How to Use Big Data in Digital Marketing

Big Data is a term in computer science and something we are increasingly coming into contact with in digital marketing. Simply put, Big Data is large or complex data that is more difficult to work with. But how can it be used in digital marketing? We’re going to figure that out now!

What is Big Data?

‘Big Data’ is a term in computer science that is used when the data is so large or complex that it causes obstacles in the systems you work in. For example, an Excel spreadsheet may crash or fail to accommodate all the data, and similarly, a database might struggle to handle both the data and the queries you run on it. Essentially, the term “Big Data” originated from the challenges posed by large databases.

You may now be asking yourself if Big Data in itself is a problem. And it absolutely is not, but large and complex data sets require a different type of organization and strategic framework for their data. And thus there are also systems that can handle it. Big Data can, of course, mean strain for those companies and teams that are affected in their daily work by data sets that are too large for current systems to handle.

The 3 V’s.

To more clearly define what Big Data actually is, we usually talk about the “three Vs”: volume, variation, and velocity. These are the characteristics of big data:

• Volume refers to the amount of data to be managed; we are talking about a larger memory capacity, such as TB and up.
• Variation to different types of data, from structured data (e.g., numbers) to unstructured (e.g. texts and images)
• Velocity refers to the speed at which new data is generated and must be processed.

Opportunities with Big Data

When you work with a large database and from multiple data sources, there are opportunities to get even closer to your customers and visitors. Big data gives you the opportunity to:

• Improved customer insights

Gain a deeper understanding of their needs, behaviours, and preferences.

• Accurate targeting

When you have a more detailed database, you can refine your segmentation and become even more accurate in your marketing.

• Predictions

If you have a larger historical database, you can utilise it for predictive analysis and use it to predict trends and potential results from future efforts in your marketing.

Example: E-commerce and Personalization

An e-commerce company uses big data to create targeted offers.

• They collect data from various sources, including website interactions, purchase history, social media, and feedback such as reviews and ratings.
• They interact with data sources and use machine learning to identify preferences and interests, as well as what products or offers a customer might be interested in based on their past behaviour.
• Based on the data, create personalized offers. For example, customized product recommendations on the site, personalized email campaigns or remarketing on products that have been shown interest in but not purchased.

Creating Conditions for Big Data

To both manage the data and utilize the potential it possesses, you need to review the system and its compatibility, security, and the competence in the organization to utilize it.

1. Set up a Technical Infrastructure

You need to ensure that you have systems that are capable of collecting and storing large amounts of data, analysing it and integrating multiple data sources. You will get a long way here with a data warehouse in your tech stack. It helps you save and query your data (such as CRM data, e-commerce data, MA data, etc.). You can advantageously use a CDP (Customer Data Platform) to collect customer data and convey it to your data warehouse. You can also supplement it with machine learning and AI to draw insights from the data. 

When it comes to large and robust data collection, you also need to ensure that different systems and flows are compatible with each other and that your solution is scalable. You also need to address privacy and handle both storage and sharing of data in a secure and legally correct manner.

2. Ensure Competence

To work with big data, your team needs skills in data analysis, statistics, and machine learning. Data analysts and data scientists are two examples of professional roles that work with large and complex data.

FAQ