Unlocking Success: Building AI Products for Retail with Nike

Table of Contents

1. Introduction
2. Background and Experience
3. The Framework for Building Successful AI Products in Retail
4. Finding a Friendly AI Problem
   • Lead Time and Granularity
   • Examples of Friendly and Unfriendly Retail Problems
5. Designing the AI Product and Implementation
   • Historical Data
   • Forecasting
   • Optimization
   • UX/UI Design
   • Enforcement Mechanism
   • Process Alignment
6. Measurement and Monitoring
   • Key Objectives
   • Adoption Rate
   • Designing a Measurement Strategy
   • Bridging APIs and KPIs
   • Importance of Data Infrastructure
   • Monitoring Performance and Iteration
7. Summary and Conclusion

📝 Building Successful AI Products in Retail

In the ever-evolving world of retail, the integration of Artificial Intelligence (AI) and Machine Learning (ML) has become increasingly important. With the potential to revolutionize decision-making processes, AI products offer opportunities for optimization, efficiency, and increased profitability. However, building a successful AI product in the retail industry requires a thoughtful and strategic approach. In this article, we will explore a framework for building successful AI products in retail, including finding a friendly AI problem, designing the product and implementation, and effective measurement and monitoring strategies. Let's dive in!

1. Introduction

The retail industry has always been highly competitive and dynamic, with constantly changing consumer demand and market trends. To stay ahead in this fast-paced industry, retailers are turning to cutting-edge technologies such as AI and ML. These technologies have the potential to provide valuable insights, optimize decision-making processes, and drive business growth.

In this article, we will explore a comprehensive framework for building successful AI products in the retail industry. This framework is Based on the experience and expertise of Elena Kuliak, the Director of Scaled Analytics at Nike. Through her extensive background in AI and machine learning, she has developed a three-step framework that encompasses finding a friendly AI problem, designing the product and implementation, and effective measurement and monitoring strategies.

2. Background and Experience

Before delving into the framework, it's important to understand Elena Kuliak's background and experience. Elena's Journey with AI and machine learning began over a decade ago at MIT, where she met her statistics professor and worked on his first startup. She then joined Select, a startup focused on cloud-based predictive analytics for retailers, where she led over 30 implementations with retailers worldwide.

Elena's expertise lies specifically in the fashion and apparel industry, which she holds close to her heart. After four years of operating as a vendor, Select was acquired by Nike, where Elena now serves as the Director of Scaled Analytics. Through her experiences at Select, Nike, and other AI and machine learning roles, Elena has gained invaluable knowledge on how to build successful AI products in the retail industry.

3. The Framework for Building Successful AI Products in Retail

Elena's framework for building successful AI products in retail is based on three crucial steps: finding a friendly AI problem, designing the product and implementation, and measurement and monitoring. By following each of these steps, retailers can maximize the potential of AI and ML technologies, leading to improved decision-making processes and ultimately, successful AI products.

4. Finding a Friendly AI Problem

The first step in building a successful AI product in retail is finding a friendly AI problem. Not all problems are suitable for AI solutions, and retailers must identify problems that are conducive to AI implementation. Two key factors to consider in determining the friendliness of an AI problem are lead time and granularity.

Lead Time and Granularity

Lead time refers to the length of time available for forecasting before a sales decision is made. Problems with shorter lead times, such as in-season pricing or fulfillment decisions, are more suitable for AI implementation. On the other HAND, problems with longer lead times, such as planning and buying decisions, are less friendly to AI.

Granularity refers to the level of Detail or specificity required in the decision-making process. Higher granularity problems, such as specific store-level decisions, are more friendly to AI implementation. Conversely, lower granularity problems, such as high-level category decisions, may not require AI implementation.

Examples of Friendly and Unfriendly Retail Problems

To illustrate the concept of friendly and unfriendly AI problems in retail, let's consider a couple of examples.

An unfriendly problem would be attempting to predict the sizes of a specific shoe, such as Manolo Blahnik stilettos, nine months in advance. With limited historical data and the uncertainty of fashion trends, accurate predictions are challenging. Instead, a more friendly approach would be to forecast the overall demand for a particular category of shoes, such as men's running footwear, for the upcoming season.

By understanding the lead time and granularity required for each problem, retailers can identify friendly AI problems that are conducive to successful AI product development.

5. Designing the AI Product and Implementation

Once a friendly AI problem is identified, the next step is designing the AI product and implementation. This involves several key components, including historical data, forecasting, optimization, UX/UI design, and enforcement mechanisms.

Historical Data

Historical data serves as the foundation for any AI product in retail. It provides insights into past performance, trends, and customer behavior, which are essential for accurate forecasting and decision-making. Retailers must ensure the availability and quality of historical data to maximize the effectiveness of their AI products.

Forecasting

Forecasting plays a crucial role in AI products by predicting future outcomes based on historical data. Accurate forecasting allows retailers to anticipate demand, optimize inventory, and make informed business decisions. By leveraging AI and ML algorithms, retailers can improve forecast accuracy and gain a competitive edge in the market.

Optimization

Optimization involves making trade-offs and decisions to achieve desired outcomes. Retailers must Align their AI products with their business objectives, whether it's maximizing revenue, margin, or other key performance indicators (KPIs). By optimizing pricing, allocation, and fulfillment decisions, retailers can improve profitability and operational efficiency.

