Generating the most relevant and personalized product recommendations by leveraging personalization algorithms without any machine learning and AI expertise. Recommendations AI draws on Google’s experience delivering personalized content across flagship properties such as Google Search, Google Shopping and YouTube to deliver personalized recommendations that suit each customer’s tastes and preferences across all your touchpoints.
Each machine learning model is created to optimize for a specific objective such as click-through rate (CTR), revenue per order, and conversion rate (CVR). Recommendations models are retrained daily, ensuring that every algorithm considers user behavior on your site. Merchandising teams are able to refine out-of-the-box recommendations further to meet the specific outcomes of the business.
Retailers and wholesalers can implement Google-quality recommendations that are based on Google’s semantic and contextual understanding of user intent on any page of the user journey, from the home page to the checkout page and across multiple channels such as your website, mobile, email, contact center, and more.
Cross-sell products throughout the user journey - search or browse, product pages, add to cart, and checkout - to increase engagement with recommendations, basket size, and average order value. Further personalize the customer experience with upsell strategies that influence buyers to purchase more expensive items, upgrades or add-ons to increase the order value.
Ease user frustration by offering recommended product alternatives on no results pages and overcome cold-start challenges where no customer or product data exists, by leveraging Google Cloud’s years of experience which draws upon recommendations and search expertise used by billions of people across the world.
Select specific recommendation models that works best with your business strategy
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Incorporate search-based recommendations to improve product findability
GroupBy’s sophisticated and easy-to-use merchandising platform, Command Center, provides the following functionality:
Ranking models optimized for revenue and business impact
Automatically boost products to a target audience
Reduce manual curation and configuration
Configure fallback logic to avoid zero recommendations
All retrieved recommendations are in real-time optimizing product findability and cross-sell/upsell opportunities
Product recommendations are updated in real-time and dynamically adapt to real-time user behavior and changes in variables like assortment, pricing, and special offers
Each retrieved recommendation considers previous user activity such as product views, add to cart events, and orders
Analyze search and browsing behavior in real-time
Personalize the entire buyer journey at every touchpoint and on every channel
Recommendations on mobile apps
Personalized email recommendations
Store kiosks or call center applications