Retailers are set to spend $7.3 billion on AI by 2022, as they look to differentiate themselves from competitors. What are eCommerce companies spending money on, when it comes to AI?
In this article, we’ll outline how eCommerce companies are using AI and what to expect in the future. We’ll go over:
- How AI helps companies create personalized product recommendations
- AI and inventory management
- How AI can lead to more accurate search and discovery for eCommerce
- Trend forecasting and AI
- How eCommerce companies are using AI to create an in-store shopping experience
AI Helps Companies Create Personalized Product Recommendations
A great use for AI in eCommerce might be unexpected for some: personalized product recommendations.
Have you ever browsed a retail website and clicked on a pair of boots–but noticed that the suggested recommendations were irrelevant?
That’s because without AI, eCommerce companies need to use complex, manual calculations to figure out the best products to show customers.
Those calculations take into account demographics such as age and location, as well as past browsing history–and the current item that the customer is browsing.
Enter AI. With AI, eCommerce companies can create personalized product recommendations without having to worry about whether customers will see relevant items.
Of course, not all AI is created equal, especially when it comes to product recommendation engines. When considering different types of AI for eCommerce product recommendations, it’s helpful to keep in mind the needs of the customer.
According to Baymard, customers browse for products with different goals in mind–some customers want a specific detail, such as a specific silhouette, and others are looking for a product category, such as “running sneakers.”
AI can recognize the above search intentions, and make relevant recommendations. For example, if a customer is browsing A-line dresses, it’s likely that they want to see similar silhouettes.
Or, if a customer is viewing running sneakers, it’s unlikely that they want to see a high-heeled boot.
While this may seem obvious to the outsider, take into consideration the sheer number of products that an eCommerce store has in stock at any given time.
Choosing the “correct” (accurate, relevant) product to show a customer can become overwhelming for eCommerce companies.
Product recommendation companies are starting to specialize in specific parts of eCommerce. For example, YesPlz focuses on fashion-specific recommendations for small and medium businesses.
There are other companies, such as Dynamic Yield (recently acquired by McDonald’s) which focuses on A/B testing and product recommendations across eCommerce.
AI Makes Inventory Management Easier for eCommerce
Inventory management can become frankly, unmanageable, for many eCommerce companies as they continue to grow their SKUs.
Accessing warehouse information in real-time can be tedious when combined with logistics challenges–and can result in eCommerce companies that are unable to keep track of their inventory.
Anyone who has spent time in inventory management will also note that inventory is not only a question of supply levels, but of consumer demand based on complex variables such as seasonality, change in consumer tastes (for example–more consumers worked from home during the pandemic, resulting in different needs than before), and income level.
When eCommerce companies are wrong about inventory management, it can result in out-of-stock products, under-sold items sitting in inventory, and angry customers.
Enter AI. Because AI can take into account hundreds of variables/decision-making criteria, eCommerce companies are using AI to manage their inventory–and relying on the AI to predict which products are likely to sell during a certain time period, to optimize shipping logistics to get products to customers as soon as possible, and identifying ways to save money.
While a human could try to identify cost savings patterns, it would be time-consuming and inaccurate. As Hypersonix AI says, there is still a relatively simple question that confounds eCommerce companies: ”how much inventory should be on hand at any given time and day?”
AI can run thousands of permutations to try to answer that question–so long as companies are willing to train the AI to understand not only the how but the why behind processes.
The future of AI in inventory management looks bright. As AI becomes more complex, eCommerce companies have even more opportunities to integrate AI into their inventory management processes.
Popular fast-fashion retailer H&M is implementing AI throughout its supply chain to optimize processes and improve the overall customer experience. For example, H&M uses AI to track purchases in stores to learn more about customer preferences in specific locations.
In addition, H&M has automated its warehouses (using AI) in Europe to allow for next-day delivery.
AI Leads to More Accurate Search and Discovery Tools
The combination of AI and search tools is powerful. More and more eCommerce companies are rethinking the search and discovery process on their websites and revamping it with AI-powered tools.
