Product recommendations are part of an ecommerce personalization strategy wherein products are dynamically populated to a user on a webpage, app, or email based on data such as customer attributes, browsing behavior, or situational context—providing a personalized shopping experience.
Product recommendations are especially valuable to organizations that have a very large, diverse product catalog. The catalog may be diverse due to a wide array of products (e.g. a department store), or it may have a small number of product categories but lots of variety within feature sets of product offerings (e.g. an eyeglass retailer).
Product Recommendations Benefits
Product Recommendations are essential to meeting the aggressive performance goals of online retailers and are a proven method for driving profitability.
With product recommendations, you can drive conversions by suggesting other products customers have bought with the item the customer is browsing, products that are in line with the customer’s search queries, or products that pair well with an item already in a customer’s cart.
Product recommendations are especially useful for businesses that have large product catalogs, since this provides them with more options to connect with and guide customers.
How Do Product Recommendations Work?
A product recommendations solution is considered a must-have for most online retailers and it’s easy to get started with basic implementation. While retailers use them extensively, product recommendations can be challenging to deploy with the desired flexibility, due to delayed data processing, limitations on complex data sets, and some solutions’ inability to push recommendations across all customer touch points.
Machine learning helps to remove those obstacles. With the ability to grab real-time behavioral context from all channels and use it to inform recommendations in the moment, marketers can offer more timely and relevant recommendations to the customer.
Using Machine Learning to Drive Product Recommendations
Machine learning-informed recommendations solutions use this real-time multi-channel data to recognize and act on patterns in customer behavior—for example, using data from the thousands of shoppers who have converted on your site to make inferences about the intent of new visitors. For returning visitors, such solutions can display products based on their customer profiles—including preferred categories, favorite brands, and even specific colors and sizes of products—which the product recommendations engine can learn over time.
Machine learning driven product recommendations capture data from multiple sources to learn the specific user’s shopping patterns, likes & dislikes, what turns them from a “window shopper” into a purchaser, and combines that information with other contextual data to serve a recommendation to that user in real-time. Additionally, product recommendations solutions with machine learning capabilities measure the reaction of the consumer and use it to constantly improve the accuracy of future recommendations for that customer and others.
Monetate Product Recommendations
Kibo’s Monetate Product Recommendations gives merchandisers & digital marketers the power to show contextually relevant product recommendations without burdening IT resources.
Increase Average Order Value
Whether you want to sell a unified outfit, or build the perfect sound system, “complete the look” recommendations are a proven way to increase average order value.
Drive Higher Profits
Not only does shopper preference play a role in product recommendation ROI, but so too does selecting those offers with the highest profit margin to display. Monetate allows for the ultimate combination of individual insights and filtering based on unique business metrics.
Secure Customer Loyalty
Surfacing product recommendations is a tricky business. Shoppers experience tremendous frustration when they fall in love with an item, only to find out it doesn’t come in their size, is out of stock or doesn’t ship to their location. Monetate makes it easy to avoid annoying shoppers by taking into consideration past shopping history and geo-targeting parameters. For example, the recommendations served can be filtered to ensure only items available in their size will be shown.