{"id":6987,"date":"2022-07-26T10:11:47","date_gmt":"2022-07-26T03:11:47","guid":{"rendered":"https:\/\/adtek.agency\/data-driven-marketing-guide\/"},"modified":"2026-10-20T22:50:56","modified_gmt":"2026-10-20T15:50:56","slug":"data-driven-marketing-guide","status":"publish","type":"post","link":"https:\/\/adtek.agency\/en\/data-driven-marketing-guide\/","title":{"rendered":"Everything you need to know about data-driven marketing"},"content":{"rendered":"

Updated on October 19, 2026: figures reviewed and updated.<\/em><\/p>\n

Everything you need to know about data-driven marketing<\/strong><\/p>\n

Customer data and customer behavior are among the most valuable resources a business has for growth. The hard part is collecting that data, analyzing it and turning it into a strategy, and that is where data-driven marketing comes in. This guide from Adtek explains what data-driven marketing is, how it works, what it can do for a business and where it tends to go wrong.<\/p>\n

Where data-driven marketing came from<\/h2>\n

Data-driven marketing improves brand communication by basing it on what you know about consumers. With the right strategy, you can anticipate customers’ needs, wants and future behavior. In return, you can build a more personalized approach and sell more.<\/p>\n

Social media and other technologies (such as digital cookies) let brands collect large amounts of data to understand their target customers better. But as people grow more concerned about online privacy in general, the overall approach to data-driven marketing is changing.<\/p>\n

In 2022, agile marketers were rethinking their strategies in an effort to market precisely after COVID-19. McKinsey surveys in 2020 showed that the pandemic pushed many consumers to try new brands, new places to shop or new online shopping channels.<\/p>\n

In just a few months, retail saw growth that had been expected to take 10 years. A shift this large in how target audiences behave created a need for data-driven marketing, for any organization that wanted to catch the growth opportunity and get a higher ROI and a stronger recovery. Companies that upgraded their marketing became better at generating revenue and built the flexibility they needed to future-proof their business.<\/p>\n\"Where\n

What is data-driven marketing?<\/h2>\n

Data-driven is a term for using data to inform marketers so they can make decisions faster. Decisions no longer rest on personal experience; they rest on actual numbers. Data-driven work is now common in many fields, especially technology and business. Businesses that make decisions and plan strategy based on data reach their business goals faster and more effectively.<\/p>\n

Data-driven marketing is a plan, a strategy, that uses consumer data as its foundation. That consumer data is collected in many ways, including interactions with the brand, artificial intelligence and third parties.<\/p>\n

Consumer data is very useful to brands. It shows them buyers’ motivations, behavior and preferences. Since personalized, targeted marketing is the most likely to attract buyers, marketers collect and use that data to deliver the best possible experience.<\/p>\n

How data-driven marketing works<\/h2>\n

Precision marketing only works with the right data. New models fed with old data can still produce inaccurate results. To capture insights, leaders in the field usually take a wide approach to data collection. They draw not only on behavioral trends and location-based insights but also on third-party analysis of businesses, customers and competitors to supplement their internal customer data. Companies starting down this path are finding the most value in combining research data from government sources with sales data and customer data from third-party providers in their models. Companies that broaden data collection in these ways can pinpoint where rising demand and new customers are coming from, and can see which existing customers have increased their spending and where lost customers have gone.<\/p>\n\"How\n

For example, before updating its modeling approach, one retail chain could only say how many customers it had gained or lost. The company then decided to bring in mobile phone data to scan for changes in competitors’ net traffic. The analysis showed that many of the customers it had won during the pandemic came from professional, higher-priced stores, while the customers it was losing were going to cheaper, value-oriented ones. Based on this information, the retailer changed its onboarding and churn-prevention campaigns. It sent emails promoting more premium products to customers who had switched from specialty stores, while offering low-priced products to value-focused customers who were at risk of leaving.<\/p>\n

In another example, a business services provider tapped new third-party data sources to identify key moments in the life cycle of small businesses. In one such effort, the provider pulled together data sources that showed, with a lag of just one day, when new companies were being formed during the uncertainty of COVID-19. Its sales staff then reached out right away with products and messages suited to the needs of newly founded companies, such as system tools. These combined efforts raised sales productivity by more than 25%.<\/p>\n

Strong data also lets companies build better insight into competitors. For example, by comparing third-party data on assortment, sales and promotions with their own numbers, marketers can judge the strength of different value propositions and see which factors appeal to which customer groups. They can then give each of those groups the right messages, content and offers.<\/p>\n

