Examples of Cognitive Computing in Retail

Introduction

Cognitive computing has revolutionized the way enterprises operate and make decisions. By leveraging advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML), businesses can now harness the power of cognitive computing to streamline processes, enhance customer experiences, and gain valuable insights. Large Language Models (LLM) and Natural Language Processing (NLP) have revolutionized many use cases, enhancing productivity and experience.

In this blog post, we will explore various use cases of cognitive computing in the retail industry, highlighting its significant impact on enterprise operations.

Personalized Recommendations

One of the most prominent use cases of cognitive computing in the retail sector is personalized customer recommendations. By analyzing vast amounts of customer data, cognitive computing systems can create highly accurate and tailored product recommendations for individual customers. This enables retailers to offer a more personalized shopping experience, increasing product recommendation relevancy and driving higher sales. In the past, most recommendations were based on "decision trees" and were primarily human-derived or leveraged simple statistical models for "others who purchased also looked." With expanded real-time datasets and ML algorithms, the systems can produce hyper-personalized and accurate recommendations, which were not possible previously. Connecting past transactions and modeling behavior that resulted in a sale can now be leveraged to build a model that recognizes similar behavior early on and offers relevant recommendations.

We have developed a framework for personalization based on our decades-long experience in
e-commerce and more recently, in Data Science and AI. As most companies have a very specific context, our framework can be leveraged to build sophisticated models leveraging both custom and existing commercial engines to optimize for the best and most relevant set of recommendations.

Inventory Management

Effective inventory management is critical for retail businesses to ensure optimal product availability while minimizing costs. Cognitive computing can play a vital role in this area by analyzing historical sales data, market trends, and external factors such as weather forecasts. By leveraging this information, cognitive systems can predict demand patterns, optimize inventory levels, and even automate reordering processes. This not only helps retailers avoid stock outs or excess inventory but also improves overall operational efficiency.

One of the leaders in demand planning and forecasting is Relex Solutions. They have been leading the pack in  retail, including groceries, helping them predict and optimize their supply chains and reduce waste. Kloud9 has developed a ML model to predict stock outs at SKU-store levels stemming from Phantom inventory. This model can also be used to identify items that are most likely potential for shoplifting. Once identified, retailers can decide on appropriate anti-theft approaches.

Fraud Detection

Fraudulent activities pose a significant threat to retail enterprises, costing billions of dollars each year. Cognitive computing can be utilized to detect and prevent fraudulent transactions in real-time. By continuously monitoring customer behavior, transaction patterns, and other relevant data, cognitive systems can identify suspicious activities and trigger alert mechanisms. This enables retailers to take immediate action, saving both financial resources and reputation.

One area in fraud detection where retailers can benefit from ML models is gift cards – (GCs) which are being used across the industry for various criminal activities ranging from theft to wire fraud. With ML, retailers can detect in real-time if the person using a GC might be engaged in illegal activity and cancel the card or contact law enforcement.

Customer Service Enhancement

Efficient and personalized customer service is crucial for retail enterprises to build strong customer relationships and retain loyalty. Cognitive computing can enhance customer service processes by leveraging chatbots and virtual assistants. These intelligent systems can understand customer queries, provide instant responses, and even handle more complex interactions. By automating certain customer service tasks, retailers can free up human resources, reduce response times, and provide instant responses with high relevancy. With advanced NLP and a refined LLM, automated customer service bots can understand context, translate languages, and offer complex service explanations.

When the global home furnishing retailer IKEA moved a large portion of their customer service engagements to the new smart bot, they could retrain half of their Customer Support individuals to act as interior designers. AI allowed IKEA to invent a new revenue stream instead of reducing staff¹.

For instance, AWS offers a sophisticated conversational AI framework called Amazon Lex, allowing organizations to develop their own intelligent service offerings. The platform can learn from past engagements and solutions, in addition to its own language models. Since the launch of ChatGPT, many other software companies have raced to deliver platforms or point solutions to customer service, reducing the time to market for their customers.

Conclusion

Cognitive computing holds immense potential in transforming the retail industry, enabling businesses to make data-driven decisions, enhance shopping journeys, and optimize enterprise operations. The use cases mentioned above are just a glimpse of the vast opportunities cognitive computing has to offer to an enterprise. As such technologies continue to evolve, we can expect greater innovations in cognitive computing - empowering retailer enterprises to stay ahead in the ever-competitive market.

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¹ - AI Chatbot Billie Takes Over

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