What is Data-Driven Marketing?

The practice of marketing has evolved beyond mere gut instincts. A new age of marketing has dawned, one that is powered by numbers, patterns, and personalised user insights. In this article, we delve deep into the world of data-driven marketing, exploring its definition, benefits, challenges, and the strategic tools that help businesses thrive in a competitive marketplace.


Defining Data Driven Marketing

Data-driven marketing is a collection of processes that come together to enable businesses to utilise their data, deriving insights about customer behaviours, enabling the crafting of personalised campaigns and refined brand communication. By analysing this data, marketers ensure that their strategies are aligned with the preferences and behaviours of their target audience.

Gavin Brogan (He/Him)

Managing Director @ Coorie Dug | Digital Analytics, Online Marketing


With over 2 decades of experience in digital analytics and marketing Gavin loves to help clients optimise their online presence, achieve their business goals and to talk about using data driven growth.

Examples of Data Driven Marketing:

  • Personalised Content Experiences – Online platforms increasingly use data about user preferences and past interactions to suggest new experiences. For media platforms such as Netflix or Spotify this could be recommendations of new shows or music. This ensures users find content they are likely to enjoy, increasing engagement and time spent on the platform or for increase conversions.
  • Personalised Marketing Campaigns – Businesses utilise user interactions data and behavioural analytics to tailor their campaigns. For instance, if a user has shown interest in a particular product but hasn’t made a purchase, the business might enter a user into an automation flow to send follow-up comms with a special offer or related product.
  • Behavioural Ad Targeting – Digital platforms like Google Ads and Meta (Facebook and Instagram) allow advertisers to target users based on their online behaviours. For instance, if a user has searched for hiking boots, they might then see ads for hiking equipment on other websites they visit.
  • Dynamic Pricing – It’s possible to adjust prices based on user behaviour and demand. For instance, a flight booking website might offer discounted prices to a user who has repeatedly checked the price of a specific flight.
  • Chatbots and Virtual Assistants – Use of chatbots is increasingly common to answer user queries. These bots often utilise user data and past interactions to provide more accurate and personalised responses, improving the user experience.
  • Location-based Promotions – Geolocation data can be utilised to offer promotions. For instance, for example a coffee shop might send a discount coupon to a user’s phone when they are near one of their outlets.

Traditional Marketing vs. Data-Driven Marketing

Traditional marketing often leans on tried-and-tested methods and past experiences however, data-driven marketing prioritises real-time insights and ongoing adaptation to optimise the user experience.

Key differences

Components Traditional Marketing Data-Driven Marketing
Data Collection Relies on collecting broad demographic data through surveys, focus groups, or purchase histories. Harnesses diverse data sources like website analytics, social media engagements, CRM data, and real-time user interactions.
Data Analysis Limited to basic statistical methods, often manually calculated or interpreted, resulting in more generalised insights. Employs advanced analytics tools, machine learning, and AI algorithms to dissect large datasets and derive specific actionable insights.
Strategy Development Uses past successes and broad demographics to shape marketing strategies. A ‘what worked before might work again’ mentality. Develops strategies based on current data insights, evolving consumer behaviour, and market trends. Always rooted in the present state of the market.
Campaign Execution Once a campaign is live, there’s minimal scope for mid-course correction. Execution largely remains static. Campaigns are adjusted in real-time based on real-time user feedback, engagement and other relevant data.
Personalisation Offers generic content to a broader audience, with maybe a few variations based on broad demographics. Delivers highly personalised, targeted content tailored to individual preferences, browsing history, and past interactions, increasing relevancy.
Feedback and Iteration Relies on post-campaign reviews to understand efficacy, often leading to delayed strategy adjustments. Allows for continuous feedback and iterative improvements even while campaigns are live, ensuring ongoing optimisation.
Measurement and ROI Gauges success with more generic metrics, and ROI calculations can be more indirect and broad. Utilises advanced tools for precise measurement, offering a clear understanding of ROI for each campaign or even specific aspects of a campaign.

While traditional marketing methods still have their place, especially in building broad brand awareness, data-driven marketing offers precision, adaptability, and direct measurability. By leveraging the power of data, businesses can more effectively engage their audience, ensuring both relevancy and higher ROI.

Navigating the Data Deluge

Many budding data-driven marketers find themselves overwhelmed when faced with the task of collecting and interpreting customer data. The common questions looming are:

  • Where should they find this data?
  • How do they manage the sheer volume of information available?

