Online shopping is a double-edged sword, isn’t it? With a few clicks, you can find anything you can ever need (or “need”). At the same time, what retailers see as “endless aisles” designed to meet every need, shoppers often experience as overwhelming and impersonal.

Add in complicated navigation, generic product details, and no way to ask clarifying questions, and the result can be a frustrating journey. Instead of guiding customers to find exactly what they need — like a live sales associate would — many online experiences push them away into the hands of a competitor.

Enter the AI shopping agent, bringing the in-store experience online.

alby’s AI shopping agent bridges the gap between shoppers’ needs and retailers’ goals. By mimicking the personalized, hands-on experience of an in-store salesperson, the AI shopping agent can help shoppers find the right products faster, make informed purchasing decisions, and drive more purchases in the process. This emerging technology, powered by the latest large language models, predicts customer needs, anticipates their next steps, and guides them toward the right product in a way that feels personal and effortless.

Understanding the AI shopping agent

While chatbots add value on the post-purchase side, they wait for customers to ask the exact right question, like “Where’s my order?” or “What’s your return policy?” But while chatbots react, AI shopping agents anticipate needs, predict behavior, and deliver real-time solutions. They use live engagement signals, product data, and machine learning to generate personalized prompts that feel intuitive, not robotic. Because they refine answers based on behavior and context, they’re able to turn intent into action and scale personalization.

Every customer arrives at your website with a goal — whether it’s exploring options, comparing products, or making a purchase. AI shopping agents recognize signals of intent in real time and guide customers toward their next step.  This helps retailers upsell complementary items, recommend upgrades, and ultimately drive incremental purchases.

This thoughtful guidance delivers a 14x increase in engagement rates and 2x higher conversion rates compared to traditional tools. The result? One leading department store saw a 2.15% lift in revenue per session.

AI shopping agent use cases for every retail vertical

alby’s platform ingests volumes of data from sources including a retailer’s product catalog, ratings, reviews, customer Q&A, and anything else a retailer may want to include. That may be an ingredients list for a health and beauty brand, sizing guides for an apparel retailer, or assembly manuals for a brand in the home goods space.

With this data, the AI shopping agent can give detailed, accurate answers about general product questions. It also translates to different skills, defined as pre-generated prompts that drive certain shopper behaviors.

We’ve outlined eight of those skills and what they mean for different retail verticals. Retailers with other, more specific needs are able to build their own custom skills with alby’s developer-friendly platform.

Review Summaries

Customer reviews are a powerful part of the buying journey. The vast majority of shoppers consult them and what they discover holds a lot of weight. Northwestern University research found that the purchase likelihood for a product with five reviews is 270% greater than that of a product with none.

However, the more popular a product, the more reviews it will have. Most people won’t comb through 300 reviews, but the AI shopping agent will.

Review summaries list out the most common sentiments. As online returns become more complicated, these summaries can give shoppers confidence about important considerations like an item’s fit or quality.

Best selling

When you designate a “best seller,” you’re essentially tagging that product as a hot item. alby’s AI shopping agent serves up those best sellers, which can help curate a brand’s catalog and instill confidence in shoppers.

Best sellers are entirely based on a brand’s inventory and don’t factor in a shopper’s taste or behavioral data. That, along with their tried and true status, makes them especially effective for converting first-time buyers who aren’t (yet) familiar with your brand. 

That’s doubly true in a conversational interface. If an AI shopping agent introduces a best seller, the shopper can provide feedback. The agent can then narrow down its recommendations to be more specific, such as “best sellers that are good to wear to a wedding.”

Visual Matching

Visual matching in particular bridges the gap between brick-and-mortar and ecommerce. alby’s large language model is trained to understand products deeply. With that knowledge, the AI shopping agent can make personalized recommendations… and then make new ones if the shopper says, “I don’t like that color. Do you have something darker?”

The review summary and best seller skills are pretty standard from one retailer to the next. Because product attributes vary so much depending on the brand, visual matching has more variance across verticals.

• Apparel: Understanding color and style, the AI shopping agent can recommend not only similar options, but complementary products from other categories. This dress would go well with those shoes, for example.
• Footwear: Similarly, the visual matching skills can display apparel that works well with a particular pair of shoes.
• Home goods: The AI shopping agent’s understanding of style and color gives it the ability to recommend not just clothes and shoes, but matching furniture and home accessories.
• Jewelry and accessories: The average person wears two or three pieces of jewelry at a time. Visual matching helps shoppers find, say, a ring that goes with the necklace they’re browsing.
• Sports and hobbies: Every piece of sporting equipment has accompanying apparel and accessories. The AI shopping agent helps shoppers determine which ones go best.
  

Co-purchase

Based on historical purchase data, the co-purchase skill is like advanced collaborative filtering. Instead of sharing what “customers like you” are looking at, it shows what other items customers tend to buy with it. An invaluable skill for retailers with diverse product catalogs or a large number of SKUs, co-purchasing is a great driver of incremental purchases.

