Embracing Data Science in Retail to Drive Revenue

by November 10, 2019 0 comments

Data Science in Retail

Today, data is everything for companies and has proved to be a powerful driving force of the industry. It has become indispensable for those who keen to take profitable decisions concerning the business. In retail, data is increasing exponentially in volume and variety, bringing value to retail business. Though, smart retailers know each one of these interactions holds the potential for profit and are leveraging the power of Data Science.

Having the potential to adapt business models, data science is the process of analyzing large datasets, both structured or unstructured. Considering reports, an IBM survey reveals 62 percent of retailers utilize Big Data techniques providing them an intense competitive edge.

Here are the top 5 ways Data Science impels revenue in retail.

 

Price Management Influenced by Data

In a Deloitte report, price management initiatives can help boost profit margins by 2 to 7 percent within a year, producing an ROI of 200 to 350 percent on average. Earlier, retailers have had to set prices using a few data points like cost of goods sold, profit margin, competitors’ pricing, and manufacturer’s suggested retail price. But modern retail data now enabled them to increase and decrease prices based on seasonality demand, customer location and behaviour, and frequency of purchase.

 

Market Sales Analysis & Promotions

Effective analysis of market sales can be a huge profit impact. However, this is not a new idea but implementing strategically can provide profit increases to the retail company. For instance, if a customer purchases a product, he/she is likely to buy related goods. So, leveraging data science in retail can assist a retail company in increasing profits without running a range of A/B tests. Even, a retailer can pitch personalised offers to their various segments of the customer to further boost conversions and sales.

 

Recommendation Engines Driven by Data History

Recommendation engines have proved to be of great use in the retail sector as it offers customers’ behaviour prediction. As per reports, over 35 percent of all Amazon sales are produced by their referral engine. Netflix also has sophisticated recommendation algorithms. These recommendation algorithms suggest products based on each customer’s purchase and search history. Today, retailers tend to utilize this engine as one of the main practices on the customers’ opinion, helping in increasing sales and commanding trends.

 

Product Visualization

Retail companies are increasingly leveraging product visualization to comprehend what customers find attractive. They are looking to lure customers based on visualization, asking questions is a white background better than black, pink or other colours? Does having a close-up photo of a product’s texture make it more salable? Or does a human model assist in selling the product? So, data science here can take it a step further to identify the optimal combination of model, texture, photo quantity, and other variables that are making the product more tempting.

 

Customers Lifetime Value Prediction

Previously, it was harder to know who is the most profitable and loyal customers for a retail business. Analytics fails to tell retailers when those buyers are starting to purchase with less frequency, and what and why leads customers to switch to a competitor altogether. But now data science can help retailers to explore those root causes and find out the dependencies between different customers’ choices and behaviours and implement that data to predict their future actions.

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