10-use-cases-ai-in-ecommerce@2xThe integration of artificial intelligence (AI) in ecommerce is shaping up to be so much more than just a technological advancement. It’s fundamentally changed almost every aspect of digital commerce — from personalizing product recommendations to managing inventory and enriching product attributes.
Let’s take a look at some of the top use cases for AI in ecommerce that forward-thinking businesses are currently using to stay competitive.
1. Improve Personalized Search Results
Personalization is one of the best ways to meet your customers where they’re at and drive business goals. And it’s perhaps one of the most necessary applications of AI in ecommerce, as 67% of U.S. consumers expect brands to provide relevant product recommendations.
For most ecommerce businesses, returning the right products in search results can mean the difference between a sale and a bounce. AI-native search engines take that pressure off by generating attractive search results for each user, thanks to clickstream data, transformers (the “t” in ChatGPT), and large language models (LLMs).
Through AI-first search, ecommerce brands can hyper personalize results driven by real-time, on-site shopping behavior. This not only enhances the customer experience, but also drives sales and customer loyalty –– each a major boost for business.
Outside of search, check out other ecommerce personalization trends to watch this year.
2. Enrich Product Data
Good product data is a must-have when it comes to satisfying your customers’ needs, helping them find the products they’re looking for, and enhancing their overall shopping experiences. After all, 60% of buyers are hoping for a better product discovery experience. If your plan is to capture lasting brand loyalty, you can’t afford to bench data enrichment efforts.
Thanks to AI, your ecommerce team doesn’t have to go it alone.
AI-powered attribute enrichment automatically helps customers find what they’re looking for faster, while exposing them to a broader range of products. The right AI solution leverages text and image data from your catalog in addition to behavioral data to dynamically update attributes and categories in real-time on a user-based level.
This personalization makes search, filtering, and product discovery less frustrating for customers — and is a must-have capability for ecommerce teams looking to decrease their workload while still hitting key business goals.
3. Assist Shoppers
There’s a fundamental problem with the way people shop online: It can take hours of research to do something simple. That’s why chatbots and shopping assistants can add an entirely new layer to ecommerce.
Driven by the power of large language models (LLMs), these recommendation engines better handle long freeform text queries (“Show me blue dresses for my niece’s summer wedding”). The speed at which they understand user intent and return accurate product results helps shoppers go from goal to reality in a fraction of the time.
Shopping assistants have a variety of use cases, from providing recommendations for outfits to fit any occasion to helping shoppers explore other categories (“What utensils and cookware do I need to prepare ratatouille?”).
By using these AI-driven engines to surface personalized products, shoppers save time exploring your extensive product catalog while you increase conversions. It’s a win-win.
4. Enable Predictive Text
Even though nearly half of online retail shoppers find items via the search bar, only 19% of sites correctly implement one of the search bar’s most important features: autocomplete.
Autocomplete, also known as predictive text, is a search feature that provides users product suggestions in real time as they type. It’s a natural extension of product search, used to facilitate the shopping experience for consumers and increase conversions.
The benefits of powering predictive text with an AI-native solution are many, such as a +13% lift conversions and 16.5% increase in average order value (AOV).
Working with an AI-powered, KPI-obsessed product discovery partner is a great first step in achieving these same results. But if you’re looking for quick wins, it’s also important to optimize the user experience of your autocomplete functionality so that you and your customers can get the most out of your solution.
Check out 5 autocomplete UX best practices that have been proven to reap additional conversions for Constructor customers.
5. Provide Actionable Merchandising Insights
Your merchandising team has enough on their plate. Lean on your product search and discovery solution to provide answers to important customer experience questions, like:
- How do customers reformulate their searches?
- How many queries end in zero results?
- What searches are trending and could be optimized with new synonyms?
- Which facets are customers using to narrow down their search or browse?
Your platform of choice should provide unleveled access to customer information and present actionable insights to inform key decision making. Think of your AI solution as a business partner. It’s there to help optimize business operations and inch you closer to hitting KPIs.
