--- name: synthesize-research description: Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw feedback into roadmap recommendations. argument-hint: "" --- # Synthesize Research > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). Synthesize user research from multiple sources into structured insights and recommendations. ## Usage ``` /synthesize-research $ARGUMENTS ``` ## Workflow ### 1. Gather Research Inputs Accept research from any combination of: - **Pasted text**: Interview notes, transcripts, survey responses, feedback - **Uploaded files**: Research documents, spreadsheets, recordings summaries - **~~knowledge base** (if connected): Search for research documents, interview notes, survey results - **~~user feedback** (if connected): Pull recent support tickets, feature requests, bug reports - **~~product analytics** (if connected): Pull usage data, funnel metrics, behavioral data - **~~meeting transcription** (if connected): Pull interview recordings, meeting summaries, and discussion notes Ask the user what they have: - What type of research? (interviews, surveys, usability tests, analytics, support tickets, sales call notes) - How many sources / participants? - Is there a specific question or hypothesis they are investigating? - What decisions will this research inform? ### 2. Process the Research For each source, extract: - **Key observations**: What did users say, do, or experience? - **Quotes**: Verbatim quotes that illustrate important points - **Behaviors**: What users actually did (vs what they said they do) - **Pain points**: Frustrations, workarounds, and unmet needs - **Positive signals**: What works well, moments of delight - **Context**: User segment, use case, experience level ### 3. Identify Themes and Patterns Apply thematic analysis — see **Research Synthesis Methodology** below for detailed guidance on thematic analysis, affinity mapping, and triangulation techniques. Group observations into themes, count frequency across participants, and assess impact severity. Note contradictions and surprises. Create a priority matrix: - **High frequency + High impact**: Top priority findings - **Low frequency + High impact**: Important for specific segments - **High frequency + Low impact**: Quality-of-life improvements - **Low frequency + Low impact**: Note but deprioritize ### 4. Generate the Synthesis Produce a structured research synthesis: #### Research Overview - Methodology: what types of research, how many participants/sources - Research question(s): what we set out to learn - Timeframe: when the research was conducted #### Key Findings For each major finding (aim for 5-8): - **Finding statement**: One clear sentence describing the insight - **Evidence**: Supporting quotes, data points, or observations (with source attribution) - **Frequency**: How many participants/sources support this finding - **Impact**: How significantly this affects the user experience or business - **Confidence level**: High (strong evidence), Medium (suggestive), Low (early signal) Order findings by priority (frequency x impact). #### User Segments / Personas If the research reveals distinct user segments: - Segment name and description - Key characteristics and behaviors - Unique needs and pain points - Size estimate if data is available #### Opportunity Areas Based on the findings, identify opportunity areas: - What user needs are unmet or underserved - Where do current solutions fall short - What new capabilities would unlock value - Prioritized by potential impact #### Recommendations Specific, actionable recommendations: - What to build, change, or investigate further - Tied back to specific findings - Prioritized by impact and feasibility #### Open Questions What the research did not answer: - Gaps in understanding - Areas needing further investigation - Suggested follow-up research methods ### 5. Review and Extend After generating the synthesis: - Ask if any findings need more detail or different framing - Offer to generate specific artifacts: persona documents, opportunity maps, research presentations - Offer to create follow-up research plans for open questions - Offer to draft product implications (how findings should influence the roadmap) ## Research Synthesis Methodology ### Thematic Analysis The core method for synthesizing qualitative research: 1. **Familiarization**: Read through all the data. Get a feel for the overall landscape before coding anything. 2. **Initial coding**: Go through the data systematically. Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later. 3. **Theme development**: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question. 4. **Theme review**: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct from each other? Do they tell a coherent story? 5. **Theme refinement**: Define and name each theme clearly. Write a 1-2 sentence description of what each theme captures. 6. **Report**: Write up the themes as findings with supporting evidence. ### Affinity Mapping A collaborative method for grouping observations: 1. **Capture observations**: Write each distinct observation, quote, or data point as a separate note 2. **Cluster**: Group related notes together based on similarity. Do not pre-define categories — let them emerge from the data. 3. **Label clusters**: Give each cluster a descriptive name that captures the common thread 4. **Organize clusters**: Arrange clusters into higher-level groups if patterns emerge 5. **Identify themes**: The clusters and their relationships reveal the key themes **Tips for affinity mapping**: - One observation per note. Do not combine multiple insights. - Move notes between clusters freely. The first grouping is rarely the best. - If a cluster gets too large, it probably contains multiple themes. Split it. - Outliers are interesting. Do not force every observation into a cluster. - The process of grouping is as valuable as the output. It builds shared understanding. ### Triangulation Strengthen findings by combining multiple data sources: - **Methodological triangulation**: Same question, different methods (interviews + survey + analytics) - **Source triangulation**: Same method, different participants or segments - **Temporal triangulation**: Same observation at different points in time A finding supported by multiple sources and methods is much stronger than one supported by a single source. When sources disagree, that is interesting — it may reveal different user segments or contexts. ## Interview Note Analysis ### Extracting Insights from Interview Notes For each interview, identify: **Observations**: What did the participant describe doing, experiencing, or feeling? - Distinguish between behaviors (what they do) and attitudes (what they think/feel) - Note context: when, where, with whom, how often - Flag workarounds — these are unmet needs in disguise **Direct quotes**: Verbatim statements that powerfully illustrate a point - Good quotes are specific and