GetResponse Introduces AI Product Recommendations to Help Businesses Boost Their Sales

AI-driven Product Recommendations provide a path to increase ROI through personalizing website visitors’ shopping experience and email campaigns.  

GetResponse, email marketing, and marketing automation software, announces the launch of AI Product Recommendations. The solution employs artificial intelligence to revolutionize the customer journey and experience.

The feature allows shop owners to maximize their conversions by showing products to visitors with the highest probability to convert, based on their past behavior and transactional history, to increase upsells and cross-sells.

Shop owners can also improve their store UX because the recommendations block serves as a navigation tool, increasing the time customers spend on the website, thus also growing conversions. Businesses can analyze store traffic and users’ behavior with AI to make better business decisions.

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“Shop owners can significantly increase their ROI and AOV this holiday season.”

“Our goal at GetResponse has always been to deliver cutting-edge tools focused on driving conversions without the extra hassle of increased budgets or lengthy implementation cycles,” said Mac Ossowski, Board Member and Director of GetResponse MAX. “After months of work, businesses can now benefit by personalizing the omnichannel experience for their customers, both on their websites and mailing.”

The AI Product Recommendations launch comes just in time for businesses to benefit from the lucrative holiday season.

“The important point is that it verifies each product against the stock availability, so there are no missed clicks for the customers. Shop owners can significantly increase their ROI and AOV,” Ossowski said.

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AI Product Recommendations powers ecommerce businesses at scale, providing the following types of product recommendations:

• Recommended for you: a self-learning model that displays recommendations with the highest probability of conversion for a specific customer.
• Most viewed in category: the algorithm recommends products with the most views within a specific category.
• Most similar in category: the algorithm displays products based on their similarities in names and descriptions.
• Recently visited in-store: visitors see product recommendations based on their previous visits to the store.
• Others also viewed/purchased in-store: AI suggests products other customers have checked or purchased in-store.
• Rule-driven recommendations: the store owner defines the products they want to promote to their customers.