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How to Use AI-Powered Recommendations to Drive Shopper Engagement

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Recommendations are a cornerstone of AI-powered commerce and can yield a 16% increase in conversions. That's why your site needs Einstein’s recommendation engine.

What if you could grow your revenue by 16%, and deepen your shopper engagement without investing in any new technology? You’d probably do it without hesitation.

That’s exactly the type of results that Einstein’s recommendation engine can provide.

Recommendations are a cornerstone of AI-powered commerce. With personalized recommendations, consumers are more engaged. And higher engagement on the site ultimately leads to higher conversion.

As a specialized success manager, I work with dozens of brands and retailers to make the most out of Einstein. The most common question I get asked is how to optimize product recommendations and leverage Einstein’s AI capabilities to grow revenue. Here are my top three tips.

Think beyond the product pages

Take stock of recommenders on your site. You may have implemented product recommenders on product detail pages and homepage. Plan for adding them on the following pages:

1. The No Search Results page:

We all know searchers have a higher buying intent than browsers. If a shopper ends up on a "No Search Results" page, don’t leave that shopper empty-handed. Show personalized product recommendations that will entice that shopper to continue to browse and look for additional products. After Ethan Allen optimized their recommenders and added a recommender on No Search Results page, their conversion rates increased an additional 40%.

2. Cart page:

Some of the highest interaction rates are recorded for the “cart” page. Many shoppers add multiple products to their cart and use it as a comparison tool. Add product recommendations to the cart page to help shoppers complete the look or encourage shoppers to add products to reach the free shipping threshold.

3. Multiple recommenders on product detail pages (PDPs):

When you plan for peak shopping days, consider adding more than one recommender on product details page. Implement different strategies for each recommender. For example, show similar products in one carousel and show complementary products in another carousel. Enrich your shoppers’ experience by improving the discoverability of products. Case in point: Party City, for example, has successfully implemented multiple recommenders on their product details page.

Go after valuable real estate

We have noticed many retailers bury recommendations at the bottom of product details pages, below product details and product ratings, as well as user-generated content (UGC). But only a small percentage of shoppers scroll below the “Add to Cart” button. Shoppers today are trained to see product details, such as additional product images and product descriptions, below the “Add to Cart” button. Consider moving product recommendations toward the top of the page, (below the “Add to Cart” button) to increase shopper engagement.

Similarly, on the "No Search Results" page, display product recommendations or part of product recommendations carousel above the fold.

Don’t forget about mobile product recommendations

Mobile sales have topped desktop sales on Black Friday for the past two years. Although shoppers increasingly prefer to visit online stores on their mobile devices, the amount of time spent on mobile devices is short. Be prepared to capitalize on shoppers’ micro-moments on mobile devices over the holidays. Retailers that lead with meaningful values and experiences will break through the holiday noise. In our State of the Connected Customer Report, we found that 84% of customers say the experience a company provides is as important as its products and services.

Design Einstein product recommendations to be part of the mobile shopper journey. Einstein recommends products not only based on that shopper’s browsing and shopping behavior but also takes into consideration devices they use. Moreover, the Einstein API is a powerful tool used to embed product recommendations within mobile apps. For example, Hibbett recently launched AI-powered product recommendations on their mobile app. You can hear Hibbett share more about how they use Einstein in this webinar.

For additional tips on jumpstarting growth in commerce, check out this comprehensive guide.

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