With customer acquisition costs only rising, website personalization is top of mind on the list of to-dos for many eCommerce retailers.
With small budgets, many online retailers often turn to advertising as a means of increasing their average order value or revenue on their website. However, instead of spending all of your marketing budget on marketing campaigns, it can often be just as important to focus on the customers who are already visiting your site, and leverage a personalized user experience to help drive sales and average order value.
Highly utilized and recognized by eCommerce giants and professionals, sites like Amazon truly showcase the importance of product recommendations in eCommerce. According to a MyBuys study of more than 100 top Internet retailers, recommending products based on a user’s purchasing or browsing behavior resulted in a 915% increase in conversion rates.
When retailers recommend a product on a site, it’s not pure coincidence – these recommendations come from data. Much can be made about what we learn about consumers’ shopping habits online, and retailers capture this data to better serve customers and meet their needs. Personalized product recommendations are based on user behavior. In displaying complimentary products that have been frequently viewed, considered or purchased with the one the customer is currently viewing, makes it easy to cross-sell or up-sell to your consumers.
Dynamically generated on an eCommerce site based on purchase habits, retailers can use product recommendations to both cross-sell and up-sell to their customers, by providing potentially higher margin products related to a shopper’s original search.
An example of cross-selling involves showing accessories for a given product or presenting information based on customer buying behavior such as “People Who Bought This Also Bought.”
Keep in mind, context is key with product recommendations – do not recommend products that a customer is not yet looking for. It is important to view product recommendations as a conversion funnel. When customers perform their initial search, product recommendations should be directly related and similar to the initial search or product visited. As customers move further along the purchase channel (after viewing different items and putting some in their virtual cart) it is then strategic to offer complimentary products. At this point in the conversion channel, do not overwhelm your customer by offering dozens of different product recommendations on your website, but offer them a reason to pair items together. Strategic product recommendations present the right products at the right price point, in a personalized manner and in the right context.
Real-Time Product Recommendations
How do you use real-time data to determine which product recommendations to display to your shoppers?
Real-time product recommendations is a growing technology that reacts and learns the behavior of your customers on an individual and aggregate level. This technology determines, in real-time, what products your customers are searching for, what pages they are visiting and what items they are adding to their virtual shopping carts.
This data comes from simple actions taken on a retailer’s website; everything from what items a shopper has bought in the past, items in the shopping cart and what was viewed and purchased. This data is usually sourced through real-time product recommendation software, which can curate real-time product recommendations based on down-to-the-minute actions on an eCommerce website. These data services use a unique algorithm to instantly determine product recommendations for shoppers, customizing the browser’s user experience. These real time algorithms develop a profile for each individual customer who visits your eCommerce website based on their shopping behavior. This data acts as a unique merchandising tool for the site and does the shopping for the user, matching your customers intent to purchase and developing customer preferences automatically.
As online shoppers continue to expect the most optimized user experience, cater directly to the rapid shopper by giving them a reason to add more to their cart instantly. Product recommendations act as the carefully paired chips next to the dip at your local grocery store, perhaps recommending batteries to someone who just put a camera in their shopping cart will create convenience for the shopper. Judging by large retailer’s success, the recommendation system must work.
How Site Search Can Determine Product Recommendations
As previously mentioned, personalized product recommendations pull data from every aspect of the consumer’s interaction with the website. Utilize and leverage your customer’s most pointed interaction with the website – the search box.
When consumers head to the search bar, they are further along in the shopping process and are looking for something more specific. In tailoring product recommendations using analytics and data from the search bar, there is a greater likelihood that you will increase the average order value. Utilize site search data and analytics to curate product recommendations for your site visitors to ensure the most accurate product matching.
As we move forward, it is important that product recommendations translate to mobile and tablet devices as well. By using data to curate your site’s product recommendations shows that you are in tune to what your customer is looking for when they visit your site. Do not let this be a missed opportunity for you. The product recommendation process can be made remarkably simple using eCommerce product recommendation technology.
In sum, as growing research supports the benefits of product recommendations in eCommerce, they should not be seen as a pushy method to increase sales, but instead, a natural extension and helpful service provided to customers. It will reduce customers browsing effort, forgetfulness and frustration. By personalizing the experience for both new and returning customers, you are able to boost the number of products added to the shopping cart as well as the conversion levels of your customers. In suggesting highly relevant products to your customers at multiple touch points of the shopping process, you will enhance the user shopping experience and make every customer feel like your store was created just for them.
Avoid missing additional sales and use product recommendations as an effective and proven method to increase average order value and revenue. Utilize the behavior of shoppers to make predictions about what customers would like, and in turn, you can even store this data to make predictions about what future customers will like.