If you can't find the answer to your question in this section, please reach out to us.

We use natural language processing and machine learning to understand item details and user behavior. This combination allows us to create personalized product recommendations in real-time.

Our solution can increase your website's revenue by 5-15% in just 30 days. It's a no-code solution, requiring minimal initial setup and no ongoing effort from your team.

Our solution is fully no-code. Your team won't need to do any engineering work, from the initial setup to ongoing maintenance and customizations.

An A/B test, also known as a split test, is a method used to compare two versions of a webpage, app feature, or product recommendations to determine which one performs better. Essentially, we divide your audience into two groups: Group A sees the original version (the control), and Group B experiences the new version (the variation). By analyzing how each group interacts with the version they see, we can measure changes in behavior, such as increases in purchases, engagement, or any other key metric of interest. This approach allows us to make data-driven decisions and continuously improve the personalization and effectiveness of our recommendations on your site. Jewel ML facilitates this process seamlessly, providing you with clear insights into the impact of our AI-driven recommendations without requiring any separate A/B testing software.

We track customer interactions using anonymous IDs, allowing us to continuously refine our recommendations to ensure they are relevant and personalized.

Yes, we can integrate seamlessly with your current systems through APIs or tags, without requiring any changes to your site code.

It takes about 20 minutes for initial setup, followed by two weeks for us to configure and test our models. Then, we launch the personalized recommendations on your site.

The process includes a quick 20-minute onboarding followed by a two-week period for us to tailor and test our models before going live with recommendations on your site.

Clients typically see a 5-15% revenue increase within the first 30 days. For example, Vans experienced a 13% increase in revenue from users who saw our recommendations.

By assigning each customer an ID and tracking their behavior, we personalize recommendations that improve over time through machine learning.

Our global server network ensures fast loading times, and our scalable machine learning models are optimized for efficiency and performance. We support up to 100 million items per site and more than 10 million concurrent users per store.

Yes, you can start with a free 30-day A/B test to evaluate our solution's impact before making any payment.

Our algorithm analyzes product images, descriptions, and customer purchase behaviors to identify items often bought together, based on visual similarities and data patterns.

We prioritize based on your objectives, such as conversion rates, revenue, or cart size, to determine which products are featured.

Each customer gets an anonymous ID, allowing us to track their behavior and understand their preferences. Our machine learning models continuously improve, ensuring relevance over time. Our recommendations become more personalized as our machine learning model learns from more data.

Our machine learning algorithms continuously learn from customer data and purchase patterns to improve recommendation relevance over time.

We capture customer signals using assigned IDs, allowing our machine learning to adapt and improve recommendations over time.

We analyze product information and images, track user behaviors like clicks, purchases, and items bought together using natural language processing and machine learning. This helps us create real-time, personalized recommendations.

Success is measured by a 5-15% increase in revenue and ROI for users seeing our recommendations within the first 30 days, among other key metrics.

Setting up access involves granting us permission to your Catalog Items API (e.g. Google merchant center), A tag management tool (e.g. Google tag manager), and analytics so we can export the A/B test results to your dashboard, which should take about 15 minutes to complete all-together.

We use local servers around the world to ensure there's minimal impact on loading speeds from our recommendations.

Absolutely, we can provide case studies and examples from other customers to showcase our solution's effectiveness. There are a few on our site as well, under the resources section.

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