AI is making an enormous impact on ecommerce businesses. A recent Algolia study found that IT and business managers saw AI as playing a critical role in search (64%) and merchandising (68%). Companies leveraging AI see average revenue increases of 10-12%. From improving conversion rates on the front end through intelligent search to cutting costs through improved inventory and logistics, AI is delivering measurable business results. 

Types of AI technologies for ecommerce

Natural language processing (NLP)

Natural language processing, or NLP, is a type of machine learning and AI that helps computers understand human language. Leveraging NLP for ecommerce search enables ecommerce search engines to better understand what customers are looking for and deliver search results that better match the products customers want. . 

Some of NLP’s best capabilities include:

• synonym recognition (searching “sofa” and “couch” return the same results.
• typo tolerance, which will return matching results even if a search has typos (“bakcpacks” will return results for “backpacks”)
• stemming, which breaks down words to their root form (if you search “swimming”, it will return results for “swim”, “swimming”, and “swimmer” since they share the same root word.

Generative AI

Generative AI uses large language models (LLMs) to create content like text, images, audio, and video. 

Common use cases for GenAI in ecommerce are: 

• writing product descriptions: generative AI can help ecommerce businesses, especially those with large catalogs, produce accurate and tailored product descriptions
• enhancing chatbots and customer service: AI-powered ecommerce chatbots can personalize responses, understand customer sentiment, give accurate product info, and provide 24/7 customer support

Machine learning 

Machine learning (ML) enables ecommerce platforms to improve the results they deliver over time by learning what works best. 

For example, any item can be offered with cross-sells at the check-out stage. A snowboard buyer could be offered wax, special boots, socks, a helmet, etc. By tracking successes, ecommerce platforms equipped with ML learn over time which of these items is most likely to result in a purchase, and then act accordingly. 

Deep learning

Deep learning is a highly sophisticated subset of machine learning modeled after the operation of the human brain. Deep learning platforms don’t need to be told what features of a data set are important for executing a task such as determining a credit rating or identifying a product image. They figure it out themselves. These platforms are very useful in ecommerce because they are fast and accurate, and able to deal with very large data sets that some AI algorithms can’t handle.

APIs and composable architectures to increase system value

The growing ecommerce ecosystem includes numerous components and solutions that have a particular function, like search, but can be linked together with other components to amplify their capabilities. This is known as composable commerce. These different components are linked by APIs which may or may not be supplied by the vendor. When planning for an AI-powered future, it’s important to take the requisite APIs into account and make sure they’re available. 

Combined, these technologies represent a win/win situation for ecommerce businesses and their customers. They improve the customer experience with a high level of personalization that customers want, and they also increase revenue. The business value of these benefits is reflected in the global market value of AI-enabled ecommerce tools. Now at $7.57 billion, it’s expected to reach $22.6 billion by 2032, with a CAGR of 14.6%.

AI use cases in ecommerce

With ecommerce platforms, AI technology is now being used to:

• Personalize ecommerce shopping experiences to delight and win over customers. A recent study indicates that 81 percent of consumers prefer a personalized experience. AI enables the deepest possible personalization, drawing from numerous sources so that sites can display the kind of items customers have liked in the past, even down to the level of preferred colors.
• Make personalized product recommendations that are more likely to result in a conversion because they’re tailored to known customer preferences. With AI, data to identify the right products can come from multiple sources (search history, purchase history, social media and more). Furthermore, AI learns from experience, and will make ever more effective recommendations over time.
• Upsell and cross-sell desirable items at the checkout stage using the same technology, resulting in higher average order value.
• Enable conversational commerce  which provides new levels of customer support. Generative AI enables automated chatbots to provide contextually relevant responses that rival the quality of an exchange with a human agent –  with no waiting on hold.
• Offer visual search to help customers find exactly what they want faster. Today, consumers often encounter items they want to buy while engaged with social media. AI-powered visual search  allows them to capture an image, paste it into a search box and get a quick result.
• Automate content generation with generative AI. The once manual process of creating product descriptions can now be automated.
• Analyze customer interactions and shopping patterns for data that can improve site performance. Because AI can handle big data at a level that isn’t possible for conventional algorithms, you can confidently identify all the critical factors (search rankings, merchandising, etc.) that influence performance, and make data-driven decisions.

Benefits and impacts of AI in ecommerce

Here are a few ways artificial intelligence has flexed its muscle within the world of ecommerce.

1. Identifying optimal pricing

When you can accurately and consistently hit the pricing optimization sweet spot, profits go up. And with AI-powered software and the large datasets it can analyze, it’s infinitely easier to hit that moving target. AI-enabled dynamic pricing lets you change prices based on supply and demand. For instance, if a competitor’s site is running low on an item, you can be alerted and increase your prices in response.

2. Making site search smarter

AI-related advances in natural language processing (NLP), which is focused on how computers understand human language, have led to breakthroughs in search engine performance. With NLP, based on AI’s understanding of the query a shopper is entering or speaking, you can more confidently assess what they’re trying to find and proactively present the search results they want.

