How can you analyze speaker feedback data?

1 Define your goals

Before you dive into the data, you need to define what you want to achieve from the analysis. What are your main questions, hypotheses, or objectives? For example, you might want to know how your presentation was received by different segments of the audience, how you performed compared to other speakers, or how you can improve your delivery, content, or engagement. Having clear goals will help you focus on the most relevant and actionable data, and avoid getting overwhelmed by irrelevant or redundant information.

2 Choose your metrics

Next, you need to choose the metrics that will help you answer your questions and measure your performance. Metrics are the quantitative or qualitative indicators that reflect some aspect of your speaking skill or impact. For example, you might use metrics such as ratings, comments, questions, applause, retention, or referrals. Depending on your goals, you might want to use different types of metrics, such as descriptive (what happened), diagnostic (why it happened), predictive (what will happen), or prescriptive (what should happen). You should also consider the validity, reliability, and comparability of your metrics, and avoid using too many or too few.

3 Collect your data

Once you have your goals and metrics, you need to collect your data from various sources. The most common source of speaker feedback data is the feedback form or survey that you or the conference organizer distribute to the audience after your presentation. However, you can also collect data from other sources, such as social media, emails, blogs, podcasts, videos, or transcripts. You should aim to collect data from a representative and diverse sample of your audience, and use consistent and clear questions, scales, and formats. You should also respect the privacy and consent of your respondents, and store your data securely.

4 Analyze your data

Now that you have your data, you need to analyze it to find patterns, insights, and recommendations. You can use different methods and tools to analyze your data, depending on the type, size, and complexity of your data. For example, you can use descriptive statistics, charts, or tables to summarize and visualize your data, or use inferential statistics, tests, or models to compare and contrast your data. You can also use text analysis, sentiment analysis, or natural language processing to extract meaning and emotion from your qualitative data. You should use appropriate and accurate methods and tools, and check for errors, biases, or outliers.

5 Interpret your results

After you analyze your data, you need to interpret your results and draw conclusions. You need to relate your results to your goals and metrics, and explain what they mean and why they matter. You should also evaluate the strengths and limitations of your results, and acknowledge any uncertainties, assumptions, or limitations. You should avoid jumping to conclusions, overgeneralizing, or cherry-picking your results, and instead use logic, evidence, and critical thinking.

6 Communicate your findings

Finally, you need to communicate your findings and recommendations to yourself and others. You need to present your findings in a clear, concise, and compelling way, using appropriate formats, languages, and visuals. You should also provide actionable and realistic recommendations, based on your findings and goals, and explain how they can help you or others improve your conference speaking skills. You should also invite feedback, questions, or suggestions from yourself and others, and use them to refine your findings, recommendations, or actions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?