Resources
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Playbooks
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The AI Shopping Assistant Playbook to Drive +60% More Conversions
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Drive more sales by recommending the perfect product, every time
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Drive more sales by recommending the perfect product, every time

Every customer wants to be treated like a VIP. Mass promotions sent to your entire email list no longer cut it. 

Today’s shoppers want one-to-one marketing that directly addresses their unique wants and needs. According to McKinsey, 71% of shoppers expect personalization, and 76% get frustrated when it doesn’t happen. But how do you deliver personalization at scale?

Shopping Assistant helps ecommerce brands deliver a VIP shopping experience with product education and recommendations. It provides personalized product suggestions based on real-time shopper data, to match individual needs. 

Shopping Assistant is also context-aware, meaning it takes behavioral signals into account, including:

  • Products viewed—Which product pages have been visited the most?
  • Pages viewed—Has the shopper explored FAQs, reviews, or category pages?
  • Purchase history—If authenticated, has the shopper bought from this brand before?
  • Cart data—What’s currently in their cart? 
  • Cart abandonment signals—Has the shopper abandoned their cart before? 
Customer profile for Jane showing high purchase intent and interest in hydrating cream, alongside a product suggestion for Glow Ritual cream priced at $52 with an 'Add to Cart' button.
Shopping Assistant is context-aware and considers shopper behavior when recommending products. 

📧 Want a full video walkthrough of Shopping Assistant from one of our experts? Sign up for email access. 

Shopping Assistant uses the specific behavioral signals to: 

  • Personalize interactions and product recommendations based on what the user is browsing, adding to their cart, or has previously purchased
  • Ask appropriate follow-up questions to evolve conversations dynamically before making a product recommendation
  • Remember conversations within the same browsing session and answer questions about a product a customer viewed (even without them explicitly mentioning the product)

For example, let’s look at how this works for Arc’teryx. A shopper is browsing women’s jackets and asks in the chat, “I’m usually a size small or medium, which size should I go with?”

With no mention of the product name, Shopping Assistant still knows the customer is referring to the Beta Lightweight Jacket based on their browsing history. 

Arc'teryx's Shopping Assistant recommends sizing suggestions based on the user's browsing behavior.
Arc’teryx Australia uses Shopping Assistant to provide contextual, personalized product recommendations.  

Based on this context, the Shopping Assistant advises the customer on the best size to choose and provides a direct link to add it to their cart from the chat. 

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Frequently Asked Questions