The first method is manually arranging the product recommendations. In this method, an e-commerce executive manually selects recommendations for all the products. Sometimes it is done based on excel– based analysis of sales and conversion data, sometimes it’s based on “make the look complete” sets of products arranged by product development, and sometimes it’s based on gut feel or personal preference. This is painstakingly time-consuming and costly.

Assume that an e-store has 5,000 products and each product may need at least 5 recommendations. It is easy to see how many hours and human resource cost an e-store will have to spend to come up with e-store wide recommendations. Moreover, these recommendations need to change constantly according to factors like changing trends, sessions, and stock availability. As a result, many of the products may not have recommendations, or they are outdated, irrelevant or out of stock

The second method is rule-based product recommendations.Rule-based product recommendations rely on explicitly stated rules, using static data models. E-commerce managers build these rules on what they feel are the right opportunities for recommendations. However, assortments and shopper trends change, resulting in new rules. It is nearly impossible to keep on adding rules to an already large rule base without introducing contradicting rules. Constant human intervention is needed to change, add or modify rules which is very time-consuming.

Most importantly both the above recommendation methods (manual and rule-based) recommend products that are the same regardless who is browsing the site and the method does not consider individual visitors’ behaviour and preferences. As a result, these recommendations will keep recommending products that aren’t relevant, or maybe even ones where the visitor clicks and finds it is out of stock. So, this poor experience means the visitor abandons the site quickly – and will be unlikely to return.

Many e-commerce sites, even large ones, manually enter recommendations using rules and human guesswork. The results of these processes are rarely effective and hardly impact the top line. This has been demonstrated by studies and surveys conducted by Gartner and other research organizations. According to these studies, the worldwide e-commerce ad spends was $58 billion in 2020 with a poor return. 85% of merchants reported that they seldom get any conversions with their current recommendation methods.

AI can help e-commerce companies move away from time-consuming and costly methods of rules-based and manual methods of personalization. Instead, this will allow e-commerce managers to focus on other issues such as industry, competition, logistics, supplier relations and customer relations.

An AI-based recommendation system can automate the whole process while moving away from a completely human-controlled driven process. AI gives the ability to interpret the intent of your customers through their behavioural data in real-time while autonomously predicting which items or content to show across the entire online customer journey.

An AI-based recommendation system can also recognize patterns, learn from data, become more intelligent over time, and generate precise personalized merchandising results, which is impossible to achieve through filters and business rules.

So, what’s holding you back?