Like best sellers, the co-purchase skill elevates the typical recommendation models by refining recommendations based on shopper input.

• Apparel: The apparel vertical is arguably where co-purchasing has the broadest reach. If a shopper is looking at a shirt, the AI shopping agent will show a complementary coat, or even something from another vertical such as a piece of jewelry or a pair of shoes.
• Consumer electronics: The first consumer electronics purchase is often a large one, such as a laptop, followed by smaller related items. Is the customer a gamer or did they purchase a laptop primarily for school? The answer to that question dictates which accessories they’re more likely to eye and buy depends on the answer. alby’s AI shopping agent makes recommendations accordingly, showing a student a frequently purchased laptop bag.
• Footwear: For footwear retailers, the co-purchase skill serves relevant clothing items. A workout set or a pair of shoes that are often purchased alongside a particular pair of sneakers, for example.
• Health and beauty: From facewash to fragrances, the average woman uses 12 different products a day and the average man uses six. Many of these products are bought in bundles. The co-purchasing skill highlights frequent pairings, like the lip liner that complements a specific lipstick.
• Home goods: Home purchases are rarely made in a vacuum. When someone decides on a dining set, this skill serves up accessories — vases or lamps, say — other customers have used to complement it.
• Jewelry and accessories: Layering necklaces is a popular jewelry trend at the moment. When a shopper looks at a necklace, the AI shopping agent can show some others to help create the perfect necklace stack.

Bundling

Bundling takes co-purchasing to the next level. Rather than suggest a complementary item, this skill allows alby to recommend an entire outfit based on a single item. It works similarly for retailers in the apparel and footwear verticals. If a shopper likes a dress or a pair of shoes, the AI shopping agent will show various themed outfits, such as “night out” or “everyday winter looks.”

For home goods retailers, bundling serves as an advanced “complete the room” recommendation engine. It can take a nightstand and spin up entire rooms for different tastes, whether a shopper prefers the modern or mid-century modern look. Similarly, a sports & hobbies brand can show a shopper everything they need to go fishing.

Fit

Because it’s trained to understand products and their unique attributes, alby’s large language model can make relevant recommendations. That same data powers the fit skill, another one that really recreates the in-store shopping experience at home.

• Apparel: Sizing isn’t standard. A large from one brand may feel like a medium from another. Without stepping into a fitting room, the fit skill helps an apparel shopper tell the difference and make the right decision.
• Footwear: Online, footwear shoppers can’t usually be discerning over arch support and cushioning, both of which are major factors in a purchase decision. Now they can be.
• Home goods: Just about every product page in the home retail space is going to have size specifications. The fit skill allows shoppers to share the dimensions of their spaces and help them determine which items may fit where.
• Jewelry and accessories: Did you know a ring should fit over your knuckle with a little resistance? Perhaps. But if you didn’t, alby’s AI shopping agent does. With the fit skill, the agent can guide inexperienced jewelry shoppers in the right direction.

Comparison

The comparison skill shows two different products side-by-side, and lets shoppers know how they differ. This is particularly helpful in verticals where products have subtle differences that a casual shopper may miss.

• Consumer electronics: Comparison charts are a staple of the consumer electronics buying experience. More than two-thirds of Americans own a laptop. A much smaller percentage know what an M2 chip is and that it’s what makes Apple devices run quickly and efficiently. The comparison skill explains that in simple language, while also sharing other key differences, such as weight and number of ports.
• Health and beauty: The comparison skill is crucial for health and beauty retailers. Why? Their products are the epitome of personalization, designed to look and react differently to every individual shopper. Someone with normal skin has vastly different needs than someone whose skin is more sensitive. alby’s AI shopping agent compares options to help shoppers find exactly what their skin needs.
• Jewelry and accessories: A 2021 survey found that just 81% of people purchased their engagement rings at a brick-and-mortar store. But before they got there, 80% researched their rings online. Engagement rings perfectly illustrate the comparison skill for jewelry and accessories retailers. This helps shoppers understand what they eventually want to buy, in terms of size, metal, and gemstone options.
• Sports and hobbies: A pair of skis has a lot of specs. There are sizes and materials as well as attributes related to a skier’s ability. Skis made for beginners tend to be narrower and built for easy turns, while another pair is well-suited for speed and powder. The AI shopping agent helps narrow that down.

Compatibility

Like co-purchase, the compatibility skill shows shoppers which items are frequently bought together. The difference is, compatibility focuses on what items can be bought together, which is an important consideration in select verticals.

• Consumer electronics: Anyone with an iPhone knows that not every phone goes with every charger. With alby’s AI shopping agent, consumer electronics retailers can make it clear to customers which chargers, cases, or cords are meant for their specific phone.
  
• Health and beauty: Face serums are designed to target specific skin concerns like acne or dryness. Like smartphones, serums are meant to work alongside specific products. With this skill, health and beauty retailers can easily let shoppers know which ones.