How ChatGPT Analyzed My LinkedIn Data and Revealed Hidden Trends
And how you can do it for your data too
We live in a world of data, where every click, like, and share tells a story. As a content creator on LinkedIn, I wanted to leverage these stories to enhance my audience engagement. But with a myriad of data points at my disposal, the task seemed daunting. That was until I turned to an unexpected ally: OpenAI’s ChatGPT. This AI language model sifted through a year’s worth of LinkedIn engagement data (which you can export from your analytics dashboard), providing me with valuable insights and strategies for growth hacking.
With the new Code Interpreter plugin and GPT 4, you can simply upload the Excel file containing all the LinkedIn engagement metrics and ask ChatGPT to explore and analyze that data, identify trends, and patterns, and consult you on social media content and growth strategies.
Using the Code Interpreter, GPT4 will execute the code to run the actual data analysis in a Python environment and then use the computed results to provide the required insights.
We can think of a step-by-step, chain of thought prompting to do this systematically. Do note that you can also combine multiple steps into a single prompt.
- Compute basic descriptive statistics for each key metric in the uploaded Excel sheet: mean, median, mode, range, variance, and standard deviation.
- Create visual representations (charts, graphs) for each key metric over time.
- Look for patterns in the data. Are there certain times or days when engagement is particularly high? Are there any recurring themes in the top-performing content? Identify any unexpected results or outliers. These could result from unique events or situations and may warrant further investigation. For each pattern or trend you identify, develop a hypothesis explaining why it is occurring. This might involve looking at the specific content that was posted or other external factors that could be influencing engagement. For each hypothesis, find additional data or conduct further analysis to test its validity. This might involve diving deeper into the content, the timing of posts, audience demographics, etc.
- Based on your analysis, identify areas where the content creator could improve. This could be specific types of content, timing of posts, use of hashtags, engagement with followers, etc. Use your findings to develop a comprehensive strategy for the content creator. This could involve recommendations on content types, posting schedules, audience engagement, etc.
This is still rudimentary but can be a great start for the deep dive.
Decoding the Data with ChatGPT
I used the above prompts with my own data. ChatGPT embarked on deciphering my LinkedIn engagement metrics, diving deep into four primary areas — ‘Engagements,’ ‘Top Posts,’ ‘Followers,’ and ‘Demographics.’ It meticulously calculated key statistical measures for each metric, revealing trends, patterns, and outliers that had previously escaped my notice.
For instance, ChatGPT discovered that my content received higher engagements and impressions on weekends, particularly Saturdays and Sundays. It also identified certain days with unusually high engagements and impressions, suggesting that unique events or specific content types led to these spikes.
Hypotheses and Validation
Armed with these findings, ChatGPT proposed hypotheses. It suggested that content posted on weekends might receive higher engagement and that certain content or events might drive exceptional engagement levels. While these hypotheses seemed promising, ChatGPT candidly acknowledged that their validation would require further detailed analysis, such as examining the content posted on high-engagement days and comparing weekday and weekend posts.
Growth Hacking Strategy
Drawing upon the trends and patterns it had unearthed, ChatGPT outlined a preliminary growth hacking strategy. It recommended optimizing my posting schedule to favor weekends when the engagement was high. It also suggested engaging more with my audience and diversifying my content to keep my followers intrigued and invested. Despite the limitations of the analysis, the insights offered by ChatGPT provided a strong foundation for enhancing my LinkedIn presence.
Limitations
This journey with ChatGPT wasn’t without its challenges. Due to the current environment and data limitations, some aspects of the analysis remained unexplored. For instance, deep-diving into the content of the top-performing posts was not possible, as the URLs couldn’t be accessed for further information as the Python environment did not have internet access. Nonetheless, despite these constraints, the insights garnered were invaluable.
Conclusion
In an era where data is gold, having an AI companion like ChatGPT to mine that gold is a game-changer. It allowed me to view my LinkedIn metrics through a fresh lens, unearthing trends and patterns hidden in plain sight. While the journey had limitations, the insights gained have opened a path to growth hacking my LinkedIn presence. This experience underscored the power of AI in interpreting data and deriving actionable strategies, making it an essential tool for any content creator in the digital age.
So, if you’re a content creator navigating the sea of data, consider letting an AI like ChatGPT take the helm. You might be surprised at what you discover!
If you are stuck anywhere or want a more intricate and nuanced analysis of how to use ChatGPT, feel free to contact me on LinkedIn.
Send a clap to signal me to make a more detailed and advanced prompting guide for this use case.
