Product Research · Example Report
Using Product Research to Decide What to Build
IntelAnvil used research with potential users to determine what ThoughtTapestry should do, which capabilities mattered most, and what should be prioritized for development.
From Product Concept to Product Direction
- ThoughtTapestry began with a broad concept: a system for recording thoughts, ideas, observations, and questions over time and using AI to understand them together.
- But a promising concept does not determine what the product should actually do.
- There were many plausible capabilities: organizing thoughts, finding connections, developing ideas, identifying gaps, generating next steps, bringing back forgotten thoughts, and more.
The Product Question
Which capabilities would potential users actually value, and what should ThoughtTapestry prioritize?
IntelAnvil investigated this through a staged research process with UK-based participants recruited through Prolific. This was the first stage of a broader research progression: determine what the product should do before testing the resulting product opportunity.
1. Discover What Potential Users Want
The first study deliberately avoided presenting participants with a predetermined feature list.
In an initial study (n = 40), participants were given the underlying product premise and asked what they would want such a system to do.
Participant Question
Imagine that over the course of a year you had recorded hundreds of ideas, observations, questions, and thoughts about things you were interested in or working on. If a system could understand all of them together, what would you want it to do with that information?
What emerged
Percentages indicate the share of responses that explicitly mentioned each theme. One response could contribute to several themes.
The results showed that participants wanted more than conventional note organization. They also wanted the system to help them understand, develop, connect, and act on their accumulated thinking.
The responses were analyzed using Text Response Hub, an AI-assisted qualitative analysis system developed by IntelAnvil.
2. Turn the Findings Into Product Possibilities
Research does not mean simply building the features mentioned most often.
IntelAnvil combined recurring participant needs, interesting ideas appearing in individual responses, and independent product hypotheses worth testing.
This produced a broader set of candidate capabilities that could then be tested against one another.
| Capability | Popularparticipant demand | Interestingindividual ideas | Independentproduct thinking |
|---|---|---|---|
| Organize my thinking | ●●● | ● | ● |
| Show me the big picture | ●●● | ●● | ●●● |
| Discover connections I missed | ●●● | ●●● | ●●● |
| Bring back relevant forgotten thoughts | ●● | ●●● | ●●● |
| Show how my thinking has evolved | ● | ●●● | ●● |
| Develop my ideas further | ●●● | ●●● | ●●● |
| Generate new ideas from my existing thinking | ● | ●●● | ●● |
| Find the ideas most worth pursuing | ●●● | ●●● | ●● |
| Turn my thinking into next steps | ●●● | ●●● | ●● |
| Find gaps in my thinking | — | ● | ●●● |
| Find contradictions in my thinking | — | — | ●●● |
| Challenge my assumptions | — | — | ●●● |
The indicators show why each capability entered the next stage. They are qualitative provenance indicators, not participant ratings.
AI helped structure the evidence and explore the possibility space. Human product judgment was used to determine which ideas represented meaningfully different capabilities and which deserved further testing.
The result was not a final feature list. It was a set of product possibilities that could now be tested against one another.
3. Test What People Value Most
The next study tested those capabilities directly.
In the prioritization study (n = 36), participants repeatedly chose which of two capabilities they would personally find more valuable.
Pairwise comparison required participants to make tradeoffs rather than simply rating every attractive feature highly.
Capability preference
Relative preference strength reconstructed from participants' pairwise choices between competing product capabilities.
The strongest preference was not for organizing notes, but for turning accumulated thinking into practical next steps. Several other highly ranked capabilities also went beyond conventional note management: developing ideas, identifying which ideas are worth pursuing, and generating new ideas from existing thinking.
Percentages are relative preference-strength estimates reconstructed from participants' pairwise choices, rather than the percentage of participants selecting each capability.
What the Research Changed
The open discovery and comparative testing answered different questions.
Open discovery revealed what potential users wanted without restricting them to IntelAnvil's existing product ideas.
Comparative testing then showed which competing capabilities participants valued most when they had to make tradeoffs.
Together, the research provided an evidence base for deciding:
- Which capabilities deserve greater emphasis
- Which ideas should remain secondary
- Where ThoughtTapestry differs from conventional note-taking products
- What should be developed and tested next
The research did not replace product judgment. It gave that judgment better evidence to work with.
From Your Idea to a Better-Supported Product
Product research can improve an existing product, shape an early concept, or help determine whether an idea is worth developing in the first place.
IntelAnvil can investigate what potential users actually want, which problems matter to them, which capabilities they value, and how different concepts, features, or directions compare.
The research can start broadly and become more specific as understanding develops. Early discovery can help determine what the product should be. Later research can compare concepts, prioritize capabilities, evaluate prototypes, or determine what should be built next.
Three Stages of ThoughtTapestry Research
Product research was the first stage in a progression of research around ThoughtTapestry. Once there was better evidence about what the product should do, the next questions concerned the product opportunity itself and how potential customers would respond to the website.
Stage 1
Current Report
Product Research
What should ThoughtTapestry do? Research explored what potential users wanted and which capabilities they valued most.
Stage 2
Market Research
Is the product opportunity worth pursuing? Potential customers were introduced to the product concept and asked about their interest and willingness to pay.
View Market Research →Stage 3
Website-Led Market Research
What happens when the website has to communicate the product itself? Participants explored the ThoughtTapestry website before their understanding and interest were evaluated.
View Website-Led Market Research →Have a Product or Product Idea to Test?
IntelAnvil can turn your questions and uncertainties into focused research and better evidence for what to build, change, or prioritize.