How is Data Science Transforming the Retail Industry?

Data science is transforming the entire global economy–and the retail industry is on the cutting-edge of this change. Retail businesses are expected to spend a combined $7.3 billion on AI-driven data analysis by 2022. That’s a huge jump from the $2 billion that they spent in 2018.

The industry has also been hiring data scientists in droves and it is one reason why data scientist was recently ranked “the hottest profession of 2019.”

However, the demand for data scientists is currently outstripping supply. This means that companies will need to develop talent internally for the long-term and find talent from alternative sources, such as Python development outsourcing firms, in the short-term.

Over the next five years, expect to see the retail continue to use data science and machine learning to increase efficiency in their manufacturing processes and supply chain system. In addition, experts believe that retail giants like Amazon and Walmart will expand their data science efforts in order to perfect their advertising and improve the customer shopping experience.

How is Data Science Transforming the Retail Industry?

Data science is poised to become the most important technology trend to affect the retail industry since the advent of online shopping. That’s because retail companies are already using advanced data analytics to increase efficiency in the manufacturing process and save money throughout the supply chain.

Additionally, they are using big data and machine learning to personalize shopping for every consumer and to improve the relevance of their product listing when consumers shop online.

Manufacturing Efficiency

The manufacturing field has been one of the first sectors of the economy to invest in big data and machine learning–and they’re already starting to see results.

Data science is transforming the manufacturing subfield of the larger retail industry by making production more efficient, streamlining inventory management, and helping managers predict equipment breakdowns and fix components before production is disrupted.

The consulting firm McKinsey estimates that data analysis and machine learning will help manufacturing companies increase their overall revenue by more than 13% if this type of technology is fully integrated into the manufacturing lifecycle. The same company predicts that data-driven machine learning will help businesses increase fuel savings by 12% and reduce material delivery times by nearly 30%.

Deloitte, another of the Big Four consulting firms, also predicts that machine learning will help manufacturing firms reduce maintenance costs by 20-30% and eliminate up to 30% of equipment downtime through preventative maintenance.

Companies like Seebo are using Python development services to build powerful data analysis software for the industrial manufacturing market. Their data analytics software uses machine learning to predict maintenance problems, forecast man-hour requirements during specific times of the year, and suggest process improvements that can increase efficiency and save money.

Reduced Supply Chain Costs

Data scientists are also transforming the retail supply chain. This is a very important development because small improvements in the logistics process can lead to huge annual savings.

The industry is using this new technology in a variety of ways. Retail companies are using data science to reduce freight costs, optimize shipping patterns, increase warehouse storage efficiency, and to forecast future demand.

For example, the long-haul trucking portion of this supply chain is using machine learning to increase route and truck efficiency. Companies like Walmart are using hybrid trucks that utilize a combination of gasoline and electric motors to reduce shipping costs and harmful emissions.

Amazon is one of the most innovative users of data science in logistics. The online retail giant is currently using data science to predict what items and what volume customers in a particular location are likely to order in the near future.

Armed with this information, the company has instituted an “anticipatory shipping” protocol that ships certain items to distribution centers before customers even place an order. This allows the firm to offer same-day and even two-hour shipping on select, high-volume items.

Personalized Shopping

One of the best ways to increase the average price of an online retail order is to improve the personalized shopping experience.

Companies like Amazon and Etsy are famous for their advanced, algorithm-powered recommended products lists. They use a customer’s past shopping experiences, and the combined patterns of similar customers, to create recommended product lists with high conversion rates.

In addition, these powerful product recommendation lists help companies predict sales numbers and streamline their manufacturing and supply chain processes. That’s because they can accurately predict sales numbers of recommended products by using past sales data and conversion rates.

This same technology will also help retail companies improve their online search results. Because companies can collect and analyze data from past product searches and purchases, they can exclude irrelevant content and focus on those items that customers with a similar shopping history purchased.

Such an approach will help companies offer better advertising and search lists to existing customers. It will also improve the consumer experience by filtering results according to the user’s established preferences–enabling customers to quickly and easily find and purchase the products they like.

In Summary

Intelligent investments in data science over the past decade have enabled the retail industry to become one of the most innovative users of data science. The industry is currently spending millions to hire experienced data scientists and collaborating with Python development companies to source these hard-to-find experts.

These investments are already beginning to pay off. The retail industry is on-pace to increase manufacturing profits, reduce machine downtime, and improve efficiency in the supply chain system. In addition, they are using data-driven machine learning to improve the shopping experience for all customers.

Expect to see even more changes over the next five years as machine learning and data science becomes a more mature discipline. Companies will begin to offer targeted advertisements with uncanny accuracy and will improve the customer experience by offering products before the customer knew they needed them.