Enhancing PR Solutions with Efficient Data Annotation- A Case Study

PR (Public relations) is an integral part of corporate communication and spreading the awareness about company's product or services.

PR generally are the most professional and strategic mode of communication, done via established media like news/magazine article, print and digital media. Finding the write channel is very important to reach the targeted audience for press releases.

In this article we'll discuss, how one of client from PR industry used AI to solve its problem.

About Customer

Our customer (US startup spin out from University of Pennsylvania) operates within the realm of Public Relations (PR) and communications software. Their core objective is to assist companies in identifying and connecting with the most relevant authors for their press releases. This involves the crucial task of pinpointing the perfect storytellers to effectively convey their brand message.

The Challenge

Customer's primary challenge lay in developing a robust recommendation engine capable of accurately matching press releases with the most appropriate authors.

This required the tagging and categorization of a vast dataset comprising millions of articles and thousands of author profiles, predominantly sourced from platforms like Twitter.

The goal was to enable users to input a press release summary and receive a curated list of relevant authors ranked by their relevance to the content.

Our Solution

To address this intricate challenge, Labellerr stepped in to streamline the topic tagging process. Leveraging our expertise in data annotation and preparation, we collaborated closely with the client's AI engineers.

Our focus was on optimizing their data pipeline to expedite the tagging of vast volumes of textual data, ensuring accuracy and efficiency in the annotation process.

Implementation and Impact

Labellerr's solution played a pivotal role in accelerating the data annotation phase. By fine-tuning the topic tagging process, our collaboration enabled the client's AI engineers to swiftly access annotated data sets for their model iterations.

This significantly shortened their development cycles, empowering them to refine and enhance their recommendation engine with more precise and relevant data.

Results

The implementation of Labellerr's streamlined data annotation process yielded remarkable outcomes for our client. With quicker access to annotated data sets, the client witnessed notable improvements in their recommendation engine's performance.

The speedier model iterations and enriched data facilitated a more accurate matching of press releases with the most relevant authors, enhancing the overall efficacy of their PR and communications software.

Conclusion

Labellerr's partnership with the client showcased the tangible impact of efficient data annotation in refining and optimizing AI-driven solutions. By expediting the annotation process, we enabled our client to strengthen their recommendation engine, providing businesses with a powerful tool to identify and engage with the perfect storytellers for their campaigns.