Data Science in the Retail Sector: Applications in E-commerce
After a stage dominated by branding and CSR , the current objective of retailers is to maximize the ROI of marketing actions to sell the stock accumulated during the months of closure, as well as to communicate new launches to a consumer who has adopted new buying habits. In the now strategically important online environment, e-commerce Guatemala WhatsApp Number List data science applications in the retail sector have long attempted to optimize the efficiency of online stores, automate processes and improve user experience to translate into maximum sales.
Data science application in the retail sector Although surveys such as the Big Data and AI Executive Survey 2019 conducted by NewVantage Partners have indicated that only 30% of companies in the United States consider themselves to be data-driven, the crisis triggered by the SARS-CoV-2 pandemic accelerated digital transformation . Retailers operating in online environments and already applying data science techniques have a competitive advantage in times of crisis. We take a look at three of the most important applications below:
Price Management With a Data-driven Approach Managing
the pricing policy based on real insights rather than intuition is a valuable tool for driving sales during slack times and taking advantage of increases in demand during peak times. This is possible on condition that the resize is able to unify its data according to common characteristics and without excluding any sales channel . With this initial classification, where data is stored in data lakes, processed and used to feed machine learning models, it would be possible to: Customize the discount or pricing policy according to the user : data analysis is responsible for detecting common patterns and identifying groups of customers based on their previous online behavior and history of interests and purchase.
It is thus possible to design a promotion strategy very well suited to the types of users encountered and, consequently, offering a greater probability of conversion. Define the prices by segments : in this case, the catalog of products and services as well as the price are fixed taking into account larger audience segments . One option would be to have, for example, standardized prices for the majority of users, but also to integrate another strategy with more aggressive discounts targeting customers whose buying engine is exclusively price or, otherwise.
Share, to Include Premium Offers With Services or Special Features
for those looking for additional security. 2. Personalized recommendations for upselling and cross-selling in e-commerce One of the great references in these data science techniques is Amazon and its advanced algorithm for showing recommendations to marketplace users. In this sense, the most established machine learning models are designed to: Offer an improved version of the chosen product (upselling). Recommend other products related to the purchased item such as, for example, a fitness ball with its air pump. Suggest items that similar users have purchased at the same time .
This is a very common phenomenon in fashion e-commerce, where the objective is to stimulate the sale of complete outfits. The advantage of using machine learning to manage these recommendations is that you gain efficiency since you don’t have to perform hundreds of A / B tests to make decisions: that’s the algorithm itself which determines the products to be displayed to each user in a personalized way. To do this, the model is subjected to prior training, that is, it is necessary to classify the data beforehand and to define characteristics that link the articles together. After their implementation, these algorithms refine the selection of recommendations and constantly optimize them.