Data Science Strategy in Retail Industry to increase revenue

Today, data is everything for business owners, and the industry has proven to be a powerful driving force. It has become indispensable to those who are keen to make profitable business decisions. In retail, the amount and variety of data are increasingly growing, adding value to retail business. Though smart retailers know each of these interactions holds the potential for profit and leverages Data Science‘s power.Data Science in Retail.

Data science, with the ability to change business models, is the process of analyzing massive datasets, both structured and unstructured. An IBM survey reveals that 62 percent of retailers use Big Data techniques to provide them with a keen competitive edge, taking into account reports.

Here are the top 5 ways Data Science in Retail Industry is boosting retail revenue.

Price Control under the Impact of Data: In a study by Deloitte, price management initiatives can help raise profit margins by 2 to 7 percent within a year, creating an average ROI of 200 to 350 percent. Previously, retailers had to set prices using a few data points such as the cost of sold goods, profit margin, the number of competitors, and the retail price suggested by the manufacturer. But modern retail data now enabled them to boost and lower rates based on demand for seasonality, consumer position and behavior, and purchasing frequency.

Analysis and Promotion of Market Sales: Efficient market sales research can have a significant profit-making impact. This isn’t a new concept, though, but strategically implementing will provide the retail business with profit increases. For example, if a customer buys a product, he/she will likely buy related goods. So, using retail data science will help a retail business maximize profitability without running a series of A/B tests. Also, to further boost conversions and sales, retailers can sell customized deals to their growing consumer segments.

Recommendation Engines Powered by a History of Data: Recommendation engines are of great value in the retail industry as it provides the prediction of customer behavior. According to reports, their recommendation system generates over 35 percent of all Amazon sales. Netflix also has advanced algorithms for recommendations. Such recommendation algorithms suggest products based on the purchasing history and search history of each customer. Retailers today tend to use this engine as one of the critical consumer sentiment activities, helping to increase revenue and patterns in order.

Product Visualization: Retail companies are increasingly using marketing analysis to consider what they find appealing to consumers. Looking for visualization-based customers to lure, asking questions is a better white background than black, pink, or other colors? Will taking a close-up view of the texture of a commodity makes it more salutary? Or does a human model help with the sales revenue? So, here, data science can take a step further in identifying the optimal combination of model, texture, photo quantity, and other variables that make the product more tempting.

Customers Prediction of Lifetime Value: Previously, it was more difficult to recognize who is the most profitable and loyal clients for a retail company. Analytics doesn’t tell retailers when those buyers start purchasing less often and what and why drives customers to turn over to a rival entirely. But now, data science will help retailers discover the root causes to figure out the correlations between the choices and behaviors of various consumers and apply it to predict their future behavior.