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How to Boost Sales & Cut Waste: Walmart's AI Secret 🤫

How to Boost Sales & Cut Waste: Walmart's AI Secret 🤫

Master Inventory Like Walmart

Andre Borczuk
October 16, 2024 • Approx. Reading Time: 9 minutes

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Hello fellow product monk!

Have you ever bought vegetables for a salad you were totally going to make, only for it to go bad in your fridge because you forgot you had it? Asking for a friend.

Now imagine you’re Walmart and you had as many as 400 million SKUs and had to throw out 383,000 metric tons of food waste in 2022. You’d probably be looking for a better solution to track your inventory also!

Join us as we unravel how Walmart used AI to advance the future of inventory management in retail! 📈🤖!

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Lessons for PMs [AWS]

Embrace AI for Scalability: AI can significantly reduce the need for manual labor in data-intensive tasks, allowing for more efficient allocation of resources.
Focus on Data Quality: High-quality data is crucial for improving customer experiences and operational efficiencies.
Integrate AI into Core Processes: AI can be used to enhance core processes such as product search and inventory management, leading to improved customer satisfaction and increased sales.
Monitor AI Performance: Regularly testing AI models for accuracy and drift is essential to ensure they continue to produce reliable results.
Leverage AI for Customer Insights: AI can provide valuable insights into customer behavior and preferences, enabling more personalized and effective marketing strategies.

Exec Summary

Walmart has leveraged generative AI to refine over 850 million product data points in its catalog, a task that would have required nearly 100 times the current headcount to complete in the same timeframe. This technological advancement has not only streamlined internal processes but also enhanced customer interactions, both in-store and online. Key benefits include improved product search functionality, enriched product descriptions, and more accurate inventory management[2][5].

Problem

The quality of data in Walmart's product catalog has a profound impact on nearly every aspect of its operations, from helping customers find and purchase items to managing inventory and fulfilling orders. Prior to the implementation of generative AI, maintaining and updating this vast catalog was a labor-intensive process prone to errors and inefficiencies. The challenge was to find a scalable and efficient solution to improve data quality without significantly increasing labor costs[4][5].

Solution

To address the challenges associated with managing its vast product catalog, Walmart implemented an advanced AI-driven approach, leveraging multiple large language models (LLMs). The primary goal was to create and enhance over 850 million pieces of data within its product catalog, a critical step in improving the overall customer experience, boosting e-commerce sales, and streamlining in-store operations. Walmart’s strategy focused on accelerating data enrichment while ensuring the highest levels of accuracy and consistency across its offerings. This comprehensive solution consisted of several key aspects:

1. Data Enrichment and Standardization:

Walmart deployed generative AI to fill in missing product attributes, such as specifications, dimensions, and materials, that were previously incomplete or inconsistent across the catalog. This enriched data provided customers with more detailed information, enabling them to make informed purchasing decisions. AI models were also used to rewrite and enhance product descriptions, standardizing the language across listings to maintain a consistent brand voice. By automating these processes, Walmart reduced manual effort and increased the speed of data enrichment, allowing for real-time updates that kept product information accurate and up-to-date.

2. Enhanced Search Functionality:

To improve online customer experience, Walmart integrated AI-powered search capabilities into its app and website. The LLMs were trained to understand conversational language, enabling the search engine to respond accurately to more complex, natural-language queries. For example, customers could type detailed or vague queries like “comfortable summer dress for a beach vacation,” and the AI system would match these queries with relevant products. This capability enhanced the precision and relevance of search results, making it easier for customers to find the products they were looking for and increasing customer satisfaction. The AI also analyzed search patterns and customer behavior over time, continuously refining the search algorithm to improve results and drive more personalized recommendations.

3. Automated Product Categorization and Tagging:

The AI models were tasked with categorizing products and adding appropriate tags automatically. This automated categorization not only sped up the process of adding new products to Walmart’s online platform but also ensured that items were correctly grouped, making them easier for customers to find through navigation filters and search queries. By organizing products into more specific and relevant categories, Walmart enhanced the visibility of its offerings, reduced bounce rates, and improved overall site navigation. Additionally, this tagging system facilitated more precise product suggestions and recommendations based on customer behavior, further enhancing the user experience.

4. In-Store Efficiency through AI Tools:

Walmart deployed AI-driven in-store technology to assist associates in managing inventory and fulfilling customer needs more efficiently. Associates could now use handheld devices equipped with AI features to quickly locate items in the store’s backroom or on the sales floor. The technology provided real-time updates on stock levels, guiding associates to restock products or fulfill online orders for in-store pickup. This system minimized time spent searching for items and enabled store associates to receive notifications for priority tasks, such as restocking high-demand products, thus improving the overall efficiency and responsiveness of in-store operations.

5. E-commerce Platform Enhancements:

The enriched and well-organized product catalog enabled Walmart to better match customer searches with the most relevant products. AI algorithms analyzed customer behavior and search data to predict which products were most likely to meet their needs. The platform dynamically adjusted product rankings based on various factors, including sales performance, customer feedback, and search trends, to optimize the visibility of high-demand items. This proactive approach increased conversion rates and drove higher sales by ensuring that customers were presented with the most relevant and appealing options based on their individual preferences and shopping history.

Through these strategic implementations, Walmart successfully leveraged AI technology to transform its product catalog, enhance search functionality, and improve both online and in-store operations. By harnessing the power of large language models, Walmart significantly boosted data accuracy, streamlined inventory management, and created a more engaging and personalized experience for its customers.

Results

The deployment of generative AI has yielded significant benefits for Walmart, including:

- Operational Efficiency: The use of AI has reduced the need for manual labor in maintaining the product catalog, allowing for more efficient allocation of resources[2][5].

- Enhanced Customer Experience: Improved product search functionality and more accurate product information have led to better customer satisfaction and increased e-commerce sales[4][8].

- E-commerce Growth: Walmart reported a 21% growth in its e-commerce function, partly attributed to the improvements made possible by generative AI[7][8].

- Marketplace Seller Support: The development of a new AI assistant to provide concise answers to sellers' queries has streamlined interactions and improved the overall selling experience[4][8].

Conclusion

Walmart's pioneering application of generative AI in inventory optimization illustrates the profound potential for AI-driven approaches to revolutionize retail operations. Product managers are gleaning from this case that integrating AI with core business processes can indeed craft bespoke solutions to multifaceted challenges, thereby driving sustainable business growth and elevating customer satisfaction.

AI's efficacy in retail serves as a strategic blueprint for other enterprises striving to enhance their operational efficiency through technology-driven innovation. It encourages deeper partnerships between product managers, data scientists, and AI technology to architect solutions that are not only intelligent but also adaptive to changing market dynamics.