Additional AI-facilitated tactics are the addition of synonyms and missing words or phrases and the automatic correction of spelling errors.

Vectors are a new AI technology being used to specifically improve search. As we’ve discovered at Algolia, accuracy can be improved dramatically by combining keyword search and vector search in a single query.

AI also makes customer queries easier to enter for people who prefer to use voice search or search with images. With visual search, AI tools can process an image, such as a photo of an item seen worn on the street, and then suggest similar items.

In short, AI site search is the next big thing. By minimizing searcher frustration through AI-powered search personalization, it can give you an immediate competitive advantage.

3. Winning shoppers with personalization

In ecommerce, a shopping experience tailored to individuals’ preferences and customer need is king. “Personalization marketing has real advantages for companies: it can reduce customer acquisition costs by as much as 50 percent, lift revenues by 5 to 15 percent, and increase marketing ROI by 10 to 30 percent,” says McKinsey in a 2023 report. The firm also found that 71% of consumers expect a personalized shopping experiences.

Ecommerce personalization has been hot for a while, but AI has put it on steroids. With AI and personalization engines, computers can capture and process huge amounts of customer data and provide insightful real-time analytics, allowing websites to act like trustworthy guides that suggest the right items and offer attractive discounts.

[The result of AI personalization for ecommerce? Robust sales. McKinsey concluded that companies utilizing personalization in the online shopping journey have profits 40 percent higher than for online retailers less focused on personalization]

4. Making the right recommendations

One aspect of personalization, recommendations, is in a class of its own. Ecommerce personalization goes considerably beyond what shoppers can expect to enjoy in a brick-and-mortar store. Algorithms can retrieve actionable insights about shoppers —  such as how they’ve browsed and their purchase history — that allow accurate prediction of what they want and then give accurate and tailored AI product recommendations that foster purchases.

5. Intuitively cross-selling and upselling

Which products would your shoppers likely add to their carts as they happily check out?

Product recommendation technology is yet another ecommerce tool enhanced by artificial intelligence. With an AI recommendation engine, you can use frequently bought together data created by collaborative filtering  to inform how you respond.

6. Automating customer support

When it comes to good customer service, chatbots and other types of virtual assistants make excellent online support reps. AI is obviously involved in pretty much everything about a chatbot. Their functionality can leverage NLP to help them determine shoppers’ needs, and with applications of machine learning solutions, they can improve their knowledge over time to become even wiser digital butlers.

Chatbots have already been a godsend for ecommerce retailers and shoppers alike. They stay up late (24×7, being ever available for night-owl shoppers), don’t need to take coffee breaks, are inherently polite and don’t lose their cool, typically have many (if not all) of the answers a shopper seeks, and productively do the drudge work that can free up live agents to address the trickier issues. However, recent AI technology advances have meant chatbots are becoming more refined in their abilities, which has led to consumers warming to them even more.

According to Statista, a third of consumers consider chatbots “very effective” in handling questions. Another good sign: more consumers are willing to buy goods and services from chatbots, some of which can also offer them personalized promotions.

7. Streamlining inventory management

AI technology uses predictive analysis that’s much more sophisticated than anything achieved by referring to current stock levels and keeping a close eye on your supply chain. With the help of machine learning, a company can accurately determine how much inventory to order and how much to keep available. According to one study, AI reduced logistics costs by 15%.

Warehouse management also stands to be streamlined with AI, as bots can take on the thankless job of storing and retrieving stocked items.

8. Preventing online selling fraud

The bad news: ecommerce businesses lose an estimated $48 billion annually to fraud. The good news: AI can fight that problem in some advanced ways. It used to be that armies of employees had to painstakingly review transactions for anomalies. How machine-learning algorithms can engage in fraud detection, using complex rules to analyze millions of big-data points and instantly spot suspicious behavior, is impressive. For instance, a criminal trying to commit a fraudulent transaction might enter an incorrect shipping address — something a human would never be able to notice or respond to, much less in the amount of time an algorithm could flag it.

9. Increasing conversion rates

Did you know that abandoned shopping carts interfere with conversion rates big time? AI can be used to improve your conversions and cut down on customer churn, letting you follow up when potential customers walk off the site and leave items still sitting in their cart. AI does this by collecting information about users and, when they leave the site empty handed, reaching out to them, such as in an email reminding them that they have items in their cart.

10. Reducing manual labor

Aside from impacting what goes on at the technology levels in an ecommerce store, as with other industries, AI can take on mundane daily tasks and lower employee headcount. The phenomenon of human retail associates being displaced by bots and automation is unfortunate, to be sure, but when it comes to the bottom line, not many companies argue with the fact that AI is the superior hire.

Here's one specific example of how AI can benefit a B2C ecommerce business.

How to get started using AI in ecommerce.

What are the opportunities?

