Generative AI is transforming industries, with eCommerce set for some of its most significant changes this decade.

One popular application of the emerging technology is in content creation, with over 80% of marketers using GenAI for social media copy and images. Within eCommerce merchandising specifically, GenAI is streamlining content creation by allowing teams to automatically enrich product attributes, improve data hygiene, enhance product descriptions, and more — all at the speed of AI.

But those aren’t its only applications. Let’s take a look at five more compelling use cases of GenAI in eCommerce below.

1. Conversational Commerce

Falling under the umbrella of “conversational commerce,” solutions like Constructor’s AI Shopping Assistant (ASA) are AI-based computer programs designed to respond to online queries in a way that mimics natural, human conversation. They’re often integrated into websites, apps, and customer service systems to provide immediate assistance and support. 

Thanks to advancements in AI and ML technologies, specifically GenAI, these solutions have come a long way in recent years. Within eCommerce specifically, their use is most common in the following two ways:

For Product Discovery

Constructor’s ASA offers online shoppers a way to express their product needs in natural language. Then, they receive product and content recommendations personalized to their preferences, history, and intent, and reflective of the eCommerce company’s real-time inventory —  in less time. 

Our AI Shopping Assistant gives online shoppers a new, useful way to discover items they need and love — disrupting the current search and product discovery paradigm. We already have good product discovery solutions for people who know what they want and just want to search for it, or people who just want to browse a category, or take a product finder quiz. But in cases where shoppers have a more complex need that they can only explain in natural language, like ‘I need healthy items for a picnic’ or ‘I want a trendy shirt to go out in,’ the current paradigms don’t work. There was no good way to explain that need to the search engines of the past. That’s where our AI Shopping Assistant comes in. ASA makes suggestions based on detailed requests from a shopper — like a trusted, in-store associate would — while also instantly factoring in everything it knows about the shopper at hand.

Eli Finkelshteyn CEO and Co-founder of Constructor

ASA is a win-win for all parties involved. The GenAI solution allows shoppers to find what they need easier and more quickly, instilling confidence in their purchases. It also helps eCommerce companies keep shoppers on-site, boosting their conversions and brand loyalty.

For Customer Support

In the same light, GenAI chatbots are handy for customer support, providing instant, 24/7 assistance to a worldwide audience. 

These chatbots leverage advanced natural language processing (NLP) to understand and respond to a wide array of customer inquiries, significantly reducing wait times. They can also personalize responses based on customer history and preferences, creating a more tailored and engaging experience. Plus, they can optimize operational efficiency as they can answer simple, repetitive questions. This saves humans time to focus on more strategic tasks.

2. Advanced Merchandising Capabilities

GenAI is the driving force behind advanced merchandising capabilities, such as:  

AI-Generated Rules

A product discovery tool like Constructor allows merchandisers to leverage AI to automatically generate rules for boosting, burying, slotting, and optimizing product result sets for their KPI of choice. 

Merchandisers then get granular, directional feedback on their work, allowing them to review and override the rules in the dashboard. This is possible via Rule Performance, a feature of Constructor’s Merchant Controls & Intelligence suite. This is true even on rules they institute on pages without a ton of data.

AI isn’t meant to replace merchandisers, nor will it ever. It works as a force multiplier that can help them optimize less visible areas so they can focus on strategic work.

AI-Generated Collections

Creating collections, or personalized landing pages, is a time-consuming, manual task for eCommerce teams to handle. 

This requires the merchandiser to have a list of SKUs selected, a deep understanding of the catalog, or  have meaningful attributes in the catalog to build collections based on conditional logic. 

With AI-generated Collections, merchandisers can describe the types of product, occasions, or styles and leverage the power of Generative AI to bring relevant items into a Collection. 

The prompt “show the customer the most attractive products for hosting a summer bbq” returned close to 300 products from the store catalog. Merchandisers can then edit the AI-generated list as they see fit. 

This feature has many benefits, including, but not limited to: 

• Improving eCommerce SEO coverage.
• Enhancing the existing manual or logic-based Collection creation workflows.
• Improving operational efficiency, as merchandisers can move at the speed of AI.
• Lowering dependency on perfect product data, as Collections are generated automatically based on merchandiser-enter prompt.

3. Virtual Try-On Experiences

Thanks to advanced algorithms and ML, GenAI can create virtual try-on experiences for products like clothing and furniture. These realistic, interactive models allow customers to see how an item will look on them or fit in their home.

For instance, shoppers can visualize furniture in their living spaces, adjusting size and placement to see how it complements their existing decor. When it comes to apparel, they can even upload their photos and virtually try on clothes, getting a sense of fit, style, and color before committing. This is also true of shoes, as seen on Birkenstock’s mobile site below.

