Top 10 Data Science Use Cases in Retail

Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data. 

Thus, data has become of great importance for those willing to take profitable decisions concerning business. Moreover, a thorough analysis of a vast amount of data allows influencing or rather manipulating the customers’ decisions. Numerous flows of information, along with channels of communication, are used for this purpose.

The sphere of the retail develops rapidly. The retailers manage to analyze data and develop a peculiar psychological portrait of a customer to learn his or her sore points. Thereby, a customer tends to be easily influenced by the tricks developed by the retailers.

This article presents top 10 data science use cases in the retail, created for you to be aware of the present trends and tendencies.

Recommendation engines proved to be of great use for the retailers as the tools for customers’ behavior prediction. The retailers tend to use recommendation engines as one of the main leverages on the customers’ opinion. Providing recommendations enables the retailers to increase sales and to dictate trends.

Recommendation engines manage to adjust depending on the choices made by the customers. Recommendation engines make a great deal of data filtering to get the insights. Usually, recommendation engines use either collaborative or content-based filtering. In this regard, the customer’s past behavior or the series of the product characteristics are under consideration. Besides, various types of data such as demographic data, usefulness, preferences, needs, previous shopping experience, etc. go via the past data learning algorithm.

Then the collaborative and content filtering association links are built. The recommendation engines compute a similarity index in the customers’ preferences and offer the goods or services accordingly. The up-sell and cross-sell recommendations depend on the detailed analysis of an online customer’s profile.

Market basket analysis may be regarded as a traditional tool of data analysis in the retail. The retailers have been making a profit out of it for years.

This process mainly depends on the organization of a considerable amount of data collected via customers’ transactions. Future decisions and choices may be predicted on a large scale by this tool. Knowledge of the present items in the basket along with all likes, dislikes, and previews is beneficial for a retailer in the spheres of layout organization, prices making and content placement. The analysis is usually conducted via rule mining algorithm. Beforehand the data undertakes transformation from data frame format to simple transactions. A specially tailored function accepts the data, splits it according to some differentiating factors and deletes useless. This data is input. On its basis, the association links between the products are built. It becomes possible due to the association rule application.

The insight information largely contributes to the improvement of the development strategies and marketing techniques of the retailers. Also, the efficiency of the selling efforts reaches its peak.

Warranty analytics entered the sphere of the retail as a tool of warranty claims monitoring, detection of fraudulent activity, reducing costs and increasing quality. This process involves data and text mining for further identification of claims patterns and problem areas. The data is transformed into actionable real-time plans, insight, and recommendations via segmentation analysis.

The methods of detecting are quite complicated, as far as they deal with vague and intensive data flows. They concentrate on the detecting anomalies in the warranty claims.  Powerful internet data platforms speed up the analysis process of a significant amount of warranty claims.