13 Aug 2021
Good recommendations can make a huge improvement to webshop performance. They increase conversion, average order value, and customer lifetime value. They also make sure that your advertising dollars spent on getting visitors to your site are converted into revenue. Using them on your site significantly increases ROAS. After reading this blog article you will know how to create personalized recommendations that turn shoppers into buyers.
There are different approaches to e-commerce recommendation systems. The older and outdated systems use rule-based algorithms to determine which product to recommend. Another outdated approach is to hardwire product recommendations manually.
Rule-based product recommendations rely on explicitly defined static rules. E-commerce managers build these rules based on what they feel is right. These rules cannot consider individual visitor’s preferences. As a result, rule-based recommendations are the same irrespective of whoever is browsing the site. For example, major e-commerce platforms like Magento and Shopify offer rule-based recommendations out of the box or through plugins. These rule-based recommendations lack conversion power for several reasons:
Another major drawback of rule-based systems is the effort needed to define and maintain the rules. It is very difficult to add further rules to an already large rule base without contradicting previous rules which are still active. The time spent on formulating and maintaining these rules can better be spent addressing real customer needs.
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AI-based product recommendation systems use Artificial Intelligence and Machine Learning (AI/ML) algorithms to determine effective personalized e-commerce product recommendations. The systems use AI/ML to combine and interpret product as well as visitors’ data – such as browsing patterns, purchase history, and demographical data. These systems build a personalized product and visitor profiles based on all this data. The algorithms can therefore display cross-sell and up-sell opportunities with astounding speed and precision. They can make sure that the product recommendations match the visitor’s intent.
For a new visitor, buying a specific product, the system maximizes the probability of an upsell, by recommending additional products based on data from previous visitors that have bought that same product, and subsequent additional products.
For a returning visitor, sophisticated algorithms can be applied, recommending products, based on the visitor’s past purchases, views, and clicks, as well as their browsing patterns in the current session.
1. Maximize conversion rates and average order value
AI-based personalized product recommendation systems provide tailored product recommendations and add opportunities to upgrade the purchase while matching customer intent. When site visitors see these products, they are far more likely to buy them. In addition to increasing conversion rates, cross-selling and up-selling are effective ways to increase average order value.
2. Enhance the shopping experience and build customer loyalty
AI-based product recommendation system will delight your visitors with highly relevant and personalized product recommendations. With personalized product recommendations, customers see relevant products which make them say, “that’s exactly what I’m looking for,” leading to customer satisfaction, a fabulous shopping experience, and improved customer loyalty. A flawed system will keep recommending products that aren’t relevant, or maybe even ones where the visitor clicks and finds it is out of stock. This poor experience will drive your visitor away from your site, to never return.
3. Manage to merchandise better, cut costs and increase efficiencies
Many e-commerce sites, even large ones, manually manage product recommendations using filters and best guesses. These recommendations deliver poor results because multiple filters and human guesswork cannot predict visitors’ intent. Building rules requires a lot of labour, leading to higher costs. Moreover, this manual process must be frequently repeated, keeping in mind the changing product catalogue and visitors’ tastes. The process also doesn’t appreciate factors such as recent poor reviews, or low or zero stock. An AI-based e-commerce product recommendation system will automate this whole process, thereby saving costs and increasing efficiencies.
Sometimes e-commerce managers want to customize product recommendations to deliver a specific desired outcome. For example, a supplier may have agreed to give additional commercial support to promote its products and give them more prominence. This is just like paying for display space in a power aisle in supermarkets. Similarly, product promotion or a new product launch may mean you want to put this near the top of recommended products in certain product categories.
In these situations, you’d want to apply rules to push certain products alongside the product assortment that has come out of the AI-based product recommendation system. AI-based product recommendation systems have differing abilities to incorporate business rules to accommodate these special needs.
So, to conclude, the best way to turn shoppers into buyers on your e-commerce site is to seduce the shoppers that come to your shop with AI-based personalized product recommendations. For the exceptional cases, make sure that the system you chose also allows you to deploy specific rules, on top of the AI algorithms, so you get the best of both worlds. This gives you the maximum conversion power and makes sure that the shoppers who browse your site start buying the products you offer, buy more products within one session, and keep coming back to the site because of the personalized experience they got.
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