Big Data & Digital Marketing: Connection between them

Big Data & Digital Marketing: Connection between them

The capabilities of companies to store large amounts of data, process this data, and make logical decisions based on it have been greatly enhanced. Consequently, this enormous amount of data present, both organized and raw, which cannot be processed manually is known as big data & digital marketing.

Furthermore, the storage capacity of modern systems as well as the infrastructure of cloud computing has enabled businesses to constantly track and store this Big Data. The processing power required to effectively and efficiently compute this data has only recently been introduced.

Thus, now most businesses are able to store and analyze Big Data. The next step in the evolution of businesses is to utilize these trends in data to identify points of weakness within their strategy and implement counteractive measures to better streamline the flow of revenue.

Digital Marketing is now majorly involved in the promotional campaign of a product. The more people are advertised and informed about a product or service, the better the chance of them becoming potential customers. In addition to Digital Marketing, if a company incorporates the analytics of big data into the campaigns, the user experience and the overall goals of the company can easily be achieved.

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Why is Big Data Important?

The significance of big data is not relatively dependent on the amounts of data available, because a major chunk of data is available online, but it is in fact dependent on how it is being used. The data can be used to find shortcomings in strategies, new cheaper ways to conduct transactions, reduce time-to-market, and offer improved statistics of a product’s performance. When big data is processed using driven analytics it can deliver the company to avenues of growth and innovation, such as:

  • Determination of the underlying issues that cause failures and defects in real time.
  • By studying a customer’s preferences, offer coupons/discounts based on recent habits.
  • Calculating risk assessments and mitigating errors.
  • Predicting, detecting, and prohibiting deceptive activities before they can impact the organization.

To have a wide spectrum of checks in place, the data analyzed must be driven through various sources in order to correctly estimate the trends. With the help of multi-dimensional systems, that are able to process raw live data as well as the ready-made structured data available, the next step in ensuring success can be achieved.


Digital marketing is referred to as the strategies invented by organizations to increase the traffic of their websites, attract target customers, and stay relevant in the industry. Thus, businesses utilize technology-enabled tools, such as articles, blogs, SEO, Email marketing, and many more, to increase the reach of their services and products.

A major challenge in digital marketing is measuring the success of a strategy due to the vastness of the internet and the large amounts of data that are created when potential customers interact with the company online. The digital realm would benefit greatly if this data could be processed and used to improve the personal experience of users.

Another challenge faced by the domain of digital marketing is the inability to process the data effectively, this is where Data science steps in. The process involves understanding the perspective of a customer and building engaging models of advertising that also help in creating “data lakes” for big data management.

In Data Science, data lakes are defined as untapped bodies of raw data that are just present and stored until someone analyses it and converts it into utilizable data. There are several innovative models that have been invented to identify trends in data. These models implement the latest technologies in software such as Machine Learning and Artificial Intelligence to correctly predict and estimate the points of friction in the digital strategy. A few of them are mentioned below:

Customer Segmentation

Each individual has separate preferences thus a model of one size fits all is not advised. By conducting thorough statistical research using the big data available in the domain of a company, they can identify the users that would be willing to pay for their products or services.

Real-time analytics

Quickly analyzing data and implementing them in the decision algorithm can support a company’s business model. Due to the effectiveness of social media in delivering valued data, this can be done with relative ease.

Predictive Analysis

Due to the enormous amounts of data already present about the possible successes and losses of companies, Regression Analyses using AI/ML algorithms can be conducted. These analyses can indicate if a venture is going to be successful or not.

Recommendation Engines

The main goal of recommendation engines is to align a user’s tastes with a brand image that he or she would enjoy. The following data science methods are commonly used by recommendation engines for this purpose: decision tree, K-nearest neighbor, support vector machines, neural networks, and so on.

Optimized Marketing

By analyzing the successes and failures of previous campaigns and combining this knowledge with modern tools of optimization, a marketing campaign can be derived that is user-oriented and targets the right customers.


The key to utilizing data models in digital marketing is to always assume that there might be a better way to do what we have to do. By accepting this consistency of change, the data gathered is processed again and again until the desired results can be achieved. With innovation and commitment to evolving in mind, Leed Software Development in Canada is keen on developing data models that can not only improve marketing strategies but also reinvent them.

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