This uncertainty often leads to hesitation or even complete avoidance of initiating data-driven campaigns. The good news? Most businesses are already sitting on a goldmine of data; it’s just a matter of tapping into it effectively.

Your Existing Data Sources:

  • CRM Systems – These platforms often hold valuable customer profiles and interaction histories.
  • Website and Social Media Analytics – Understand user behaviour, site visits, brand engagement, audience demographics, and sentiment analysis.
  • E-commerce Platforms – Track purchase histories, cart abandonment rates, and user preferences.
  • Advertising Tools – Gauge the effectiveness of ad campaigns, user engagement, and more.

Striving for Comprehensive Insights

Relying solely on historical sales data or isolated datasets can provide a skewed perspective. It’s essential to constantly update and broaden data sources, especially in today’s dynamic market where consumer interests and behaviours shift rapidly.

While initiating with data from past years is commendable, a truly effective data-driven strategy demands a holistic view. Therefore, we recommend always aim for larger, more comprehensive enriched datasets that can offer a panoramic view of the market and customer behaviours.

Using Data to Tell Your Story

Navigating the digital age, marketers are privy to vast amounts of data. The challenge lies not just in collection but in efficient aggregation and real-time utilisation.

Staying Updated

For data to be impactful, its freshness is paramount. Real-time information is the gold standard, but if that’s unattainable, updating data frequently – daily or weekly – is crucial. Yet, the task of manually aggregating and refreshing this data, especially if you’re resorting to manual spreadsheet entries, can be tedious and prone to errors.


Modern Solutions for Data Aggregation

One way to circumvent this challenge is to employ a marketing dashboard. Advanced analytics and visualisation tools, like Power BI, Tableau and Looker Studio, are designed to seamlessly integrate with various data sources. Strive for a unified view of all campaign data, ensuring that marketers have a holistic perspective at their fingertips.


The Power of Data Warehousing

However, for organisations dealing with vast data volumes spanning multiple departments, simple dashboards might not suffice. This is where advanced solutions like Enterprise Data Platforms (EDP) and Data Warehousing come into play. They don’t just aggregate; they store, process, and manage data from varied sources, ensuring that marketers have a consolidated and structured data repository. With such tools, marketers can delve deeper, uncovering patterns and insights that can be pivotal in crafting compelling narratives.

Creating a Data-Driven Marketing Strategy

Creating a data-driven marketing strategy doesn’t simply mean looking at numbers and making decisions based on them. It’s a systematic process that requires integrating data insights into every stage of your strategy. Below is a step-by-step guide to help you formulate a compelling, data-backed marketing strategy.

Define Clear Objectives

Before diving into data, set clear, measurable objectives. Whether it’s increasing website traffic, boosting sales by a certain percentage, or improving brand awareness, your goals will guide your data collection and interpretation.

Gather Relevant Data

Utilise tools to collect data from various sources like CRM, website analytics, social media platforms, and more. Ensure that the data aligns with your set objectives. For instance, if your goal is to enhance website traffic, focusing on website analytics will be pivotal.

Data Analysis

Once you’ve amassed the data, analyse it to extract actionable insights. Look for patterns, trends, and anomalies. Tools like Google Analytics, Tableau, and HubSpot can be indispensable in this phase.+

Segmentation and Targeting

Use the data to segment your audience based on parameters like behaviour, demographics, and purchasing patterns. Targeting specific segments ensures your marketing efforts are more tailored and effective.

Strategy Formulation

Now, with a clear understanding of your audience’s behaviour and preferences, draft your marketing strategy. This should encompass channels to focus on, content types, advertising strategies, and more.


With your strategy in hand, it’s time to implement. Use automated tools and platforms to execute your campaigns. Ensure that there are mechanisms in place to continually collect data even in this phase.

Review and Optimize

A data-driven approach is iterative. Post-implementation, collect data on the campaign’s performance. Review this data to understand what worked and what didn’t. Refine your strategy based on these insights for future campaigns.

Stay Updated

The digital landscape is ever evolving. Regularly update your data sets and tools. Attend webinars, workshops, and conferences to stay abreast of the latest in data-driven marketing.

Iterate and Refine

Data-driven marketing is not a one-time process. Continually revisit and refine your strategy based on new data and insights. This ensures your marketing efforts remain agile and relevant.