How to Use Machine Learning and AI in Ecommerce: Benefits and Examples

Ecommerce trends
17mid read May 18, 2023
Ecommerce trends
How to Use Machine Learning and AI in Ecommerce: Benefits and Examples

When ChatGPT first appeared last year, the world woed. The chatbot has quickly become one of the most prominent machine learning use cases in customer service and showed that artificial intelligence (AI) has reached a point where technology can perform certain tasks much better than humans.

But machine learning (ML) and AI in ecommerce go way beyond chatbots. Retailers use AI for personalization, data analytics, dynamic pricing, and recommendation engines. Big names like Zalando and Asos are setting up entire deep learning departments to better understand the customers’ the moment they are on the site. 

It seems like AI brings about irreversible changes to ecommerce.

At Elogic, we’ve stayed at the forefront of top ecommerce trends since 2009 and can surely say that ML and AI are here to stay. Being a platform-agnostic company, we see many major ecommerce platforms like Adobe Commerce and Salesforce Commerce Cloud leveraging ML algorithms to offer outstanding customer experience (CX) and deeper insights into analytics.

In this article, you’ll see how ecommerce companies are using AI in ecommerce, why you might want to invest in it, and how you can start implementing it to streamline your daily business operations and improve your CX.

How Do Machine Learning and Artificial Intelligence Work?

Even though the terms ‌ML and AI are often used interchangeably, they imply slightly different things.

Machine learning (ML) is a subset of artificial intelligence (AI) which literally teaches a machine… to learn! ML models feed on data and look for patterns in it trying to draw conclusions, like a human would. The system is not explicitly programmed but rather learns ‌to make predictions or take some decisions using historical data.

Recommendation engines are a classic example of ecommerce machine learning. The system learns the relevant details of the user, like last purchased products, the colors they prefer, budgets, etc. and derives an algorithm to recommend products that the customer is likely to buy.

Read more: 20 Best eCommerce Tools to Boost Your Online Business 

Meanwhile, artificial intelligence (AI) is a much broader term referring to any technique that allows computers to imitate human intelligence. Siri, Cortana, and Alexa Voice Assistance are all examples of AI.

Whenever you see voice-enabled search in a store or personalized product offerings, you’ll know these are AI and ecommerce in action.

Still, AI and ML go hand-in-hand in online shopping; and while it might be an evolving field for retailers, they pave the way for new customer interactions and business opportunities.

Seizing Business Opportunities: How Can AI and ML Benefit Ecommerce?

AI and ML have a profound effect on the ecommerce industry. Here are the main advantages of AI and machine learning in ecommerce for companies to start transforming their businesses today.

Higher ROI

Few people actually realize how AI can increase ecommerce sales. According to the McKinsey State of AI Report, 79% of respondents stated that integrating AI into marketing and sales has increased business revenue. Integrating it into your CRM might create a more efficient sales process. Adding an AI-based ecommerce platform, like CDPs or business intelligence (BI), will pave your way to personalization, which will increase your average order value (AOV) and customer loyalty.

In fact, there are many cases in point that illustrate this benefit. Amazon’s recommendation engine drives 35% of the company’s annual sales, and Alibaba has reduced delivery errors by 40% after investing in its smart logistics program.

Targeted marketing and advertising

Salesforce, the top CRM and ecommerce solution and Elogic partner, states that customers expect a personalized experience. Still, only 26% of marketers are confident that their organization has a successful strategy for personalization. One of the biggest challenges is siloed data — when departments don’t have access to the same information about the customer — which leads to disconnected customer experiences.

Unifying data is one of the benefits of artificial intelligence in ecommerce. Because AI and ML draw from multiple data sources across a business, AI technology can break these silos by generating visible, accessible, and actionable insights. For instance, AI-driven customer data platforms (CDPs) will unify your data and analyze large volumes of data and accelerate the process of testing and refining marketing campaigns.

You can use these insights to identify trends, predict potential customer trends, and recommend products similar to the ones preciously purchased or viewed. And most importantly, you can ‌personalize at scale tailoring user experiences across channels.

Informed business decisions

Many businesses find it quite difficult to not only collect data but also to make sense of it. Traditional analytics tools have served a purpose so far but certainly not like those embracing AI/ML in ecommerce.

AI-driven predictive analytics deserves a special mention here. It can make your business decisions more informed and accurately forecast future product demand patterns for specific items or entire categories within an ecommerce store. 

