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New ratings & reviews integration: Reviews.io customers can now power their social proof personalization via Fresh Relevance

September 13th 2022

Lucy Russell

By Lucy Russell

Head of Product Management

A new ratings and reviews integration has been added to Fresh Relevance’s rapidly expanding portfolio – Reviews.io. The Fresh Relevance system can now be easily linked up to Reviews.io, so joint customers can use our data driven behavioral tracking platform to enhance the use of the ratings and reviews platform and improve social proof used.

The Fresh Relevance personalization platform captures behavioral and transactional data from website visitors, identifies visitors with sophisticated data capture tools and provides a comprehensive suite of cross-channel personalization tactics to provide customers with a tailored experience that sets our clients apart from their competitors.

Fresh Relevance’s features integrate seamlessly with the Reviews.io capabilities to boost conversions on a cross-channel basis. Here’s how our joint customers can benefit from the integration.

Cross-channel social proof

Reviews.io allow you to display ratings and reviews from your customers in bulk and triggered emails, and on your website.

For example, you can use them in service and product review carousels on homepages, product pages and checkouts powered by Fresh Relevance’s data, including shoppers’ browsing history, location, customer segment and more.

You can also use dynamic product star ratings on personalized product recommendations in email newsletters to give shoppers an added incentive to click through.

Additionally, product star ratings and review snippets can be used in cart and browse abandonment emails to give customers the confidence to complete their purchase.

User-generated content

With Fresh Relevance, you can take your social proof one step further by including user-generated content. This means that you can use customers’ social media posts to aid your marketing.

Try using them in your triggered emails, such as cart and browse abandonment, price drop and back in stock emails to highlight a particular product and bring shoppers back to your site.

Smart filtering

Filtering by rating and number of reviews

Our joint technology allows you to recommend products based on the average product rating and the number of reviews to ensure you showcase your top rated items. You can create recommendation blocks such as “top rated products” by applying filters to existing data sources and you can control the products that are shown by picking a minimum rating to be given (for example, no ratings below 3) as well as the minimum number of reviews (for example, only show products with more than five reviews). In this case, at least five of your customers would have to rate your product with 3 stars or more for the rating to appear in your web and email content.

Filtering by keywords

You can also filter your reviews by keywords, such as ‘fast shipping’, to ensure you show content that’s right for your campaign. Additionally, these keyword filters can be combined with your existing filters, such as ‘service reviews’ and ‘4+ stars’.

Filter by photo reviews

You can now filter your reviews by the number of images, as well as filtering to only show reviews that have photos attached.

Lead with the most popular product

With the new integration, it’s easy to define the order of products within an email. For example, in a cart or browse abandonment email you could choose a “lead” product with a high rating to be highlighted to your shopper, increasing the chances of them returning to their cart to complete the purchase.

Book a demo with us today to find out how Fresh Relevance and Reviews.io can help you achieve your business goals.

Book a demo to learn more about Fresh Relevance and Reviews.io

Lucy Russell

By Lucy Russell

Head of Product Management

As Head of Product Management at Fresh Relevance, Lucy works closely with our customers and the wider team to shape the product roadmap and oversees the roll-out of all new features.