Shopping is a necessity for all human beings. However, the retail landscape is changing dramatically, and today it looks much more different than it was only ten years ago. Naturally, technology plays a huge role in this process. The way consumers make their purchasing decisions is not based on personal preferences and looks anymore. Even when buying offline, consumers stand in stores and use their smartphones to compare prices and reviews of the products. Most people also use social media platforms to ask for opinions from friends and family.

Because of the fast changes in the retail sector, industry observers predict that the retail will not exist in the way we know it. According to their forecasts, the industry will change more in the coming five years than it did over the past century. Moreover, the extinction of the brick-and-mortar stores seems to be in the not-so-far future. This statement is indeed quite dramatic, yet realistically, retail stores must be very cautious about their existence.

As an example, let's consider one of the most successful retail stores in the US that recently went bankrupt. Sears, the store that changed the shopping culture of America declared bankruptcy in October of this year. The 132 years old company has been struggling with debts for several years now, and the main guilty is the e-commerce sector. Sears was unable to keep up with the fast rise of the internet shopping. So, it does not matter that you're a brick-and-mortar store. Creating an online platform for your clients to shop is essential for your survival.

Product recommendation engines were created specifically to create the best online shopping experience for your customers. Ensuring that your customers are engaged in the process will not only increase their loyalty, but it will also help generate more revenue. Customers are the most important asset of any business, so their satisfaction rate is closely connected to your sales. Therefore, using recommendation engines will increase the effectiveness of online shopping both for your customers and your business.

Boost Your Sales with a Product Recommendation Engine

Recommendation engines are automated tools that use algorithms to filter data and make smart recommendations based on each user's data. They are basically an automated type of a shop counter guy, who helps you find a particular product. The number of internet users is increasing per day. Thus, e-commerce businesses should feel more responsible than ever to provide their customers with the most relevant recommendations based on their preferences and tastes.

Product recommendation engine is the source that drives sales for e-commerce stores. This is true for both upselling and cross-selling. It ensures that the user engages with the content once more, and perhaps, finds the product he / she looks for. As of now, about 35% of Amazon's revenue comes from the recommendation engines. And 75% of what people watch on Netflix is ​​based on the algorithms of recommendation engines too.

Recommendation systems Netflix

Most of the e-commerce businesses are currently using a product recommendation engine to boost their sales. People love to make their decisions based on recommendations. Online retailers are taking advantage of this. An online store with, on average, 20,000 monthly views can not ignore the effectiveness of product recommendation engines. If you want to boost your CTR by displaying relevant and relatable products, then a product recommendation engine is what you should consider.

How A Recommendation Engine Works

Machine learning search engines are revolutionizing the way product recommendation automation works. Before, the results of search engines where sorted based on where and how frequently the text-match was found in the metadata product. Today, search algorithms are capable of using more detailed data. The recommendation results are based on click, add-to-cart, and purchase behavior decisions.

First thing first, the recommendation system gathers data. Data can be explicit and implicit. Explicit data consists of data inputted by users, like comments and ratings of products. Implicit data includes order history, return history, cart history, pageviews, clicks, and search log. This data is collected for every user who visits any given website. The more data is available to the engine algorithms, the better and more relevant would be its recommendations.

When the data is collected and stored, you can then proceed to its analysis and filtering. There are different analysis methods, considering how fast you want to provide recommendations.

Product recommendations

Product recommendation engines can learn about customer behavior in real-time, customizing all the pages they interact with. Using past behavior data, recommendation engines determine the best product matches of the intended meaning of the query.

Thereby, product recommendations become relevant to the visitors and very personalized. With the help of personalized and relevant recommendations, websites accurately target experience by showing the right products at the right time to each individual. Knowing your customer's preferences well, you'll earn their trust and boost your sales.

Types of Recommendation Systems

There are 4 types of product recommendation engines.

