8 Examples of Advanced Product Recommendation Techniques That Drive Growth

Use artificial intelligence (AI) and other technology to connect shoppers to products they’ll love.
June 6, 2021. 5 MIN READ
Product recommendations help shoppers find exactly what they are looking for, whether that’s groceries for the week or gear for an upcoming hiking trip. Retailers use product recommendation engines to find items customers will love based on what they type into search fields, click on, favorite, and add to their wish lists or carts. This helps to drive revenue and boost conversion.

Discover what product recommendation engines are and how they work

But offering product recommendations is just the first step. Brands can stand out from their competitors by deploying advanced techniques that take product recommendations to the next level.
The most sophisticated product recommendation engines generate suggestions through machine learning and artificial intelligence (AI). Customer behavior data gleaned from browser history, search history, viewed items, preferences, and more flow through complex algorithms. These algorithms surface individualized product offerings to each shopper as they browse in-app, on-site, or in future marketing communications.
Here are eight savvy ways to make the most of product recommendation engines and show shoppers exactly what they need.

Product recommendation example #1: Use machine learning technology

Machine learning allows predictive software to use the data it gathers to provide AI-driven product recommendations. As more information is incorporated into the algorithm, the accuracy of the recommendations improves and evolves.
Most product recommendation engines run on predictive analytics, which make sense of disparate customer data inputs to predict future behaviors. Once this data is analyzed and “trains” the software’s algorithm, it can begin matching customers to the products they’re most likely to buy.

Product recommendation example #2: Automate your product recommendations system

Automation is a key feature of product recommendation engines. It allows brands to easily scale their personalized recommendation efforts. Effective product recommendation engines automate nearly the entire process by collecting data, analyzing it, and delivering recommendations directly to customers. The less often a marketer or merchandiser has to manually step in, the more businesses save time, effort, and money.

Product recommendation example #3: Integrate your product recommendation engine with your CRM

Effective suggested selling depends heavily on an accurate analysis of customer data. That’s why it’s important for your product recommendation engine to integrate with your existing customer relationship management (CRM) platform.

Your product recommendations should be based on all available customer data, including goals and important life events. The ability to automate tasks, including tracking, analysis, and reporting, ensures that customers are always guided along the path to brand advocacy.

Product recommendation example #4: Highlight your top-selling products

Multiple studies have found that roughly 80% of a company’s profits come from 20% of the products sold. Review past data to identify your best-selling products and continuously showcase these products to potential buyers. That way, you are always surfacing the items that are most likely to result in a sale.

Recommend your top-selling products as people search your website, add items to their carts, or interact with your marketing content.

Product recommendation example #5: Showcase discounts and special offers

Sales and discounts are an easy way to draw people in and spur an influx of sales, both on discounted items and regularly priced items. Use product recommendation software to hook users who are searching for a sweet deal. A promotion or discount might sway those shoppers who are on the fence and direct them to items they might not have bought otherwise.

Product recommendation example #6: Build new customer experiences with a headless architecture

Headless commerce separates the front end and back end of an ecommerce application to give brands more freedom in their architecture. With an AI-powered headless commerce architecture, you’re able to bring commerce to – and collect information from – wherever customers engage. A system with flexible application programming interfaces (APIs) makes it possible to embed AI in mobile apps, clienteling tools, and more. With access to more customer data, your product recommendation engines provide more powerful insights. For example, you can use customers’ past purchasing behavior to offer tailored suggestions across digital platforms. Ultimately, you offer a cohesive and personalized shopping experience across all possible touchpoints.
When your brand offers a popular item, you’re likely to see a surge of web traffic from customers searching for it. Let’s say that you sell a newly released version of a mobile device. Shoppers also need phone accessories like cases, screen protectors, and back ring holders. You can capitalize on these trends by highlighting the additional products shoppers would likely be interested in. “Complete the set” functionality lets you organize product groupings, such as a head-to-toe outfit, or everything one needs to go camping. Then, when a customer seeks out one of the products, you can show them everything in the set in case they’d like those additional items, too.

Predictive sort technology uses customer data to deliver tailored results to shoppers searching for products on your website. With the help of AI, you can effectively offer personalized product suggestions for the search drop down, search pages, and category pages.

This technology interweaves personalized product suggestions throughout the shopper journey. The result: Shoppers find exactly what they’re looking for, fast. Customers spend less time searching for the products they want and conversion increases.


Discover how to deliver personalized experiences and boost revenue

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  • Eliminate guesswork
  • Boost productivity
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More resources

What Is a Retail Product Recommendation Engine – And Why Do You Need One?
How to Uplevel Smart Merchandising
Power Personalized Product Results With Just a Few Clicks

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