Skip to content

What is Data-driven Marketing? Benefits, Challenges, Examples

Data-driven marketing is an approach that improves the effectiveness of B2B marketing by fuelling it with information. Marketers source this data from prior campaigns, marketing technologies or third parties. Then it’s used as a bedrock to drive efficiencies and optimisations.

This page includes a wealth of information on data-driven marketing and its role in an efficient marketing team.

Scroll 👇 or use the menu to skip to a section.

What is a data-driven marketing strategy?

A data-driven marketing strategy is an approach that uses data analysis to guide decision-making and optimise marketing efforts. It involves collecting, analysing, and leveraging data to understand customer behaviour, preferences, and trends. This, in turn, helps create more effective marketing campaigns and achieve business goals.

Discover the best data-driven techniques for marketing - press ▶️ to watch Cognism’s tutorial.

Here’s a comprehensive breakdown of what constitutes a data-driven marketing strategy:

Key components of a data-driven marketing strategy

1. Data collection

  • Data types: Marketers collect data from various sources such as website analytics, social media interactions, customer surveys, CRM systems, sales data, and third-party data providers.
  • Data quality: Your data must be accurate, complete, and relevant. Poor data quality can lead to incorrect insights and ineffective strategies.

2. Data integration 

  • Unify data sources: Integrating data from different sources creates a comprehensive view of your customer. This can involve combining data from online and offline channels.
  • Data management platforms (DMPs): Use DMPs or Customer Data Platforms (CDPs) to aggregate and manage customer data from multiple sources in one place.

3. Data analysis

  • Descriptive analytics: Understand what happened in the past through historical data analysis (e.g., website traffic reports, sales trends).
  • Predictive analytics: Use machine learning and statistical models to predict future outcomes based on current and historical data.
  • Prescriptive analytics: Recommend actions based on data analysis and predictive models to optimise your marketing strategies.

4. Audience segmentation

  • Segment customers: Divide your customer base into distinct groups based on demographics, behaviour, buying patterns, etc., to tailor your marketing efforts more effectively.
  • Persona development: Create detailed ideal customer profiles that represent key audience segments and guide personalised marketing.

5. Personalisation

  • Customised messaging: Use data insights to personalise communication and offers for each customer segment or even individual customers.
  • Dynamic content: Deliver content that changes based on real-time data, such as customer behaviour or preferences.

6. Omnichannel marketing

  • Consistent experience: Providing a seamless and consistent experience across all customer touchpoints, including websites, social media, email marketing, and in-store interactions.
  • Cross-channel campaigns: Design campaigns that leverage multiple channels; use data to ensure consistent messaging and optimal timing.

7. Customer journey mapping

  • Map touchpoints: Identify and map all the touchpoints a customer interacts with throughout their journey with your brand.
  • Journey optimisation: Use data to understand how customers move through different stages of their journey. Identify opportunities to enhance their experience and increase conversions.

8. Performance measurement 

  • KPIs and metrics: Define key performance indicators (KPIs) such as conversion rates, customer acquisition cost, lifetime value, and engagement rates.
  • Continuous monitoring: Use analytics tools to continuously monitor the performance of your marketing activities and campaigns.

9. Optimisation and testing 

  • A/B testing: Conduct experiments to test different versions of marketing elements (e.g., emails, landing pages) to determine what works best.
  • Continuous improvement: Use data insights to continually refine and optimise your digital marketing strategies.

10. Data-driven decision making 

  • Insight-driven actions: Base marketing decisions on data insights rather than intuition or guesswork.
  • Agility: Be prepared to adapt strategies quickly; you must respond to data-driven insights and changing market conditions.

11. Ethical data use 

What are the benefits of data-driven marketing?

The future of marketing is data-driven. We’ve identified several reasons to commit to a data-driven approach vs non data-driven, which we’ve laid out in the infographic below 👇

Data-driven marketing infographic

Who conducts data-driven marketing?

In simple terms, it’s the entire marketing team!

Everyone must understand your target market, team goals, and KPIs. It’s the only way to ensure that your team works together and provides a unified experience for your customers.

We’ve outlined the three main groups of data-driven marketers below:

1. Content marketers

Content has historically sat in a pretty unusual position, caught somewhere between creative marketing and analytical marketing.

But the modern content marketing team should be just as data-driven as the others. Engagement/search data can inform every decision, from publishing blogs to posting on social media.

2. Campaign marketers

Campaign marketers (also known as demand generation marketers) rely heavily on data, which should fuel their every move.