Why are AI and search tools so complementary? AI thrives in helping with complex decisions—and the search and discovery process for eCommerce websites is extremely complicated.
For example, customer search intentions can differ depending on factors such as regionalisms, understanding of product taxonomy (the naming and categorization of products), and overall search goal.
Effective eCommerce companies understand that a product can have various names (such as running shoes vs. sneakers) and plan these alternative search terms.
Or, customers can have different search intentions, which can be difficult to capture with traditional (non-AI) search tools.
Once again, in order to create a robust system to understand search intention, eCommerce companies would need to hire specialists to consider the world of possibilities that a customer might consider when typing into a search box.
With AI, eCommerce companies can avoid having to tag thousands of different product names to capture search terms—and altogether avoid the complex search filters as seen below:
As you can see, there are hundreds of possible ways to describe products. AI circumvents the need to manually describe each and every product attribute in a few different ways.
Firstly, AI can be trained to recognize key product attributes, and can pull those attributes from an image. Secondly, visual search has become increasingly popular in eCommerce.
Customers can search for an item of clothing by using visual tools to select the cut, silhouette, and style of clothes they’re looking for. The search results that appear are relevant and accurate because they’re powered by AI.
AI Can Accurately Forecast Fashion Trends for eCommerce
Where do fashion trends come from? Sometimes it can seem like trends appear out of nowhere, but in reality, they were carefully selected by analysts who combed through data about fashion trends, including colors, products, and silhouettes.
The process of predicting trends is time-consuming and laborious, but with AI, patterns can be identified faster and more accurately.
There is debate about whether AI can replace human trend forecasters, but the question shouldn’t be whether human forecasters are replaceable, but rather, how can trend forecasters use AI’s ability to quickly identify patterns to help predict trends?
For example, IBM’s Watson is a tool that can look at images from runway shows, and record similarities in colors, patterns, and silhouettes to create an aggregate view of fashion trends.
Then, human analysts can take the pre-sorted data, and apply a human component—perhaps the analysts know certain data patterns are irrelevant.
The combination of human and AI-powered trend prediction has the potential for massive impact.
If eCommerce companies can better understand trends, it can lead to better decisions when stocking inventory and less products sitting in inventory.
Companies such as Finesse are using AI trend insights to decide which fashion items to produce and sell to customers. While fashion eCommerce usually lets fashion, then data, dictate which items to stock, Finesse is taking the inverse approach.
AI as a Substitute for In-Person Shopping Experiences
As a result of the pandemic, customers are shopping online now, more than ever. However, there are certain parts of the in-store experience that customers want and can be difficult to replicate.
For example, have you ever felt frustrated after ordering a clothing item, and it doesn’t fit as expected?
AI is solving many of those needs, and re-creating the in-store experience to meet customer expectations.
Whether using chatbots, virtual try-on experiences, or 3D models of clothing, eCommerce companies are using AI to elevate the overall customer experience.
Through AI-powered chatbots, eCommerce companies can ensure around-the-clock support and provide superior customer service.
Chatbots have come a long way from their first inception, when conversations sounded robotic and frankly, artificial.
Now, AI chatbots can have conversations that mimic the conversational patterns of a human and can take on the brand tone.
Another use of AI to replicate the in-store experience involves a dreaded task for many–trying on clothing. And they’re creating the experience in unexpected ways.
Companies such as Drapr use fashion AI to create 3D models of a brand’s catalog.
Customers can then see the difference between the item of clothing and their body to identify which size works best for them.
Forma can create a virtual try-on experience from just one picture, using fashion AI. Users can upload a selfie and experience the fitting room experience from home.
The Future of AI for eCommerce
The use cases for AI in eCommerce ranges from product personalization to inventory management. In fact, it’s nearly inevitable that every eCommerce will use some form of AI in the future. The question is how?
Depending on business goals and strategy, AI-powered tools can help eCommerce companies increase conversion rates, improve the overall customer experience, and support the customer search and discovery process.
The future is bright for AI and eCommerce–how will your eCommerce use AI?