Marketing data: Types, benefits, and solutions

Marketing teams are using 230% more data compared to 2020. While it can be a good sign that marketers have become data-savvy and incorporated data in their day-to-day tasks, the reality shows that 56% don’t even have enough time to analyze and act on this massive amount of data generated from different campaigns.

Why is it so hard to make sense of marketing data, and how can we, as marketers, break through the noise? Let’s find out in this article.

4 types of data in marketing

Marketing data is a vital asset that provides in-depth insights into customer behavior, market trends, and campaign performance. It is gathered from various public and private sources and is instrumental in identifying potential customers, creating engaging content, and designing effective marketing strategies. When analyzed correctly, this data can significantly enhance a company's marketing efforts and increase sales and customer satisfaction.

Understanding these categories is essential for comprehensive marketing data management. Each has distinct advantages—and challenges—you’ll want to consider.

Zero-party data

Zero-party data is information your customers voluntarily share, such as through quizzes, surveys, or polls. It’s often the most accurate because people directly state their preferences.

Source zero-party data through:

• Quizzes
• Polls
• Surveys

Compared to third-party data, zero-party data provides more accurate information about your customers (since it comes directly from them) while staying privacy-compliant. But according to the 2025 Marketing Data Report, only 16% of marketers are using zero-party data—it’s a missed opportunity.

First-party data

First-party data is information collected about a customer by interacting with an owned property. Examples of first-party marketing data up for grabs include:

• Website: Time on site, bounce rate, and pages per session.
• Mobile app: Notifications opened, monthly active users, or retention rate.
• Ecommerce platform: Order value, products purchased, or several purchases.
• Customer relationship management (CRM): Deal size, sales cycle length, or preferred communication channel.
• Social media analytics: Engagement rate, website clicks, or time active.
• Point of sale system: Preferred payment method, purchase location, or sell-through rate.

Marketing data reveals much about potential customers, but clarity on your tracking is essential—especially for EU audiences. GDPR mandates that consumers understand what data websites collect.

Plus, third-party marketing data's quality and accuracy are declining. Major operating systems, including Google Chrome and Apple, respond to consumer privacy concerns with cookie tracking limitations. No wonder 87% of marketers say that they’re using first-party data in their marketing strategies.

Second-party data

Second-party data is another company’s first-party data, shared with you through partnerships or agreements. For example, if you partner with another business and they share their customer data with you, like a retailer sharing customer data with a complementary brand.

The benefit of this is that it offers a broader audience view without resorting to cold, aggregated information. Still, it is essential to consider that both parties must trust each other and clarify how data is shared, stored, and used.

Third-party data

Third-party data is information obtained from external sources. However, with increasing privacy and declining third-party cookies:

• 66% worry about tracking users across channels
• 57% expect less effective targeted advertising
• 57% predict more difficulty in marketing attribution and measuring marketing

Wondering how we found this insights?

In our 2025 Marketing Data Report, discover how global marketing teams use and manage marketing data to tackle these challenges and adapt beyond third-party data.

Explore the full report
Why is marketing data important?

Gone are the days when gut feelings and guesswork formed the backbone of your marketing strategy. Businesses that leverage marketing data analytics can make data-driven decisions rooted in real-world evidence—helping you focus on campaigns and channels that deliver the most significant impact.

• More time for productive work: Collecting, cleaning, and analyzing marketing data manually can consume enormous time. Instead, automated data management solutions pull multiple data streams into a single source of truth.
  • Case in point: Nestlé, the global FMCG giant, consolidated UK-specific data into one dashboard and cut 80% of its reporting time. With that extra bandwidth, Nestlé’s team could focus on turning marketing analytics insights into outcomes—testing new campaigns, improving targeting, and refining product positioning.
• Gain powerful insights: Each campaign generates key performance indicators (KPIs)—like click-through rates, conversions, or revenue. You'll see how marketing efforts fuel growth by comparing these marketing metrics with broader business metrics (e.g., churn, net revenue).
  • For instance, content marketing professionals can track organic traffic or time on site, while paid search managers might focus on ROAS. These insights shape future investments and help convince stakeholders to approve additional marketing budgets. 
• Improve return on ad spend: When spending money on advertising, you must ensure your decision-making process is foolproof to earn cash back through paying customers. Marketing data allows you to do that.
  • For example, let’s put that into practice and say gut instinct tells you that women aged 20–30 are most likely to purchase your products. However, you found that previous advertising data shows women in the 20–25 age bracket account for 80% of all sales.
• Marketing data = opportunities and competitive advantage: Knowing your marketing data closely, including who your customers are and what they respond best to, allows you to create personalized campaigns that reach your target market.
  • In a 2024 survey, when asked about their attitudes toward online advertising, most U.S. respondents answered, "I am often annoyed by advertising on the internet." Most people appreciate data-driven ads that reveal products of genuine interest to them.
• Less hassle with maintenance: From creating client reports to refreshing ad creatives, maintenance is hassle-free when leaning on reliable data. Manual data entry invites errors, confusion, and endless hours of debugging dashboards or spreadsheets. With automated reporting tools, you can keep performance dashboards updated without the headache of constant upkeep.

