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Data-Driven Marketing: Make Kick-Ass Campaigns Backed by Data

You're here because you're interested in how data can disrupt marketing.

Or, maybe you're looking for inspiration to create data-driven marketing strategies.

Or, maybe you searched for your favorite yogurt, but Google's search algorithms have gone mad.

Either way, stay with us to learn exciting insights about data-driven marketing.

Today, companies use data-driven marketing to reach out to new prospects with target ads and creatives. Data skyrocketed marketing transformation and made digital campaigns much more effective.

However, the rising quantity of information created a demand for solutions that can process and store these data. Marketing startups and large companies, in response to the situation, have rolled out various technologies that simplify the process and provide a personalized marketing experience. 

Here are the primary objectives of using data to improve marketing performance:

How marketers use data in their day-to-day workflow
Real-world application of data in marketing

According to Google, 72% of marketers are willing to invest in the quality of gathered data. Data-driven marketing allows companies to optimize their promotional strategies and reach out to new audiences.

In this post, we’ll dive into the concept of data-driven marketing, single out its benefits, and figure out why it’s crucial for modern brands and agencies. 

What is Data-Driven Marketing?

The meaning of the term lies in the name itself.

💡Data-driven marketing means collecting and using the information to drive your marketing decisions.💡

That's it, very simple.

If we'll dive into detail, data-driven marketing uses strategies that are driven by the insights extracted from data and which we use to make predictions, decisions, run tests, and adjust marketing efforts to match the end customers' needs.

This type of marketing requires teams to understand the data they’ve already gathered, know how they can aggregate new data, and find ways to harmonize, organize, analyze, and apply it to their marketing strategies.

🚀Read What is Data Harmonization and Why Is It Crucial for Performance Marketing? 🚀

Why data-driven marketing is all the rage

To understand why marketers care about data and analytics, we need to go back in time and review how marketing was arranged a few decades ago.

Basically, marketers used a spray-and-pray approach. It means marketers spread their messaging and communication to a broad audience and would pray that the message hits the company's target audience.

In the past, advertisers had near-to-zero information about their audiences, customers' preferences, engagement level, and so on. That's why their decision-making process was based solely on very broad parameters like age, gender, location, and so on.

Here's a great example of how misleading demographic data can be.

How misleading demographic data can be
It's highly unlikely that Prince Charles and Ozzy Osbourne fall into the same buyer category

Companies could hardly understand what inspired customers to purchase their products or services.

Nowadays, marketers possess an incredible amount of information. We're not talking about very broad data about geolocation or the model of the customer's device.

Real data-driven marketers know the prospects' decision-making behavior, hobbies, how they interact with the brand, what touchpoints they use throughout the customer journey, and more.

Data-driven marketing allows customers to build buyer personas around customers' problems and challenges, rather than their gender or wealth.

The difference between traditional and data-driven marketing

Traditional (or legacy) marketing is based on hunches, guesses, and assumptions about what customers want and need. Relying on intuition and someone's experience isn't an option when businesses have all data at their fingertips.

Traditional marketing is a process of trial and error. But, sometimes, these errors cost a fortune, and failing marketing campaigns may deal a serious blow to the company's budget or even brand image.

At the same time, data-driven marketing is based on facts and stats. It allows marketers to reach out to the right audiences and avoid expensive mistakes. Besides, customer data can be used in different ways, such as:

  • Create personalized marketing campaigns
  • Assess the performance of existing campaigns and modify them if needed
  • Explore new markets

Data-driven marketing as a part of analytical company culture

It's important to understand that data-driven marketing may not work on its own. The utilization of data in marketing will definitely increase the company's marketing performance, but it won't affect overall growth too much.

To improve your revenue growth, the whole company should be data-driven. It's really hard to achieve meaningful results if all the other departments in the company have data silos and minimal visibility into the processes in the company.

Data-driven marketing relies on a sales department and well-developed data and IT infrastructure. When it comes to data, companies can't prioritize just a single operation (marketing, sales, or else), and ignore all the rest. The company should have a unified data ecosystem that ensures maximal visibility into all operations and facilitates collaboration between departments.

How Data-Driven Marketing Benefits Businesses

Businesses and consumers can benefit from data-driven digital marketing. While companies get more visibility into the clients’ behavior patterns and subjects of interest, customers can find out about new products right on time and make a purchase in just a few clicks. Now, let’s break down all data-driven marketing benefits to understand the real value it brings to companies. 

