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AI for E-commerce : Forms, Benefits & Application | Engati

Shubhangi Srivastava
last edited on
June 28, 2024
5 minutes

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Whenever the term AI for Ecommerce comes up, one instantly thinks of a robot pretending to be a human and selling products. Rather, it is so much more about understanding algorithms and fundamentals. Essentially, Artificial Intelligence in E-commerce aids to bring down the time spent on repetitive and manual tasks. 

Artificial Intelligence is revolutionizing the ecommerce landscape, transforming how businesses operate and interact with their customers. From personalized shopping experiences to optimized supply chains, AI is becoming an indispensable tool for ecommerce platforms. We recently did a masterclass ‘AI for Ecommerce’ where we covered the entire journey of the evolution of AI for Ecommerce to various use cases across departments. This blog is a deeper explanation of the same.

Various forms of AI in Ecommerce

AI technologies encompass a broad range of methods and applications that significantly impact the ecommerce industry. Here, we'll delve into four critical forms of AI: data mining, machine learning, natural language processing (NLP), and computer vision.

1. Data Mining

Data mining involves extracting valuable information from large datasets to uncover patterns, trends, and correlations. In Ecommerce, data mining is used to analyze customer behavior, sales trends, and market dynamics, which can inform decision-making and strategy development.

Applications in Ecommerce:

  • Customer Segmentation: By analyzing purchase history and browsing behavior, ecommerce platforms can segment customers into different groups based on their preferences and buying habits. This allows for targeted marketing and personalized offers.
  • Sales Forecasting: Data mining techniques help predict future sales by analyzing historical sales data and market trends. This enables businesses to plan inventory and marketing strategies more effectively.
  • Recommendation Systems: Data mining is the backbone of recommendation systems that suggest products to customers based on their past behavior and preferences.

2. Machine Learning

Machine learning (ML) is a subset of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed. In ecommerce, ML algorithms can automate processes, optimize operations, and enhance customer experiences.

Applications in Ecommerce:

  • Personalized Recommendations: Machine learning algorithms analyze customer data to provide personalized product recommendations, increasing the chances of purchase and customer satisfaction.
  • Dynamic Pricing: Machine learning models can adjust prices in real-time based on demand, competition, and other factors, ensuring competitive pricing and maximizing revenue.
  • Customer Lifetime Value Prediction: ML can predict the customer lifetime value by analyzing their behavior and interactions, helping businesses identify high-value customers and tailor strategies accordingly.

3. Natural Language Processing (NLP)

Natural Language Processing (NLP) enables machines to understand, interpret, and respond to human language. In ecommerce, NLP is used to improve customer interactions, enhance search functionality, and analyze customer feedback.

Applications in Ecommerce:

  • Chatbots and Virtual Assistants: NLP powers chatbots that can interact with customers in natural language, providing instant support, answering queries, and guiding users through the purchasing process.
  • Sentiment Analysis: By analyzing customer reviews and feedback, NLP algorithms can gauge customer sentiment, helping businesses understand customer satisfaction and address issues proactively.
  • Enhanced Search: NLP improves the accuracy of search results by understanding the context and intent behind customer queries, making it easier for customers to find the products they are looking for.

4. Computer Vision

Computer vision involves the use of AI to interpret and process visual information from the world. In ecommerce, computer vision enhances the shopping experience by enabling visual search, improving product displays, and ensuring quality control.

Applications in Ecommerce:

  • Visual Search: Customers can search for products using images instead of text. Computer vision algorithms analyze the images to find similar products, making the search process more intuitive and efficient.
  • Augmented Reality (AR): AR applications use computer vision to overlay digital information on the real world. In ecommerce, this allows customers to visualize products in their environment before making a purchase, such as trying on clothes virtually or seeing how furniture fits in their home.
  • Quality Control: Computer vision systems can inspect products for defects and ensure they meet quality standards, reducing the risk of returns and improving customer satisfaction.

Why include AI in your Ecommerce?

AI can be integrated into ecommerce in numerous ways to enhance efficiency, customer satisfaction, and overall business performance. Here are some key areas where AI can make a significant impact:

1. Personalized Product Recommendations and Discovery

Personalized recommendations are a powerful tool for enhancing the customer experience and boosting sales. AI algorithms analyze customer data such as browsing history, past purchases, and preferences to suggest products that are most likely to interest individual customers.


  • Increased Sales: Personalized recommendations can lead to higher conversion rates as customers are more likely to purchase items that match their interests.
  • Enhanced Customer Experience: Tailored suggestions make shopping more enjoyable and efficient for customers.
  • Customer Loyalty: Personalized experiences can lead to increased customer loyalty and repeat purchases.


  • Amazon uses AI to recommend products to users based on their browsing and purchase history, significantly increasing sales and customer satisfaction.

