Since the beginning of 2023, generative AI has been experiencing an unprecedented boom. In the retail, fashion and luxury sectors, product data plays a fundamental role in the consumer experience, as well as in sales performance. This sector poses a double challenge: working with large volumes of data subject to product seasonality, but also ensuring its quality. Will AI be up to the challenge?
The challenges of the product repository, the cornerstone of sales processes
Brands are increasingly constrained by laws or regulations that relate to their data. This is the case with the famous RGPD, which has turned the way we consider personal data upside down, but also with the INCON Law (INformation CONsommateur) or newly with the AGEC law, which obliges companies to communicate on the environmental impacts of their products.
One of the special features of product data is the complexity and richness of the attributes that describe a product. Comprehensive data is not only essential for optimal product management, but also for an enriching customer experience. Qualifying thousands of products with attributes such as dimension, color and weight can become a tedious and error-prone task.
Add to these constraints the notion of seasonality and trends, and the flow of data becomes increasingly complex and cumbersome to manage. In the case of omnichannel, for example, it is essential to have precise knowledge of the points of sale and their products, for example when setting up a Click & Collect system. Given the sheer volume of data processed by hand, human error and misinterpretation will undoubtedly tarnish the quality of the data.
Ultra-customization of product sheets finally possible
Product Information Management (PIM) solutions make it possible to contextualize content according to customer segments, but this functionality is rarely used due to the complexity of generating dedicated sales pitches or product visuals.
With generative AI, contextualizing sales pitches or digital assets to provide an exceptional product experience to attract and retain customers is finally becoming a reality. This will enable the personalization of the product experience throughout the consumer's buying journey.
With AI, the promise of taking consumers' emotions, values and aspirations into account when presenting products becomes achievable.
A Golden Record Product thanks to AI?
While for personal data, it is extremely difficult to find a functional model involving artificial intelligence for data quality, for product data on the contrary, AI opens the way to an El Dorado for a revolution in terms of productivity of referencing processes and data quality.
Where humans are limited in terms of the volume of non-quality processing, machines are not. In a product repository, AI can become a tool that boosts Data Management actions.
AI as an aid to normalization, standardization and self-classification of supplier data
Data Managers can train the AI to reconcile supplier classifications with those of the brand's repository. By learning from a sample, it can then replicate infinitely while continuing to learn. Understanding "ocean" as the color blue is not self-evident, especially as this type of denomination can be subject to waves of fashion. AI is capable of processing huge volumes of data in record time. Data teams can then concentrate on enriching the data to make the system ultra high-performance and relevant.
Detecting and reducing errors with AI
Thanks to the use of AI, we avoid the problems of typing errors (typos, spelling mistakes) and interpretation that are typically human. Able to read and understand all kinds of media, AI can break down an image, check the match between a product sheet and a photo to detect inconsistencies (a description of a blue V-neck sweater alongside a photo of a red turtleneck sweater).
AI, marketing's right-hand man
In marketing terms, AI could have a dual use: extracting product data from a marketing pitch (relieving teams of a tedious task), and learning to reproduce the brand tone and build a pitch using product data while perpetuating the customer experience.
In addition, using RAG (retrieval augmented generation) approaches, AI will be able to draw on the company's product data repository to optimize the results of generative AI. This approach makes it possible to formulate answers based primarily on internal knowledge, like a kind of editorial charter.
Data enrichment assistance
While technically, an AI could be asked to generate product images based on data, this process still seems largely controversial. On the other hand, since AI is capable of reading an image, it can learn to make connections to enrich product data. It can detect that the product is a short, floral dress and define it as a summer dress. This type of process is extremely interesting, especially for businesses with high product turnover and seasonality, as the processing time by the AI is unbeatable.
Data Managers boosted by AI
This doesn't mean that data managers are going to disappear - quite the contrary. If AI represents an undeniable asset in terms of reliability and speed, this is only true if the AI has been properly trained with quality data. A miracle solution, perhaps, but one that needs human expertise to happen. Data Managers will play a crucial role in training and quality control, as well as in enriching data to make the repository more relevant and efficient for a high-performance customer journey.
The potential of AI has not gone unnoticed, with PIM and DAM tools beginning to be "augmented" with AI, offering a glimpse of the power of AI in data quality. Are you ready to enhance your data with AI?
Pascal Anthoine
Director of Data Governance & Management
Jérôme Malzac
Innovation Director
