Multilingual Brand Monitoring with Lingvanex Technologies
Challenge
A leading marketing agency* needed an efficient way to monitor brand mentions across a wide range of media sources, including social networks, websites, YouTube, podcasts, and more. The company’s existing processes were overwhelmed by the sheer volume of data and the complexity of tracking thousands of sources in over 20 languages. They lacked the tools to efficiently process diverse content formats, such as audio and video, or to deliver timely insights on public sentiment and emerging trends. As a result, delays and inaccuracies in analysis threatened their ability to maintain brand reputation and provide actionable intelligence to clients. The agency urgently needed a scalable, multilingual solution capable of real-time monitoring to stay ahead in a rapidly evolving digital landscape.
*The company’s name is not disclosed in compliance with confidentiality agreements.
Solution
Product: Natural Language Processing
To address these challenges, the agency implemented Lingvanex’s advanced natural language processing (NLP) tools. The tool included technologies for Named Entity Recognition (NER), speech transcription, and machine translation, all designed to streamline and automate brand monitoring. These tools allowed the agency to extract key brand mentions from both text and audio sources, classify them by sentiment and relevance, and translate non-English content into the desired language. For audio and video content, such as podcasts and YouTube videos, Lingvanex’s transcription technology converted spoken language into text, which was then processed with NER and translation tools. The agency could now monitor thousands of sources every hour, providing real-time insights into brand perception and enabling proactive responses to emerging issues or trends.
Results
The results of this implementation were transformative. Monitoring coverage expanded significantly, bringing previously overlooked platforms and languages into focus. Data processing times were reduced by 80%, enabling real-time reporting for clients. This rapid and efficient data processing allowed the agency to cover more platforms and languages than ever before. The improved accuracy of sentiment analysis and contextual understanding enhanced client trust and satisfaction.
Contact Us
Completed
Your request has been sent successfully