“Let’s say you’ve set out to boost your company’s revenues”, says Igor Iakovliev, the Managing Partner and COO at Elogic Commerce. “Based on your collected data sample, the system sees that service Y has the highest profit margin. It scans the type of customers requesting that service and suggests you promote that service to a particular target group. Add AI to this type of analytics tool, and you’ll get predictive analytics.”

Optimized logistics and inventory management

Inventory management is one of the biggest B2B and B2C challenges as you might have too much or limited stock at hand. The same accounts for logistics, with retailers investing in effective supply chain strategies to lower the cost of purchasing and manufacturing.

Streamlined logistics and a clear view of the inventory are one of the benefits of AI in ecommerce. Advanced real-time inventory management systems rely on AI to inform you on your inventory availability throughout warehouses and channels. They might also analyze data to forecast demand patters and optimize your warehouse replenishment plans.

In fact, McKinsey & Company reports that AI-driven forecasting can reduce supply chain errors by 20 to 50 percent, which translates into higher sales. For instance, if you sell shoes online, you might see that the demand for winter shoes increases during the fall season and plan, stock, and schedule deliveries accordingly considering the risk of supply chain disruptions.

Higher customer conversions

AI algorithms allow marketers to quickly analyze and optimize pages for better customer engagement and higher conversions. 

For instance, a DTC brand and a subsidiary of PepsiCo, SodaStream, used AI and machine learning for ecommerce to analyze the effectiveness of their marketing campaigns in 46 markets around the world. The results showed that ads appealed differently to consumers depending on the channel. The brand saw a 3%-5% increase in email conversion rates and a 10-15% increase in SMS text conversion rates.

This is only one application of artificial intelligence in ecommerce. You can also apply it to your: 

  • site search (because the faster your customers will find what they need, the faster you’ll make a sale)
  • remarketing campaigns (send your users personalized promotions and incentives to encourage them to return and complete the purchase after abandoning their cart)
  • customer service (cut through the endless aisle of customer support line by offering your shoppers self-service AI-powered chatbots).

What Are the Most Successful ML and AI in Ecommerce Examples?

Big players, such as eBay and Amazon, have a winning experience of AI integration throughout the entire sales cycle. However, you do not necessarily need to be a market leader to make use of these technologies. Successful AI use cases in ecommerce show that regardless of your store size, you can integrate AI and ML technologies to gain competitive benefits.

Read more: Leader in Ecommerce: 7 Reasons Why Amazon Is So Successful 

Recommendation engines

Recommender systems help companies elevate sales by providing personalized offers and enhanced customer experience. Recommendations usually speed up website search, ease users’ access to the needed content, and are excellent cross-selling and up-selling examples of artificial intelligence in online shopping. 

They also contribute to a higher purchase rate and boost user loyalty, which translates into higher sales. After the Elogic team ‌integrated Certona AI-powered personalization solution for a US fashion retailer, Carbon38, the brand saw a huge increase in average order value (AOV) and returning customers.

“You may also like” feature on Carbon38 website.

Pricing strategy

AI-powered pricing will use the algorithm to analyze large amounts of data and make pricing decisions based on that analysis. This is one of the most prominent examples of AI in B2B ecommerce.

Advanced tools for data analysis gain information from multichannel sources and determine the flexibility of prices. The influencing factors include location, customer buying attitude, seasoning, and the market prices in the specific segment. 

Furthermore, the algorithm conducts customer segmentation and real-time optimization, allowing you to personalize pricing schemes.

For instance, our Finnish client, a B2B technical component specialist Wexon, can now analyze user behavior and adjust price tiers around registered/new customers, order volumes, and market conditions.

Visual search

Although shoppers tend to browse visual content before making a purchase, they sometimes fail to find the right words to describe what they are searching for. Visual search makes it much easier. Customers can simply upload an image instead of typing a long and detailed query. As a result, the customer can narrow the search down and get more relevant items.

Bing Visual Search, Google Lens, and Image Search are all powerful AI tools for ecommerce that have turned this type of search into a trend. The market is making use of the Lens Your Look search engine by Pinterest that enables you to find outfit options relevant to your existing wardrobe.

For instance, ASOS has beautifully combined machine learning and ecommerce and built the Style Match feature for its mobile app. It lets shoppers take a picture and discover products from their catalog that match it. This tool encourages shoppers to buy from the brand.

The trend yields particularly positive results if coupled with voice search and conversational commerce. Brands can integrate Amazon Lex machine learning models for ecommerce and take advantage of automatic speech recognition to interpret ‌users’ voice input in search.

Style match feature by ASOS. Source: BusinessInsider.

Customer sentiment analysis

Traditional sentiment analysis tools rely on customer interviews, social monitoring, ratings, and polling, all of which present an enormous amount of raw data. If you start analyzing it manually, something will surely slip. 

