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Artificial Intelligence Retailers entrust their sales to the AI

The percentage of retailers that have implemented solutions based on Artificial Intelligence increases at a significant rate each year. The benefits are presented not only as an increase in sales, but also in significant cost savings associated with customer service, as well as improvements in the supply chain and performance.

Artificial Intelligence
Artificial Intelligence

Extreme competitiveness in the retail market

Retail is undoubtedly one of the most competitive markets. Every day new stores open, the quantity of products increases and SMEs feel pressured by the big companies of the sector.

In this context, having just the right amount of inventory is critical to customer satisfaction, especially when certain products are perishable or deteriorate when stored.

Predicting Demand as a survival method

It is in this context that new demand forecasting techniques, based on data science, can anticipate and allocate the optimal amount of resources to minimize stagnant inventory or reduce the opportunity cost of leaving customers unattended.

The synergy between Data Analytics and Artificial Intelligence has made it possible to approach demand analysis and establish predictive models through two approaches supported by two IA disciplines: Machine Learning and Deep Learning.

Artificial Intelligence

Machine and Deep Learning bring a new perspective on customer demands

1. Deep Learning: product comparison based on image analysis

One of the most important indicators, to ensure the correctness of our prediction, is to identify which marketed products are most similar in order to project the possible sales trend of new products. For this purpose, an analysis of the product images can be carried out using Deep Learning techniques in order to be able to compare them both with the company’s products and with competing products. Obtaining the advantages of predicting how many sales a product will have, as well as anticipating the failure or success of a new product.

The most used techniques in these cases are:

  • Convolutional Neural Networks
  • Autoencoder
  • LSTM (Long Short Term Memory networks)

2. Machine Learning: product demand prediction

Once obtained which products are the most similar we proceed, through the analysis of historical data, to make predictions of sales of a product applying Machine Learning techniques.

Different demand prediction models will be used using different programming and automatic learning languages. Python and R are the languages of reference today. However, success will come from the hand of the model that offers the best results:

  • Adjustment and precision
  • Execution time
  • Scalability
  • Ease of use

In terms of learning techniques, the following should be highlighted:

  • Linear Regression
  • XG Boost
  • Arima
  • SVC
Artificial Intelligence

* In this graph we can see how the evolution of sales of two similar products reach better figures in the first case (red line), which would have applied comparative models and prediction based on Artificial Intelligence for its launch.

Artificial Intelligence with direct impact on the business.

  • Product knowledge

    Product knowledge

    Greater precision in the knowledge of a product through a better learning of its life cycle.

  • Business Scalability

    Business Scalability

    Self-learning enables rapid business escalation.

  • Decision-making


    The automation of processes and the automatic prediction, allows to speed up the decision making, and to improve the productivity.

  • Immediate benefits

    Immediate benefits

    The benefits become apparent in the short term, and consolidate in the medium and long term. Favouring the survival of businesses in the face of competition.

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