At its inception, Algocat's promise was to dramatically improve the completeness and quality of large online catalogs through artificial intelligence. The generative AI (LLM) revolution now enables us to help online retailers with small catalogs to improve their SEO and conversion, too.
The completeness and quality of online catalogs helps to meet several fundamental challenges for e-commerce players: increasing natural traffic (SEO), boosting conversion and reducing returns.
The first lever for increasing traffic is to respond to search intentions: responding to them via dedicated landing pages can significantly increase traffic.
The creation of these landing pages requires the ability to filter products according to a precise criterion, which must be correctly structured and standardized in the database.
The second lever is to work on the SEO long tail and prepare for the arrival of Google Search Generative Experience, which requires complete product descriptions that answer potential buyers' questions about the product.
To increase conversion, search filters need to work properly, which requires product attributes (color, dimensions, screen size, etc.) to be well informed and standardized (to avoid, for example, having a color filter with 15 different blues).
Good product descriptions are also crucial: 78% of consumers are looking for detailed information on product features¹.
Last but not least, comprehensive, high-quality product informationensures that customers don't buy the wrong product: almost 30% of returns are caused by a lack of information provided by the buyer on the site², and 87% will no longer buy from a site that has provided them with inaccurate information¹.
A number of artificial intelligence solutions meet these challenges, performing classification,information extraction, normalization and text generation processes.
However, the implementation of these solutions often requires dedicated learning (to feed the machine learning model), which leads to a high implementation cost and requires the customer to be able to provide training data.
This reserves them for players with large catalogs and sufficiently structured product information management.
By integrating generative artificial intelligence technologies, Algocat's new platform speeds up implementation considerably and, in most cases, eliminates the need for costly and time-consuming specific training.
This enables e-tailers of all sizes to implement artificial intelligence to save precious time and develop their offering more rapidly.
The first customers to use the new Algocat platform have catalogs ranging from a few hundred to several thousand items, and the solution was up and running in less than 2 weeks.