Meanwhile, AI-powered tools will analyze large volumes of data much faster and identify the smallest shifts in buyer behavior. ML techs use language processing to define words that imply a positive or negative attitude. Therefore, these feedback forms provide a solid and insightful background for product or service improvement.

In fact, businesses can use smart customer sentiment analysis in their customer journey mapping. This is an example of a map Elogic has done for one of our clients:

Customer journey mapping example

Inventory management

Merchants aim to perform proper inventory management to provide customers with the right products, at the right time and place, and in a proper condition. The process involves monitoring and deep analysis of the stock and the supply chains. 

When it comes to inventory management, machine learning in ecommerce detects patterns and correlations among the elements and supply chains. The algorithm determines the optimal strategies for stock and inventory. Correspondingly, the analysts optimize delivery and run the stock, implementing the data obtained.

Customer support

One of the brightest applications of machine learning in ecommerce, chatbots are an excellent way to help merchants partly automate the interaction with customers. What’s more, you can considerably reduce costs while maintaining quality. In the case of a complex query, a bot will detect the need for human intervention and redirect the client to a customer support agent. 

Generative AI plays an essential role here. As AI tools learn more about individual shoppers, online interactions with customers may become more like those with a stylist or personal shopper. For instance, Mercari, the second-hand consumer goods marketplace, has introduced an AI-driven shopping assistant that runs on ChatGPT software and can not only respond to customers’ queries but also recommend products based on the input question.

Mercari AI-powered chatbot. Source: Retail Dive.

Practical Use Cases of AI and ML Application in Ecommerce

So far, you’ve seen the benefits and applications of AI and ML in ecommerce backed by a few case-scenarios from real retailers. Now, it’s time to present you with some big names and, without a doubt, gurus of taking the max out of these cutting-edge technologies in the industry.

Read more: List of Famous Brands that are Using Adobe Commerce 

Amazon and its winning customer service 

Amazon focuses on impeccable customer service as one of its main competitive advantages of ecommerce. And this service is maintained with the help of AI for ecommerce. So, in which specific spheres do they apply the tech?

  • Product recommendations. Amazon utilizes Collaborative filtering and Next-in-Sequence models to work out predictions regarding the goods each specific customer may need next. The tool is enabled by the collected data of customer purchase behavior.
  • Logistics. AI makes changes in routing, delivery times, and other delivery parameters for greater efficiency and accuracy. Drone delivery will be the next step Amazon takes.
  • Natural Language Processing. This newest deep learning technique is powering the digital assistant Alexa by Amazon.

Alibaba and its customer-centric approach

The company is continually utilizing the most advanced tools enabled by AI and ML. Alibaba applies augmented reality mirrors, facial recognition payments, interactive mobile phone games, and many other features and tools. Specifically, Alibaba is focusing on:

  • Smart business operations. Alibaba’s own ChatGPT-style product called Tongyi Qianwen, released on April 11, 2023, is allegedly optimizing efficiency at the workplace. The tool performs a number of tasks, such as turning oral conversations into written notes and drafting business proposals. This will save employees time and resources in the long run and allow them to focus on business rather than tedious daily tasks.
  • Sharp personalization. Creating an engaging customer experience is the cornerstone for most modern merchants. Alibaba achieves this by implementing highly targeted AI ecommerce platform. Wherever a customer has shopped before, it is possible to match their purchased products with new goods in the Alibaba pool. 
  • Smart supply chain. Alibaba has created Ali Smart Supply Chain – an AI-powered tool that predicts product demand, optimizes inventory, determines the right product offers, and develops pricing strategies.

IKEA and the use of augmented reality

Merchants who sell furniture online know how hard it is to manage returns. The bulky nature of the products makes it difficult for shoppers to imagine the piece in their surroundings, which skyrockets return costs. IKEA is one of the brands that is tackling the issue with the help of AI and augmented reality (AR): 

  • Better offline and online CX. The brand’s new feature of IKEA Kreativ for their website and an app allows the customers to design and visualize their own living spaces with digitalized furniture. They no longer need to travel to a brick-and-mortar store to see the piece; a simple click on the phone will be enough. 
  • Visual search. A user may point their camera at a piece of furniture, and an IKEA Place app will find others like it. GrokStyle’s point-and-search functionality has been added to the app and is considered to be the future of search.

