Data analysis with AI: how to measure the effectiveness of employee communication
In today’s organizations, the intranet is a fundamental internal communication tool. However, simply having content isn’t enough to guarantee its effectiveness. What’s crucial is regular data analysis, which allows you to precisely evaluate what works and what needs improvement. This is where artificial intelligence comes in. In this article, we’ll explore how, with AI support, you can effectively analyze intranet data, turning numbers and statistics into actionable recommendations.
Data analysis with AI – the crucial preparation phase
Every effective data analysis with AI starts with careful data preparation. Intranet data should include information about employee activity, for example, how many times an article was viewed, how long users stayed on a page, and how often different sections were used. The easiest way to export such data is from analytics tools like Workai Analytics, in CSV or Excel format.
When preparing data, make sure it’s well-organized, with titled columns, view counts, reading times, and publication dates. This structure allows AI to quickly understand the context, leading to better quality insights.
Feeding data into AI step by step
Once your data is ready, you can share it directly with a tool like ChatGPT. In practice, this means pasting part or all of your table and adding a detailed instruction. The more precise your question, the more useful the answers you’ll get.
For example, if your goal is to identify which content is worth continuing, you might ask:
„Based on the intranet data below, indicate the three most-read articles from the last quarter. Briefly explain what might have driven their popularity.”
The result will be a quick summary of what especially interests your employees, along with suggestions for future content.
Asking the right questions
AI can help provide highly specific insights, provided you frame your questions correctly. For instance, if you notice a drop in engagement in the news section, your question might be:
“Analyzing the article view data from the last three months, do you notice any trends? Suggest actions we could take to increase interest in intranet publications.”
Another valuable question could be:
“Which topics covered on the intranet in the last quarter generated the most engagement (reading time and clicks)? List them with brief explanations.”
You can also ask for a time-based summary, such as:
“Based on the data I provided, what is the best day of the week and time to publish new content on the intranet?”
Such responses allow you to optimize your publishing schedule and improve communication effectiveness.
Ongoing support in monitoring content
Thanks to AI, data analysis on the intranet doesn’t have to be a one-time action. You can easily automate this process by regularly asking AI for fresh recommendations. For example, you might share article view statistics weekly and request updated suggestions for upcoming publications.
A sample prompt you can use regularly:
“Analyzing the latest article readership data, suggest three topics we should cover in the next newsletter or on the intranet homepage. Include topics that have gained popularity recently.”
This approach helps you quickly capture shifts in employee preferences and dynamically adjust your communication to current needs.
Supporting the interpretation of qualitative data
AI can analyze not only numbers but also help interpret qualitative data, like comments, opinions, or employee feedback. For example, you might copy a few comments from under an article or feedback survey responses into ChatGPT and ask for sentiment analysis, such as:
“Asses the sentiment of comments under the latest article about hybrid work. Provide conclusions that can help us improve future communication on this topic.”
This gives you a quick overview of whether the content generated positive reactions or sparked concerns, allowing you to adjust future messaging.
Example use cases
Data analysis with AI is increasingly used by companies to evaluate the effectiveness of newsletters and tailor content to real employee needs. For instance, one organization noticed a drop in newsletter open rates and lower audience engagement. After analyzing the data, AI revealed that repeatedly covering the same topics decreased interest. By introducing more diverse content and testing new formats, such as video summaries or interactive elements, the open rate increased by several percentage points.
Another use case is optimizing send times. In one company, AI analyzed historical data and found that newsletters sent on Wednesday mornings performed significantly better than those sent on Mondays or Fridays. Adjusting the schedule helped improve open rates and reach.
AI can also support evaluating content structure. In one case, the system noticed that longer articles at the end of the newsletter were often skipped. After reordering the layout and adding short summaries at the beginning, click-through rates in less popular sections increased substantially. Thanks to such analyses, organizations can continuously optimize their newsletters, better align with employee expectations, and improve internal communication effectiveness.
Summary
Data analysis with AI enables quick, data-driven decision-making, improving corporate communication efficiency. By asking precise questions, you can easily translate data into practical recommendations, leading to more targeted and engaging internal messaging. Remember, AI doesn’t replace the communicator, but it greatly strengthens their capabilities.
When used properly, AI turns intranet data analysis into a regular, simple, and highly effective task. Start using these capabilities today and watch how your company’s communication evolves.