  • Collaborative Filtering

Collaborative filtering is used to create a highly personalized experience. This technique looks at the user's date, such as ratings and purchase data. This information is then matched with other users that have similar data. Thus, through collaborative filtering, you gain new knowledge about the products that the user has not interacted with yet. This is how product recommendations are generated.

Collaborative filtering works through the following steps:

  • Matching current visitor with other customers based on the previous purchase of similar (or the same) products.
  • Grouping together all of the items
  • Disregarding products that the user has already bought.
  • Recommending the rest of the products to the visitors.

Collaborative filtering uses other users' actions to predict the preferences of another user. For example, if a user browses the site looking at shoes, but eventually buys a purse, the software would find the correlation between these two actions and categories. Then, as more people repeat the same actions, a product recommendation will be created.

  • Content-Based Filtering

Content-based filtering concentrates on a specific shopper. The software is built upon a keyword-based description of the products and items the person likes. The algorithms track actions the web pages visited, time spent on different categories, items added to the cart and products clicked on. Customer profiles are created based on such user-specific data. The recommendations are then created by comparing user profiles to the product catalogs and identifying which items to show.

  • Demographic Filtering

Demographic filtering is the easiest and least effective technique. This software does not consider any personal or user-specific data, unlike collaborative and content-based filtering. Instead, users are divided into groups based on demographic attributes, such as age, gender, and location. Recommendations are then tailored according to the demographic profiles.

  • Hybrid Recommendation System

Hybrid product recommendation system is the most effective one out of the four, as it is a combination of any of the above mentioned systems. The software aims to maximize the strengths and minimize the weaknesses of given techniques. By combining techniques, this product recommendation engine gathers the most relevant and user-specific data.

Many big e-commerce businesses use hybrid product recommendation engine. For example, Netflix uses this approach to create suggestions for their visitors. Their recommendations are based on the user's ratings of previous content and comparing the actions of similar users. This is an example of combining content-based and collaborative filtering. The hybrid approach helps to get the most accurate predictions and provide the best recommendations.

The Benefits of Product Recommendation Engines for E-commerce

Recommendations engines can offer valuable benefits to e-commerce benefits. For example, Netflix estimated its own recommendation engine to be $ 1 billion per year. Here are the best benefits of product recommendation engines:

  • Customer Satisfaction - with the help of recommendations engines, the experience with your website becomes much more enjoyable for the visitors. For example, when the user leaves the site and then comes back later, they would love to see that their previous browsing data is available. It would guide their further shopping activities. These product engines, basically, must act like a shopping assistant in a brick-and-mortar store. If used properly, they need to make the whole process more engaging and increase the satisfaction rate of your visitors.
  • Revenue - the algorithms of recommendation engines have been explored and executed. They've been proven to drive higher conversion rates. Non-personalized product recommendations have no benefits. To earn higher revenue, you need to be closer to your customers, know their preferences and provide valuable recommendations that are user-specific.

Ecommerce product recommendations

  • Personalization - The recommendation engine is supposed to model your friend or family, who knows you well enough to give good recommendations. When we feel indecisive about a certain purchase, we always look for an advice from friends and family. That's because they know us well and can give good recommendations. Using a product recommendation engine, you will collect data to personalize your offers and ensure that your customers are happy.
  • Reports and Statistics - providing reports is an integral part of the personalization process. When the engine gives the visitors relevant and timely recommendations, reporting allows them to make decisions. With the help of reports, the engine generates recommendations for slow-moving products and shifts sales.

To conclude, it's worth saying that product recommendation engines are essential for any e-commerce business. If properly set-up and configured, they can boost sales, revenues, CTRs, conversions, and many more important metrics. They can create a better and more enjoyable customer experience with your site. The better the user experience, the better the engagement. And all of this, of course, translates into increased sales, higher customer satisfaction, and higher retention rates.

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About the author:

Founder and business director of SmartHint, he is also regional director Paraná of ​​ABComm, with more than 10 years of experience in electronic commerce for major brands in the most varied segments.