Artificial intelligence

The PIM and artificial intelligence: what advantages for e-merchants?

In e-commerce, the quality of product data is a key element to stand out from the competition. To ensure this quality, retailers must demonstrate good data management, in order to gain efficiency and offer a better service.

Salomé Furlan

In e-commerce, the quality of product data is a key element to stand out from the competition. To ensure this quality, retailers must demonstrate good data management in order to gain efficiency and offer a more satisfactory product and customer experience.

To facilitate the management of this valuable data, the use of a PIM (for Product Information Management) solution is essential. When importing new supplier data into PIM, a large part of the tasks are automated; however, some must be done manually by business operators (error correction, deleting duplicates, product categorization, etc.). For several hundred or thousands of products, this is time-consuming and tedious, if not impossible for small teams, which can force a retailer to reduce the size of the product assortment or prevent the integration of new products.

This is where solutions using artificial intelligence (AI), such asUnifaicome into play : an intelligent system analyzes all product data and identifies irregularities, corrects errors and, better yet, enriches the data. AI is therefore an incomparable asset for better cleaning, categorizing and enriching product data through a PIM.


AI at the heart of solutions PIM 

The solutions PIMsolutions, combined with MDM and DAM solutions, facilitate the storage and verification of product data (whether marketing, sales or technical). However, they remain closer to a database than to an intelligent robot. For example, a PIM can detect errors in product information, but the corrections will have to be made by the operators.

The quality of the information is therefore conditioned by what the business operators enter into the system, which increases the risk of incorrect data entry - especially when the teams have very little time available. These manual corrections, in addition to being very time-consuming, are likely to cause a whole series of problems: incomplete or incorrect fields, duplicate products, poor image, etc.

In order to avoid these errors and to gain efficiency, AI models have been integrated upstream of the solutions PIM.

The role of AI

Simply put, artificial intelligence aims to reproduce some of the features of a human brain by relying on a set of complex techniques and theories. In PIM, AI will analyze data and compare them to deduce statistical rules and identify elements that do not respect them. However, it will initially need the expertise of a business operator to handle the more complex cases. Thanks to human intervention, the algorithm will be able to learn and progress over time.

The use of AI thus allows for a more complete and robust automation than if one were to be satisfied with the basic functionalities of a PIM. In concrete terms, an automation platform like Unifai can be integrated upstream or downstream of a PIM to:

  • Standardize, i.e. eliminate errors and structure the catalogs to facilitate their integration;
  • Enrich data by extracting product characteristics from descriptions, creating new attributes to improve user search and supplementing product attributes from an external source;
  • Reconcile data, i.e., reconcile databases to create a single product repository and avoid duplicate product offerings.

The e-commerce sector is very buoyant today, which means that the volumes of data to be integrated are ever greater and that customer requirements in terms of product information quality are increasingly high. The credo for e-tailers is clear: process more and process better. Combining PIM and artificial intelligence will become essential to stand out and offer the best possible customer experience.

How artificial intelligence is helping PIM

In order to automate data cleansing and attribute enrichment in a PIM, several AI technologies are involved, including Machine Learning and Natural Language Processing. If these terms are relatively unclear to you, here is a brief explanation:

  • The Machine LearningMachine Learning, a sub-category of artificial intelligence, is a set of techniques that give machines the ability to learn. These techniques are therefore different from programming, which consists in the execution of predetermined rules. This is also where AI differs from the functionality of a PIM. In Machine Learning, learning requires very large amounts of data and many iterations.

  • The Natural Language Processing (or NLP) is a technology that allows machines to understand human language. In the case of categorization, for example, NLP models will read the product information (description, title, characteristics) to propose the most relevant category. NLP can also understand the context and better understand the meaning of words according to the product universe.

In addition, a DAM is sometimes associated with PIM. As a reminder, the DAM (Digital Asset Management) is a solution to manage, store and centralize all the digital tools of a company, such as graphic supports, audio files, images, etc. If the PIM is focused on the product, the DAM is focused on the digital aspect of it. In these cases, AI also facilitates the storage and integration of the digital elements of the product sheets.

What are the requirements?

For an AI model to properly process your product data and achieve a satisfactory result, it will need to rely on existing "knowledge bases". These knowledge bases must contain a large amount of data, ideally of very good quality - in other words, homogeneous product files of consistent quality - to train the AI.

Fortunately, as a rule, an e-merchant already has a large database of previously sold products, which is a very satisfactory database from an AI point of view and thus generates very good performance.

On the contrary, if these conditions of quantity and quality are not fully met, AI will not be able to deploy its full potential. In particular if:

  • The amount of data is insufficient
  • The quality of the data is uncertain or too fluctuating: the learning of the AI will also be uncertain, it will not be able to deduce rules (at least correct ones) and will not produce good results.
  • The data to be processed is too heterogeneous: logically, no "statistical trend" will emerge correctly, and the AI will be powerless.

Nevertheless, once the problem is detected, a few days work on highly identified products allows to initiate the AI and to start to put the data in quality.

Things to remember

Product data has become an essential part of the growth of e-commerce companies. To stand out from the competition in a fast-growing industry, e-retailers must focus on good data management to improve the product and customer experience.

The automation of data processing in a PIM thanks to AI is a real time saver for the product launch and avoids errors in the product sheets; this is provided that a sufficiently large and high quality knowledge base is available to train the models.

Before or after the integration of your data in a PIM, Unifai categorizes, enriches and makes your product data reliable. If you would like to know more about our product data enrichment and reliability modules, please contact us or request a demo.

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