Experts confirm that personalization is a leading e-commerce trend and a must-have for any player in the market. Demanding customers expect a personalized offer greeting them upon entering the store.
Matching products with unique users and the circumstances in which they visit a website is a priority, if not a necessity. Luckily, there is a tool that can handle this task with ease and with no extra work on the seller’s side: a recommendation engine. Learn how the system works and what challenges it can handle in today’s e-commerce landscape.
The recommendation engine plays the role of a seasoned shop clerk
According to research by Gemius, over 35% of respondents point to the ease of online shopping as the leading factor in choosing this method over the brick-and-mortar alternatives.
The expectations of consumers grow and to be able to fulfill them – the business owners need to provide the highest-quality services. Thus, an e-shop needs to be intuitive and user-friendly in allowing users to find the product they are looking for.
Only a personalized offer has the power to convince the consumer to make a purchase. Amazon was the first to understand that an e-store should have as many versions of itself, as many users visit it so that the displayed suggestions would invoke an impulse to buy. On average, you have only 90 seconds to present the consumer with just the relevant product. If you don’t manage to do that, a demanding client will just move on to your competition’s offer. After all, the most important factor for this group of recipients is time and ease of use – whichever store can offer that wins
Currently, the most popular tool that automatizes the personalization process is the recommendation engine. It is a system that automatically filters the catalogue of the store and subsequently predicts which items may be interesting to potential customers. The engine recognizes the individual preferences of a user on its own and in turn: offers products that are right for a unique visitor.
How do Recommendation Engines Work?
- 1. The customer visits the store and begins looking for a product;
- 2. The system analyzes the preferences of customers based on their search history (category, price, parameters, etc.);
- 3. Recommendation frames, entitled: ”Selected for you”, or ”You might also like…”, display personalized product offers.
Is such an engine enough to reach satisfactory sales numbers?
Unfortunately, the implementation of a recommendation engine might not be enough to reach your sales goals at this point. Especially since quite a few applications available currently on the market only mimic the actual functionality of such a system. Full personalization can only be obtained by using cutting-edge technology solutions. What does it need to be capable of? The answer is simple: absolutely everything that modern e-commerce expects it to. The time to merge e-commerce with big data and AI is now.
Recommendation Engine: Intelligent Algorithms
Machine learning, or AI, in other words, is a leading technological trend.
Machine learning looks for practical implementations for AI research that is capable of creating a system that can learn, make predictions, and perfect itself based on collected data.
This is of course reflected in e-commerce. AI simplifies and makes the process of looking for a product much more efficient. Until now, the search results were dependent on keywords and synonyms. Now, apart from the actual input, other factors are taken into consideration: the number of clicks, conversion rate, user reviews, and even the availability of an item – and this is just the tip of the iceberg.
Right now, the system predicts what consumers might be thinking, instead of relying solely on their input. However, that is not all – all the activities of unique users are monitored. The cutting-edge recommendation system uses advanced algorithms that learn from the collected data and react in real-time. Entering a search-phrase, or clicking on a particular area of the website (a product, a category, the promotions tab) is just the beginning of the process…
By employing AI, the algorithms process the behavioral data of unique users and use it to display personalized recommendations. Most importantly perhaps: and all of this happens in real-time.
Real-time personalization is considered to be one of the most effective methods of boosting sales in an e-store. Research conducted by Researchscape indicates that this kind of system increases the engagement of consumers by 73%!
The QuarticON recommendation engine is based on this kind of sophisticated AI. It is used in over 1000 stores across the globe, for example in the US, Spain, or Estonia. Also, many Polish companies have decided to implement the solution in their stores, among them: Black Red White, Leroy Merlin, and Gino Rossi.
Where Does the Recommendation Engine System Get the Information About User Preferences?
For the entire personalization process to reach its full potential a lot of data needs to be analyzed (thus: big data). It’s a very profitable activity that always provides new insights about any business.
Big data has revolutionized e-commerce. Processing broad data sources that offer a lot of variables provides information that can increase sales. This sort of analysis allows us to reach customers with fully personalized offers. Big data also allows the understanding of user needs. We can learn what products they find engaging, what have they been looking for in the past, and what is completely outside their scope of interest – an intelligent algorithm will draw conclusions from kind of data.
A personalized website is not just based on search history. It simultaneously divides the users into groups and creates their behavioral profiles. This means, that aside from the preferences of unique users, the system will also consider the content of the cart of other, similar users and the activity of individual consumers during their last session on the website.
Transform your store into a modern technological enterprise!
Recommendation engines have taken e-commerce by storm a few years ago. However, the industry is currently raising the bar – it now expects us to take personalization to a new level.
The stakes are high: personalizing processes boost customer satisfaction, which, in turn, can be converted into the sales numbers produced by a given business. As we all know: a customer who is well taken care of is more likely to make a purchase, to come back to the store, and produce a higher conversion rate.
By using self-learning AI algorithms, that utilize big data, we can offer a fully personalized offer. The effects are visible from the get-go and envelope all factors: the number of clicks, conversion rates, and customer satisfaction. An e-store is no longer just a business. Modern, cutting-edge solutions can transform it into a technological enterprise.