Text automation is the automated process of creating text with minimal human intervention, also known as Natural Language Generation (NLG). It aims to create texts more efficiently, scalably and consistently.
The benefits of text automation include faster and more cost-effective text creation, the creation of large volumes of text, consistent text across different channels, personalised content and automatic updating. By automating content processes, natural barriers to detailed product communication can be overcome.
Areas of application for text automation
- Product descriptions
Automated creation of customised, appealing product descriptions based on structured product data such as specifications, sizes and features.
- SEO-optimised content
Generation of SEO-friendly texts that integrate targeted keywords to increase visibility in search engines and thus boost organic traffic.
- Category texts and Product Listing Pages (PLP)
Automatic creation and customisation of category descriptions and PLPs that highlight relevant products and are adapted to current marketing campaigns.
- Personalised recommendations
Automated generation of personalised product recommendations based on the behaviour and preferences of individual customers.
- Email marketing campaigns
Automated creation of marketing emails tailored to the needs and interests of customers, e.g. through product recommendations or special offers.
- Technical documentation
Automated creation of technical manuals, user guides or FAQ documentation based on product data to ensure accurate and consistent communication.
- Reporting and analyses
Automated creation of reports, e.g. sales figures, stock levels or performance analyses, in order to make informed decisions and optimise processes
Technologies for text automation
Important technologies and methods include natural language generation (NLG), data-to-text, artificial intelligence (AI), machine translation (MT), robotic process automation (RPA) and GPT.
Various approaches exist:
- Data-driven text generation (data-to-text): Converts structured data into natural language text.
- AI-based text generation: Uses artificial intelligence to create texts based on existing texts or data sources.
- Rule-based systems: Use predefined rules and templates to generate text.
The future lies in data-driven content operations that enable improved product communication and increased sales. Quality assurance ensures that the content and style of generated texts comply with communication guidelines. Efficiency is demonstrated by the automatic updating of content without manual intervention.