What if We told you, there is a copywriter, available 24/7 who can complete missing descriptions and improve data quality of your Data Objects?

One of the most important issues that can arise when ecommerce businesses do not have proper product data descriptions is a lack of customer trust and satisfaction. Accurate and detailed product descriptions are essential for customers to make informed purchasing decisions. Without this information, customers may feel uncertain about the product's quality, features, and benefits, which can lead to a lack of confidence in the business. This can result in a higher rate of returns, decreased customer loyalty, and a lower overall conversion rate. Additionally, if product data is incomplete or inaccurate, it can also lead to issues with inventory management and order fulfillment, leading to additional costs and operational headaches. Furthermore, poor product data can lead to lower visibility and fewer sales on search engines and marketplaces, as the search algorithm might not be able to match the product with the customer's search queries.

There are several reasons why using AI to enrich product descriptions can be beneficial. One reason is that AI can help to improve the accuracy and relevance of product information by analyzing data and identifying patterns. This can help to ensure that the product descriptions are accurate and up-to-date, which can increase customer trust and satisfaction. Additionally, AI can help to automate the process of creating product descriptions, which can save time and resources for businesses. Furthermore, AI can be used to generate personalized product recommendations for customers, which can increase customer engagement and sales. Finally, AI can help to identify and correct errors in product descriptions, which can help to improve the customer experience and reduce the risk of returns.

What if I told you, there is a copywriter, available 24/7 who can complete missing descriptions and improve data quality of your Data Objects? This copywriter is OpenAI GPT-3, let’s see how we can integrate it with Pimcore.

According to OpenAI GPT-3 itself:

“OpenAI GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language processing model developed by the company OpenAI. It is pre-trained on a massive amount of text data and can generate human-like text, complete tasks such as translation and summarization, and respond to prompts in a conversational manner. GPT-3 has been trained on a diverse range of internet text, allowing it to have a wide range of knowledge and understanding.“

With such powerful capabilities GPT-3 can be used to automatically generate product descriptions, or assist with content creation, it can also be used to assist with content tagging and categorization. Today, we'll talk about how we used it to generate missing product descriptions and generate articles.

Everyone of us encountered a situation, where a lot of descriptions were simply missing. But, if the rest of the product data is there, we can generate them using AI.

Basic idea was

• We use attributes stored in Pimcore such as color, material, dimensions, intended use etc. to create a prompt used by AI to generate text

• It can be done automatically if description is missing or on demand by using a button in Data Object

• Generated description can be used, modified or updated by human editor

• We can also generate text by entering prompt manually

There are a variety of options to do it automatically such as Event Listeners or Value Providers but we decided to go with ‘on demand’ option, so we created custom buttons and modals on Data Object view.

After clicking on one of them we can configure expected length of response (max. 4000 characters). In Article generation you can manually enter a prompt, in description generator it is created automatically using filled in product attributes.

Then in case of description generator we are simply asking AI over an API with a generated question based on product attributes:

Create a description for product named Cobra 427 which is a Sports car, production year is 1966, country of origination is GB, body style is 2-door roadster. It has 2 doors, 2 seats, rear-wheel-drive, 8 cylinder, front located engine that generates 305 kw of power.

Receiving response from GPT-3 can take some time, so we decided to incorporate Symfony Messenger to our functionality.

If you don’t know what Symfony Messenger is, or how to use it with Pimcore, make sure to check out an article by Mateusz Soroka about that.

Symfony messenger allows us to complete operation asynchronously, user gets notification that his text will be ready in a few seconds.

When the response is ready, whenever if it is description or article content, object fields will be automatically updated by Symfony Messenger handler.

By using GPT-3 to generate product descriptions based on data from product attributes, we can improve the quality and consistency of product data, saving time and effort for human editors while also providing customers with accurate and informative content. It's needless to say it also has a positive impact on SEO. We can't wait to see more use cases of AI and Pimcore!

Interested in implementing an AI product description bundle in your Pimcore platform? Contact us.