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A case study on establishing data-driven media relations
How does the world see my company? Do reporters understand our strategy? Do our communications move the needle?
These are questions that bedevil communications professionals. In earlier years, agency interns pored over articles and scored them using their personal judgment. Modern AI tools were supposed to make the job easier, but data that is filled with junk publications and off-topic articles often stands in the way of an accurate read on the media landscape. An intelligent combination of human brilliance and AI is the answer.
The Client Challenge
A large multinational consumer brand was looking for a better way to quantify media sentiment based on coverage of its strategic goals and stock. The firm's corporate communications department used a manual human-driven approach for assessing sentiment of its corporate strategy in the news. This posed a challenge due to the limitations of staff to process large data samples and the subjectivity and lack of consistency that comes with human assessment.
The LAI Solution
Literate AI (LAI) worked with the corporate communications team to achieve the following key objectives:
- Establish a consistent, repeatable process for assessing media perception
- Identify individual reporters’ bias toward the brand (positive and negative)
- Evolve the media relations strategy based on data-driven analysis
We employed our unique methodology to achieve these objectives.
Step 1: AI Transparency and Analysis
Combining human brilliance and artificial intelligence, the LAI team first identified the most relevant press coverage to include in the analysis. We then processed the cleansed data through our proprietary platform, TLDR, to achieve high-quality, objective results. The output provided a fact-based and defensible brand sentiment score, as well as detail on the sentiment of specific reporters toward the brand.
Step 2: Strategy and Execution
Leveraging the data-driven analysis, LAI developed a media relations strategy focused on pitching the most relevant reporters for the brand. The team also suggested specific keywords to use in the pitch, based on the reporters’ past coverage.
Step 3: Tracking AI Optimization
Tracking the impact of these efforts, the client saw an overall improvement in sentiment across the media landscape. They also established an objective brand sentiment score against which they will benchmark future analysis. In addition, they received a personalized media relations playbook to optimize their ongoing corporate communications initiatives.
