eCommerce Tech

AI in eCommerce: Benefits & Use Cases

Jan 8, 2024
8 min read
AI in eCommerce

In the past, artificial intelligence (AI) was confined to the realm of science fiction, but now it has seamlessly integrated into our everyday routines.

From crafting emails to enhancing entertainment recommendations and even streamlining financial transactions, AI exerts a substantial impact on diverse facets of our lives. Its significance is especially notable in the realm of eCommerce.


Until quite recently, AI was nearly a luxury employed mainly to impress customers and foster loyalty. Today, AI has become a new norm for eCommerce businesses, serving as a key tool for both satisfying customer needs and streamlining business processes.


In this article, we’ll delve into the benefits of using AI in eCommerce and most common use cases. Let’s start.

AI implementation statistics

Benefits of AI in eCommerce

Artificial intelligence offers numerous benefits for eCommerce businesses:

Personalized сustomer уxperience:

  • AI analyzes relevant data to make product recommendations
  • Provides customized offers and discounts
  • Utilizes natural language processing for interactive customer engagement
  • Boosts customer satisfaction and loyalty, leading to increased sales


Improved search results:

  • AI algorithms enhance the search experience based on customer behavior
  • Proposes products relevant to customer interests
  • Increases accuracy in search results, leading to higher conversion rates and revenue


Streamlined operations:

  • AI automates routine tasks like supply chain management and customer service
  • Reduces costs and improves overall operational efficiency
  • Frees up time and resources for business growth and innovation


Fraud detection:

  • AI analyzes customer behavior to detect and prevent fraudulent activity
  • Identifies patterns indicating suspicious behavior, such as unusual purchase patterns or login locations
  • Protects eCommerce businesses’ revenue and reputation without excessive resource investment


Improved inventory management:

  • AI optimizes inventory levels by analyzing historical data and customer behavior
  • Predicts future product popularity
  • Helps avoid stockouts or overstocking, preventing lost sales and customer dissatisfaction

Concerns about AI in eCommerce

Despite the numerous and undeniable advantages of AI, there still exist some potential concerns you should be aware of before implementing the technology to navigate its integration successfully:

  • Cost

Investing in AI can be a hefty financial commitment, encompassing infrastructure, skilled personnel, and ongoing upkeep. This could pose a significant hurdle for small businesses, especially if the returns on investment aren’t guaranteed.

  • Privacy

AI’s effectiveness often hinges on accessing vast amounts of customer data, sparking concerns about privacy. eCommerce entities utilizing AI must be upfront about their data collection practices, assuring customers that their personal information is safeguarded.

  • The human touch dilemma

While AI can offer a personalized customer experience, some individuals still crave human interaction. AI chatbots might struggle to match the support and empathy provided by human customer service representatives, potentially leading to customer frustration. Achieving a balance between AI and human interaction is crucial for a positive customer experience and maintaining a sterling reputation.

  • Security

AI systems aren’t impervious to cyber threats, making them susceptible to hacking and other attacks. eCommerce businesses employing AI must fortify their systems with robust encryption measures and continually invest in security protocols to protect customer data. Vigilance and ongoing commitment to security are paramount.

8 AI use cases in eCommerce

Let’s dive into how AI can change the game for both shoppers and businesses:

#1 Chatbots & voice assistant

AI-powered chatbots and virtual assistants have revolutionized the online shopping experience by seamlessly blending convenience with personalized interactions. These digital assistants, armed with natural language processing capabilities, act as knowledgeable guides for customers navigating the vast digital aisles.


Imagine a virtual concierge that understands and anticipates user preferences, offering tailored product recommendations and aiding in the decision-making process. From swiftly resolving queries about product details to facilitating secure and efficient transactions, these AI-driven companions provide real-time assistance, transforming the often solitary act of online shopping into an engaging and supportive experience.


Moreover, their round-the-clock availability ensures that customers can seek guidance at any hour, fostering a sense of reliability and accessibility. As these technologies continue to evolve, the line between in-store assistance and digital support blurs, creating a harmonious and enhanced online shopping journey that caters to the individual needs and preferences of each customer.

