The retail industry is evolving rapidly. The way consumers shop is constantly evolving. The line between online and offline is blurring and more retailers are now adopting a data first strategy, which is helping them to understand how their customers are behaving and ensuring that they can match the consumer requirements.
Retail data science is the “new normal”. Brands that have access to high-quality data, and know how to use it, are the ones that are delivering unprecedented value to their customers. The competition is fierce across retailers and data science has been disrupting the industry for a while now. By leveraging meaningful business insights, brands can take more targeted approach, deliver better-focused goods, and services and discover constant opportunities for growth.
Let’s look at how Data science can be used in retail to gain a powerful competitive advantage in a highly competitive space.
It is a system that filters the information and predicts users’ preferences, while they are browsing the Internet. It is proved to be a great tool for retailers to predict customer’s behavior. By providing recommendations based on the user behavior, retailers can upsell/cross-sell and thereby increase sales.
Product recommendation engines use advanced algorithms and build a unique profile for every customer. These engines filter through their product catalog and based on complex algorithms, recommend products that could likely be of interest to the customer. It considers customer data including purchase history, preferences, and feedback. The gathered customer data is then automatically personalized into accurate recommendations. These algorithms influence sales and improve customer satisfaction.
One of the biggest retailers, Amazon, has access to its customer’s information such as their names, search histories, payment modes, and addresses. By making use of all your details in hand, Amazon creates personalized recommendations and provides efficient customer care.
A significant advantage that Data Science brings in is having the right price for both the customer and the retailer. With the help of price optimization tools, the price for a particular product is freezed upon. Here, retailers try to analyze the impact of change in prices of the various products.
“What-if” analysis helps in understanding the impact of price on sales, customer’s purchasing decision, product selection, the location of the customer, the buying attitude of a customer, season of purchase, the competitors’ pricing, etc. Considering all these factors, data science helps retailers to estimate the optimal price, which will increase sales and thereby generate maximum revenue.
Leverage on predictive forecasting
Predictive forecasting uses different data sources to make predictions, including the history of previous sales, economic indicators, customer searches, and demographic data.
For example – How does Starbucks stay successful in all of their outlets? They analyze the data available with them with the help of data science tools and techniques to decide on every new opening location by area demographics, footfalls, and customer behavior. It helps them to determine whether opening a new store in a particular area will be successful for the brand and if it can bring them significant revenue.
One of the concerns that retailers face is when it comes to forecasting sales, ordering inventory to meet demand, and reserving capital to invest in other parts of their businesses. Predictive analytics can replace time-consuming manual methods of retail sales forecasting and analyze larger volumes of data from diverse sources to produce a more accurate forecast.
Retailers use various data analysis platforms and machine learning algorithms to identify and detect patterns and correlation among supply chains. It helps to define the optimal stock and inventory strategies. Patterns that are identified are related to sales trends and strategies are made to optimize the delivery of goods and manage the stock.
With the help of data science, extremely large datasets can be rapidly analyzed to reveal patterns and trends, which include-
- How much inventory is required to meet demand while keeping stock levels to a minimum
- How to manage stock flow, make solid predictions, smart decisions, and forecasting that help improve the store’s bottom line.
Operational and Supply Chain Improvements
Using analytics in the supply chain is one of the recent ways by which retailers are upping their game. The data can be used for everything from product tracking to improving quality, real-time inventory management, better forecasting and it also speeds up delivery of goods. Making better decisions at this level of the operations not only cuts down on costs but also impact bottom line positively
Retailers who leverage the power of data science will continue to win more customers. Data science is transforming inventory management capabilities, reducing costs, improving operational efficiency, maximizing sales and in turn, is increasing customer satisfaction. Data Science helps to build stronger bonds with the customers using actionable insights from the data and helps in taking the business to the next level. It helps retailers to understand exactly what the customers are expecting from them.
The increasing customer data provides a number of opportunities for retailers to use this data for their growth. Data Science in retail enables retailers to take the right decision regarding the different aspects of the Retail Industry at the right time.