How GenAI Solutions for Ecommerce and Long-Form Queries Can Help Merchandisers Understand Shopper Intent

Any merchandiser will tell you that one of the most challenging aspects of their job is trying to read the minds of their customers and understand shoppers’ intentions when they come to their site. But what if generative AI could change the way consumers shopped so that they revealed more information in the search bar?

With increasing adoption of tools like ChatGPT and even Rufus, more and more consumers are learning to formulate search queries using natural, conversational language rather than traditional keyword-based searches. This presents a unique opportunity for merchandisers to learn more about shoppers’ intents when they visit their eCommerce sites, which in turn leads to better merchandising decisions.

By analyzing the rich, detailed queries that customers use, merchandisers can identify trends, understand specific needs, and personalize the shopping experience at user-level, ultimately driving higher conversion rates and customer satisfaction.

Why It’s So Hard for Merchandisers to Understand Shoppers’ Intent

Ecommerce merchandisers are faced with the unique challenge of interpreting shoppers’ intent based on their digital footprint. 

Shoppers leave behind a vast amount of data, from search queries and click-through rates to purchase history and abandoned carts. This data is often unstructured and spread across multiple platforms, making it difficult to consolidate and analyze comprehensively. Merchandisers are spending a large chunk of their time investigating under the hood of their site’s search tool to interpret this data, looking at popular search queries and connecting the dots of a customer’s journey. 

In short, it’s an extremely complicated and frustrating task not that far off from mind-reading. 

For instance, a traditional keyword-based query like “blue denim” doesn’t give much for merchandisers to go off of –– was this shopper searching for a new wardrobe staple within a certain budget? What style of jeans were they looking for? Were they looking for pants or a denim button down? With so much context missing, it’s nearly impossible for merchandisers to know the right products to show. 

Google and ChatGPT Are Retraining Consumers On How to Search 

Thankfully, the way consumers interact with search engines is evolving. Google and ChatGPT, two giants in the world of artificial intelligence and natural language processing, are fundamentally retraining consumers to be more conversational with their search queries, making interactions more intuitive and effective. 

Advanced technology like natural language processing has enabled users to ask questions in a more conversational manner without worrying about specific keywords. This more relaxed, dialogue-based approach to search becomes more about solving problems than simply searching for products. 

This conversational search is a game-changer for merchandisers. A search question like  “What are the most comfortable mens jeans under $50?” reveals so much more about shopper intent than “blue denim.” The more information about shoppers’ intent available, the easier it is for merchandisers to make more effective decisions, and ultimately, provide a satisfying customer journey.

Don’t Fall Behind: How Retailers Can Build This Functionality Into Their Search Experience

Building conversational search functionality into an eCommerce platform requires a combination of advanced technologies, thoughtful design, and continuous improvement. At Constructor, we believe product adoption should take a user-centric approach when it comes to implementing emerging tech –– and one of the best ways to do that is to provide customers the opportunity to speak with an expert. 

Constructor’s AI Shopping Assistant (ASA) is a conversational product search and discovery tool that combines Generative AI with Constructor’s user-level personalization to optimize ecommerce KPIs. ASA allows shoppers to ask longer-form, detailed questions, and receive recommendations tailored to their unique preferences, shopping history, and intent, all aligned with real-time inventory.

Intent-based recommendations can also assist in creating an overall search experience that feels natural and intuitive for users. Features like autocomplete can suggest natural language queries that reflect common conversational phrases, training users to interact with the search bar in a more conversational manner. This helps to capture the shopper's intent early in the search process, making the shopping experience more efficient.

Most merchandisers wouldn’t be surprised to see “mind-reader” as a requirement on a job listing. But thanks to generative AI, the shift from traditional keyword-based searches to natural, conversational queries is allowing for a deeper understanding of customers’ true intentions –– no mind-reading skills required. 

This blog post was created by Akeneo partner, Constructor. You can learn more about their AI Shopping Assistant here.

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Nate Roy, Director of Brand & Content

Constructor