How to Leverage Big Data for Smarter Marketing

Whether to improve loyalty or optimize business performance, leveraging big data can help you achieve your goals.

Leveraging Big Data

In the next decade, a major part of your business success will depend on how you leverage ‘big data’. Businesses today collect huge volumes of data at incredibly high speeds. With the rise of social media, 2.5 quintillion bytes of data are produced every day. This influx of big data can be overwhelming, especially if you aren’t sure how to analyze the information. Yet when this data is analyzed thoroughly, you can gain insight into your customers’ needs and continue to scale your business.

Question is, who is going to utilize this valuable information more effectively – you or your competition?

Whether you are trying to improve customer loyalty, optimize your business performance, or drive engagement, incorporating big data into your marketing is proven to be an indispensable tool.

Big Data is a term used to describe the technology and practice of working with data that’s not only large in volume, but also fast and comes in many different forms. Marketers are interested in three types of big data: customer, financial, and operational.

Customer data helps you understand your target audience. This includes facts like names, email addresses, purchase histories, and web searches. Also, your audience’s attitudes can be gathered from social media activity, surveys, and online communities.

Financial data helps you measure performance. Your organization’s sales and marketing statistics, margins, costs, and financial data of your competition fall into this category.

Operational data relates to business processes. From customer relationship management systems to feedback from technology and other sources, analysis of this data can lead to improved performance and reduced costs.

When it comes to maximizing big data, the ultimate goal should be to gain knowledge on the ‘5 W’s’:

WHY people choose your product over the competition

What makes your product stand out? How does your product provide a certain want or need? By analyzing certain information, you can apply the feedback and market to specific audiences to gain new customers.

WHO your consumers are in a 1:1 connection

It’s crucial for any business to ‘know your customer’ (KYC). Accessibility to big data can provide insight into customer wants, needs, behaviors and patterns. Customer engagement, specifically how people view and interact with your brand, is a key factor in your marketing efforts.

WHAT products or services they want

Collecting and properly analyzing big data can pinpoint the products and services your customers want, need, and desire. Analytics will help dial in your marketing efforts to lead your audience to the right product or service.

WHEN they want them

Knowing when a customer wants a product may be just as important as the product itself. Utilizing big data will help decipher such information to get your product to the right customer at the right time.

WHERE they want them

Also, knowing the right place or location your customers want a certain product or service is a key insight.

First-Party Data

First-party data is the information brands and creators collect directly from their customers and consumers. This data generally comes from sources like websites, CRM, surveys, and customer feedback. Since first-party data is data that you yourself have collected, it is typically the most reliable data available to you. Every market has a finite amount of potential customers, so it’s a race between you and your competition to get this information. As a brand marketer, it’s vital to leverage this data to attract more customers than your competition. Once you’ve collected first-party data, you can gain audience insights, create a personalized experience for your users, improve your retargeting strategy, identify patterns and predict future trends.


Companies collect big data to analyze and interpret daily transactions and traffic data, aiming to keep track of the operations, forecast needs or implement new programs. But how to collect big data directly? To begin, you need a sound strategy to collect this vital information. Here a few ways big data can be collected:

Online Marketing Analytics

Online marketing analytics is the driving force for digital marketing. eCommerce companies serve millions of customers per day and process tons of data gathered in their buying experiences. Insights drawn from this data are needed to personalize customer journeys and improve customer service. While it allows businesses to learn about their audience, such data volumes require effective big data collection tools that enable fast and accurate data processing. A few examples include popular tools like Xplenty, BrightData, or Suma.

Loyalty Programs

Loyalty programs still remain popular among retailers striving to build brand loyalty. A loyalty program encourages customers to collect points with each purchase and exchange them for rewards. With this incentive, businesses create a buyer’s profile with detailed consumer preferences and habits. This profile may be sold or used to achieve more effective merchandising.

Social Media Activity

Without a doubt, social network users are big suppliers of unstructured data in the form of video, audio, photo, etc. Considered a major opportunity for creating user profiles, the massive influx of data from social networking is expected to grow exponentially. As mentioned previously, big data tools are a must to process content shared on social media as well as gather the data on user activity.

Email tracking

Email tracking gives marketers specific information on how people use email. Detecting when someone’s email is opened, this method provides behavioral patterns and other insightful information.


Now you’ve gathered this pertinent information, how will you analyze it? You will need to develop tools and processes to understand and formulate insights from big data. Here are five techniques to help interpret the constant flow of information.

1. A/B testing

This data analysis technique involves comparing a control group with a variety of test groups, in order to discern what treatments or changes will improve a given objective variable.

2. Data integration

Combining a set of techniques that analyze and integrate data from multiple sources and solutions, the insights are more efficient and potentially more accurate than if developed through a single source of data.

3. Data mining

Data mining extracts patterns from large data sets by combining methods from statistics and machine learning.

4. Machine learning

Popular in the field of AI, machine learning is also used for data analysis. Machine learning works with computer algorithms to produce assumptions based on data, providing predictions that would be impossible for human analysts.

5. Natural language processing

Known as a component of computer science, artificial intelligence, and linguistics, this data analysis tool uses algorithms to analyze human, or natural, language.

Other data analysis techniques include spatial analysis, predictive modelling, association rule learning, and network analysis. Managed accurately and effectively, these tools can reveal many business, product, and market insights.


Once you've amassed and analyzed information about your customers, you can now use that data to launch new products, get feedback on new product development, improve customer acquisition , and more.

By combining big data with an integrated marketing strategy, you can also make an impact in these key areas:

  • Customer engagement – Pivotal information can deliver insight into not just who your customers are, but where they are, what they want, and how they want to be approached.
  • Customer loyalty – Big data can help you discover what generates customer loyalty and what retains their business.
  • Marketing optimization – With big data, you can determine your marketing spend across multiple channels and optimize marketing programs through testing, measurement, and analysis.


Almost every country has some sort of data privacy laws to regulate how information is collected, how data subjects are informed, and what control a data subject has over his information once it is transferred. In the United States, there is no one comprehensive law that currently regulates big data specifically. Rather, companies leveraging big data must ensure that their activities comply with privacy laws applicable to their operations. Also, there's a network of sector and medium-specific regulations that address telecommunications, health information, and marketing. It’s important that your company knows such regulations and has a strategy to navigate privacy laws regarding data storage, security, and use of first-party data.

To sum it all up – leveraging big data is a big deal toward the success of your organization. As marketers, not only is it crucial to process this information correctly (and quickly), but you must utilize it effectively to connect with your audience and grow your brand. We hope this article helped define big data and outlined how you can use this information to solidify your marketing plan.

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