A step-by-step guide to managing marketing data

Below is a practical guide to collecting, storing, and analyzing your marketing database to make informed decisions.

1. Identify required sources of marketing data

Data can come from various sources—website analytics, social media, CRM systems, offline events, and beyond. Each channel has its pros and cons compared to the digital ones:

• Website analytics
  • Examples: Google Analytics, Adobe Analytics
  • Pros: Offers a detailed look at user behavior—pageviews, session durations, and conversions. Useful for understanding top-visited pages, bounce rates, and where users drop off in the funnel.
  • Cons: Requires proper tagging and configuration to capture the right events. It can become overwhelming if you track too many metrics without a clear goal.
• Social media platforms
  • Examples: Facebook Ads, Instagram Ads, LinkedIn Ads, Pinterest Ads
  • Pros: Great for targeting specific demographics and measuring engagement metrics like clicks, likes, and shares. Allows for direct conversion tracking with tools like the Facebook Pixel or LinkedIn Insight Tag.
  • Cons: Each platform has its own reporting interface, making manual comparison time-consuming. Also, social algorithms and data policies can change frequently.
• CRM systems
  • Examples: Salesforce, HubSpot, Pipedrive
  • Pros: Centralizes lead and customer data—helpful for tracking lifetime value, lead scoring, and nurturing campaigns. Ties marketing efforts to actual revenue or closed deals in B2B settings.
  • Cons: Requires consistent data entry and ownership. Without proper workflows, information can become outdated or inaccurate.
• Ecommerce platforms
  • Examples: Shopify, WooCommerce, Magento
  • Pros: Provides direct revenue, order history, and customer details, linking marketing spend to actual sales. Ideal for understanding average order value (AOV), purchase frequency, and cart abandonment rates.
  • Cons: It may require extra integrations for multi-channel attribution, mainly if sales also occur in brick-and-mortar locations.

2. Collecting marketing data

Successful data analysis starts with effective data collection. If the information entering your system is incomplete, outdated, or inaccessible, you can’t make well-informed marketing decisions.

Here are key methods and principles to keep in mind:

• Automated connectors and integrations: Manual data exports from platforms like Google Ads or Facebook Ads can be time-consuming and error-prone. Automated connectors transfer data directly to your chosen destination. This ensures you get timely, accurate information without tedious downloads or CSV uploads.

Owning your data foundation: Storing data in a place you control (e.g., a data warehouse) is critical. When you rely solely on in-platform analytics, you risk losing access or facing limitations if the platform changes policies. Owning your data also means you can blend first-, second-, and third-party sources more freely, maintain historical records, and perform deeper analysis down the road.

Here are some best practices for ethical data collection:

• Obtain clear consent: Respect privacy laws such as GDPR or CCPA. Ensure users know what data you’re collecting and why.
• Collect only what’s necessary: More data isn’t always better. Focus on metrics that align with your objectives so you don’t overwhelm your storage or compromise user trust.
• Regularly audit data sources: Double-check that integrations remain functional, permissions are current, and data fields match your reporting requirements.
• Secure your pipelines: Encrypt data in transit and at rest and restrict who can access it. This prevents unauthorized changes and ensures customer information stays protected.

Data quality checks: Before analysis, review your data for inconsistencies. If metrics appear out of sync, investigate whether an API delay or a misconfiguration in your platforms might be to blame. The time you spend on quality assurance now will save you from drawing false conclusions later.

3. Analyzing marketing data

Deciphering marketing data usually involves four main approaches, each serving a different purpose:

• Descriptive analytics: Summarizes what has happened—like monthly website visits, email opens, or total revenue. It’s a snapshot of past performance that helps you identify general trends.
• Diagnostic analytics: Examines why certain events occurred. For instance, a sudden conversion jump might be traced back to a PR feature or a new ad campaign. This step digs deeper into cause-and-effect relationships.
• Predictive analytics: Forecasts future outcomes by examining historical data patterns, such as seasonality or user churn. Marketers often use machine learning or statistical models to estimate the likelihood of various scenarios.
• Prescriptive analytics: It goes one step further by suggesting actions based on predicted outcomes. For example, it might recommend allocating more budget to a high-performing channel or pausing an underperforming campaign.