Improved Personalization

The digital marketer’s main task is to deliver a convincing message to the right target audience that needs it at that very moment. With data-driven advertising, brands create personalized marketing campaigns that help attract more prospects due to a deeper understanding of the customer’s preferences and requirements.

McKinsey's recent research shows that 71% of consumers expect personalization from businesses, and 76% of consumers get frustrated if they don't get a personalized experience.

Consumers' expectations on personalized experiences
Personalized experience is what customers want from today's brands


A personalized advertising approach helps brands with the following:

  • Keeping in touch with each of their customers on an individual level
  • Raise brand awareness
  • Increase customer loyalty
  • Improve ROI and ROMI.

And it pays off. Personalization plays a vital role in the company's growth. Businesses that develop personalized experiences for customers grow 40% faster than others.

Personaluzed experience growth statistics
Personalized experiences can significantly accelerate thecomapny's growth


Holistic View of Your Audience

With the right tools, marketers can filter large volumes of personalized information to identify the most actionable insights about their target audience and prospects. Data-driven marketing combined with the right data processing system makes it easier to segment, group, and reach out to relevant audiences. With segmentation and improved visibility of your audience, you can create more personalized marketing campaigns and keep clients engaged.

Cross-Channel Advertising

Marketing teams use data to increase their outreach and promote their brand to multiple channels. Omnichannel marketing ensures that your company sends consistent, aligned messages that reach the right users at the right time. 

Recent research by Fluent has found that 62% of customers who interact with the brand via 10+ channels make purchases at least once per week.

However, according to London Research, only 38% of businesses have synchronized customer journey across all physical and digital channels.

How companies manage cross-channel communication
As seen, a lot of companies have poor management of cross-channel communication

This means a lot of companies are missing a competitive edge and additional revenue by overlooking omnichannel advertising.

However, it’s hard to align cross-channel marketing data without proper data tools. ETL marketing solutions help companies in cases such as:

  • Extracting data from different sources simultaneously 
  • Unifying disparate data
  • Storing insights in a dedicated data warehouse 
  • Streamlining analysis-ready data to visualization tools

With organized data processes and all marketing data in one place, advertisers can reduce repetitive work and concentrate on marketing analysis rather than manual data operations.

Data-driven attribution

While on the subject of omnichannel advertising, we need to pay attention to omnichannel attribution. Modern marketers use a range of attribution models to understand what touchpoints inspired customers to make the purchase.

Let's review the most popular ones.

First-touch attribution model

The first-touch attribution model gives 100% credits for the conversion to the first channel in the funnel.

First-touch attribution model
First-touch attribution model

Last-touch attribution model

The last-touch attribution model assigns 100% credits to the last channel in the funnel.

Last-touch attribution model
Last-touch attribution model

Linear attribution model

The linear attribution model gives an equal amount of credit to all channels in the funnel.

Linear attribution model
Linear attribution model

Position-based attribution model

The position-based attribution model gives higher priority to the first and last touchpoints in the funnel.

Position-based attribution model
Position-based attribution model

Time decay attribution model

The time decay attribution model allocates more credit to touchpoints that are closer to the conversion.

Time decay attribution model
Time decay attribution model

All of these attribution models have their own pros and cons, but in reality, they have one common, significant drawback. Each of these models neglects certain touchpoints. Hence, it's probable that the touchpoint that impacts the conversion the most will be overlooked.

That's why marketers move to data-driven attribution.

Data-driven attribution

This omnichannel attribution approach uses AI and machine learning to identify the touchpoints that drive sales and allocate credits automatically, based on data.\

Data-driven attribution model
Data-driven attribution model

The utilization of any other attribution model except data-driven may lead to expensive mistakes. Without data, businesses can't see the holistic picture of each touchpoint's performance. It eventually results in wrong decisions, ignoring high-performing channels, and loss of revenue.

Improved Understanding of Your Customers

Companies can use data-driven marketing to understand what their customers lack and how they can improve customers’ perception of their experience with the brand.

For example, data allows companies to reduce the customer churn rate and win back customers faster. In a world where one single mistake can cost businesses one-third of their customer base, failing isn't an option. PWC states that 32% of customers will stop doing business with brands after a single negative experience.

Customer churn statistics
Customers aren't very tolerant to mistakes

With data-driven marketing, companies can reach out to loyal clients who stopped visiting websites or make purchases in stores. Offering customers a unique proposition can help bring them back, restore their trust, and improve the brand image.

To learn more about the customers' opinion of your company, you can send them surveys. For example, NPS surveys help to effectively gather customer feedback and bad experiences that customers come across while interacting with your brand.

