Case Studies

Social Media Analytics for Category Benchmarking

Posted by | Fuld & Company

A leading global luxury magazine sought to develop a luxury brand index to compare the performance of luxury brands based on several parameters, including social media.

Objective

To leverage social media and other metrics as inputs to create a luxury brand index.

Approach

  • Collected social media data from Twitter, Instagram, and Facebook, using web scrapers to capture public data.
  • Recorded parameters such as number of followers, number of posts, frequency of posts, number of shares, sentiment, key trending topics, etc., for each social media channel.
  • Captured data related to revenue, profitability, new product launches, brand ambassadors, brand website and engagement, mobile app and its features, etc., via desk research or from third-party data providers (e.g., App IQ and Capital IQ).
  • Leveraged Python and its NLP libraries to perform sentiment and bag-of-words analyses.

Outcome

The computed index and sub-indices provided critical insights into brands’ social media performance and benchmarked it against direct competitors and overall product category.

Algorithms Used: NLP, VADER, and bag-of-words analysis

Tools

  

Tags: , , , , ,

Related Resources

Read More

Global medical equipment company discovers a new competitor…and a new market

A leading medical equipment manufacturer was concerned about a new entrant that had moved into one of its high-value, diagnostic […]

Read More

Identifying Drug Repurposing Collaboration Partners

A top pharma company actively working in the drug repurposing sector wanted to expand its portfolio by adding new indications […]

Read More

Identifying the most effective prophylactic/therapeutic oral anti-inflammatory agents

A global FMCG leader sought to expand their current oral care product range with a line of products that had […]

Subscribe to our mailing list for our latest updates:

Wednesday, February 15

10:30 ET | 15:30 GMT

Join us to learn about:

  • Cultural nuance issues
  • Methods to mitigate bias
  • Optimizing survey design
  • Interpreting global results