AI in ecommerce: personalisation

How to use artificial intelligence to deliver unique shopping experiences to every customer

9 min

AI-powered personalisation has become the primary competitive differentiator in ecommerce. Amazon attributes 35% of its sales to its recommendation engine, and consumers increasingly expect experiences tailored to their preferences and behaviour.

From product recommendations to dynamic pricing, AI lets each user see a store adapted to their needs without a human intervening in every decision. This guide covers the key applications and how to implement them.

Recommendation engines

Recommendation engines are the most visible AI application in ecommerce. They use collaborative filtering, content-based filtering or hybrid models to suggest relevant products to each user.

  • Collaborative filtering: "customers who bought X also bought Y". Based on collective behaviour patterns
  • Content-based: recommends products similar to the one the user is viewing, based on product attributes
  • Hybrid models: combine both approaches with contextual data (time, device, location) for greater precision
  • Tools: Algolia Recommend, Dynamic Yield, Nosto, Amazon Personalize

Dynamic pricing with AI

Dynamic pricing adjusts prices in real time based on demand, inventory, competition and user behaviour. AI analyses thousands of signals simultaneously to find the optimal price that maximises margin without losing conversion.

It’s standard practice in sectors like airlines and hotels, and is expanding into retail and B2B ecommerce. The key is implementing it transparently to avoid eroding consumer trust.

  • Input variables: real-time demand, available stock, competitor prices, historical elasticity
  • Tools: Prisync, Competera, Intelligence Node for monitoring and automatic adjustment
  • Guardrails: set maximum and minimum price limits to prevent anomalies

Visual AI: image recognition

Visual AI enables image-based search ("find products similar to this photo"), automatic catalogue tagging and visual trend detection. It’s especially powerful in fashion, home decor and retail.

Tools like Syte, Vue.ai or Google Vision API let you integrate these capabilities without building custom models.

Chatbots and shopping assistants

Chatbots powered by generative AI go far beyond the traditional FAQ bot. They can act as personalised shopping assistants: understanding user needs, recommending products, answering availability questions and guiding through to checkout.

  • Conversational assistants that understand natural language and conversation context
  • Catalogue integration to answer questions about stock, sizes, materials and shipping
  • Guided recommendation: "I’m looking for a gift for someone who loves cooking, budget €50"
  • Tools: Tidio, Gorgias with AI, Zendesk AI, custom solutions with GPT-4 API

How to start with AI in your ecommerce

AI personalisation doesn’t require starting with a complex system. The first steps deliver the highest ROI: improving internal search, adding basic recommendations and segmenting email marketing with behavioural data.

Prioritise by conversion impact and implementation ease. SaaS tools like Algolia, Nosto or Dynamic Yield let you launch personalisation features in weeks, not months.

Key Takeaways

  • AI-powered personalisation is the primary competitive differentiator in ecommerce
  • Recommendation engines combine collaborative and content-based filtering for greater precision
  • Smart search with NLP can double or triple conversion rate
  • Dynamic pricing optimises margins but requires transparency and guardrails
  • Start with search, recommendations and email: these are the highest-ROI quick wins

Want to personalise your ecommerce with AI?

We help you implement recommendations, smart search and personalisation to improve conversion and average order value.