🚀Learn how to visualize customer satisfaction rate with detailed revenue charts🚀

Minimized Failure Risks

With data-driven advertising and an enhanced understanding of the market, companies minimize the risk of failure when launching new products or new product lines to the market.

With granular insights into their prospects and the market as a whole, marketers conduct initial testing, identify potential customers beforehand, and guarantee a successful product launch.

It’s hard to list all of the benefits of data-driven marketing at once. The advantages that a company can extract from this advertising approach strictly depend on its business objectives and business model. However, there’s no denying that data-driven advertising is useful for any kind of company.

How Do Businesses Use Data?

Today, marketers have found dozens of ways to use data for lead generation and promoting their brands across different channels. Let’s take a look at some of them.

Recommendation Engines

Recommendation algorithms and feed pages always try to show the most relevant content for each user and keep them engaged. The software suggests the most suitable content based on your activities inside the app and all of your interactions with external channels.

🚀Learn what product recommendation is and how marketers use it🚀

For example, YouTube recommends content based on the user’s viewing history, search queries, and the type of content they interact with. 

Ecommerce platforms gather a lot of yields from product recommendation engines. helps over 18,000 websites provide personalized user experiences across email, social media, site search, and more. The platform offers 15 pre-built recommendation features such as hot products, best cross-sell products, analytics, and more.

Data-driven content marketing

Companies always want to learn more about their market, customers, their behavior, and so more. Crafting content backed with actionable data provides much more value than plain text and empty words.

That's why B2B companies often share reports, researches, surveys, and more. Sharing your expertise on the topic you're into makes your company look more trustworthy and authoritative.

🚀Check out Improvado's Global Digital Advertising Spend by Industry in 2021 🚀

However, B2C companies also share data with clients to make them feel more engaged. For example, Spotify recently released its "2021 Wrapped". It provides users with statistics into the music they listen to.

The platform shows each user's favorite artists, genres, songs. It even visualizes the user's music aura based on the types of music they listen to.

Data-driven content: Spotify's 2021 Wrapped
Spotify's interaction with users is always on point

Such interactions with the audience make brands stand out among competitors and provide unique experiences to users.

A/B testing

Marketers constantly run A/B testing campaigns to discover which ad format, wording, and graphic design works better for their target audience.

A/B testing is a great way to find out not only what types of ads perform best, but also how many interactions are needed for potential customers to make a purchase.

Without data, A/B tests would be completely useless. How will you know which ad or blog generates more clicks if you don't gather this data?

A/B testing in marketing
A/B testing example

Attribution modeling

We've already discussed attribution modeling earlier.

If you’re dealing with omnichannel advertising, you can use gathered data to identify the channel that generates the most revenue. It also allows you to pinpoint exactly why it performs better than others and make improvements to other campaigns with attribution modeling.

🚀Read How to Build a Custom Marketing Attribution Model [Guide] 🚀


Geolocation is extremely valuable data for marketers. The majority of social media ad channels provide information about the user’s current position. This data can be used to make special offers tailored to each user's location.

If you have precise information about your customers' location, you can successfully use it with paid ads campaigns. For example, Google Ads allows marketers to target prospects based on countries, regions, or even a specific radius around a certain location.

Besides, you can also exclude users based on their location. For example, if you only ship in the U.S., you don't want to show your ads to Europe-based users.

However, to get the most out of geotargeting, you should have demographics about your users. Neil Patel explained in detail how to use geotargeting and what data you'll need.

There are millions of use cases for data-driven marketing. However, companies often face the challenge of structuring their raw cross-channel data. Right now, it’s time to discuss all of the challenges of data-driven advertising and figure out how you can overcome them.

There are millions of use cases for data-driven marketing. However, companies often face the challenge of structuring their raw cross-channel data. Right now, it’s time to discuss all of the challenges of data-driven advertising and figure out how you can overcome them.

Data-Driven Marketing: Pitfalls to Watch Out For

Like any other strategy, data-driven marketing has its own downsides.

Kimberly A. Whitler, former CMO, professor, and Forbes contributor, highlights the following issues as main blockers to converting data into dollars in her article.

Why companies fail to turn data into revenue
Data-driven marketing main challenges

If you’re planning to build a data-driven marketing strategy you have to be aware of all of the challenges and know how you can overcome them. That’s why we’ve compiled a list of the main pitfalls and shared practical advice on how you can deal with them.

Small businesses neglect data-driven marketing

One of the main problems with data-driven marketing is that small companies think they can't afford it. Startups and small businesses feel that they're not mature enough and they have more significant issues that require their attention.

