The Impact of Recommendation Algorithms: Analyzing the Influence of Data on Marketing Strategies in the Media Sector

Abstract

The recommendation algorithms used by businesses in the media sector have contributed to their marketing strategies by analyzing consumer engagement and observing where interest is most prominent. Through implementing such algorithms, companies have gained a better understanding of the demographic they are reaching, and are able to better cater to their customers’ needs. Algorithms often collect data on the area of a business known to have the most impact on a business’s projected growth, as this is the most efficient way to expand a business. The data collected by these algorithms keeps track of metrics that can later be used to grow the business, such as the social, financial, and emotional aspects surrounding each customer. Even details that may be more inconspicuous, such as how long the page is kept open, or how the mouse is moved along the page, greatly influence the value of data from the recommender systems. Therefore, since this data is often contextualized, this study expects to discover a direct correlation between available data and marketing strategy optimization, which further increases consumer retention. This cycle continues due to evolving algorithms that increasingly get more accurate, leading to a customer’s experience becoming more individualized. This paper aims to review the different types of recommendation algorithms used by businesses and analyze the data sets that they gather, as well as the ways in which this data is gathered. Then, it will provide an explanation of what type of contribution each data set has on a business, and how marketing strategies are tailored every time a significant conclusion has been made. By doing so, the paper provides a much-needed review of how companies are able to decide on the tangible steps for their business platform, and what areas to concentrate on to optimize consumer experience.