Companies will achieve the best results if they begin with a business-first (rather than a technology-first) approach to AI. What are the pain points? What are the desired business results from AI? Some  companies may be looking for a better customer experience that requires personalization. Others may be plagued by constant stock-outs. Basic problems with search, such as the inability to identify synonyms (sofa vs. couch), may be reducing sales. 

Once problems are identified, it’s important to “do the math” and quantify the ROI an AI initiative can promise. 

Data Issues

AI is first and foremost associated with algorithms, but companies that want to incorporate AI into their ecommerce business often find that data readiness is the greatest challenge. For example, AI-based determinations about what is presented to the customer depend on data from multiple sources, including customer demographics, product data, buying history and more. This data resides in multiple applications (ERM, CRM, PIM, etc.), and it will most likely be stored in different formats that are not compatible with one another. Companies may need to consult with a data engineer (“data wrangler”) to verify that their data is clean and to understand what’s involved in leveraging all the appropriate data available.

Finding the right vendor

The ecommerce space continues to grow, with numerous vendors offering AI-powered capabilities. Making a careful review of your options to fill your specific business needs is the final step in getting started. Here are some top vendors worth consideration. 

Personalization Engines

• Bloomreach offers a variety of products focused on personalized online marketing, including a headless customer data platform and a search engine. These are  all powered by a centralized AI capability that uses real-time customer and product data to personalize search and other marketing functions such as conversational shopping and abandoned cart follow-up. Bloomreach is a particularly appropriate vendor for enterprise, C2C ecommerce business. It is a SaaS company that belongs to the MACH alliance. It runs on both AWS and GCP with limited autoscaling due to capacity.
• Algolia provides an AI personalization platform focused on connecting potential customers with products they want. Its capabilities include search, browsing, recommendation generation and merchandising. In all these cases, sophisticated algorithms deliver relevant user experiences based on data from multiple sources. Products are designed for use by non-technical staff. Algolia is a SaaS company that belongs to the MACH alliance. It runs on all major cloud platforms.

Chatbots

• Intercom offers two AI-powered chat products. One provides automated 24/7 support to customers, while the other is designed to support sales reps. These products can capture consumer data, perform segmentation and create targeted marketing messages. The company also has a significant focus on customer service use cases. Intercom is a SaaS company that runs on all major cloud platforms.
• Drift (recently acquired by outbound marketing company, Salesloft) offers an AI-powered chatbot that was designed from the beginning to augment the sales pipeline. The chatbot can understand conversational language and has multilingual capabilities. It uses GPT not only in AI chat, but also to provide suggested responses to live agents and generate follow-up email messages. Salesloft, and Drift before its acquisition, are SaaS companies that run on all major cloud platforms.

AI Search

• Coveo markets itself as a search and genAI platform. Its semantic search product offers a full complement of features from which customers can choose, although implementing them is likely to require developer skills. Coveo positions its search offering as suitable for B2B as well as B2C use cases. Its focus is on large enterprises, and it has a significant learning curve. Coveo is a SaaS solution that’s compatible only with AWS.
• Algolia offers a comprehensive, composable, end-to-end AI search and discovery platform built with an API-first approach. Its technology combines keyword and vector search to deliver more accurate results faster. Both voice and image search are supported, along with dynamic ranking. Business teams can work in a no-code environment to modify customer-facing front ends, and set up analytics to monitor campaigns.

Best practices for implementation, monitoring and optimization

It’s important to establish realistic timelines. Some implementations may be “easy,” but others may involve technical challenges. For example, it’s not wise to assume that your data is clean. This needs to be verified. Most AI systems are cloud-native, and it's important to be familiar with the target cloud’s idiosyncrasies. 

Security is a critical issue. For example, APIs have data access privileges just like humans, and these privileges should be strictly limited, as hackers can use APIs as paths to sensitive data. 

If non-technical people are to work with the system on a daily basis, they need to be trained. 

More than anything, it’s important that business teams and IT teams work in constant collaboration.

When it comes to monitoring, AI-powered systems involve a lot of automation, and previously manual processes, like generating product descriptions, should be spot-checked to ensure that they’re functioning properly. Traffic should be monitored to make sure that you’re not paying for unused capacity.

With machine learning, AI-based systems are designed to optimize themselves, but the performance of an entire ecommerce business can be optimized in many ways that go beyond automatic function, starting with retail basics, such as product choices. On the technical side, new capabilities are constantly appearing, and it’s smart to keep an eye out for new capabilities that not only improve the customer experience but also the bottom line.

Impact your revenue with AI search

There are currently over 16.6 million ecommerce sites world wide, and more than 2,500 are launched every day. This is certainly a  daunting competitive environment, but of all these sites, only about 15% have actually deploying AI capabilities, which means that AI can deliver a significant competitive advantage to your business. 

If you want expert help sorting through the AI possibilities for your ecommerce needs, connect with our team. More than 50% of retail professionals we surveyed said effective AI site search has been a revenue driver for them, and we have an impressive record of helping retailers with AI-driven ecommerce solutions.