However, this mindset is totally wrong. Even small companies have to understand where their marketing budget goes and measure the effectiveness of their marketing efforts.

Moreover, there's a risk to miss the moment when data-driven marketing becomes necessary and miss an opportunity to optimize marketing expenses.

That's why, if you're a small business, you need to get down to data-driven marketing as soon as possible. We suggest using marketing analytics maturity models to understand the status of your current data-driven marketing analytics and how you can improve it.

🚀Read Analytics Maturity Model: The Path to Analytics Perfection 🚀

Here's a marketing analytics maturity model by Improvado.

Marketing analytics maturity model
Improvado's marketing analytics maturity model

There are also popular models by Gartner, SAS, Association Analytics, and other companies. You can find them all in our guide.

Data privacy

Data privacy regulations affect the workflow of data-driven marketers. Non-compliance with GDPR or CCPA regulations may lead to million-dollar fines. Quite a penalty for making the user experience more personalized.

Additionally, Apple's recent privacy changes dealt a serious blow to the marketing world. For example, Snap stock fell 22% after reporting its third-quarter revenue. The company lost its revenue after its advertising business was disrupted by Apple's new privacy policies.

We highlighted the influence of privacy policies on marketing activities and the retail industry in our recent article.

🚀Read 12 Ecommerce Trends for 2022: Will the Growth Keep Slowing Down? 🚀

Cookies also threaten user data privacy. Deloitte's study shows that only 26% of cookies around six different industries were secured. The rest of personal data was left unsecured.

Cookies' security statistics
As seen, cookies pose a threat to users' personal data

In a nutshell, companies have to take data privacy very seriously. Moreover, cookies are no longer a reliable source of information for omnichannel analytics. Only around 30% of users consent to the usage of cookies in 2021. Meaning marketers have to search for new ways of tracking the attribution such as cookieless attribution.

Data Organization

Gathering raw data from disparate sources always leads to poor organization, low analysis potential, and data discrepancies. Marketing analysts have to waste their time on normalizing and unifying unstructured data. This leads to a loss of focus on the main objective, slow workflow, and decreased marketing performance. 

Data-driven marketing companies solve this problem with automation of all data processes by streamlining them via a data pipeline. ETL data-driven marketing solutions handle all routine data operations, starting with data extraction and ending with visualization dashboards.

🚀Read A Guide to ETL Processes: ETL Stages and Benefits Explained🚀

Marketing analysts get analysis-ready data stored in an external data warehouse and up-to-date actionable insights on their marketing performance. With solutions like Improvado or Funnel, analysts get a granular image of their marketing efforts and can focus on higher priority tasks.

Choosing the Right Team

Another important factor is to choose a proper team. When brands have insignificant or even zero experience with data-driven marketing, it may be hard to understand all of the marketing metrics, advertising approaches, omnichannel strategies, marketing dashboards, and tons of other aspects. 

Another common scenario is when companies don’t have enough resources to adjust their marketing dashboards, configure BI tools, create proper templates for marketing reporting, and more. Focus on other tasks and lack of time leads to an unstructured analysis workflow and gaps in the company’s data infrastructure. 

To get the required expertise and missing resources, you can augment your team with off-site marketing specialists. Large marketing data solutions vendors offer professional services packages. With this service, you can get access to marketing data specialists who can adjust your marketing dashboards, configure a reporting process, set metrics tracking according to your needs, and even conduct professional training of your employees. 

Aligned Data Between Departments

The company’s marketing success is a result of the cooperation between each department of the organization. Unfortunately, siloed data doesn't contribute to efficient collaboration.

Michael Paladino, CEO and co-founder of RevUnit states, the following in his recent Forbes article:

"In 2021, connected data is table stakes. The ability to view data, derive insights from it, and have one source of truth could be your advantage over competitors who are held back by silos and poor quality data."

And he's completely right. According to McKinsey, investments in different data areas may reduce your future spending from 10% to 50% in the following three years.

How data reduces businesses' potential expenses
Potential savings from investements in data operations

To achieve the highest level of collaboration, you have to integrate a common data ecosystem.

Data Extraction

Extracting data manually can be a marketing analyst’s worst nightmare.The majority of marketing channels offer access to accumulated data via external APIs. In order to extract data, analysts have to write code to invoke the API and push data to a data warehouse. What’s more, if you need to extract new data fields or rearrange the extraction order, you’ll have to rewrite the code from scratch. This fact requires analysts to have coding experience. In any other case, you’ll have to engage your developers, distracting them from their direct duties.

