Streamlining E-Commerce Experiences With AI-Based Product Search
The way consumers discover products online is undergoing a profound transformation. Traditional methods of browsing and researching are giving way to more dynamic, artificial intelligence-powered experiences that better cater to individual preferences. New research from The Business of Fashion and McKinsey highlights this shift: 50 percent of fashion executives believe generative AI (“GenAI”) will revolutionize product discovery, while 82 percent of consumers want AI to help speed up the research process.
Major retailers are already integrating AI into their platforms to revolutionize product discovery. Look no further than Walmart’s GenAI-powered search tools, Amazon.com's AI shopping guides, and Target’s interactive gift finder. The race is on to best anticipate customer needs and secure market share; the winners will be those that effectively leverage technology to capture consumer attention and deliver the frictionless experiences they demand.
However, before they jump the gun, retailers must take the time to thoughtfully integrate AI, ensuring it enhances the buyer journey while aligning with brand and customer needs.
Use AI to Optimize Product Discovery
At the top of the funnel, AI can help connect consumers with the right products based on their search keywords. Understanding buyer intent (whether through their digital behaviors or their search keywords) can go a long way in improving customer experience and, ultimately, driving sales.
AI can optimize the search experience in several ways:
- Natural language processing (NLP) understands user queries in natural language, even if they’re not perfectly phrased.
- Predictive algorithms identify which products customers are likely to click on or purchase based on past searches, purchase history, and session data.
- Predictive search displays relevant search suggestions as users type their queries.
- Suggested filters refine searches based on size, brand, and price, making it easier for shoppers to find exactly what they’re looking for.
- Error tolerance drives results despite incomplete or misspelled queries.
- Mobile optimization lets consumers shop their own way with fast-loading results and easy-to-find search bars.
These features are absolute musts for optimizing retailers’ search and discovery experiences, but it’s not enough to merely offer them. Businesses must fortify these offerings by ensuring the underlying data is accurate and structured.
Develop a Catalog of Comprehensive Product Information
Data is the backbone of any AI strategy. Retailers that hope to use the technology to improve product discovery can start by developing catalogs with comprehensive product information.
These catalogs should include:
- Product attributes like size, color, and brand to facilitate filtering and enable AI to make the most accurate recommendations possible.
- Rich metadata, including descriptive titles, tags and details, to help AI match products accurately.
- Image tagging to help AI improve visual search results by identifying comparable products based on appearance.
- Consistent categorization to ensure accurate results.
This isn’t an exhaustive list, but it’s a good way for retailers to get started. Accurate and thorough data is essential for AI-driven search tools to process queries and deliver highly relevant results.
Ensuring Long-Term Success Through Tailored Shopping Experiences
Retailers are under increasing pressure to meet growing consumer demand for hassle-free shopping experiences. AI-powered product search offers retailers a way to sharpen their competitive edge and make it easier for customers to discover and purchase the items they want. NLP and predictive search can transform the shopping journey, but success depends on having well-organized product data and thoughtful implementation. To take personalized shopping to the next level, retailers can also tap into real-time behavioral insights to go deep on customer preferences and decision-making. By prioritizing these elements, retailers can deliver the kind of tailored experiences customers expect while staying competitive in a rapidly changing marketplace.
Claire Fang is chief product and technology officer at Fullstory, a behavioral analytics platform.
Claire Fang, Chief Product and Technology Officer, Fullstory
As Fullstory’s chief product and technology officer, Claire Fang brings more than two decades of product leadership experience to the executive team. With a background spanning public companies and startups, Fang brings a wealth of expertise in delivering innovation in enterprise software, building world-class product and engineering organizations, and driving exponential business growth at a global scale.
Before joining Fullstory, Claire served as the chief product officer at SeekOut. In this role, she led the company’s product management, design, and marketing functions and was responsible for product vision, strategy, roadmap, and execution. Prior to that, she was the chief product officer for Qualtric’s EmployeeXM business, where she oversaw the product management, product marketing, and product science functions and led the business through a 5x growth. She also gained extensive experience in product management at industry giants Facebook and Microsoft, where she helped develop Microsoft Azure into an industry-leading platform, realizing 50x revenue growth.
In her current role, Claire is responsible for setting Fullstory’s strategic product direction and leading the product, design, and engineering teams.
Claire holds a Bachelor of Engineering degree from Southeast University and a Ph.D. in electrical and computer engineering from Carnegie Mellon University.