6. Set Dynamic Pricing
Just as surfacing the right product to the right customer creates interest, matching that customer with the right price drives the sale home.
Here are some clever strategies that ecommerce businesses — including B2B vendors — are using to set dynamic pricing:
- Predicting the best price for each product. Just as AI-native solutions can alter product rankings, they can also alter the pricing of certain items based on how likely users are to purchase said products at specific price points. (This includes the highest price point customers are willing to pay.) This strategy takes a macro look at customer interactions and sets the global price for that product to achieve the maximum profit or revenue.
- Predicting the best price presented to each customer. By looking at what products an individual clicks, adds to their cart, and purchases, AI can alter the price of an item to increase the likelihood of a conversion for that particular customer. For example, AI may choose to entice first-time purchasers or price-sensitive customers with lower rates. In other words, AI can serve dynamic, personalized prices.
- Showing the best price across accounts and buyers. In B2B ecommerce, there are many variations of products, restrictions, and/or conditions to consider for each account when pricing products. With the right personalization software, you can customize how buyers across accounts interact with your products and the prices they see. You’ll also have the ability to govern contracts to support product assortment and account-based pricing.
7. Fuel Product Recommendations
With an AI-powered product recommendation engine, merchandisers can more easily make targeted, on-brand product suggestions throughout a customer’s journey.
By tapping into real-time data, ecommerce teams can program the AI to surface product recommendations that prioritize their ecommerce business objectives, like revenue per visitor (RPV), profit margin, or abandoned cart rate. They can also place recommendations where shoppers are likely to interact, like the homepage, category pages, pop-ups, emails, and more.
Depending on your platform, each product interaction then fine tunes the rest of a customer’s product discovery experience. For example, if a customer shows affinity for a certain brand in a “You Might Also Like” recommendations pod, products from that brand rank higher in category pages, search results, Collections, and across your ecommerce site.
8. Manage Inventory and Forecast Sales
With ever-evolving product catalogs and changing customer demands (not to mention supply chain disruptions), knowing how much inventory to keep on hand can be way too much to handle manually.
With an AI platform specifically designed for ecommerce inventory management, you can take the guesswork out of your inventory management process. Deep analysis of buyer data such as buying behavior and seasonality allows you to more accurately predict and plan for stocking.
Using AI for inventory management and sales forecasting can help you:
- Analyze previous, current, and projected sales throughput.
- Predict, report on, and quickly solve vendor issues.
- Predict changes in customer demand.
- Analyze market changes that could affect sales.
All of this can help make your business better at predicting demand and meeting it with the right product at the right time.
9. Segment Customers
With segmentation, a clearer picture of your customer can come into focus, which also allows for greater personalization efforts. With that clarity comes a better game plan.
Segmenting customers into specific categories based on their behavior, preferences, and demographics enables businesses to create highly targeted marketing campaigns. AI-powered solutions can help brands deliver more relevant promotions and products, leading to a lift in conversion rates and other business KPIs.
10. Boost Omnichannel Presence
AI isn’t only a powerful tool for improving on-site customer experiences.
It can also be used to process the behavioral and zero-party data your customers willingly provide to deliver personalized content across multiple touch points — like email marketing, social media, advertising, and more.
Use an AI-powered product search and discovery platform to collect that data and push it to other ecommerce platforms, building a powerful, holistic omnichannel customer experience.
AI in Ecommerce: Use Cases for Success
There’s a breadth of potential for AI in ecommerce, spanning use cases from dynamic pricing to merchandising insights, product recommendations, and everything in between.
The key to not getting lost in the hype boils down to one word: focus.
As we barrel into a more AI-heavy (digital) world, the real winners will be those who focus on leveraging AI to drive real business results while reducing the manual workload of their time-strapped teams.
The shift toward data-driven AI tool usage is happening now. Will you join along?
