Digital purchases across various industries have been consistently increasing. This upward trend is evident in on-demand service industries such as food delivery and online retail.
In the realm of food delivery services, there has been substantial growth worldwide. For instance, the global market for online food delivery services grew from $111.11 billion in 2021 to $128.32 billion in 2022, indicating an impressive compound annual growth rate (CAGR) of 11.5%. Furthermore, this market is projected to reach $159.46 billion by 2026, growing at a CAGR of 5.6%. This growth is largely attributed to the increase in smartphone users, further boosted by the COVID-19 pandemic which significantly elevated the demand for online food delivery services.
Likewise, the online retail sector has seen an exponential increase in digital purchases. The e-commerce market, already substantial at $4.28 trillion, is anticipated to grow even further to $6.38 trillion by 2024. With the e-commerce landscape rapidly expanding, competition among companies in this space has intensified. This escalation has led to the realization that a robust and effective search engine is instrumental for success. Studies highlight that a large portion of e-commerce site visitors, around 47%, expect a search bar in the website's header, and an overwhelming 70% would abandon a site if the search function yields irrelevant results. This highlights the potential for lost sales and diminished customer loyalty if customers struggle to find their desired products.
With the increasing importance of efficient online search functionality, industry leaders like Sunny Agarwal, a machine learning expert and product manager for a commerce giant, are taking proactive steps. These professionals are at the forefront of transforming the customer shopping experience, underlining the significance of digital purchases and their continued growth across different industries.
Machine learning and artificial intelligence
Machine learning (ML) serves as a cornerstone in the broader field of artificial intelligence (AI), empowering computers to learn from and adapt to data without explicit programming. By employing algorithms and statistical models, ML enables AI systems to recognize patterns, make decisions, and enhance their performance over time as they process increasing volumes of data. This self-learning capability equips AI systems to address complex tasks and deliver solutions in various domains, including natural language processing, image recognition, and autonomous systems. Consequently, ML has emerged as an essential component of contemporary AI, fueling technological advancements and significantly expanding its potential applications across diverse industries.
The challenge of understanding 'user intent'
As digital purchases gain momentum, particularly in on-demand industries like food delivery, understanding 'user intent' becomes a formidable challenge for businesses to navigate. For example, when a user searches for "chili," the challenge lies in discerning whether they want to explore restaurants offering chili-based dishes or if they're seeking takeout from the restaurant chain "Chili's." These intricate conundrums only scratch the surface of what companies have to address to ensure efficient service delivery.
Simultaneously, mastering food delivery search systems calls for not just decoding user intent but orchestrating various operational elements. A smooth transaction needs accurate alignment of the user's request with open restaurants ready to serve, coupled with the availability of drivers to deliver along the required route. Without this synergy, even a perfectly prepared meal can lead to a disappointing user experience.
Sunny Agarwal, an expert in this complex field, brings an exceptional blend of academic and professional experience. He embarked on his academic journey with a degree in Computer Science, specializing in Machine Learning, from the Indian Institute of Technology and extended his knowledge with a Master's in Management Science from Columbia Business School in 2016.
His deep interest in applying machine learning to search engines led him to Grubhub, a renowned food delivery platform. Here, Agarwal faced the complex task of optimizing search functionality. His role entailed understanding user queries and matching them with relevant local restaurants capable of delivering to the specified address.
Today, as a product manager for a top 5 retailer's e-commerce business, Agarwal enhances the search experience on the company's website and apps, catering to millions of customers daily. His prowess in machine learning-powered search ranking allows the company to outperform competitors and provide seamless, efficient search experiences.
The rise of e-commerce and the sheer volume of online products available underscores the importance of robust search engines that guide customers efficiently. Integrating machine learning into these engines enables more personalized and accurate user experiences. Yet, the challenge lies in understanding a user's intent even when they may lack the precise words to express their needs, such as searching for "Obama last book" when they mean "A Promised Land."
Addressing these challenges requires extensive data analysis and dynamic collaboration between product managers, data scientists, and engineers. Additionally, keeping up with evolving trends and customer expectations adds another layer of complexity to the development and optimization of search algorithms.
Agarwal's primary goal is to ensure customers find their desired items at the top of the search page. Given the platform's vast number of sellers and items, he employs multiple machine-learning models to analyze user queries and a swift, scalable infrastructure to provide near-instantaneous search results. The success of his approach hinges on delivering a list of 40 relevant, highly-rated items with competitive prices and quick shipping times—all in a blink of an eye to prevent customers from perceiving the experience as slow.
These nuanced improvements may often go unnoticed by users, but their implementation requires a high level of expertise. Thanks to his vast experience with machine learning models for search ranking, Agarwal is able to enhance the online shopping experience significantly. His work serves as a testament to the importance of these intricate enhancements in the ever-growing field of e-commerce.
The future of ML in e-commerce
With machine learning advancements accelerating, experts like Sunny Agarwal are tapping into their immense potential across diverse industries such as ride-sharing, food delivery, and e-commerce. Their focus is on ML-powered search engines, which are evolving rapidly to offer personalized, seamless user experiences in a swiftly progressing technological landscape.
The challenge for these innovators is to ensure ML models are bias-free, explainable, and regulatory compliant, given the growing role of ML in shaping customer experiences. From finding the fastest ride route or the most satisfying local takeout to making the perfect online purchase, Agarwal aims to make these tasks effortless.
He is pushing the limits of ML integration across these sectors, striving to perfect the art of matching a user's needs with the right product, meal, or service. As we glimpse into a future where the benefits of ML are becoming intrinsic to our daily experiences, pioneers like Agarwal stand at the forefront, exploring and leveraging the vast potential of AI across ride-sharing, food delivery, and e-commerce.