Best example: Ikea

Ikea logo

Billie, Ikea’s innovative chatbot, boasts the ability to comprehend customer inquiries, furnish product details, suggest recommendations, and even contribute to designing entire interior spaces autonomously, eliminating the need for human intervention. Operating 24/7, Billie demonstrates remarkable multitasking capabilities, managing multiple conversations concurrently.


Launched in 2021, from 2021 to 2023, it efficiently addressed approximately 47% of customer queries, totaling 3.2 million interactions and resulting in substantial savings of nearly EUR 13 million. With Billie adeptly handling straightforward inquiries, Ikea’s staff can now take on more value-added roles, particularly in the realm of remote selling.

Ikea chatbot

#2 Personalized search

Utilizing advanced algorithms and machine learning, AI analyzes user behavior, preferences, and purchase history to deliver tailored search results. This means that when a customer searches for a product, the AI system understands their individual context and showcases items that align with their interests. This personalized search not only enhances the speed and accuracy of finding desired products but also creates a more engaging and user-friendly interface.


It’s like having a virtual shopping assistant that learns and adapts to each customer’s unique preferences, providing a curated selection that significantly improves the chances of finding the perfect item. This not only enhances customer satisfaction but can also drive increased sales and customer loyalty, as users feel more understood and catered to in their online shopping journey.

#3 AI-Driven Voice Shopping

According to OnlineDasher’s report, approximately 71% of shoppers prefer verbalizing their queries through voice commands as opposed to manually typing them. Enabled by advanced natural language processing and machine learning, voice assistants like Amazon’s Alexa and Google Assistant have become personal shopping companions. Users can effortlessly place orders, inquire about product details, and even receive personalized recommendations through simple vocal commands.

Best example: Carrefour

Carrefour logo

This French grocery retailer provides customers with the convenience of shopping through the Google voice assistant. Users have the ability to add items to their shopping lists and make modifications through voice commands, using everyday language (such as “chocolate” or “butter”) or specific product names. Subsequently, Google Assistant transforms the list into a comprehensive shopping cart on the Carrefour website. Here, shoppers can complete their orders, process payments, and select their preferred delivery options.

#4 Product recommendations

By offering tailored product suggestions, store owners can customize customer engagements and deliver more pertinent online shopping encounters, leading to increased conversion rates, higher average order values, and enhanced customer loyalty. The integration of AI-powered recommendations proves particularly beneficial in achieving these outcomes.

Best example: Netflix

Netflix logo

Netflix’s AI-driven Recommendations Engine (NRE) employs sophisticated algorithms to sift through content based on individual user profiles. Capable of filtering over 3,000 titles simultaneously using 1,300 recommendation clusters, it precisely identifies the titles that align with a user’s potential interests.


Through the analysis of data garnered from each customer’s visit and clicks, the recommendation engine develops a profound understanding of individual preferences. This enables it to generate hyper-personalized recommendations for each user, irrespective of the scale of the customer base. The engine excels in swiftly matching each customer with the right products or offers.

#5 Dynamic pricing optimization

Determining the optimal price for your product involves considering various factors, including competitor pricing, production costs, and customer demand – a time-consuming task, especially with a large product inventory.


Imagine if AI could handle this for you, dynamically adjusting pricing based on its analysis. This capability is known as Dynamic Pricing. With it, you can set your product prices optimally at any moment, factoring in costs and competitor pricing through extensive data analysis. These systems can even forecast when it’s advantageous to increase prices or launch a sale based on accurate data.


Moreover, AI simplifies the process by automating price adjustments for hundreds or thousands of products in your store, eliminating the need for manual changes. In practice, you could leverage this by, for instance, raising prices when competitors’ stocks are low. When customers urgently seek a product unavailable elsewhere, they are more likely to accept a higher price if it means obtaining it sooner from your store.

Best example: Amazon

Amazon logo

Amazon effectively employs a Dynamic Pricing Strategy, adapting its product prices in response to promotions or discounts offered by competitors, often making adjustments of up to 20%. Importantly, these changes are implemented gradually, strategically aligning with sales forecasts to ensure optimal profitability. This approach enables Amazon to consistently offer competitive prices while maintaining control over its profit margins.

#6 Customer segmentation

80% of customers prefer engaging with a brand that tailors their user experience. Effective audience segmentation emerges as a crucial strategy for delivering personalized marketing communications.


