AI in Ecommerce: Benefits and Use Cases

Artificial intelligence (AI) is increasingly being used for e-commerce, with global expenditure on AI e-commerce integration rising to $8 billion by 2024.

Businesses that don’t evolve and integrate AI into their online shopping model risk becoming obsolete or losing their competitive advantage.

In this article, we will review the importance of incorporating AI into your e-commerce business, sharing several real-world case studies as well as implementable use cases.

Why Use AI for eCommerce?

A report from BusinessWire predicts that by 2025, the profit generated from the application of AI software will increase to $59.8 billion.

In addition to the monetary gain, there are several reasons why you should be implementing AI technology for e-commerce.

To name a few:

  • Automate Redundant Tasks – AI algorithms can automate the extraction and processing of data from various sources, eliminating the need for manual data entry and analysis. This is particularly useful for tasks such as reviewing website analytics or generating content for your business.
  • Analyzes Trends – AI tools can churn out large piles of data to derive actionable insights for your business. For example, the AI-powered tool Boomtrain analyzes customer data from multiple touchpoints to gauge how they perform online interactions. These insights ensure digital retailers can provide a consistent digital experience across all devices and make appropriate product recommendations.
  • Targeted Marketing and Advertising– AI-powered platforms in the e-commerce industry are better equipped to track real-time user buying patterns and prepare reports on customer preferences and decision-making. This enables retailers to run targeted marketing and advertising campaigns catered to specific demographics, ensuring better conversions.
  • Efficient Query Management – Using natural language processing (NLP) algorithms for chatbots, you can better understand customer queries and pain points to improve user experience. With machine learning, such bots can even learn independently by picking up patterns to provide personalized solutions to customers without you having to intervene.

AI Use Cases in eCommerce: 7 Ways

Let’s now go over several use cases and accompanying case studies on the use of AI in eCommerce.

1. Pricing Optimization using AI

Several factors influence a product’s price, including customer segmentation, market demand, and product supply.

Online stores need to consider all these elements to determine the best price for their products. AI tools can simplify this process, enabling you to devise the most effective pricing strategy.

For instance, AI can help adjust prices and tailor discount offers on your platform, particularly if competitors face inventory shortages, allowing you to attract more customers and increase sales.

Assortment intelligence is another pricing optimization strategy. This includes a data-driven assortment of your SKUs and personalized product recommendations to provide the right amount of variety and options.

Additionally, this strategy offers deep insights into how your competitors’ products perform and are priced, enabling you to give the best deals to attract customers.

By understanding and adapting to market dynamics and consumer preferences, you can position your offerings more competitively.

For multichannel retailers, managing product pricing across various sales platforms can be challenging. AI algorithms simplify this by identifying sales channels with higher demand, allowing for tailored pricing adjustments.

These systems also account for seasonal trends and supply chain disruptions. For example, a clothing retailer might receive automated suggestions to increase prices for winter apparel like hoodies and sweaters ahead of the cold season.

AI’s dynamic pricing capability also considers the Purchasing Power Parity (PPP) of different regions to recommend appropriate prices.

For example, customers in Africa have lower purchasing power than Americans. As a result, you should strategically price your products lower in these locations to get sales.

Example: Tools like Prisync leverage automated processes that combine inventory data with competitive and internal pricing information in real-time.

This enables businesses to adopt a dynamic pricing strategy, adjusting prices based on current market conditions and demand.

2. Inventory Management with AI-Driven Forecasting

Forecasting demand and allocating the right amount of inventory is essential for the efficiency of every ecommerce business. This is where AI-powered inventory management tools come in handy during supply chain forecasting.

These tools run the numbers for you to do a predictive analysis of the current stock levels in your storage.

Next, they calculate the supply chain performance and extrapolate the optimal levels you should keep in hand to satisfy customer demand.

RELEX stands out as a prime example of such technology, employing machine learning algorithms to fine-tune supply chain activities.

Key functionalities of RELEX include advanced demand forecasting, real-time inventory tracking, and seamless integration with existing systems.

This enables businesses to adopt and maintain a just-in-time inventory approach, a strategy that has propelled companies like Walmart to the forefront of the global market.

Through such innovations, RELEX not only streamlines supply chain processes but also significantly enhances operational agility and responsiveness.

Moreover, AI plays a pivotal role in inventory management by advising on what and where to store items for efficient retrieval, especially critical for perishable goods at risk of expiring quickly.

This approach minimizes lost sales by ensuring products are readily available when needed.

Example: An innovative application of this technology is seen with the Indian quick commerce platform, Zepto.