2. Inventory Management

AI can revolutionize inventory management by predicting demand, optimizing stock levels, and reducing waste. Machine learning algorithms analyze sales data, market trends, and seasonal variations to forecast demand accurately.


  • Optimized Stock Levels: AI ensures that businesses maintain optimal inventory levels, reducing the risk of overstocking or stockouts.
  • Cost Savings: Improved inventory management reduces storage costs and minimizes waste.
  • Improved Customer Satisfaction: Ensuring product availability enhances the customer experience.


  • Zara uses AI to manage inventory efficiently, predicting which items will be popular and ensuring they are stocked appropriately in their stores.

3. Chatbots and Virtual Assistance

AI-powered chatbots and virtual assistants provide instant customer support, answer queries, and assist with transactions. They can handle a wide range of tasks, from providing product information to processing returns.


  • 24/7 Support: Chatbots offer round-the-clock assistance, ensuring customers can get help whenever they need it.
  • Cost Efficiency: Automating customer service reduces the need for large customer support teams.
  • Improved Customer Experience: Quick and efficient support enhances customer satisfaction.


  • H&M uses chatbots on their website and social media platforms to interact with customers, providing personalized shopping assistance and support.

4. Fraud Detection and Prevention

AI plays a crucial role in detecting and preventing fraud in ecommerce transactions. Machine learning algorithms analyze transaction data to identify unusual patterns and flag potential fraudulent activities.


  • Increased Security: AI enhances transaction security, protecting both businesses and customers.
  • Reduced Fraud Losses: Early detection of fraud minimizes financial losses.
  • Improved Trust: Enhanced security measures build trust with customers.


  • PayPal employs AI to monitor transactions for fraudulent activity, ensuring a secure payment process for its users.

5. Better Targeting and Data-Backed Consumer Research

AI helps businesses understand their customers better by analyzing large volumes of data to uncover insights about consumer behavior and preferences. This enables more effective targeting and marketing strategies.


  • Effective Marketing: Data-driven insights allow for more accurate targeting of marketing campaigns.
  • Increased ROI: Better targeting leads to higher conversion rates and a better return on investment.
  • Consumer Insights: AI provides deep insights into customer preferences and trends, informing product development and marketing strategies.


  • Netflix uses AI to analyze viewer data and tailor its content recommendations and marketing campaigns to individual user preferences.

6. FAQs (Powered by LLMs and NLP)

AI-driven FAQ systems use large language models (LLMs) and natural language processing (NLP) to understand and respond to customer inquiries. These systems can provide accurate and contextually relevant answers to a wide range of questions.


  • Efficient Customer Support: AI-powered FAQs reduce the need for human intervention, handling common queries automatically.
  • Improved Accuracy: LLMs and NLP provide precise and relevant answers, improving customer satisfaction.
  • Scalability: AI systems can handle a large volume of inquiries simultaneously, making them highly scalable.


  • Sephora uses an AI-powered FAQ system to provide customers with instant answers to their questions, enhancing the overall shopping experience.

Personalized Shopping Experiences

One of the most significant impacts of AI in ecommerce is personalization. AI algorithms analyze customer data, such as browsing history, purchase behavior, and preferences, to provide personalized product recommendations. This not only improves the shopping experience but also increases the likelihood of conversions.

Example: Product Recommendations

Platforms like Amazon and Netflix use AI to recommend products and content based on user behavior. By analyzing patterns and preferences, these systems can suggest items that customers are more likely to purchase, thereby enhancing user satisfaction and boosting sales.

AI for every function of an E-commerce company

AI in Marketing Department

AI is a game-changer for ecommerce marketing departments, offering advanced tools and techniques that enhance efficiency, effectiveness, and customer engagement. Here's how AI can be integrated into various marketing functions:

1. Automation

Automation in marketing involves using AI to streamline repetitive tasks, allowing marketers to focus on more strategic activities. AI-powered automation can handle tasks such as email marketing, social media posting, and customer segmentation.

  • Time Savings: Automates routine tasks, freeing up time for strategic planning and creative work.
  • Consistency: Ensures consistent execution of marketing activities, such as posting on social media at optimal times.
  • Scalability: Allows marketing campaigns to be scaled up without a proportional increase in workload.
  • Mailchimp uses AI to automate email marketing campaigns, from personalized email content to optimal sending times, ensuring higher engagement rates and customer retention.

2. Better Retargeting

Retargeting involves re-engaging customers who have previously interacted with a brand but have not completed a purchase. AI enhances retargeting by analyzing user behavior and preferences to deliver personalized ads that are more likely to convert.