Gap and their virtual dressing room

When Heather Mickman came to be the interim CIO of Gap, one of the biggest clothing and accessories retailers in the world, he made it his mission to make AI a part of the DNA for how they work within Gap. Here are the areas in which they certainly succeed:

  • Optimized inventory movement. Their ML-powered solution produces automated and accurate size profiles that determine the size selling for a particular item is a specific store. This way, the brand keeps up with ‌customer demand and satisfaction.
  • Virtual fitting rooms. The company offers an AR app that allows shoppers to try Gap outfits on without entering a store. A user can select one of the five body types featured in the app, apply the Gap garment to it, and buy it online if they like what they see.
A computer simulation of a female model trying on a blue embroidered dress.

How to Implement AI and Machine Learning in Your Ecommerce Business?

The machine learning use cases in ecommerce are impressive and they embrace all spheres, from improving customer services to providing higher security for your business. The implementation of  AI-driven automation in retail is projected to increase from 40% to 80% in the next 3 years. 

So, what are the specific procedures that help your business to catch the big wave and make use of machine learning in ecommerce? Several steps will help you structure the process and develop the respective strategy before rushing into the unknown.

1. Identify which of your business processes can be ML-enabled 

Analyze your workflows and ask yourself the following questions:

  • Which processes are human-intensive?
  • Which processes are repeatable?
  • Which processes require human intervention to study large volumes of data?

The answers will indicate where exactly the application of AI and ML will help to save time and resources in your business.

2. Consider data collection and feature extraction

Data is the basis for the efficient use of AI and machine learning in ecommerce. A wise decision will be to store all data in a database, which allows analyzing and managing it in the future.

3. Determine your goals and capabilities

Trying to embrace a larger scope of AI implementation than it is necessary may lead to unreasonable expenses. Focus on your goals and start with something simple. For example, you can concentrate on predicting and preventing customer churn. If you are satisfied with the outcomes, you can scale up the implementation of AI.

4. Choose the appropriate tools and platforms

In general, the ecommerce software you choose is crucial for your business as it largely influences the cost and efficiency of running your online retail store. Sometimes you’ll even need to replatform to find a suitable solution that will meet your business needs. Modern computing technology in particular allows using ML in the cloud, which will further save you time and effort. 

Depending on the field of your business, you can enjoy multiple AI and ML tools aimed to optimize your operations and enhance sales. For example, Adobe Sensei automates numerous time-consuming tasks and leaves more time to spend on the creation process. Nosto is a comprehensive marketing solution that uses AI to automatically deliver a highly personalized customer experience in real-time. As a result, you get enhanced engagement and greater sales.

5. Create a dedicated team and determine which vendors you need

To properly manage the adoption process, you need a dedicated team that will keep things on track. The team will be closely collaborating with the third parties needed for the project and making sure that the process is being led towards the goals you set.  

ML/AI Ecommerce Takeaways

You may be frightened to adopt the new AI/ML in ecommerce because of the organizational challenges; or, on the contrary, inspired to follow an example of big industry names who have successfully integrated the technology. 

Whatever your feeling, no retailer should stay indifferent to innovations in the sector.

They will make your business processes more efficient. Streamline your customer experience. Improve your targeting and even help you scale into new markets.

The only thing you need to do is ‌come up with a plan, create a team that believes in these technologies, and have the organizational patience to learn, improve, and pivot when necessary.

Elogic has been enhancing retailers’ teams as ecommerce developers and consultants for over 14 years. We can help you evaluate your as-is state of business, plan out the steps and projects you’ll need to undertake to achieve your goals, and even implement and integrate the required tech end-to-end.

Integrate AI in your ecommerce application

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AI Ecommerce FAQs

How to use AI in ecommerce?

The use of AI in ecommerce is never limited to a single case scenario. You can leverage it for analytics, customer recommendation and personalization engines, inventory management, and logistics, among others. You just have to find the right AI tool that will match your business objectives and integrate it with your ecommerce system.

How is AI changing ecommerce?

The growth of artificial intelligence in ecommerce presents huge benefits for businesses. It can help increase sales, improve operational efficiency, and boost customer satisfaction. Retailers can better understand customer purchasing patterns and tailor their product offerings accordingly.

What are some AI personalization ecommerce examples?

Some examples of personalization in ecommerce include:

  • Personalized product search: when the store displays search results based on the user prior queries on the same website;
  • Product selection and categories: when the website reorders product categories in line with the preferences, geographical location, and prior search of your shoppers.
  • Product bundles: when a user receives ‌personalized recommendations based on the algorithm “people who bought X also bought Y” after completing a certain action on a website.
  • Dynamic content: when all customer profiles are segmented and the store tailors UI, landing pages, calls-to-action, pop-ups, etc. to different user categories.

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