How Product Recommendations Can Replicate the In-Store Experience

More consumers are turning to online shopping, which means an optimal customer experience is more important than ever. According to McKinsey and Company, 80% of respondents want personalization from retailers. Putting customer experience at the forefront of your digital strategy is the key to a brand’s success. Optimizing the online shopping journey isn’t just about better, faster technology, though; it’s also about bringing the in-store experience online. COVID-19 has drastically changed the in-store shopping experiences and led to a 30.1% increase in online sales as pre-pandemic. However, human touch and empathy in our digital experiences are crucial in building customer trust and loyalty. One simple way to create that in-store feeling online is through product recommendations. By offering timely, customized product recommendations to your shoppers, you’ll enhance the customer experience and improve customer loyalty. Here are a few of the best product recommendation algorithms to help you replicate the in-store experience online. 

Popular

In stores, retailers often display popular items in the front for everyone to see as they walk in. Similarly, “Popular” product recommendations allow you to get your hottest products in front of your online shoppers. This algorithm delivers the most purchased items within a set timeframe and is a great option for new and returning customers alike. If someone is coming to your site for the first time, you can easily deliver “Popular” product recommendations as they do not require previous user behavior data. Popular items are also extremely valuable to display during the holidays since many people are buying gifts and might not be familiar with your brand. Just like a store associate would highlight top-selling items to customers, Popular product recommendations can create that same in-store feeling digitally.

People Also Like

Shoppers at brick-and-mortar stores are often drawn to items they see other customers buying, and “People Also Like” product recommendations can help you deliver a similar experience online. This algorithm is based on a product’s “signature,” which is made up of the most common actions (i.e. searches or clicks) that lead to purchasing that item. Products with the most similar signatures are considered to be the most similar items. “People Also Like” product recommendations allow your customers to see what like-minded people would purchase alongside a certain item, all without leaving the comfort of their home.

Personalized

Many store associates work to create a personalized shopping experience by hand-picking items for customers or helping them build and style outfits. Online shoppers can still get this tailored experience through “Personalized” product recommendations. “Personalized” product recommendations are based on an individual user’s previous and in-session behavior on your site. The user behavior is then compared to product signatures, and the signatures that best align with the user’s behavior will be recommended. According to Invesp, 75% of consumers are more likely to buy an item if it is delivered in a “Personalized” product recommendation. Not only do “Personalized” product recommendations have a huge impact on your bottom line, but they are an excellent way to bring the in-store experience to your online shoppers.

Summary

If you’re just beginning your website optimization, testing product recommendations is a great way to increase conversions on your e-commerce website. You will improve the customer experience as well as increase revenue. Looking for more resources?

Check out: How Moonpig Optimized Product Recommendations

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