Birkenstock lets shoppers virtually try-on select models via their mobile site.

These virtual try-on experiences not only enhance the shopping experience, but also help customers make more informed purchasing decisions, reducing the likelihood of returns and increasing overall satisfaction.

4. Search 

What started off as a search bar has evolved into much more, thanks to the advent of GenAI. In addition to the chatbot functionalities, GenAI-powered ASA can also power the Search experience, taking the shape of:  

Autocompletion

Improve the utility and impact of autocomplete functionality with ASA intent-based suggestions. For a query such as “office clothes,” ASA could suggest search terms including “office clothes for summer,” “office clothes for women – business casual,” “office clothes comfort fit,” and so on. 

These suggestions likely wouldn’t appear in traditional autocomplete due to incomplete product catalog data. But when suggesting the “office clothes comfort fit” query, ASA infers beyond product catalog details. The Generative AI-powered tool recognizes the material in product images, content from reviews, and more to populate suggestions. Then, when shoppers select a suggested search query, the results shown are personalized to them.

Use ASA to help autocomplete fulfill shopper intent more accurately, building value to shoppers and your business.

Search Modes

When shoppers have a complex need and aren’t sure how to address it, ASA can generate intent-based recommendations, acting as a personal shopping assistant. These various search modes include:

• Recipe. E.g., “What’s a good peach cobbler recipe for someone who’s gluten free?” or “gluten-free peach cobbler recipe.” In addition to generating recipes with items the grocer has available, ASA makes personalized recommendations for each ingredient. (So, if the recipe calls for flour, and the shopper tends to buy organically, then options for organic gluten-free flour are shown.) Customers can easily add every product to cart from the recipe page.
• List. E.g., “Blush, eyeshadow, mascara” or “litter box, scratching post, collar.” In the first example, makeup recommendations map to the shopper’s preferred brands, colors, and palettes. In the second, ASA infers meaning based on other items listed and the shopper’s history to return cat collars (not dog collars) among the other recommended items.
• Complete the Look. E.g., “What goes with chinos?” ASA automatically generates in-stock suggestions across categories (shirts, ties, shoes, etc.) that’ll complement the chinos and reflect the shopper’s preferred styles, colors, price points, etc. This is possible via clickstream, or first-party behavioral data.
• Style Assistant. E.g., “I’m attending a state fair in August. What can I wear in the heat?” Again, ASA recommendations are easy to navigate, make sense contextually, are personalized, and span various in-stock products and categories (dresses, shoes, hats, etc.) to promote bundling.
• Suggestion. E.g., “I’m going camping with my kids for the first time at Yosemite National Park in October. What do we need?” or “What do I need to mount a 60-inch TV to my wall?” Suggestions highlight relevant products across categories. Recommendations are often based not only on product data and shopper affinities, but also on on-site content. In the last example, DIY wall-mounting guides can also be displayed to the shopper, alongside product suggestions.
  

5. Supply Chain Optimization

Generative AI can predict supply chain disruptions and provide intelligent inventory forecasting, enabling businesses to manage their stock with greater efficiency and precision. 

The technology leverages vast amounts of data from market trends, historical sales figures, weather patterns, global events, and other sources to identify potential disruptions in the supply chain before they occur. This allows companies to take preemptive measures to mitigate risks, like adjusting procurement strategies or finding alternative suppliers. 

GenAI can also forecast inventory needs with high accuracy, ensuring that businesses maintain optimal stock levels to meet customer demand. This not only reduces costs associated with excess inventory and stockouts, but also enhances overall operational efficiency, leading to improved customer satisfaction and profitability.

How to Prioritize GenAI Use Cases in eCommerce 

From content creation to conversational commerce, advanced merchandising abilities, virtual try-on experiences, and more, GenAI will continue to offer the eCommerce industry a world of opportunities. 

Before diving head first into a digital transformation project, companies should strategically decide where GenAI offers the most benefit. This often takes the shape of a customer-centric approach.  

“[eCommerce companies] will need to think wisely about where AI can help their business and how,” shares Finkelshteyn. “It won’t be about using AI for its own sake but instead about making smart bets on where AI can genuinely improve the user experience for their shoppers. [Ecommerce companies] will need to tread carefully between two extremes: they will need to ensure they don’t use AI as a gimmick in places where it looks flashy but doesn’t actually help users, while also not being so afraid of getting AI wrong that they’re the last to use it.” 

By thoughtfully integrating GenAI where it can truly enhance the customer experience, eCommerce companies can harness its full potential to drive innovation and growth.

The State of Generative AI

This report conducted by Forrester dives into how this revolutionary technology is impacting the B2C commerce industry and the tangible benefits of genAI.

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Nate Roy, Director of Brand and Content

Constructor