UX/UI Design

User experience (UX) and user interface (UI) design are critical in ensuring the seamless integration of AI products into existing business processes. The AI product should be user-friendly, intuitive, and align with the retailer's workflow. Effective UX/UI design enhances user adoption and promotes the successful implementation of AI products.

Enforcement Mechanism

The enforcement mechanism determines how AI recommendations are implemented and integrated into the decision-making process. Whether it's pricing, allocation, or fulfillment decisions, retailers need a robust enforcement mechanism to ensure the AI product's recommendations are followed and executed. This mechanism may involve pricing algorithms, inventory allocation algorithms, or other automated processes.

Process Alignment

One of the challenges in designing AI products is aligning them with existing processes. Retailers must evaluate their Current processes and identify areas where AI can be effectively integrated. In some cases, process redesign may be necessary to leverage the full potential of AI products. By aligning the AI product with existing processes, retailers can streamline operations and maximize the product's value.

6. Measurement and Monitoring

Measurement and monitoring are crucial aspects of building successful AI products in retail. To evaluate the effectiveness of AI implementations, retailers must design a robust measurement strategy and continuously monitor performance.

Key Objectives

The measurement strategy should align with the optimization objectives of the AI product. Whether it's revenue maximization, margin improvement, or other KPIs, retailers need to define key objectives and metrics to assess the product's impact. By measuring success against these objectives, retailers can gauge the effectiveness of their AI products.

Adoption Rate

In addition to measuring the product's impact, retailers must also monitor the adoption rate of AI recommendations. This metric indicates the percentage of AI recommendations that are executed by users. Understanding the adoption rate helps retailers identify any barriers or challenges to user acceptance and adoption. By addressing these barriers, retailers can maximize the value of their AI products.

Designing a Measurement Strategy

Designing a comprehensive measurement strategy requires careful consideration of various factors. Retailers need to bridge the gap between product APIs (Application Programming Interfaces) and business KPIs (Key Performance Indicators). This bridge allows retailers to connect the AI product's performance to the broader business objectives. Additionally, retailers must design a data infrastructure that enables effective measurement and monitoring of the AI product's performance. By tracking adoption rates, product APIs, business constraints, and circumstances, retailers can continuously evaluate and iterate their AI products.

Importance of Data Infrastructure

A robust data infrastructure is crucial for effective measurement and monitoring. It enables retailers to capture and analyze data related to AI product performance, user adoption, and business outcomes. Retailers should invest in developing data pipelines and monitoring systems that provide real-time insights into the AI product's performance. By collecting and analyzing Relevant data, retailers can make data-driven decisions and optimize their AI products.

Monitoring Performance and Iteration

Monitoring the performance of AI products is an ongoing process. Retailers should continuously evaluate the product's impact, analyze adoption rates, and assess its alignment with business objectives. By leveraging the data infrastructure and measurement strategy, retailers can iterate and improve their AI products over time. This iterative approach allows retailers to fine-tune their AI products, optimize their performance, and drive continuous improvement.

7. Summary and Conclusion

Building successful AI products in retail requires a thorough understanding of the industry, strategic problem-solving, and effective implementation and measurement strategies. By following a framework that encompasses finding a friendly AI problem, designing the product and implementation, and employing effective measurement and monitoring strategies, retailers can unlock the full potential of AI and drive business growth.

Elena Kuliak's framework provides valuable insights into the key considerations and best practices for building successful AI products in retail. By leveraging AI and ML technologies, retailers can optimize decision-making processes, improve operational efficiency, and enhance the overall customer experience. As the retail industry continues to evolve, staying ahead of the curve with AI products will be crucial for retailers to thrive in the competitive market.

Thank You for joining us as we explored the framework for building successful AI products in retail. By implementing these strategies and leveraging the power of AI, retailers can unlock new opportunities, drive growth, and Shape the future of the industry.

Highlights

• The integration of AI and ML technologies has the potential to revolutionize decision-making processes in the retail industry.
• Elena Kuliak, the Director of Scaled Analytics at Nike, has developed a three-step framework for building successful AI products in retail.
• Finding a friendly AI problem involves considering lead time and granularity.
• Designing the AI product and implementation involves leveraging historical data, forecasting, optimization, UX/UI design, and enforcement mechanisms.
• Measurement and monitoring are crucial for evaluating the effectiveness of AI implementations.
• A comprehensive measurement strategy should bridge the gap between product APIs and business KPIs.
• A robust data infrastructure enables effective measurement and monitoring of AI product performance.
• Continuous monitoring and iteration are essential for optimizing AI products over time.

FAQs

Q: What is the lead time and granularity in finding a friendly AI problem? A: Lead time refers to the length of time available for forecasting before a sales decision is made, while granularity refers to the level of detail or specificity required in the decision-making process.

Q: How important is the measurement and monitoring of AI products in retail? A: Measurement and monitoring are crucial for evaluating the effectiveness of AI implementations and optimizing product performance over time.

Q: What are some key components of designing AI products in retail? A: Some key components include leveraging historical data, accurate forecasting, optimization for desired outcomes, UX/UI design, enforcement mechanisms, and process alignment.

Q: How can retailers ensure the adoption of AI recommendations? A: Monitoring the adoption rate of AI recommendations and addressing any barriers to user acceptance and adoption can maximize the value of AI products.

Q: Why is a robust data infrastructure important for AI product measurement and monitoring? A: A robust data infrastructure enables retailers to capture, analyze, and leverage relevant data for effective measurement and monitoring, leading to data-driven decision-making and optimization.

Resources:

• MIT
• Nike
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