Discoverist AI Recommendations

Develop by Shopify

$1299-$599

/ Month

30 Days Free Trial

Real Time AI Curated Product Recommendations and Bundles

Checkout integration

A/b testing

Api integration

Product suggestions

Drag & drop

Marketing emails

Real-time curation

Related bundles

Product pairs

About Discoverist AI Recommendations

Launched Feb 05, 2024

Built by Shopify

Address

2237 Northwest 18th Street, Oklahoma City, OK, 73107, US

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Description

Real Time AI Curated Product Recommendations and Related Product Suggestion Bundles & Combination.

As your customer is browsing your online store, discoverist is dynamically in real-time curating product recommendations that match what your customer is interested in today resulting in lift in cross sell and upsell. We created drag & drop modules to easily add Product Suggestions, Bundles of Related Products and Product Pairs that can be dropped onto Home Pages, Product Pages, Category Pages, Checkout or Marketing Emails.

Dynamic & Real Time, product suggestions are created as the customer browses.
Accurate without data, we use proprietary language models to suggest products.
Products Bought Together, Suggested Related Products, Bundles and Combinations.
Emails, Also Purchased Suggestions, Basket recommendation
API Integration Available, SMS, A/B Testing

Pros

Real-time AI curated product recommendations increase cross-sell and upsell opportunities
Drag & drop modules make it easy to implement product suggestions on various pages and emails
Dynamic product suggestions that adapt as the customer browses improve personalization
Accurate product recommendations without relying heavily on existing customer data through proprietary language models
Offers a variety of recommendation types such as products bought together, related products, bundles, and combinations
Integration options with API, SMS, and support for A/B testing

Cons

Over-reliance on AI may not capture nuanced customer preferences that human curation might
Initial setup and configuration for personalized recommendation performance may require time and resources
Potentially limited user control over customization of recommendation logic/design
Performance may greatly vary depending on the type and amount of data available