With constantly increasing interaction with the digital space, customers today expect improved and enhanced experiences every time they interact with a digital platform. Consumers are looking for personalized experiences when it comes to mobile applications, news sites, social media platforms, and e-commerce websites. They expect the digital space they engage with to remember their interests, likes, dislikes, and, based on the legacy data, make relevant recommendations for content and products to look out for.
Recommendations are the new must-have for all online platforms that wish to retain and grow their customer base. With this personalization in effect, brands can assist customers in finding appropriate content/products based on their previous choice, buying patterns, trends, and attitudes. This site, in turn, benefits from upsells, greater cart purchases, and higher average order rates, all while fostering long-term consumer relationships. This is where a Product Recommendation System comes into play.
What is a Product Recommendation System?
A product recommendation system is a software that makes use of machine learning solutions to generate suggestions for products or content a specific individual would like to purchase or engage with. It involves complex recommendation algorithms that mine consumer data, purchase patterns, products interacted with, and contextual data to create an enhanced web of complex connections between the products and the customers. This allows the platform to offer a personalized experience to every user.
A product recommendation system employs various strategies using machine learning predictive analytics that offer recommendations of multiple complexities and granularities. To choose the best strategy for a particular digital platform, it is imperative to assess the customer data and product data available. Other details, such as customer demographics, geographical location, etc., play an essential role in choosing the recommendation strategy that best fits your platform.
- Global strategies
This strategy is the most basic and can be used for new as well as returning customers. This captures the most frequently bought, trending, or most common product or content.
- Contextual strategies
This particular strategy is slightly more complicated than the previous one. It necessitates a thorough examination of product characteristics such as color, shape, category, and how often it is bought in conjunction with other products. In terms of viewing content, it analyses the genre, short-form, or long-form content before the recommender systems make a suggestion to the consumer.
- Personalized strategies
This is the most advanced of all the strategies that makes use of customer data and product context to device recommendations that are personalized at an individual level. This implies that the brand/platform needs to have access to consumer’s behavioral data collated over a period of time.
Advantages of a Recommendation Engine
- Increase Website/Application Traffic
A product recommendation system uses custom emails, targeted blasts to drive traffic to your website.
- User-Level Personalization
Recommender systems provide relevant product/content recommendations as the user engages with the product. This is done by assessing the customer’s current web use and past browsing history. This data is collected in real-time so that the application can adjust to his changing shopping preferences.
- Customer Satisfaction
When customers are given personalized item/content recommendations, they become more involved with the website. They will delve much further into the product line without having to conduct recurring research. This improves the overall experience with the platform and boosts customer engagement that eventually converts to long-term customer retention and brand loyalty.
- Increased Revenue
It has been noted that when a recommendation engine is used to display customized options, the average order value rises. Usage of a product recommendation system boosts the number of items per order in addition to increasing the average order size. When a customer is presented with options that pique his curiosity, he is more inclined to add them to his shopping cart.
- Generate Reports
A product recommendation system also helps create extensive reports covering multiple facets of the website, sale-purchase, backlog, changing demand for a particular product, etc. This allows the business to evaluate its campaign and make well-informed decisions.
- Discovering New Products
Consumers appreciate and acknowledge suggestions regarding products and content that they would like. When they engage with a platform that constantly relates to their choices, they are automatically attracted to the platform and tend to return.
- Inventory Control
A recommendation engine can be used to market items/products in the inventory that are promotionally priced, on clearance, or overstocked by highlighting the products on the recommendation rack for customers.
Conclusion
Today, recommendation engines are critical to every online business’s growth. However, a good recommendation engine must be able to compare not just the product but also user, inventory, and logistics to make specific recommendations in real-time.