The Future of Retail with Behavioral Data

By Data Science Salon

Data-driven shopping experiences and higher customer expectations define the modern retail dynamics. As the industry becomes more competitive, retailers face the challenge of providing a meaningful and personalized buying experience.

Retailers are increasingly investing in AI and machine learning technologies to improve sales performance and save costs. According to Markets & Markets, the global AI in retail market size is predicted to expand at a CAGR of 38.3 percent from USD $736.1 million in 2016 to USD $5,034 million in 2022.

Companies implement AI systems to turn data into valuable insights and automate tedious tasks. This leads to higher efficiency, better customer experiences, faster time to market, higher inventory accuracy, and more intelligent operations — all of which help differentiate a retailer from its competitors and increase its revenue.

Leveraging Behavioral Data for a Unique shopping Experience

Data is a key component of a successful AI strategy and retailers are in the fortunate situation to have vast amounts of data sources provided by their customers. They leverage behavioral data from web-based events such as customer’s digital footprints, including page clicks, searches, purchase history, and more to create unique shopping experiences and automate processes. Furthermore, they can integrate this data with other channels like loyalty programs and in-store engagements to create more comprehensive intelligence applications.

AI Use Cases in the Retail Industry 

Combining the data with AI based systems can help retailers improve performance across the entire value chain. Here are some of the most popular use cases:

Product classification

Product categorization, sometimes referred to as product classification, remains one of the biggest challenges for retailers with large category trees. Combining product images and descriptions with machine learning and NLP techniques, retailers are able to solve complex product categorization tasks efficiently and with high accuracy.

Recommender systems

Recommender systems offer customers personalized content, based on their own shopping history or on the user behavior of similar customers. The engines filter and select the most relevant products to satisfy specific customer interests. This helps retailers grow sales and attract new customers. According to McKinsey, the product recommendation system accounts for 35% of all Amazon purchases.

Personalized advertising

Retails can use cameras in combination with facial recognition to gain information about their customers demographics, such as the gender and approximate age, as well as about their shopping behavior. The systems are able to analyze when customers enter the store, what products they looked at for how long. These insights enable marketers to segment their customers much more specifically and offer hyper-relevant and personalized advertisements that make 1:1 offers a reality. As a result, customers benefit from an improved shopping experience and companies from more efficient marketing campaigns.

Inventory management

Computer vision-based cameras are also used to track and manage inventories in retail stores. For instance, intelligent in-place cameras are able to recognize when display shelves are at their minimum levels, so digital signage can be adjusted and guide customers to similar products. Additionally, computer vision technology is used to measure in-store traffic and enable real-time pricing, based on current demand. Another opportunity for retailers is to offer discounts for short shelf life products to boost their immediate consumption.Thus, AI enabled inventory management facilitates efficient logistics and proves a blessing when it comes to storing shorter shelf life perishable products.

Retail demand forecasting

Forecasting sales is complex because numerous elements influence purchases at any given time: weather, shopping trends, regulations, new products, buying habits, and a pandemic, to name a few. Machine learning can help address sales forecasting challenges by integrating several factors that influence sales into model building. Predictive models can forecast sales months in advance, taking account of seasonality, consumption trends, price levels, etc.

Conclusion

Using AI, retailers can leverage vast amounts of behavioral data from multiple sources, learn from patterns, and respond in real-time. Computer vision, NLP, and machine learning techniques enable them to provide engaging customer experiences, automate tasks and ultimately stay ahead of the competition.

Learn how to apply cutting-edge AI and machine learning techniques to the retail industry at the virtual Mini Data Science Salon on October 21. Be a part of an immersive learning experience on The Future of Retail with Behavioral Data, where you will interact with data practitioners, innovators, and engineers as we explore the exciting possibilities of the future with AI in retail.

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