To organize the process and make it less time-consuming, you can implement an automated data extraction and loading system. Solutions like Improvado offer prebuilt data extraction templates that allow you to extract data right away. With templates for 200+ marketing connectors, you’ll definitely find the right one for you.

Steps to Build a Data-Driven Marketing Campaign

Now, it’s time to break down a data-driven marketing process into steps and take a closer look at each of them. Let’s get started. 

Step 1. Define Requirements

Before you proceed with collecting data, you must clearly define the objectives that you want to achieve with data-driven marketing. Analyze your current marketing channels and cross-reference them to understand what metrics you can monitor. You should also set clear KPIs that will help you understand what data you should collect. 

Step 2. Data Extraction

Take a look at your objectives and make a final list of the connectors you need to extract data from. You can extract data manually by invoking APIs during each data update, or you can find a data extraction tool that will automate the whole process.

Step 3. Data Normalization and Loading

For this step, you need to choose your storage solution, which is where you’ll aggregate your future data. Solutions like Amazon S3, Snowflake, and Google BigQuery are the data warehouses most popular among marketing data analysts. 

Before loading your data to a warehouse, you have to cleanse and unify it. Manually eliminating duplicates, excess fields, and columns may take up too much time, and that’s why we recommend that you use ETL systems. While some teams prefer to do mapping and cleansing on their own, experienced data-driven marketers delegate such tasks to software solutions. For example, Improvado offers MCDM (Marketing Common Data Model), which allows you to transform unstructured marketing data in a way you need and store it in a warehouse.

Step 4. Get Actionable Insights with Visualization Tools

When your analysts have a warehouse filled with analysis-ready data, it’s time to create marketing analysis dashboards and feed them with the gathered data. Tools like Google Data Studio, Looker, Tableau, and others provide you with a granular picture of your marketing performance and highlight previously overlooked customer behavior patterns. A quality ETL system will streamline all your normalized data into a visualization tool without any help.

Google Data Studio data sources
Google Data Studio Connectors

Step 5. Handle Your Campaign

With all of the required data on the dashboard, you can optimize your existing campaigns or launch new ones. Now, you have to focus on metrics like ROMI, ROI, CPA, CPC, and others to identify the differences and track the progress of your efforts. 

Scatter plot for CPC and ROAS marketing
CPC and ROAS marketing dashboard template

Step 6. Monitor Your Marketing Performance 

To clearly understand the outcomes of your efforts, your marketing analysts should continuously track performance, calculate ROMI, and monitor marketing reports. Only in that way can you truly assess the effectiveness of data-driven marketing.

Boost Your Data-Driven Marketing Performance with Improvado

It’s evident that data-driven marketing is the future of digital advertising. Predictive analytics and artificial intelligence will soon change the way we perceive marketing. However, right now, data processes still require optimization and automation. To accelerate the performance of your marketing analysts and eliminate time-consuming manual data processes, you have to find the right ETL marketing solution.

Improvado is a full-cycle ETL marketing solution that helps businesses automate routine processes and make informed decisions faster. With our platform, you can choose from 200+ marketing data sources and integrate any of them into your data infrastructure. You don’t need to handle complex SQL queries or maintain in-house ETL developers. We’ll connect all data sources and arrange a data pipeline on our own. Now, you can focus on campaign optimization instead of routine manual processes.

Frequently Asked Questions

What is a data-driven marketing strategy?

A data-driven marketing strategy is a way of communicating with potential clients based on the data gathered about prospects. Data-driven marketing companies use clients’ data to identify possible touchpoints, customers’ pains, and foresee their behavior. These data allow businesses to build and scale effective personalized marketing campaigns that attract more customers at a lower cost.

Why is data-driven marketing important?

With the right insights, marketers can assess their marketing performance over time and adjust campaigns in a way to generate more conversions. Data-driven campaigns based on precise data can save marketers’ time on repetitive optimization and allow them to focus on the analysis of acquired results.

How do you obtain marketing data?

To dive into data-driven marketing you have to gather required insights first. Extracting data from multiple sources by manually triggering APIs is a time-consuming and ineffective process. That’s why experienced marketers use data-driven marketing solutions such as Improvado or Adverity. With this type of software, advertisers connect marketing data sources in a matter of seconds and get cleansed and normalized data right into their warehouse without effortlessly. This allows to free specialists’ time for more important tasks, such as marketing strategy planning and analyzing.

Our recommendation:

Check out Top 15 enterprise marketing tools that will skyrocket your marketing performance in 2022

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