Product recommendation by AI: A life-savior solution for all modern retails

35 percent of Amazon’s revenue is driven by its product recommendation engines.

Ikea, one of the most famous home furnishing retailers, grew 2 percent of its Global Average Order Value for eCommerce last year with product recommendation AI.

These statistics are significantly intriguing, given that the majority of our global sales now come from online shopping platforms. Burdens have fallen upon all traditional retailers that must digitalize their business by building their online shopping applications or joining online shopping platforms, along with designing online marketing strategies and assembling an online team to take care of everything from the back office to the delivery.

The main question is how do we sustain our sales and revenue in a time when all purchases are made through a smartphone application, and shop assistants can no longer interact with customers face to face?. And how to turn this crisis into an opportunity to boost more and more sales?

As shown by the statistics above, it is made clear that product recommendation can be a super-useful assistant for online shopping, by suggesting a personalized recommendation of products and services to customers as if it could read their minds.

Sertis would like to introduce you to our product recommendation solutions that will help you exponentially boost your sales and drive your business forward.

How does a product recommendation solution work?

On an online shopping platform, product recommendation engines use AI to analyze customers’ interests based on their purchase history or recently viewed products to suggest options of products and services that customers may like. Some engines are even able to provide real-time recommendations as if they were shop assistants walking us through a range of products and providing a personalized experience.

Product recommendation engines work by using these 2 types of AI models:

  • Collaborative filtering is a predictive model that analyzes and provides recommendations based on data from a wide range of users with similar tastes. it can be divided into two subtypes:
  1. The user-based approach works by analyzing a group of customers with similar interests to a target customer and recommending the product those people bought.
  2. The item-based approach analyzes the products customers bought, e.g. the model found that customers who bought A tended to buy B as well, so the model would recommend B to customers who show interest in A.
  • Content-based filtering predicts customers’ interest based on their profiles and behaviors, e.g. the target customer shows interest in a specific product group, so the model recommends more products from that group.

Product recommendation’s benefits

Recommended movies and tv shows on Netflix, songs you might like on Spotify, and people you may know on social media, these magic are made possible by recommendation engines that analyze users’ interests and suggest options of content they might have been looking for.

It is acknowledged that this engine is one of the key elements in these big companies’ success, especially during the pandemic when everything is failing. Effective product recommendations are proven to significantly boost your sales.

Let’s see what benefits the product recommendation engines can give to businesses

  1. Boosting sales by encouraging purchase decisions and facilitating in-demand product finding.
  2. Enhancing better experience by AI functioning as a personalized assistant.
  3. Increase sales in products with poor sales by recommending them to customers through a recommendation system.
  4. More understanding of your customers to design a more profitable and personalized marketing campaign.

Product recommendation from Sertis

Our product recommendation utilizes a deep learning model that simulates human learning and thinking to make informed predictions and analytics and provide insightful results.

Here is how our product recommendation solution works:

  1. Collecting — we collect data on customers’ purchase history, behaviors, and decision-making patterns.
  2. Analyzing — our model analyzes those data to predict the interest score of specific customers toward certain product groups. Different models are built for different product groups or new customers with no data.
  3. Scoring — the model will give an interest score to every customer and product combination to find top potential customers for product-specific promotions.
  4. Recommending — the recommending products that are presented to customers varied from the scores.
  5. Adapting — the insight can be combined with churn prediction for a personalized retention campaign.

Why should you consider Sertis AI solutions?

Accuracy — Sertis strives to develop the best AI solution by exploring various approaches and tailoring the solution to each client’s unique business challenges in order to find the best pain reliever for clients in every industry.

Self-learning — AI models automatically learn from new data and adapt to new situations, without any bias that is generic for human beings. In other words, they can learn just like us but, unlike us, they have no bias, which makes them our best assistants.

Granular-level analysis — Our AI model analyzes and forecasts at the granular level being as detailed as the data is available. We will get in-depth results covering every single factor counted.

Time and cost-efficiency — AI and automation are prominent for saving time and resources. They help free up personnel from these manual routine tasks and spare more of their time for value-added tasks.

Join forces with us to design and customize the right solutions for your business.

Written by: Sertis

Originally published at



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