  • Increased Conversions: Personalized retargeting ads are more relevant to users, increasing the likelihood of conversion.
  • Cost Efficiency: By targeting users who have already shown interest, marketing budgets are used more effectively.
  • Improved Customer Experience: Personalized ads provide value to customers by showing them products that match their interests.
  • Google Ads uses AI to create dynamic retargeting ads that showcase products users have viewed or added to their cart, improving the chances of completing the purchase.

3. Campaign Analytics

AI-driven campaign analytics provide deep insights into the performance of marketing campaigns. By analyzing vast amounts of data, AI can uncover patterns, identify successful tactics, and provide actionable recommendations for optimization.

  • Data-Driven Decisions: AI analytics offer precise insights that help marketers make informed decisions.
  • Real-Time Analysis: Provides up-to-date performance metrics, allowing for timely adjustments to campaigns.
  • Predictive Insights: AI can predict future trends and outcomes based on historical data, helping in proactive campaign planning.
  • HubSpot employs AI to analyze marketing campaign data, offering insights on which content performs best, the optimal times for posting, and the most effective channels for reaching target audiences.

4. Visual Search and Image Recognition

Visual search and image recognition technologies allow customers to search for products using images rather than text. This AI capability enhances the shopping experience by making it easier for customers to find exactly what they’re looking for.

  • Enhanced User Experience: Simplifies the search process, making it more intuitive and engaging.
  • Higher Conversion Rates: Customers are more likely to find and purchase products they visually identify with.
  • Innovative Marketing: Enables creative marketing strategies, such as shoppable images and interactive catalogs.
  • Pinterest Lens allows users to take photos of objects and find similar items available for purchase, leveraging AI to bridge the gap between offline inspiration and online shopping.

AI in Sales Department

AI technology offers significant benefits for ecommerce sales departments by improving efficiency, enhancing customer interactions, and driving sales growth. Here’s how AI can be effectively integrated into sales functions:

1. Lead Qualification

Lead qualification is the process of identifying which potential customers (leads) are most likely to make a purchase. AI enhances lead qualification by analyzing large datasets to score and prioritize leads based on various factors such as behavior, engagement, and demographics.

  • Improved Accuracy: AI algorithms can evaluate leads more accurately by considering multiple data points and patterns.
  • Increased Efficiency: Automating lead scoring saves time for sales teams, allowing them to focus on high-potential leads.
  • Higher Conversion Rates: By prioritizing the most promising leads, sales teams can tailor their efforts to those more likely to convert, increasing sales effectiveness.
  • Salesforce Einstein uses AI to analyze customer data and predict which leads are most likely to convert, enabling sales teams to prioritize their efforts on high-value opportunities.

2. Automate Tasks for Better Reallocation of Time and Resources

AI can automate a wide range of administrative and routine tasks, freeing up time for sales teams to focus on more strategic activities, such as building relationships and closing deals.

  • Time Savings: Automating tasks such as data entry, scheduling, and follow-ups reduces the time spent on manual activities.
  • Resource Optimization: Sales teams can allocate more time and resources to high-impact activities, improving overall productivity.
  • Consistency and Accuracy: AI ensures that repetitive tasks are performed consistently and accurately, reducing the risk of human error.
  • HubSpot Sales Hub employs AI to automate tasks like logging emails, setting up follow-up reminders, and updating contact information, allowing sales representatives to concentrate on selling.

3. Gather Data to Engage Better with Prospects

AI-driven tools can gather and analyze data from various sources to provide sales teams with deep insights into customer behavior, preferences, and needs. This information helps in crafting personalized engagement strategies that resonate with prospects.

  • Enhanced Customer Understanding: AI analyzes customer interactions and behaviors, providing valuable insights that help in understanding their needs and preferences.
  • Personalized Engagement: Sales teams can use AI-driven insights to tailor their communication and offers, making interactions more relevant and compelling.
  • Proactive Engagement: AI can identify signals and trends that indicate when a prospect is ready to buy, enabling sales teams to engage at the right moment.
  • Conversica uses AI to engage with leads via email, assessing their level of interest and providing sales teams with actionable insights on which prospects are ready for a follow-up, enhancing the likelihood of conversion.


AI is reshaping the ecommerce industry, providing numerous benefits that enhance customer experiences, streamline operations, and drive growth. As AI technology continues to evolve, its applications in ecommerce will expand, offering even more innovative solutions for businesses and consumers.

Embracing AI in ecommerce is no longer an option but a necessity for businesses looking to stay competitive in today's digital marketplace. By leveraging AI technologies, ecommerce platforms can offer personalized experiences, improve efficiency, and ultimately achieve greater success.

Shubhangi Srivastava

Shubhangi is the Content Lead at Engati. With more than 4 years of experience working across various marketing teams, she specialises in user engagement, lead generation and conversions. When not working, she likes learning about various cultures across the world.

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