Data Science: How E-commerce giants are reaping the benefits?

With the increased usage of advanced data handling techniques and data management systems, a wide range of organizations have started using data to their benefit. With the globalization of the retail sector and the advent of e-commerce, this business has grown at a global scale leading to an exponential rise in the number of consumers. This amounts to huge consumer data that needs to be processed and analyzed. With different consumers having varied choices, requirements, demographics and backgrounds, their buying preferences are also extremely different. This brings in a lot of data which needs to be structured and analyzed by retail brands to prepare customer profiles, learn their buying preferences and market products accordingly. Data Science applications and algorithms lend a lot of support to improve sales.

1. Recommendation engines: It is an important tool that drives customer preferences by recommending them products. This practice helps retailers increase sales and dictate the trends. These recommendation engines are made up of complex machine learning algorithms and deep learning codes. They are designed to analyze the online behavior of prospective buyers and accordingly suggest them with products. A research study on the topic reveals that a large percentage of consumers does buy as per the recommendations made. Of the total revenue generated by amazon, about 35% alone is from its recommendation engine. This, however, involves a lot of data filtering and spontaneous data analyzing methods.

2. Market Basket Analysis: This is a self-learning tool that also analyzes the buying history of customers. It is very evident that a customer who has purchased a certain set of items is less likely to buy the same products again. Hence, the prospective product list can be curtailed accordingly. Market Basket analysis works on the probability or the likelihood of buyer’s purchasing.

3. Warranty Analytics: An essential tool for retail businesses is to keep a track of the data associated with particular products. It helps manufacturers and retails keep a track of the warranty information, expiry dates, return information and also keeps fraudulent activity in check. Predominantly, a check on the number of units sold and the number of units returned due to warranty claims can be accounted for by this data.

4. Price Optimization: Optimal pricing is a challenge for the retailers as they have to justify costs to both the manufacturers and to the customers. Machine learning algorithms take into account the flexibility in pricing because the final pricing does not only need to include its manufacturing costs, but it must also include all the necessary costs that were incurred for the product to be made available to the customer. It is an important tool for retailers to market their product in the best way possible with attractive pricing.

5. Inventory Management: Market trends change almost every day. Hence, it is important for retailers and manufacturers to keep their inventories updated as per the current trends. Analyzing the buying preference of the majority of customers, retailers should stock their stores with the more commercially viable products. This helps in increasing sales and reducing overheads.

 

Data Sciences have changed how the retail industry used to conduct its business with radically different methods to operate and approach its customers. Hence, Data Scientists have become all the more important. If you are in Pune and looking for a data science course, you should definitely visit ExcelR Data Sciences Course training in pune.