The added uncertainty of the new normal requires marketers to test better and respond faster. A more agile operating model is important, but it is also increasingly necessary to work with technology that learns at scale. That means building technical capabilities that can read and interpret signals of consumer intent and how consumers respond to marketing messages, then feed those signals back into the marketing engine to learn what works and what does not.<\/p>\n

Marketers who are really pushing the limits use artificial intelligence (AI) to monitor campaigns and assess responses at a granular level, to learn which elements work and which do not, or which segments customers come from, at what times and through which channels, and then adjust strategy based on those insights. Getting specific insights like these with standard analytics can take days of sorting through marketing activity. AI-assisted monitoring can do it in minutes, sometimes seconds.<\/p>\n

For example, one consumer services company launched retention campaigns as communities came out of lockdown. Conventional analytics could only evaluate the campaigns as a whole and found them moderately effective. The company then tested a new AI tool that could look deep enough to evaluate responses at the level of core-based statistical areas (CBSAs), and this showed the campaign was highly effective in specific niches with similar economic and epidemiological profiles. (CBSAs are statistical regions used in the United States, each built around a city and the surrounding areas tied to it by commuting.) The AI tool identified how the campaign’s performance patterns were developing, which let marketers configure the system so that AI-based analytics data fed directly into the campaign’s targeting logic every night. This campaign and others like it were a key part of a broader data-driven marketing program that helped the company raise its trial rate fivefold.<\/p>\n\"How\n

3 trends that shaped data-driven marketing in 2022<\/h2>\n

Balancing privacy and personalization<\/h3>\n

Notably, brands are shifting their data collection strategy from understanding customers better to understanding them well enough.<\/p>\n

Because of growing global concern about data privacy, forward-thinking brands are looking for ways to collect only the consumer information they need for specific purposes. Widespread and growing frustration with big tech companies such as Facebook shows how collecting more data than necessary can breed anger and distrust.<\/p>\n

If you need to collect sensitive information about customers, ask for permission and explain up front how you plan to use it.<\/p>\n\"3\n

More emphasis on behavioral data<\/h3>\n

Behavioral data captures how customers interact with a product or service. Common sources of behavioral data include websites, mobile apps, help desks and chatbots.<\/p>\n

This type of data is usually created and stored as “events”, meaning an action taken by an individual user.<\/p>\n

The rise in interest in behavioral data has been driven by changes in the privacy environment and by renewed interest in tools such as Google Analytics or Matomo. Behavioral analytics can improve conversion, engagement and retention.<\/p>\n

As this trend takes hold, marketers are now looking to dig deeper, rather than wider, when they collect data.<\/p>\n

More data tools call for better integration<\/h3>\n

The range of new data tools for marketers keeps growing, and many companies use different platforms and solutions for different areas of the business. Data tools can measure key metrics such as website traffic, pageviews and click-through rate (CTR) to show what works best for the brand.<\/p>\n

With so many data solutions in use, though, better integration is needed so data can flow freely between systems and overall complexity goes down.<\/p>\n

All of these trends point to a move away from collecting as much data as possible toward a more strategic, surgical approach. Start by deciding exactly what your brand needs to know about its customers. Then think about the best way to collect that information without crossing any lines.<\/p>\n

Benefits of data-driven marketing for businesses<\/h2>\n

Lower marketing costs<\/h3>\n

A data-driven strategy aims for quality of leads rather than sheer quantity. Because the business uses the right marketing channels to target its ideal customers directly, it gets higher conversion rates. Marketers who know their current customers well can spend half as much as a traditional campaign and still get better results.<\/p>\n

Data-driven marketing solves a long-running struggle for marketers: figuring out where budget is being wasted and reallocating it to get more out of every dollar. With more detailed, higher-quality data, marketers can combine aggregate data and the insights from attribution models into one overall measurement, known as unified marketing measurement, or UMM.<\/p>\n\"Benefits\n

Using attribution models, marketers evaluate the customer journey, examining the many touchpoints along the marketing funnel to get a full view of the purchase path for each target segment. Once the marketing team has identified what moves new customers into a new channel, it can allocate marketing spend where it fits best.<\/p>\n

New technical methods<\/h3>\n

Prospects face plenty of choice, and if a marketing message does not fit them, they will simply go elsewhere. Using sophisticated data and analytics can improve the customer’s experience with a brand, because the organization understands better what customers want and can then personalize the customer journey. In essence, it can replace blanket campaigns with relevant ones.<\/p>\n

Data-driven marketing strategies make precise audience segmentation possible, personalize the experience and raise engagement among new customers. Specific knowledge of how different audience groups behave makes it possible to upsell based on their interests and to use the right mix of channels for each demographic.<\/p>\n

