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digital marketing

Application of data mining in digital marketing

Digital marketing is one of the areas that is expanding day by day with the increasing use of the Internet. This field is widely used in the Internet and social networks and today can be recognized as one of the main branches of any business. Now the question is, with the increasing use of the web and the Internet, can data mining help digital marketing? To find the answer to this question, we can review some examples of data mining applications in the field of digital marketing.

Application of data mining in digital marketing

Suppose a store website has a lot of customers, and these customers each perform specific operations. For example, some of them may read most of the site’s articles, while others may just view or purchase products. Therefore, it can be that each of these customers has different behavioral patterns. By analyzing these behaviors, data mining leads us to new decisions. In this section, with a few examples, we examine the application of data mining in digital marketing.

Customer shopping cart analysis

One of the sub-branches of data mining is clustering. For example, according to different clustering algorithms, customers of a website can be divided into different clusters and groups. This division can create different groups, each with its own characteristics. For example, in a group, people may read more articles and buy less, and usually enter the site from search engines. So this group can be identified using clustering and their tricks can be collected by various tricks.

By collecting this information, these people can be encouraged to buy through email marketing or other methods. Or, for example, suppose there is a cluster of customers who have a small number of purchases, but each time they buy expensive products. By identifying this group of customers, luxury and expensive products can be advertised with a special discount for them.

Application of data mining in email marketing

In another example, suppose you receive a large number of emails from your website. Customers and you know which article the customer entered their email when reading. Now suppose you want to generate custom emails for each person instead of sending one email to all of them in a way that makes them more likely to buy. To do this, you can probably use the text processing technique to analyze the text of the article that the user entered their email after reading. Then try to introduce the closest product to him and by doing so, increase the chances of selling your products.

Application of data mining in customer network expansion

Another thing could be to monitor the behavior of users on your website and show the appropriate product to this user according to his behavior. To do this, you can use one of the sub-domains of data mining, called classification. For example, consider the specific behavior of a group of users who, through search engines, saw different pages of the site and paused for about 40 seconds on each page. Of course, other features and behaviors can also be in this collection. So that by aggregating this data, we can identify a set of user behaviors in purchasing the product.

Suppose in the example above one of these people buys a particular product. Using classification algorithms, we examine the behavior of new customers to connect each new customer to old customers who behave similarly. Now we can see what products the previous customers who were closer to the current customer. (In terms of behavior) bought, so that we can show the same products to the new customer as an advertisement. Such an analysis, in addition to the regular integration and categorization of customers, also facilitates the work for future business planning.

Application of data mining and machine learning in digital marketing

The common denominator of all the above examples is the data-driven nature and use of user-generated data to identify patterns of behavior and offer different products to users. There are many other applications for data mining and machine learning in the field of digital marketing, some of which are in below:

  • Manufacturing and creating products related to the user’s shopping cart by Recommender Systems
  • Website and application page design using user statistical information in order to maximize user satisfaction and earn more profit
  • Targeted advertisements in different parts of the website or application
  • Understanding user activities and examining the reasons for improving the user relationship with the website or its downfall
  • Understanding users’ opinions about an article or product. (For example, positive or negative comments about the characteristics of a particular product using text mining processes)

Many other examples and applications can be in the field of digital marketing. Each of which in turn can add value to an Internet-based business. This data-driven domain requires an understanding of the business and an analysis of the terms of an online business. For example, an expert in the field of data science, to create added value in an Internet business, must hold several meetings with managers and sales experts and various marketing departments in order to have a broad view of that business and potential and actual customers.

Conclusion

Given the above, data mining can be very useful in the field of marketing and advertising. One of the most important pillars of digital marketing is to know the behavior and needs of the user. By processing behavior and data from customer behavior, data mining provides managers with information that is essential to competition. In fact, data mining is a trick to predict the next step for your competitors. Hence, many large businesses are looking to hire data science specialists to strengthen their position with customers.

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