time and create false confidence.

The Way To Achieve Data-Driven Marketing Success

The businesses best placed to lead their industries in the coming years are likely to be those building data-driven marketing capabilities today. Netflix didn't become a streaming leader by accident; it deliberately developed data capabilities that improve customer experiences and business results. Amazon didn't stumble into successful recommendations; it invested in understanding customer behavior and anticipating needs.

You're not going to have some special, exclusive data that no one else has. It will come from doing much better with data in order to understand customers, optimize experiences and grow businesses. The required tools are available, the methods work, and the potential is enormous.

It's not a matter of if you will one day need data-driven marketing capabilities; market forces are driving adoption. The key is whether you develop these capabilities at the same pace as your competitors, or faster.

Start where you are, use what you have and do what you can. You don't need perfect data infrastructure to start getting value from customer insights. Quick wins such as simple segmentation, early personalization, team training and basic analytics can deliver measurable benefits and prepare the organization for more complex work later.

Mark Jeffery's book Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know makes a related point: focus on the metrics that matter. Data alone is not enough; you need insights, action and results.

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"Data-driven marketing isn't just about collecting information, it's about transforming customer insights into meaningful experiences that drive sustainable business growth. The companies that master this integration between data science and human creativity will dominate their markets for the next decade."

Tessar Napitupulu, Founder and CEO of Arfadia

The road ahead takes determination, time and the open-mindedness to question assumptions with facts. But for those organizations that truly adopt data-driven ways of working, the benefits go so much further than being better at marketing-related metrics. You can develop closer customer relationships, make better strategic choices and build long-term competitive advantages.

And your customers are producing data with every interaction. The only question is whether you'll leverage that information to better serve them, grow your business more quickly and outperform competitors who are still marketing based on gut feelings rather than truths.

The data-fueled marketing revolution is not on its way; it is already here. Companies like Netflix, Spotify and American Express have demonstrated what is possible when you put data at the heart of a marketing strategy. It's your turn to continue that success story and write the next chapter.