They introduced a Dark Store model, using AI to analyze order trends by location and identify frequently repurchased products.

Zepto then strategically opens warehouses in those specific areas, stocking them with in-demand items, significantly mitigating their issue with unsold inventory.

3. AI-Powered Product Recommendations

In physical stores, personal interaction and product recommendations come naturally, but replicating this personalized experience online presents a challenge.

This is where AI-powered online stores shine, offering personalized product recommendations to customers during their visit.

AI-driven recommendation engines, such as Granify and Boomtrain, leverage vast amounts of consumer data sets from multiple sources to enhance the shopping experience.

These platforms can significantly reduce cart abandonment rates by delivering automated product suggestions and calls to action at strategic points on eCommerce sites.

These systems also perform site search analysis to identify what customers frequently search for, enabling more effective targeted marketing strategies for high-demand products.

This targeting can be refined through media purchases on social media where your target audience is most active.

For example, if your primary audience is young adults who use Instagram and Facebook, you can create and promote content specifically for them using generative AI tools.

Additionally, these AI solutions analyze purchasing trends to offer product suggestions that align with individual customer preferences, enhancing satisfaction and loyalty over time.

Crucially, AI algorithms review the contents of a shopper’s cart in real-time, recommending similar or complementary products during the checkout process.

This strategy of upselling and cross-selling not only enriches the shopping experience but also increases the average order value and overall revenue.

4. AI-Powered Chatbots

With so many companies competing for the same customers in the e-commerce industry, time is of the essence.

However, it’s not feasible to maintain constant interaction with consumers manually.

This is where AI-powered chatbots, equipped with Natural Language Processing (NLP), become invaluable.

NLP enables chatbots to understand and respond to human language, allowing customers to receive immediate assistance, whether they’re navigating to a product page or resolving queries, just as they would with a human representative.

The unique advantage of these AI chatbots is their ability to improve with each interaction, thanks to machine learning algorithms, reducing the need for dedicated customer service staff.

Deploying an AI chatbot serves like a 24/7 virtual assistant, freeing up valuable time for you to focus on strategic decisions and business growth.

Moreover, the data collected from customer interactions with chatbots provides insights into consumer behavior, helping refine your website and product offerings to better meet customer needs.

AI technology also supports voice and visual search, enabling customers to search for products by uploading images or speaking, a feature particularly useful for finding products similar to those seen in real life.

Example: Leading AI chatbots such as Zendesk AI, aiPDF and Dialogflow are revolutionizing customer service for major corporations like Stripe, Twitter, and Microsoft by offering a comprehensive, automated customer support system.

5. Customer Data Analysis and Management

Utilizing the latest AI tools and machine learning capabilities offers deep insights into customer behaviors, enabling businesses to tailor their marketing efforts effectively.

An example of how AI has streamlined ecommerce sales is by sending customers personalized bulk messages and emails.

By understanding individual preferences and purchasing patterns, companies can deliver highly personalized marketing that resonates with the audience, enhancing engagement and loyalty.

AI-driven retargeting campaigns leverage this data to reconnect with interested yet undecided customers, boosting conversion rates by presenting customized content or offers.

AI also supports precise A/B testing, allowing for the optimization of marketing strategies through analysis of customer reactions.

This segmentation facilitates a deeper understanding of diverse customer bases, guiding product and brand messaging adjustments to better match target demographics.

Example: Amazon is a company that extensively employs AI for customer data analysis. One example is their recommendation engine, which suggests products based on individual browsing and purchasing histories.

This application has been pivotal in Amazon’s ability to offer a personalized shopping experience to millions, significantly contributing to its global market share.

6. User Reviews Moderation and Filtering using AI

The ultimate form of marketing is word of mouth. If users like your product and service, they are going to recommend it to other people and promote it as well.

For e-commerce companies, the number one spokesperson is the ratings and user reviews. Product reviews play a pivotal role in purchasing decisions.

In fact, a staggering 89% of online shoppers take the time to read reviews before making a purchase.

However, in the digital age, reviews can serve as a double-edged sword. This is particularly true for businesses leveraging AI, which thrives on data to refine and optimize their operations.

Since artificial intelligence models use deep learning technology, the more data it receive, the more optimized it gets.

However, the downside to this is that the AI can get biased if your website gets spammed by skewed user reviews.

There are instances where fake accounts give false reviews, or reviews bomb your website to bring down its ratings.

If you implement AI solutions, the algorithm employs pattern recognition and content analysis to identify and filter out spammy reviews, ensuring that genuine feedback from users is highlighted while irrelevant or misleading content is minimized.