Data visualization: An automated dashboard simplifies performance tracking and effectively communicates results to stakeholders.

4. Applying marketing data insights

Data matters only if it drives smart decisions. Turning insights into concrete actions ensures your analytics effort pays off. Here’s how:

• Refine ad spend and targeting: If your attribution data reveals that a particular channel delivers high-quality leads at a lower cost, allocate more of the budget. Similarly, reduce investment in channels that yield unprofitable traffic or conversions.
• Optimize content and messaging: Use engagement metrics—like time on page or bounce rate—to see which topics or formats resonate most. Focus on high-performing themes to attract a broader, more qualified audience.

Personalize campaigns: When you spot patterns in user demographics or behavior (e.g., higher conversion among certain age groups), tailor ads and landing pages to them. This approach boosts relevance, click-through rates, and overall ROI.

Privacy and compliance for marketing data

Collecting all this data is a privilege that comes with ethical and legal responsibilities. Regulations like GDPR and CCPA mandate that users know how, why, and where their data is collected and stored. Compliance isn’t optional; violations risk hefty fines and a damaged reputation.

Data security solutions for marketers help comply by not permanently storing data. They can act as a secure pipe that transfers information from your sources to your chosen destination.

All data is encrypted in transit, and only you decide what to extract, process, or retain. Supermetrics is SOC 2 Type II, GDPR, and CCPA compliant, so your customers’ data remains safe.

Emerging trends in marketing data

Marketers are embracing new technologies and privacy-friendly approaches to maintain a competitive edge. Here are some key trends shaping the field:

• AI & machine learning: Predictive models and AI-driven tools help forecast user behavior, optimize bidding, and even automate A/B tests. While these solutions can be powerful, teams must understand how the models work to avoid misinterpretation. McKinsey’s State of AI Report 2024 found that for the past six years, AI adoption by respondents’ organizations has hovered at about 50%. In 2024, the survey finds that adoption has jumped to 72%.
• Cookieless tracking: As third-party cookies “phase out,” marketers rely more on first-, second-, and zero-party data for personalization. This shift demands flexible solutions that respect user privacy without losing critical insights.

Predictive analytics: Tools that merge historical data with advanced algorithms can anticipate campaign performance, enabling proactive budget allocation and swift adjustments. This technology lets you connect data from diverse sources for deeper analysis with your BI tool of choice.

How to stop drowning in data and find actionable insights

1. Agree on how often you’re looking at marketing reports. In our 2025 Marketing Data Report, we asked marketers how frequently they check their reports. The results:

• 46% check them weekly
• 25% check them monthly
• 21% check them daily
• 7% check them quarterly

So, which one is the correct one? It depends on how often you actually make decisions based on your data. Sit down with your team and clarify how frequently you want to review and act on your metrics.

2. Aim for data-informed (not just data-driven). Being “data-driven” can imply blindly following the numbers. Instead, aim to be data-informed—gather the information you truly need for better decisions, then filter your findings through strategic judgment and experience. You’ll always need human insight to interpret the data in context.

3. Right-size your data. In many cases, you’re more likely to have too much data than too little. The right amount of data is what you can realistically understand and use. Anything beyond that doesn’t just clutter your dashboard—it can actively hinder your reporting and progress. For more guidance on which marketing metrics will be most helpful for your team, check out our guide to marketing reporting.

4. Agree on what you’re tracking and measuring. It sounds obvious, but many marketing teams never formally decide on their core metrics or the questions they need those metrics to answer. If you’re stuck here, check out our guide on data-driven marketing decisions to improve the quality and speed of your decision-making.

5. Ask whether you really need real-time data. Real-time updates can help in fast-paced scenarios, like pacing a flash sale or adjusting ad bids hourly. But if your decisions unfold weekly or monthly, daily metrics might suffice. Think about how quickly you act on your data. If you aren’t making changes multiple times a day, real-time tracking might introduce complexity without genuine benefit.

6. Skip the perfectionism. Getting comfortable with your data often means accepting that it won’t ever be 100% perfect. And that’s okay. Perfection isn’t the goal—if you think your data is flawless, you’re almost certainly overlooking something. Instead, aim for consistency and practical utility.

Start building a marketing data foundation with Supermetrics

Marketing data is a powerful tool—provided you focus on the insights that truly matter. Aim to be data-informed, not just data-driven: decide which metrics are relevant, gather only as much data as you can realistically understand, and steer clear of chasing perfect but impractical numbers.

Pull, organize, and interpret data from hundreds of platforms with Supermetrics. Start your 14-day free trial today.