More and more businesses and retailers are harnessing the power of data to improve their sales by implementing recommendation systems in their spaces.

Recommendation systems based on Artificial Intelligence techniques, with the aim of predicting users’ tastes and recommending product proposals and services of interest to them.

Based on algorithms, they analyse and predict the needs of the public using data.

However, how do they manage to reach these levels of prediction? Below, we are going to explain a series of the most applied and effective methodologies at points of sale.

How do Recommendation Systems work?

By analysing data and developing behavioural patterns, which, when combined, are adapted according to the characteristics of each point of sale.

An increasingly demanding market forces us to look for more sophisticated solutions that can be found through the use of different technologies applied to retail.

Let’s take a look at some of them:

Content-based filters

They are based on the prediction of the product and its characteristics, i.e., for a specific user, their tastes or purchases are analysed, showing products with similar characteristics.

The recommendation is based on the content of the products without taking into account the subjective opinion of other users. This provides more understandable predictions for users.

Making it easier: “if you liked this product, it is very likely that you will like this one” because of its similarity, features, etc.

These techniques can be applied in upselling techniques where we offer an alternative product with a higher price.

Cross-recommendation

It works in a similar way to content-based filters. In this case, products are grouped together that are usually bought together or that are valued in a similar way by users.
For a user who likes or regularly buys a product, similar products will be recommended according to this criterion.

behavioural filters

These filters are based on information about the users. The system analyses the purchases, likes or ratings of all consumers; this information is used to apply it to groups of similar consumers or consumers with the same tastes.

We create behavioural clusters that allow us to know the profile of the public of your business according to days and time slots.

We can recommend products to a user, even if they have not consumed them, because they have been liked by similar users and may arouse their interest.

Less obvious recommendations that  use of a greater volume of information, as we have taken into account all the tastes of the user and those similar to him.

Ladorian offers a unique system of recommendations

Ladorian is a technological company specialised in the implementation of Intelligent Recommendation Systems based on Artificial Intelligence set up at all retails.

How do we achieve this?

Our Prime Time algorithm analyses data at all levels: product, categories, customer purchases, consumption habits, etc. This allows us to develop an intelligent recommendation system based on segmentation and cross-selling techniques.

An exclusive system for the retail sector that makes the most of your data in real time and with real results.

These are some of the most commonly used techniques.

With a enough large data set, we can generate personalised recommendations with a high probability of matching users’ particular tastes.

As a general rule, it is necessary to combine different methodologies and, in many cases, this combination is conditioned by the characteristics of the retail or point of sale.

As you will have seen, we have the solution you are looking for for your retail, contact us without obligation and we will help you to implement an Intelligent Recommendation System in your point of sale!

Shall we start?