This is a key attribute to look for when hiring. Make sure that the interviewee is data-driven and analytical as well as creative. They must have the spark to get a campaign going and the ruthlessness to cut one off when it’s not working.

These hires are hard to find, but worth every penny.

3. Performance marketers

This role is already set up to work closely with B2B data - which they use when running and monitoring their paid ads.

The performance marketer must also be focused. To stay on top of their game, they need a keen understanding and interest in PPC and bidding strategies.

💡 Looking for more info? Click to read Cognism’s guide on the ultimate marketing team structure.

What are the top examples of data-driven marketing?

Data-driven marketing involves various marketing activities, which are different for each of the three data-driven marketer types.

Here’s our list 👇

Content marketing

Content marketers will study data to plan, execute and review their content activities.

The major activities for content marketing are:


SEO research is an integral part of content planning. Conduct keyword research using tools like Ahrefs and SEMrush. These tools help you identify low-competition, high-traffic keywords.

Without the data provided by these sites, a content marketer would be writing aimlessly, hoping to strike a chord using just their writing ability and guesswork.

In contrast, the data-driven content marketer doesn’t guess.

Instead, they gather data and review their competitors’ strategies. Then they create plans which are far more likely to succeed.

Market trends

This often involves reviewing third-party data, along with your own collected data. Having access to other companies’ studies and reports gives you more content to work with.

Blog posts that are full of facts and statistics perform better. Many companies provide these for free, and they’re often great backlink plays.

Before writing an article, the data-driven content marketer researches the topic and gathers stats to include in the copy. Their goal must always be to improve their articles’ Google rankings.

Competitor analysis

Innovation is highly prized in B2B content marketing. But there’s also a lot to be said for following closely behind - for using other companies’ experiences to inform your decisions without taking as much risk.

This is why data-driven content marketer will review their competitors so they know which ones to emulate and which ones to avoid.

Content performance metrics

If hindsight is 20/20, then hindsight backed up by data is at least 30/30!

It allows the data-driven marketer to examine previous campaigns and decide whether to do more of the same (or not!).

Here are some of the key marketing metrics that content marketers must track:

  • Content ROI.
  • Conversion rate.
  • Unique page views.
  • Form submissions.
  • Content shares.
  • Content downloads.
  • Leads generated.
  • Website ROI.

Campaign marketing

Campaign marketers use data for planning, reviewing, continuing or terminating their campaigns.

Their major activities are:

Lead nurturing

Lead nurturing is the process of keeping in touch with outbound leads until they’re ready to buy. It’s a hard line to walk: too much interaction, and you’ll scare them off; too little interaction and you’ll miss your shot.

Collecting data on your nurtured leads will grant you more precision. It will give insight into the content types they’re interested in and how close they are to purchasing your product.

With this information, the lead can be put on a customer journey that suits them.

Marketing attribution

Marketing attribution is often the next big step for marketing teams. It can take a little while to figure out, but it can drastically improve how you understand your campaigns.

It’s the process of identifying the customer journey more specifically. Good questions to ask at the start of this process are:

  • Where are our leads coming from?
  • Where are they dropping off?
  • How are we gaining or losing leads?

Market trends

Much like the content marketer, the campaign marketer should keep a close eye on trends. Using information generated by third parties and their own insights, they can create a more informed campaign strategy.

While this information is not often included in the campaigns themselves, it’s invaluable for campaign planning.

Email marketing metrics

Without a doubt, email marketing is highly dependent on data. It’s also a vital channel for campaign marketers, in terms of promoting content, boosting brand awareness and generating leads.

When running email campaigns, these are the metrics that campaign marketers focus on:

  • List size.
  • Bounce rate.
  • Open rate.
  • Read rate.
  • Deliverability rate.
  • Conversion rate.
  • Sharing rate.
  • Click-through rate.

These data points should all be accessible through your email automation platform, so there’s no excuse for not using them!

If your email automation platform gives you access to any additional stats, then take a look at them. We can’t give you a definitive list of the additional stats you need to be measuring (other than the above) - because it’s different for every company.

The trick to working out which stats to use is to consider your company goals and identify the steps you need to take to reach them. If any of those steps can be measured with the data points you have available, you’re in business!

💡 Looking for more info? Find out why you need email marketing automation.

Performance marketing

You’ll struggle to find an aspect of the performance marketer’s role that isn’t data-driven!

Their major activities are:


This is almost as important for the performance marketer as it is for the content marketer. If they’re creating Google Ads, they’ll need a well-thought-out keyword strategy.

Market trends

The performance marketer also needs to keep a close eye on market trends. It’ll help them make informed decisions about their campaign’s positioning and messaging.

