Artifical Intelligence use cases for ecommerce

AI Applications for Ecommerce: A Technical Comparison

Table of Contents

AI Applications for Ecommerce: A Technical Comparison

Virtual Assistants

  • Technologies: Natural Language Processing (NLP), Machine Learning (ML)
  • Purpose: Engage customers in conversation and complete tasks
  • Integration: Separate to website, available via SMS messaging, voice, webchat etc
  • Analytics: Can collect data from customer actions including intent, sentiment and task completion
  • Benefits: Reduced customer enquiries, tailored conversations

Product Recommendations

  • Technologies: Predictive Analytics, Machine Learning (ML)
  • Purpose: Increase conversions and average order value
  • Integration: Site-wide or specific products on certain pages
  • Analytics: Can be tailored to weekly, monthly, and annual sales patterns
  • Benefits: Increase sales, reduce returns, support discovery of products

Pricing Algorithms

  • Technologies: Predictive Analytics, Machine Learning (ML)
  • Purpose: Maximize revenue per customer by personalizing prices
  • Integration: Various levels of personalization, e.g. non-personalized pricing, personalized product pricing, real-time price optimization
  • Analytics: Measurement of customer response to various pricing models
  • Benefits: Increased average order value, more efficient customer segmentation

Image Recognition

  • Technologies: Computer Vision, Deep Learning, Machine Learning (ML)
  • Purpose: Automate tagging images on product pages for faster search and sorting
  • Integration: Every product page via APIs
  • Analytics: Automatically detect and categorize image tags for product pages
  • Benefits: Increases discoverability of products, personalize the customer experience

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