How to Increase Conversion and Average Order Value by AI?
ui42 solutions are based on cutting-edge AI technology. UI42 uses the latest AI algorithms and techniques to develop recommender systems. Our solutions are highly customizable. UI42's recommender systems can be customized to meet the specific needs of each e-commerce business. This ensures that businesses can get the most out of their investment. UI42's recommender systems are designed to be easy to use and integrate with existing e-commerce platforms. This makes it easy for businesses to get started with AI-powered recommendations.
UI42's recommender systems have a proven track record of success. We have helped e-commerce businesses increase their conversion rates by up to 15% and their average order value by up to 6%.
Abstract:
- How AI recommenders work in general
- What data can be collected and used to train AI models
- How AI recommenders work on our customers' websites.
- Offer customers what they might want, even before they want it
- How to increase average order value
- The cost of AI recommenders
1. How AI recommenders work in general:
AI recommenders are algorithms that use machine learning to recommend products or services to customers. These algorithms learn from data about customers, such as their purchase history, interests, and behavior on the web.
There are several different types of AI recommenders. The most common types used in e-commerce are:
Image distance recommender: This type of recommender uses the similarity of images to identify products that might be interesting to a customer.
Graph neural network (GNN) recommender: This type of recommender uses the relationships between products to identify products that are compatible with each other.
Soft max recommender: In the context of recommender systems, soft max can be used to calculate the probability that a customer will purchase a certain product. For example, if we have inputs that represent products that a customer has already added to their cart, soft max can be used to calculate the probability that the customer will purchase another product.
2. What data can be collected and used to train AI models:
Customer purchase history: This history includes products that customers have purchased in the past, as well as the prices they paid for them. Purchase history is one of the most important sources of data for recommender systems, as it provides information about what customers have preferred in the past.
Customer behavior on the web: This behavior includes various data about how customers interact with a web page. This data can be useful for understanding customer interests and recommending relevant products.
- Products viewed by customers
- Products clicked by customers
- Device from which the customer came
- Time spent on pages data
- first_visit
- begin purchase
- add to cart
- remove from cart
- purchase value
Customer demographic data: This data includes age, gender, location, and interests of customers. Demographic data can be useful for recommending products that are relevant to a certain demographic group.
3. How AI recommenders work on our customers' websites:
At ui42, we use a combination of image distance and GNN recommenders. The image distance recommender is active until enough data is collected from the page about visitors. When there are at least 5,000 people on a specific page, the GNN model is directly launched. In this way, we ensure that the recommender is started immediately, and at the same time, we have the option to automatically add the GNN model when enough data is collected on the page.
The results of our tests show that AI recommenders help us increase conversion by an average of 4.8%.
4. Offer customers what they might want, even before they want it:
Categories are an important part of every e-shop from an SEO perspective and also from a UX perspective. They allow customers to quickly and easily find the products they are looking for.
In the past, products on category pages were typically sorted by lowest price or popularity. However, this may not always be the most relevant for customers.
Personalized recommendations: With the use of recommender systems, products on categories can be sorted in a personalized way. This means that products that are relevant to a specific customer are recommended.
Website Speed: The biggest problem with implementing recommender systems on categories is speed. If an e-shop has a lot of traffic, it can be difficult to display individual products in a given category to each visitor. For this reason, we at UI42 have developed an API that helps to address this issue. The API allows websites to quickly and efficiently generate personalized recommendations on category pages.
5. How to increase average order value:
In the cart, we found that image distance and GNN models are not effective enough. Our customers did not want to see products in the cart that they already intended to buy.
Since the classic AI model could not generate sufficiently accurate recommendations, we decided to use a different model, soft max. Soft max works with probability, so it is more suitable for this purpose. We used data from all orders from a given e-shop to train it.
The soft max model is an effective way to increase the average order value in the cart. If you want to increase the turnover of your e-shop, we recommend that you consider implementing the soft max model into your cart.
The soft max model helped to increase the content of the cart by 5%
6. The cost of AI recommenders
The cost of AI recommenders is dependent on the number of products they recommend. The average e-shop pays for recommendations 1.6% of the turnover that they have demonstrably mediated. This price can vary depending on various factors, such as:
The scope of recommendations: Recommender systems can recommend products at different levels, such as on category pages, product detail pages, or in the cart. The price of recommendations increases with the increasing scope of recommendations.
The type of recommendations: There are different types of recommendations, such as recommendations based on customer interests, product similarity, or current events. The price of recommendations can vary depending on the type of recommendations.
The quality of recommendations: The quality of recommendations can vary depending on the model that is used to generate them. Recommender systems that generate more accurate recommendations are typically more expensive.
The cost of running recommender systems: These costs include the costs of hosting, maintenance, and updating recommender systems.
Customization of recommender systems for a specific e-shop is a one-time investment that can help to increase the accuracy of recommendations, improve the user experience, and increase the competitiveness of the e-shop.
Conclusion:
In conclusion, we can say that every e-shop should serve personalized content using recommenders. It is an effective way to increase turnover and not have to waste time setting up products that will be displayed in the e-shop.
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