Collecting accurate, detailed data can also give you a better picture of customer satisfaction after the sale. Most companies run surveys to support product and service improvements. Customer experience analysis can reveal a great deal about target customers and how to serve them better. In practice, many businesses use customer satisfaction scores (CSAT, NPS) to analyze and improve the customer experience. CSAT asks how satisfied a customer was with a specific purchase or interaction. NPS asks how likely they are to recommend the brand to someone else.<\/p>\n

Planning ahead<\/h3>\n

More actionable consumer data leads to focused, sharp insights. When data-driven marketers understand customers at the individual level, they can predict how customers will respond to different campaigns and make strategic decisions about delivering personalized messages that give the best possible experience.<\/p>\n

Data-driven marketing tools make personalization a smooth, simple process. If you know your target audience is young mothers, for instance, you probably will not create marketing messages aimed at women without children. Decisions about marketing campaigns can also be tracked quickly, so organizations can drop what is not working and optimize what is.<\/p>\n

A solid set of consumer data greatly reduces the risk of failure, because the budget spent on marketing campaigns and product development is matched to user needs and expectations from the start. Sales data can also reveal untapped cross-selling opportunities across the sales funnel that may have been missed before.<\/p>\n\"Benefits\n

Finding the right patterns<\/h3>\n

Advertising has drawn on patterns in consumer behavior since it began, though those patterns can be harder to spot today. Pinpointing the behavioral indicators that stand out requires data that is constantly refreshed from a combination of sources at a granular level. In some cases, this is similar to closely studying individual city neighborhoods, or comparing the buying habits of one upscale suburb with those of the affluent areas next to it.<\/p>\n

For example, with the right customer data, retailers can assess which products different segments are likely to buy for Thanksgiving and adjust their marketing messages to increase sales. Strong data also gives companies better insight into competitors. Comparing third-party data, such as sales and promotions, with their own metrics helps them evaluate and improve what they offer.<\/p>\n

As another example, an algorithm might learn that consumers who engage in showrooming tend to buy the products they looked at later, at a lower price online. Indicators like this can help businesses trigger the right offers to turn a “showrooming customer” into a buyer while they are still in the store, which lets marketers spend on more profitable segments.<\/p>\n

A quick explanation of showrooming: it means visiting retail stores to look at merchandise before buying it online at a lower price. This consumer behavior is typical for higher-priced goods.<\/p>\n

Building connections<\/h3>\n

An organization’s marketing goals determine how it uses data to connect sales, customer behavior and effective channel use. When evaluating marketing goals, there is often overlap between the chosen KPIs and the data needed to make better decisions. A modern data stack (customer relationship management software) can capture key insights across touchpoints better than integrating data by hand, which has a higher error rate.<\/p>\n

Data-driven marketing is an invaluable tool for developing marketing strategy. For example, the customer data you collect may show that most of your target market shops on smartphones. That can inform mobile strategies such as mobile paid ads. Building connections like these has become important for organizations that want to keep relationships with, and stay relevant to, both new and loyal customers.<\/p>\n

For this reason, using a suitable customer relationship management platform that centralizes data is very important to stop information and teams from working in silos. From strategists to creators to salespeople, a good marketing team works as one system, and everyone needs to collaborate to bring in leads and improve revenue. Building connections around consumer behavior through data and teamwork is key to success.<\/p>\n

A better customer experience<\/h3>\n

Data-driven marketing focuses on using in-depth consumer profiles to improve the customer experience. That matters for success, because a poor website experience sends many customers to buy somewhere else.<\/p>\n

The extra personalization that data-driven marketing makes possible builds trust between consumers and brands and creates a positive customer experience. Personalizing the experience can produce real results: according to McKinsey, personalization typically lifts revenue by 10% to 15% (McKinsey, 2021<\/a>).<\/p>\n

Better allocation to make the most of spend<\/h3>\n

A common challenge for marketers is working out where their ad budget is being wasted. Data-driven marketing, guided by analytics tools, lets marketing teams discover which part of the ad budget has the biggest impact on conversions or brand awareness. This is done by evaluating the customer journey with attribution models such as unified marketing measurement (UMM). UMM looks at multi-touch attribution and media mix modeling together to give a full view of the purchase path. Organizations can then see what moves prospects and customers down the funnel and allocate budget accordingly.<\/p>\n

Producing relevant content<\/h3>\n

Evaluating consumer data gives marketing teams insight into the kinds of ads, images, copy and content your target audience wants to engage with. Delivering the right message, one that serves the person’s own interests and creates value at the right moment, is essential to connecting with consumers. Unfortunately, marketers struggle to match content to their audience, for example:<\/p>\n