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Top 19 AI E-commerce Applications & Use Cases in 2024

Top 19 AI E-commerce Applications & Use Cases in 2024Top 19 AI E-commerce Applications & Use Cases in 2024

AI e-commerce can help businesses to:

  • increase the revenues of e-commerce businesses by helping them
    • understand their customers better
    • reduce churn
    • improve sales, cross-sell, and upsell by offering relevant products at the right time, and through the right channel
  • reduce costs by automation

However, there are many ways to leverage AI/machine learning in online shops:

  • Some e-commerce giants are spending millions on internal data science teams with limited impact.
  • Others leveraging products in the market have been able to achieve rapid impact.

Understanding AI use cases is critical to setting the right targets for e-commerce AI efforts and the below list of use cases can help focus efforts on real-life use cases.

Personalized Services

1. Chatbots and voice assistants in customer service

Digital assistants such as  Siri and Alexa are playing an increasingly important role in people’s lives. Similarly, assistants and chatbots can offer your customers real-time answers to their questions, solve their problems, and even identify cross-sell opportunities (conversational commerce).

To learn more about e-commerce technologies you can read our Top 10+ eCommerce Technologies: Use Cases & Examples article.

2. Website personalization

Improving customer experience results in higher revenues for e-commerce companies, and personalization is one of the most important levers for customer satisfaction.

To learn more, check out our detailed article on website personalization.

3. Recommendation systems

Recommendation systems can leverage natural language processing (NLP) to turn AI capabilities into sales. That’s why companies have been investing in better algorithms to improve their recommendations since the 2000s.

To learn more, check out our detailed article on the topic.

Site search is a valuable tool for the eCommerce industry because it ensures that customers get introduced to the products that they’re looking for. 

So making sure that your algorithm provides a relevant search result for specific written queries increases the chances of YoY making a sale. 

To enhance the effectiveness of site search engines, businesses can use AI-powered search engines, also referred to as insight engines. Insight engines enable businesses to combine search with machine learning capabilities so that internal search becomes more personalized and accurate.

Improving products and services

An improved e-commerce product results in additional sales without new marketing activities.

5. Pricing Optimization

Has it happened that just as you are browsing a product, a limited-time offer pops up on your screen? This can feel like the system is reading your mind to prepare the right offer at the right time but it is applied statistics to present dynamic prices to users.

6. Image tagging/recognition

An example comes from Pinterest, the image-sharing social network. Pinterest allows users to select an item from any online photo and then use Pinterest’s image recognition software to identify similar items.

Figure: Pinterest visual search engine

Source: Pinterest1Our crazy-fun new visual search tool”. Pinterest. Retrieved December 12, 2023.[/efn_note

Along with finding matching products, an AI system can provide shoppers with complementary product recommendations based on product attributes like size, color, shape, fabric, or brand.

7. Auto-generated product descriptions

Product descriptions are shop assistants of eCommerce. Customers make their purchase decisions after reading the description. If they have not already decided to buy, this is a business’ last chance to influence customers. 

Creating content is challenging and time-consuming for businesses. It takes about 18 months to complete 10,000 descriptions while AI content generation tools could do it in a few hours. By leveraging AI, businesses can also create a dynamic description that addresses the interests of each buyer.

8. Conversion rate optimization testing

Artificial Intelligence improves the productivity of testing programs. For example, Qubit is a personalization software that helps businesses deliver 1:1 e-commerce customer experiences. Qubit employs AI to verify A/B and multivariate testing results to identify false positives among user actions that lead to conversion.

9. Retail Analytics

Analytics is the science of producing insight from the patterns in complex data to make better decisions. Starting with sales and customer data, businesses used analytics in their decision-making processes but, in the age of AI, it is evolving with advances in machine learning.

Retail analytics is using enterprise data to find insights related to customer interactions, supply chain & inventory management, and target marketing. With those insights, eCommerce companies can make improvements in their performance. 

For more information about AI applications in retail, check out our detailed article on the topic.

10. Self Checkout Systems

For e-commerce players that want to expand their audience, physical stores can be interesting. Technologies such as those used in Amazon Go can be a good fit for e-commerce players expanding into physical retail.

To learn more about IoT use cases in retail, click here.

Website performance improvements

11. Review and forum moderation

Customer reviews have become the cornerstone of online shopping. Fake reviews can easily erode customer loyalty. Some e-commerce companies use AI to challenge this issue, it is commonly called “astroturfing”.

For instance, Amazon is fighting “astroturfing” with AI. The Amazon system ensures that it is the reviews of verified purchasers that are boosted, as opposed to fake reviews.

12. Marketplace moderation

Fake products can negatively impact the consumer’s perception of the original company that makes the authentic product. Al can also help spot fake products. 3 PM Marketplace Solution helps marketplace owners ensure that their merchants provide original products with the help of a self-learning algorithm.

Supply Chain Applications

13. Inventory Planning

Artificial intelligence can process more data than humans. Therefore, AI-powered demand forecasting solutions can provide more accurate results and prevent cash-in-stock and out-of-stock cases that businesses may encounter.

To learn more about inventory management automation, click here.

14. Automated Warehouses

Multiple fields in AI can enhance workflow in eCommerce companies’ warehouses. Some examples are:

  • Warehouse Robots
  • Damage detection
  • Predictive Maintenance

For more information on AI applications in logistics, feel free to check our detailed article on the topic.

Marketing & Sales Applications

According to research from Demandbase

  • Generate a better sales close rate (59%)
  • Increase revenues (58%)
  • Improve website traffic and engagement (54%)
  • Convert more leads (52%)

We’ve written about AI in Marketing and AI in Sales that’s why we don’t go into detail. Here are the top use cases:

Cybersecurity Applications

19. Using AI to improve cybersecurity

As data is considered the greatest asset of businesses and the average cost of a data breach is ~4 million, eCommerce companies should invest in AI security tools to protect their enterprise and customer data.

You can read our cybersecurity best practices article to learn more about the ways that improve your cybersecurity posture.

If you want to get started with AI in your business, feel free to check our lists of:

AI will be more impactful in the e-commerce area in the coming years. If you don’t use some of the solutions we mentioned to power your business, this may be a good time to start that. Just let us know the area you are interested in and we can help you choose the right AI technology partner for your e-commerce business:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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