Simplify your life
Recommendation modules have changed a lot in recent years. You have the choice between many specialized modules. The complexity of setting up these modules depends on three main factors:
1) The number of recommendation modules
It may seem attractive to install specialized modules for relatively simple services such as New arrivals, Sale and Best Sellers. You manage each service independently, check compatibility between each module, and when you edit a product in your catalogue, you must check whether it affects manual configurations in all modules.
Installing one module that covers all recommendation services simplifies this management.
2) The complexity to select products to recommend
There are 2 main methods to select products to recommend:
- With manual configurations between products. All new e-merchants start by setting up associations between similar and complementary products in each Prestashop product sheet.. This operation takes on average 3 minutes. It can reach 7 hours with 150 products and 50 hours with 1500 products. It requires to know well the characteristics of each product, and to evaluate the products to associate in each banner. It’s relatively effective for products you know well. But the efficiency is much more random for products that you know less well. When you edit a product in your catalogue, you must analyse all potential changes to be made manually. This method can become very tedious if you have a lot of products and a strong rotation.
- By learning with the AI. The treatments are automatic. Some results are updated every day, or even every visitor’s click. Artificial intelligence algorithms based on the “wisdom of the crowd” come up with amazing recommendations, which you have not thought of, that optimize your conversion rate. You can also deploy Matching recommendations that match the tastes and intentions of users at each stage of their navigation. And the Analytics page on your dashboard helps you understand the changing behaviour of your customers and the contribution of each product to the success of your sales.
3) The complexity to configure the recommendations
It is essential to evaluate the complexity to configure the recommendations. A priori, a simple service should be simple to configure, and a service that requires learning algorithms and Artificial Intelligence should have many options to choose the right algorithm, select internal parameters, and configure filtering rules. Indeed, some modules publish hundreds of pages installation manuals that allow you to configure hundreds of parameters.
How long is it necessary to understand these manuals, configure the first recommendation banner and adjust the parameters to optimize the efficiency of the algorithms?
Is it the role of the e-merchant to understand the intricacies of each algorithm?
We believe that learning methods should not be limited to the treatment of recommendations but to all configuration processes. MyDreamMatch considers your industry to automate the most complex tasks and reduce the configuration of your recommendations to a minimum.
The MyDreamMatch module allows you to activate each type of recommendation on each page in 1 click. You can optionally change the title, the number of products to display on each banner or move the banner on each page in 1 click. The banners integrate perfectly with your design by taking the style of your site. A unique method of automatic placement of products to highlight on all banners of your site allows you to master your business rules very simply.
The objectives of the MyDreamMatch are to save you time and money. The very simple management of the module saves you considerable time. The 7 types of recommendations based on the AI guarantee you an exceptional conversion and loyalty rate.
Software engineer from Supélec Paris, I worked as a developer, architect and then manager for many years at the Research Center of IBM and then in the Cisco European organization.
Passionate about Artificial Intelligence, I decided in 2015 to create my company MyDreamMatch in order to make personalized recommendations accessible and easy to use by the largest number of web sites.