Without this, the performance marketer will forever be playing catch up.

Paid ads performance metrics

This is the performance marketer’s bread and butter. They must check their campaign statistics every day, or they’ll never know when to suspend or double down on a campaign.

This information also helps them create more successful campaigns. They can review past results to determine the most effective channels, messaging, and creative assets.

These are the most important statistics for a performance marketer to measure:

  • Impressions.
  • Ad position.
  • Click rate.
  • Lead rate.
  • Closed deal rate.
  • Cost per impression.
  • Cost per click.
  • Cost per lead.

These data points will all be available through your chosen paid ads platform.

💡 Looking for more info? Discover Cognism’s guide to LinkedIn advertising.

How do you set up data-driven marketing?

A data-driven marketing strategy will make your marketing efforts more predictable and successful.

There are 10 steps to implementing one:

1. Get to know the stakeholders

This involves your current team, other teams in the company, and your customers.

Learn what they want from your company, who they are, and what types of content they regularly engage with.

2. Dive into the data

You have to familiarise yourself with the data that already exists in the company. What’s being tracked, how’s it being tracked, and are there any gaps?

Then you can start thinking about filling those gaps.

3. Build a content strategy

Look at your existing content and find out what’s missing. What should you be producing more of?

If you’re struggling to identify anything, take a look at your competitors for inspiration. They’ll either let you know what to do, or what to avoid.

4. Make marketing revenue driven

It’s easy for marketing to take a back seat in a business’s journey, simply creating articles and assets for other departments when needed. A high-performing marketing team needs to stand up on its own.

This means ensuring the marketing team understands the wider business goals and owns the metrics specific to their roles.

5. Become a product specialist

Top marketers need to understand every level of the business, not just revenue.

This means meeting with the product team and having training sessions to ensure they have thorough product knowledge. The content they produce will improve tenfold.

6. Get systematic with paid

This means starting small, seeing what works, and growing gradually.

Don’t be afraid to close any channels which aren’t working. When it comes to performance marketing, it pays to be ruthless!

7. Set targets for your team

Each marketer should have their own revenue-based targets to hit. Give the separate teams a fraction of the revenue target to hit each month - this ensures they stay revenue-driven.

It’s also important to give them measurable KPIs, so they can take charge of the data for their own role.

8. Review your tech stack

Find out which marketing technologies your company already uses and whether they provide value. If not, drop them!

9. Establish nurture streams

These can take time, but they’re so important. Make sure you focus on each individual stage of the stream, rather than just the end goal.

This means measuring and changing things on the fly. Great marketing starts with creativity - but is driven by data.

10. Get things done!

Do as much as you can, and get as much life as possible. Then worry about the bigger picture afterwards.

Until you have data to work with and existing campaigns to review, you can’t be data-driven. So get to work!

What are the challenges of data-driven marketing?

Data-driven marketing presents a range of problems that marketers must navigate.

Here are some of the key challenges:

1. Data privacy and compliance

  • Regulations: Stringent data protection laws like the GDPR in Europe and CCPA in California require companies to handle data carefully and transparently. Compliance with these regulations can be complex and resource-intensive.
  • Consent management: Marketers must ensure that they have explicit consent to use personal data, which can complicate data collection processes.

2. Data quality and integration

  • Data silos: Data often resides in disparate systems and formats, making it challenging to integrate and create a cohesive customer view.
  • Accuracy and cleanliness: Poor data quality, including inaccuracies, duplicates, and outdated information, can lead to incorrect insights and ineffective marketing strategies.

3. Technology and tools

  • Choosing the right tools: With a vast array of technologies available, selecting the right marketing tools that align with business goals can be daunting.
  • Integration complexity: Integrating tools and platforms to create a seamless data flow is often technically challenging and resource-intensive.

4. Skills and expertise

  • Data analysis skills: There’s a high demand for skilled data analysts and scientists who can interpret complex data and extract actionable insights.
  • Continuous learning: The field is rapidly evolving, requiring marketers to constantly update their skills and knowledge of new tools and techniques.

5. Data overload

  • Information overload: The sheer volume of data available can be overwhelming, making it difficult to determine which data points are most relevant and useful for marketing.
  • Analysis paralysis: Too much data can lead to decision paralysis, where marketers struggle to make decisions due to excessive data and conflicting insights.

6. Customer trust and engagement

  • Privacy concerns: Customers are increasingly wary of how their data is used. Misuse (or perceived misuse) of customer data can lead to loss of trust and damage to a brand’s reputation.
  • Transparency: Marketers must be transparent about how they use customer data to maintain trust and meet regulatory requirements.

