We retrieve real time records of user transaction history, attributes of payment methods and user-payment method interactions from our databases. This massive collection of data allows us to suggest highly accurate recommendations for our customers. Our state-of-the-art Hybrid Recommender System, which can combine collaborative and content-based methods and take the strengths of both systems, can deliver personalised recommendations of payment methods that suit each customer’s tastes and preferences. Not only can this increase recommendation accuracy but can, in turn, increase user engagement and reduce churn.
Some examples of recommender systems that the average consumer will be familiar with are the likes of product recommendations on online shopping sites after particular google searches are made, suggestions for movies and TV shows on streaming services based on what you have been watching up to that point and recommended videos on social media and music streaming sites. AI payment recommendation systems and the requisite payment recommendation engine are part of a family of consumer tactics that are so commonplace now that you are probably experiencing them all the time and don’t even understand that it’s happening. We want to make our experience for users of your website as smooth as that of Netflix or YouTube, so that they continue to engage with your payment method and streamline their payments when they return to your site.
A user dataset will be utilised to analyse the patterns of each of your website users and their payment preferences. It will contain different important metrics and ultimately will say what the recommendation may be for the user after the analysis of that data. Each row of the dataset will represent a specific behavior of a user towards your product, such as how long they engage, if they tend to buy products on every visit or if they are infrequent shoppers or paid users and more regular browsers. If the behavior is to keep sticking around and engaging with the product you’re selling, the value associated with it will likely correspond to the amount of time with which the player has been engaging. If the behavior is to stop engaging after a certain point, the computer will analyse what that point is and will attempt to keep this user coming back to your site or product.
Users’ behavior data is vital documented information that tells you what you need to know about the engagement of the user on the website, which can be collected through a variety of methods and studies of patterns. It is used in conjunction with User demographic information. This brand of data is related to the user’s personal information which they have volunteered to the site and signed terms and conditions forms to allow you to use for this and other purposes. They will examine these datasets along with product attribute data, which is information related to the product itself such as the type of thing that the user is engaging with from your website.
There will also be a certain degree of user similarity employed, where our technology will cross reference the behaviors of certain users to get a general idea of what to recommend to slightly newer users whose data we don’t have the full information on as of yet. This will engage people early, and keep players on the site and getting involved with your products, which is the most important.
People are also likely to spread the word about your website if they find that the experience was smooth and easy, which can be helped by the correct implementation of your algorithms. AIDock will often create an environment where users want to stick around on the website for hours, which is great for your bottom line. We have a number of satisfied clients who will swear by the methods that we implement, such as the payment recommendation engine, which is constantly developing as technology and payment methods and patterns begin to change.