Scaling AI Initiatives in Retail

Editor’s note: Fatih is a speaker for ODSC West this October 29th-31st! Be sure to check out his talk, “Scaling AI Initiatives in Retail,” there!

The retail landscape is undergoing a transformative shift, driven by the integration of artificial intelligence (AI) across multiple facets of the industry. As Vice President of Data & AI at ALDO Group and a faculty lecturer at McGill University, I have led the charge in harnessing AI to not only sustain but significantly enhance our competitive edge and consumer experiences. At ODSC West 2024, my talk titled “Scaling AI Initiatives in Retail” will explore advanced AI strategies including causal inference, markdown optimization, and generative AI applications, reshaping retail operations.

Revolutionizing Retail with Advanced AI

Our strategic agenda at ALDO deeply embeds AI into areas such as pricing, inventory management, and customer engagement. This initiative aims to enhance operational efficiencies and maximize profitability. Through detailed case studies, we will explore potential improvements in sales and customer engagement driven by these advanced AI tools.

Demand Forecasting at Different Granularities and Hierarchies

A cornerstone of our AI strategy is sophisticated demand forecasting at multiple levels of granularity and hierarchy — from individual SKUs to broader categories and overall market trends. Advanced AI is employed to dissect vast amounts of data and predict future needs accurately, allowing us to make precise adjustments in inventory and marketing strategies. This nuanced approach ensures that our responses are finely tuned to meet specific demand curves and consumer needs, optimizing resource allocation and enhancing customer satisfaction.

Enhancing Sales with Causal Inference

Causal inference is crucial for moving beyond correlation to understanding the true impact of business actions on sales outcomes. This approach facilitates the development of sophisticated sales enhancement strategies that anticipate changes in consumer behavior and market dynamics.

Approaches to Markdown and Order Fulfillment Optimization

AI could potentially deliver substantial benefits in markdown optimization by analyzing price elasticity and demand forecasts, allowing for dynamic price adjustments to optimize stock levels and maximize revenue. Similarly, AI-driven strategies are poised to enhance supply chain efficiency in order fulfillment, aligning inventory distribution perfectly with demand patterns, thereby aiming to reduce operational costs and improve customer satisfaction.

Exploring Generative AI for Marketing and Operations

Generative AI is poised to transform content management and internal communications:

• SEO and Product Descriptions: Generative AI could be utilized to create SEO-friendly product descriptions that appeal to consumers and perform well in search engine results, potentially driving increased traffic and sales.
• RAG for Internal Employee Chatbots: The Retrieval-Augmented Generation (RAG) model could power internal chatbots, providing employees with instant access to accurate information, which could enhance decision-making and operational efficiency.
• Natural Language-Enabled Search: The integration of natural language processing with search functions could revolutionize how customers find products on platforms, making search intuitive and user-friendly.

Personalization and Recommendations

AI is poised to play a critical role in personalizing the shopping experience. By analyzing customer data and behavior, AI models could offer personalized recommendations on search and product list pages, significantly enhancing user engagement and conversion rates. These recommendations could be continuously refined through real-time data to remain relevant and compelling.

Comprehensive Data Strategies and Advanced Analytics

Our innovative approach to data employs sophisticated feature engineering techniques to predict complex retail patterns:

• Feature Engineering: Utilizing temporal data points such as lagging and differencing at various levels — country, article ID, style, channel — to capture dependencies over time frames ranging from 4 to 52 weeks. Advanced encoding techniques are essential for efficiently handling high-dimensional categorical data.
• Data Preparation: Robust data preparation includes addressing missing values from anomalies like COVID-19 impacts and applying sophisticated scaling and transformation techniques to ensure model accuracy.
• Modeling Techniques: Advanced machine learning models, including gradient boosting methods and Transformer-based architectures, are essential for handling our diverse and dynamic datasets.

Scaling AI Deployments: From Pilot to Full-Scale Implementation

We will cover the full lifecycle of AI projects from their inception as pilot programs to their evolution into full-scale deployments. This discussion will include the challenges encountered, the solutions devised, and the best practices that have emerged. Attendees will gain insights into creating a robust data infrastructure, developing AI talent, and integrating AI with existing technological systems.

Conclusion

“Scaling AI Initiatives in Retail” is designed to provide a holistic view of how AI can be effectively implemented to transform retail operations. Attendees will leave equipped with the knowledge and tools to apply these strategies within their own businesses, driving growth and enhancing customer connections through innovative AI applications.

About the Author/ODSC West 2024 Speaker:

Fatih Nayebi is the Vice President of Data & AI at the ALDO Group, where he leads AI initiatives that revolutionize retail operations and customer experiences. With over 15 years of experience in AI, Fatih excels at integrating AI technologies into business environments, significantly improving customer engagement and operational efficiency.

Fatih holds a Ph.D. in Machine Learning and is a distinguished Faculty Lecturer at McGill University’s Desautels Faculty of Management. He teaches courses in enterprise data science, machine learning engineering, and deep learning, blending academic rigor with practical industry insights.

An accomplished author, Fatih has written extensively on AI and programming, including key works on Swift functional programming. His numerous published articles and patents highlight his expertise. Fatih’s unique ability to bridge academic research with real-world applications makes him a sought-after thought leader in AI and data science.

Originally posted on OpenDataScience.com

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