How product recommendations can increase revenue for e-commerce

• What are product recommendation engines?

• Why should you care?

• How do product recommendations work?

• How are product recommendations implemented?

• Call to action 

Product recommendation engines are one of the most powerful tools e-commerce companies can use to increase sales and revenue. Using a recommendation engine, e-commerce companies can create hyper-specific outreach campaigns; that can be anything from email outreach, website ranking, and social media ads. Occurring across a variety of mediums, these recommendations serve to get the most relevant products in front of a high volume of high-intent customers. 

Using artificial intelligence (AI), machine learning (ML) algorithms, recommendation engines predict what products will be of most interest to the company's current and future customers. Automating this process with a recommendation engine alleviates any guesswork that comes from creating traditional marketing and sales campaigns; instead, applying data-driven and personalized promotions to customers that boost visibility and sales. 

Applying customized product recommendations ensures that the most relevant products appear first on a customer’s search, maximizing the chances they will find something of interest, and minimizing the chances of churn. With so many options for web-based shopping, the need to stay competitive has never been greater. According to a 

, 61% of users said if they didn’t find what they were looking for right away, they would quickly move on to another site. 

While the specific benefits of using product recommendations range by company, across the board, product recommendation tools can: 

• Increase revenue 

• Increase ROI

• Improve the customer experience 

Using a product recommendation engine is extremely useful to 

increase revenue

, as it gives retailers the opportunity to naturally introduce and cross-sell relevant products. As customers interact more with the platform, the recommendation tool can learn more about unique customer preferences and offer intelligent recommendations. A study done by 

found that product recommendations accounted for 31% of e-commerce site revenue in 2021. 

The success of product recommendations can be seen on Amazon and China’s Alibaba. In 2016, Alibaba ran a marketing campaign that generated personalized shopping pages for their users. After the event, Alibaba saw their conversion rate improve by 

. 

Along with increasing revenue, using a product recommendation tool can 

increase ROI 

(return on investment). With product recommendation engines being fairly inexpensive to integrate, and their implementation being run on renewable and virtually free data, there is little overhead cost involved when running these profitable algorithms.

As shown by the boost in revenue, product recommendations engines can 

improve the customer experience

. By making it easier for customers to find products that are relevant to their preferences, e-commerce brands establish themselves as an intuitive source for customers to return to. 

The heart of any AI/ML tool is good data. With the right data, algorithms can identify high-intent customers and increase visibility of relevant products on a company’s website and outreach campaigns. 

The most effective product recommendations engines feed their algorithms data from a variety of sources in order to make the best predictions. The more data an algorithm receives, the better it will be at predicting user behavior. 

Not all e-commerce companies have unlocked the full potential of their data. However, the majority of e-commerce companies have the abundance of data needed to incorporate a product recommendation algorithm. These data points can include: 

• Shopping carts 

• Search history 

• Purchase history 

• Reviews

• Browser history

Using any or all of these types of data is enough to begin training an effective product recommendation algorithm. Even for companies that fall short on the volume of data needed to train a model, AI/ML can be used to augment customer data. 

Depending on the available data, product recommendations are typically integrated using either content-based or collaborative filtering. 

Collaborative filtering

finds commonalities in crowd data to predict what an individual user would be interested in. For example, a customer who is purchasing a pair of shoes might be shown shirts and pants other customers purchased along with the shoes. This increases the likelihood that a customer will see related items and be incentivized to make another purchase. 

Collaborative filtering would be a great implementation for e-commerce companies with a large amount of user data and transactions. 

Content-based filtering

uses customers' search and purchasing history to offer up unique recommendations. For example, a customer being shown an ad for sweatpants to match a recent purchase of a sweatshirt. 

As there isn’t a ton of data required to implement content-based filtering, this system is perfect for any size company. 

While one algorithm may be better fit for an individual use case or company than another, it’s common for companies to deploy both types of algorithms within their software. 

Any company with data is capable of using a product recommendation engine. Product recommendations have shown to be a powerful sales tool, boosting the average annual revenue of e-commerce sites by 31% in 2021. Using low-code AI/ML tools is the easiest and most accessible way of integrating custom product recommendations into your software. 

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