Case Studies

Competitive Intelligence through conference tracking

Posted by | Fuld & Company

A leading US-based global manufacturer, specializing in scientific and lab equipment, supplies, and software for scientists, faced a challenge. They wanted to stay ahead in the rapidly evolving fields of mass spectroscopy, diagnostics, and analytics. To do this, they needed to track scientific updates and competitor activity efficiently.

Objective

The primary goal was to track key industry conferences, such as ASMS, HPLC, and ANALYTICA, by leveraging social media. The manufacturer aimed to capture earned media at scale and analyze these data for actionable insights.

Solution

Our approach encompassed several strategic steps:

  • Data Collection
    • Designed web data collectors for two social platforms – X (Twitter) and Facebook
    • Collected data for the event days ±2 days to ensure comprehensive coverage
  • Data Processing
    • Utilized Python for parsing and cleaning data with techniques such as lemmatization and stop-words removal for efficient Natural Language Processing (NLP) and text analytics
  • Sentiment Analysis
    • Implemented VADER and BERT algorithms to identify the sentiment of social posts
  • Topic Identification
    • Employed the Latent Dirichlet Allocation (LDA) algorithm to pinpoint dominant topics discussed within the community
    • Further analyzed the sentiment related to these topics
  • Engagement Analysis
    • Analyzed engagement data (likes, retweets, etc.) along with findings from the above models to:
      • Estimate the Share of Voice for own vs. competitor brands
      • Identify key speakers/contributors who received positive and negative attention
      • Recognize key opinion leaders

Outcome

  • Dynamic Dashboard
    • A custom Tableau dashboard enabled the client to navigate the results dynamically and interactively

 

 

 

 

 

 

 

 

 

 

 

 

 

  • Key Insights
    • We provided the client’s Competitive Intelligence (CI) team with crucial learnings and implications on themes such as:
      • Successful trials and research
      • New technologies and patents
      • Popular topics and trends within the industry

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Impact

This strategic approach enabled the manufacturer to gain a comprehensive and nuanced understanding of their industry landscape. By harnessing social media data and employing advanced analytical methods, they were not only able to track competitor activities and industry trends but also identify opportunities for innovation and growth.

 

Tools used

Python

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