Furthermore, employing segmentation tactics can substantially enhance the productivity, efficacy, and return on investment of marketing campaigns. However, manually creating customer groups is a time-consuming task.


This is where AI steps in to streamline the process. Artificial intelligence tools, leveraging data analysis, can rapidly generate customer segments based on identified similarities. Notably, AI conducts this analysis without preconceived assumptions or biases, ensuring a more accurate understanding of the audience. Additionally, these tools can unveil entirely new segments that might be overlooked, providing valuable insights even when working with the same data that you or your team have examined.


#7 Sales and demand forecasting

Forecasting is widely used by eCommerce businesses to streamline inventory management, logistics planning, warehouse allocation, and pricing strategies. However, the task of accurately predicting demand has become increasingly complex, as reliance solely on historical sales data, even when supplemented with seasonal information, is proving insufficient.


Unlike traditional methods that rely on historical data, AI leverages real-time information, incorporating factors such as demographics, weather conditions, performance metrics of similar products, and insights from online reviews or social media. Machine learning further refines these predictions over time as more data becomes available.

Best example: Danone

Danone logo

Danone has implemented a machine learning system to elevate its demand forecasting capabilities. Beyond generating more accurate estimates for short-life products, Danone’s machine learning system has facilitated improved coordination among various departments, including sales, supply chain, finance, and marketing. This enhancement in efficiency has led to a better balance in inventory, enabling Danone to achieve targeted service levels at both the channel and store levels.

#8 Autogenerated product descriptions

Crafting product descriptions that are both persuasive and optimized for SEO poses a challenging task, even for seasoned copywriters. The larger your product inventory, the more time-consuming it becomes to create unique descriptions for each item.


Why not leverage AI-powered tools for this task? While they may not match the finesse of professional copywriters, AI excels at swiftly generating engaging, distinct, and optimized content based on product specifications, particularly for straightforward descriptions.


Best example: Restaurant Supply (Case study by GoMage)


GoMage’s client, a supplier of restaurant equipment, sought an opportunity to streamline product description creation and turn generic descriptions into catchy and SEO-optimized ones. To achieve this, we developed the AI-powered content creation and management automation solution. Our solution goes beyond simple content creation – it understands intricate specifications, ensuring a cohesive brand voice across comprehensive product narratives and nuanced subcategories.

Step by step AI implementation

While the allure of embracing novel technologies is undeniable, it’s essential to refrain from diving in without a well-defined roadmap to avoid unnecessary time and financial investments in misguided endeavors.


Step 1. Develop a strategic foundation

Initiate the process with a well-thought-out strategy that maps the journey from your starting point to the ultimate AI objective. Avoid delegating this crucial step solely to a recently hired AI expert, CIO, or CTO. Delve into the specifics of what you aim to achieve with AI, taking a pragmatic approach and commencing with manageable steps that can be expanded upon later.


Step 2. Identify targeted use cases aligned with corporate strategy

Identify specific use cases that align with overall corporate goals. Optimal AI use cases often thrive at the intersection of business objectives, data distinctiveness, and existing AI models. Prioritize revenue-generating opportunities where you possess a data advantage within a context suitable for proven AI technology.


Step 3. Tap into external expertise

Even if you harbor a passion for AI, seeking expert guidance is crucial. Engage a dedicated team, either as a project-based or part-time arrangement, to delve into the intricacies and assist in crafting a strategic AI roadmap. Third-party expertise can also prove helpful in bringing your MVP to life.


Step 4. Implement a comprehensive solution

Once confident in your team’s efforts, proceed to build the full-scale solution. Anticipate iterations before achieving the desired functionality. With growing familiarity in the realm of AI, you and your team will gradually realize enhanced benefits from the implemented projects.

Final word

Undoubtedly, the eCommerce landscape is on the brink of a significant transformation in the coming years, fueled by the integration of machine learning and AI technologies. While the full impact is yet to unfold, numerous instances of artificial intelligence are already reshaping online shopping. AI’s substantial influence is evident in the enhancement of customer experiences and the introduction of innovative solutions within the eCommerce sector.


If there were any lingering doubts about the imminent revolution in how customers discover products online, the current utilization of AI has laid them to rest. For businesses yet to leverage the mentioned solutions to empower their operations, now stands as an opportune moment to embark on this transformative journey.

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