7. Cost and resource allocation

  • High costs: Implementing and maintaining data-driven marketing solutions can be costly, including expenses for technology, data acquisition, and skilled personnel.
  • Resource allocation: Balancing investment in data-driven tools and traditional marketing approaches requires careful planning and resource allocation.

8. Measuring ROI

  • Attribution challenges: Accurately attributing sales and conversions to specific marketing efforts across multiple channels is complex, making it difficult to measure the true ROI of data-driven campaigns.
  • Long sales cycles: For products with long sales cycles, it can be challenging to track and attribute the impact of marketing efforts over extended periods.

9. Rapidly changing environment

  • Market dynamics: Rapid changes in market conditions, customer preferences, and technology can make it difficult to keep data-driven strategies relevant and effective.
  • Algorithm updates: Changes in algorithms used by platforms like Google or Facebook can impact the effectiveness of marketing strategies, requiring continuous adaptation.

10. Ethical considerations

  • Bias in data: Data-driven approaches can inadvertently perpetuate biases present in the data, leading to unfair targeting and exclusion of certain groups.
  • Ethical use of data: Ensuring that data is used ethically, especially when it involves sensitive information, is crucial for maintaining customer trust and brand integrity.

What is the future of data-driven marketing?

The future of data-driven marketing will be shaped by several transformative trends and technological advancements.

Here’s a detailed look at the key aspects that are likely to define the future of data-driven marketing 👇

1. Advanced AI and machine learning integration

  • Predictive analytics: AI will play a crucial role in more accurately predicting customer behaviours and preferences, allowing for highly personalised marketing efforts.
  • Automation: Marketing processes, such as content creation, customer segmentation, and campaign optimisation, will become increasingly automated, freeing up time for strategic decision-making.

2. Enhanced personalisation

  • Hyper-Personalisation: Future campaigns will focus on delivering personalised content and offers in real-time, based on a deep understanding of individual customer profiles and behaviours.
  • Contextual marketing: Marketing will become more contextually aware, using data such as location, time of day, and current activity to tailor messages that resonate more effectively with customers.

3. Omnichannel integration

  • Seamless customer experience: Integrating data across multiple channels (e.g., social media, email, in-store, mobile apps) will provide a unified view of the customer, ensuring consistent and seamless experiences.
  • Cross-channel insights: Data-driven marketing will leverage insights across channels to optimise engagement and conversion strategies, breaking down silos and enabling a holistic approach to customer engagement.

4. Rise of first-party data

  • Decreased reliance on third-party cookies: With increased restrictions on third-party cookies, businesses will turn to first-party data (data collected directly from their own customers) to inform their marketing efforts.
  • Building direct relationships: Companies will invest in direct channels like loyalty programs and customer portals to collect and leverage first-party data more effectively.

5. Real-time data processing

  • Immediate Insights: Real-time data analytics will become more prevalent, allowing marketers to react quickly to changing customer behaviours and market conditions.
  • Dynamic campaigns: Marketers will adjust their campaigns dynamically based on real-time data, leading to more responsive and agile marketing strategies.

6. Voice and conversational AI

  • Voice search optimisation: As the use of voice assistants (e.g., Alexa, Google Assistant) grows, marketing strategies will adapt to include voice search optimisation and voice-activated content.
  • Chatbots and virtual assistants: AI-powered chatbots (such as ChatGPT) will handle customer inquiries and provide personalised recommendations, enhancing customer interaction and data collection.

7. Augmented Reality (AR) and Virtual Reality (VR)

  • Immersive experiences: AR and VR will enable more engaging and interactive marketing experiences, providing customers with immersive ways to explore products and services.
  • Data from interactions: Data collected from AR/VR interactions will offer new insights into customer preferences and behaviours, enhancing the ability to tailor marketing strategies.

8. Integration of IoT (Internet of Things)

  • Smart devices: Data from connected devices will provide new opportunities for personalised marketing based on how customers interact with their smart environments.
  • Context-aware marketing: IoT data will enable marketers to understand customer contexts better, allowing for more precise targeting and relevant messaging.

💡 Looking for more info? Read Cognism’s B2B marketing predictions.

Data-driven marketing: additional resources

The Cognism blog is your number-one resource for marketing blog content.

We also have a podcast, which you can listen to on Apple or Spotify.

Finally, gain a deeper understanding of B2B marketing by reading “The Diary of a First-Time CMO” - written by Cognism’s CMO, Alice de Courcy! Click the banner to get started 👇

Cognism Diary of a CMO