Fuel Cycle’s AI Summary for Qualitative Reports uses your research objectives to generate data summaries automatically. While AI is powerful, it still needs clear direction to produce optimal results.
This article provides a quick review of the basics for crafting strong research objectives and discusses how to tailor them specifically for AI-based analysis. You will also find sample objectives that have proven effective in practice to help you begin.
Research Objectives 101
Research objectives are clear statements that show what you want to find out or understand. They help your research have a clear purpose and focus on the most important things. Without them, it's easy to lose focus, expand your research too much, or gather data that doesn't answer your main questions.
Writing strong research objectives isn’t just about staying organized—it’s about ensuring everyone involved in the project, including your AI tools, is aligned. With well-defined objectives, you’ll not only gather more relevant insights but also ensure AI can provide the most accurate, useful analysis possible.
When you use our AI summary feature, the summary you receive includes a narrative summary of the data and a summary table that counts the frequency of each tag. This quantifies your qualitative data, providing a clear, measurable outcome of your research objectives. This concrete grounding makes our summaries so powerful — they are relevant and specific to your needs.
Research objectives often try to achieve too much. They are filled with various stakeholder needs and complex sentences. However, these types of research objectives do not translate well to AI. For AI to effectively analyze and summarize your data, develop your objectives according to the "SCRAM" mnemonic: Specific, Clear, Relevant, Actionable, and Measurable.
- Specific—Objectives should be focused on a particular (and narrow) aspect of the research.
- Clear—Vague goals produce vague outcomes. Ensure your objective is clear. If you have to read it twice to understand it, rewrite it!
- Relevant—Your research objective should be suitable to the topic and feasible for data analysis to yield results.
- Actionable—Use action verbs! This is nearly an extension of being clear – instruct the AI explicitly, and it will comply.
- Measurable—Give the AI something to calculate.
Sample Research Objectives
Keeping these best practices in mind, here are some research objectives we’ve seen in the past and how we’d adjust them to get the best results from our AI Summary feature.
| Original | For AI |
|---|---|
| Understand how important small business owners perceive social media to be to their business, and why they think it is important or unimportant. |
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| Learn more about how customers shop on social media. Understand how many people shop for home products on social media, identify what drives shopping on social media, identify why some may be hesitant to purchase on social media and identify which social media channel is most shopped or preferred and why. |
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| Understand consumer perceptions, behaviors, and attitudes around [topic]. |
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| Understand the preferences and behaviors of TV show viewers. |
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In all cases, the change reasoning remains consistent: writing specific, clear, measurable, relevant, and actionable objectives that enable AI to develop useful and sensible tags when summarized.
Final Thoughts
The research objectives for AI are quite similar to the ones you have been writing for years, but they are now divided into specific components. The more detailed your objectives are, the clearer the AI-generated tags and analyses will be.
If you have a great prompt idea you would like to share or need some help fine-tuning your objectives, please let us know. Our team is here to help you get the most out of your AI-powered analysis.