AI’s capability extends to detecting unusual patterns or surges in review activities and identifying coordinated review bombing campaigns aimed at manipulating product ratings.

Through the integration of AI/ML, NLP, and Robotic Process Automation (RPA), e-commerce platforms can swiftly identify and address potentially problematic reviews, safeguarding the integrity of user feedback and maintaining the trustworthiness of their review systems.

7. Fraudulent Transactions (Payments) Detection and Prevention

The ongoing development of AI models greatly aids in detecting and preventing fraudulent transactions, especially when dealing with the growing problem of payment fraud in international eCommerce.

Statistics show that the expected losses from eCommerce payment theft in 2022 alone were an astounding $48 billion and are expected to rise to $343 billion from 2024 until 2027.

AI is essential to the fight against this threat because it uses sophisticated algorithms to monitor and examine different fraud indications.

Artificial Intelligence (AI) models are particularly good at monitoring fake IP addresses, analyzing trends linked to questionable client behavior, and looking for abnormalities in purchase histories.

These models may adjust and change by utilizing machine learning, gaining knowledge from fresh data, and staying ahead of scammers’ shifting strategies.

By using a dynamic method, fraud detection becomes more accurate and efficient, enabling organizations to discover and stop unlawful activities quickly.

Example: Renowned financial institutions like JP Morgan Chase and CitiBank employ fraud detection using AI in banking. Applying AI-driven fraud detection techniques allows these platforms to decrease fraudulent transactions drastically.

The technology highlights possible fraudulent activity in real time, continually evaluates transaction patterns, and finds abnormalities.

The adoption of AI in fraud detection has not only led to a marked decrease in fraudulent transactions but also bolstered chargeback protection and reinforced overall security measures.

As a result, these platforms have experienced a boost in consumer trust and the safeguarding of their payment ecosystems.

8. AI Text Generation for Product Descriptions, Blog, and Social Media Posts

Large language models (LLMs) have recently made a breakthrough with the introduction of ChatGPT and Google Gemini. Not only do these platforms produce unique copies of your product descriptions, but they also generate them in large volumes in such a short time.

In the realm of e-commerce, AI excels at crafting relevant product descriptions that provide accurate details, highlight unique value propositions, and address potential customer pain points.

By analyzing product features and customer preferences, AI-generated content can be personalized to resonate with the target audience, ultimately driving engagement and conversions.

Furthermore, AI facilitates social media posting automation, streamlining the process of regularly updating social channels with compelling content.

From product announcements and marketing campaigns to promotional updates, AI-generated social media posts maintain consistency and relevance, allowing marketing teams time to focus on more important strategies.

Programmatic SEO is another way to automate AI-generated content in large volumes to rank and index on search engines quickly.

By incorporating relevant keywords and ensuring content aligns with SEO best practices, businesses can improve their online visibility and bring organic traffic to their e-commerce platforms.


The bottom line is that AI technology is evolving at such a rapid rate, and so is its application in the e-commerce industry.

By adopting AI, e-commerce platforms are not only streamlining operations but also gaining critical insights that enhance decision-making.

We hope the AI use cases explored in this article will inspire practical applications, driving innovation and success in your e-commerce operations.


What are the risks of AI in ecommerce?

AI integration in e-commerce introduces risks such as data privacy and security concerns, potentially compromised by unethical practices. Ethical considerations may lead to biased algorithms, impacting fairness and trust. Proactive measures, including ethical AI practices and regular audits, are crucial to navigating these risks, ensuring responsible and secure implementation for the benefit of businesses and consumer trust.

What are the benefits of using AI in e-commerce?

Using AI with e-commerce stores brings many benefits, which include enhancing the overall customer experience and operational efficiency. For instance, personalized shopping experiences, predictive analytics for demand forecasting, and dynamic pricing optimization increase customer satisfaction and revenue. AI-driven chatbots streamline customer support, while recommendation engines boost cross-selling. Supply chain optimization and fraud detection improve backend operations.

How is AI being used in ecommerce marketing?

Recent advancements in AI have transformed e-commerce marketing by enabling more personalized customer interactions. Recommendation engines analyze user behavior for targeted product suggestions, increasing conversion rates. AI-driven chatbots provide instant and customized assistance, enhancing customer engagement. Predictive analytics anticipates trends, optimizing inventory management and supply chain processes. Dynamic pricing adjusts in real-time for competitiveness.

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Our team has been at the forefront of Artificial Intelligence and Machine Learning research for more than 15 years and we're using our collective intelligence to help others learn, understand and grow using these new technologies in ethical and sustainable ways.

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