vivid, not generic - Attribute to participant type, not name: "Enterprise admin, 200-person team" not "Sarah" - A quote is evidence, not a finding. The finding is your interpretation of what the quote means. **Behaviors vs stated preferences**: What people DO often differs from what they SAY they want - Behavioral observations are stronger evidence than stated preferences - If a participant says "I want feature X" but their workflow shows they never use similar features, note the contradiction - Look for revealed preferences through actual behavior **Signals of intensity**: How much does this matter to the participant? - Emotional language: frustration, excitement, resignation - Frequency: how often do they encounter this issue - Workarounds: how much effort do they expend working around the problem - Impact: what is the consequence when things go wrong ### Cross-Interview Analysis After processing individual interviews: - Look for patterns: which observations appear across multiple participants? - Note frequency: how many participants mentioned each theme? - Identify segments: do different types of users have different patterns? - Surface contradictions: where do participants disagree? This often reveals meaningful segments. - Find surprises: what challenged your prior assumptions? ## Survey Data Interpretation ### Quantitative Survey Analysis - **Response rate**: How representative is the sample? Low response rates may introduce bias. - **Distribution**: Look at the shape of responses, not just averages. A bimodal distribution (lots of 1s and 5s) tells a different story than a normal distribution (lots of 3s). - **Segmentation**: Break down responses by user segment. Aggregates can mask important differences. - **Statistical significance**: For small samples, be cautious about drawing conclusions from small differences. - **Benchmark comparison**: How do scores compare to industry benchmarks or previous surveys? ### Open-Ended Survey Response Analysis - Treat open-ended responses like mini interview notes - Code each response with themes - Count frequency of themes across responses - Pull representative quotes for each theme - Look for themes that appear in open-ended responses but not in structured questions — these are things you did not think to ask about ### Common Survey Analysis Mistakes - Reporting averages without distributions. A 3.5 average could mean everyone is lukewarm or half love it and half hate it. - Ignoring non-response bias. The people who did not respond may be systematically different. - Over-interpreting small differences. A 0.1 point change in NPS is noise, not signal. - Treating Likert scales as interval data. The difference between "Strongly Agree" and "Agree" is not necessarily the same as between "Agree" and "Neutral." - Confusing correlation with causation in cross-tabulations. ## Combining Qualitative and Quantitative Insights ### The Qual-Quant Feedback Loop - **Qualitative first**: Interviews and observation reveal WHAT is happening and WHY. They generate hypotheses. - **Quantitative validation**: Surveys and analytics reveal HOW MUCH and HOW MANY. They test hypotheses at scale. - **Qualitative deep-dive**: Return to qualitative methods to understand unexpected quantitative findings. ### Integration Strategies - Use quantitative data to prioritize qualitative findings. A theme from interviews is more important if usage data shows it affects many users. - Use qualitative data to explain quantitative anomalies. A drop in retention is a number; interviews reveal it is because of a confusing onboarding change. - Present combined evidence: "47% of surveyed users report difficulty with X (survey), and interviews reveal this is because Y (qualitative finding)." ### When Sources Disagree - Quantitative and qualitative sources may tell different stories. This is signal, not error. - Check if the disagreement is due to different populations being measured - Check if stated preferences (survey) differ from actual behavior (analytics) - Check if the quantitative question captured what you think it captured - Report the disagreement honestly and investigate further rather than choosing one source ## Persona Development from Research ### Building Evidence-Based Personas Personas should emerge from research data, not imagination: 1. **Identify behavioral patterns**: Look for clusters of similar behaviors, goals, and contexts across participants 2. **Define distinguishing variables**: What dimensions differentiate one cluster from another? (e.g., company size, technical skill, usage frequency, primary use case) 3. **Create persona profiles**: For each behavioral cluster: - Name and brief description - Key behaviors and goals - Pain points and needs - Context (role, company, tools used) - Representative quotes 4. **Validate with data**: Can you size each persona segment using quantitative data? ### Persona Template ``` [Persona Name] — [One-line description] Who they are: - Role, company type/size, experience level - How they found/started using the product What they are trying to accomplish: - Primary goals and jobs to be done - How they measure success How they use the product: - Frequency and depth of usage - Key workflows and features used - Tools they use alongside this product Key pain points: - Top 3 frustrations or unmet needs - Workarounds they have developed What they value: - What matters most in a solution - What would make them switch or churn Representative quotes: - 2-3 verbatim quotes that capture this persona's perspective ``` ### Common Persona Mistakes - Demographic personas: defining by age/gender/location instead of behavior. Behavior predicts product needs better than demographics. - Too many personas: 3-5 is the sweet spot. More than that and they are not actionable. - Fictional personas: made up based on assumptions rather than research data. - Static personas: never updated as the product and market evolve. - Personas without implications: a persona that does not change any product decisions is not useful. ## Opportunity Sizing ### Estimating Opportunity Size For each research finding or opportunity area, estimate: - **Addressable users**: How many users could benefit from addressing this? Use product analytics, survey data, or market data to estimate. - **Frequency**: How often do affected users encounter this issue? (Daily, weekly, monthly, one-time) - **Severity**: How much does this issue impact users when it occurs? (Blocker, significant friction, minor annoyance) - **Willingness to pay**: Would addressing this drive upgrades, retention, or new customer acquisition? ### Opportunity Scoring Score opportunities on a simple matrix: - **Impact**: (Users affected) x (Frequency) x (Severity) = impact score - **Evidence strength**: How confident are we in the finding? (Multiple sources > single source, behavioral data > stated preferences) - **Strategic alignment**: Does this opportunity align with company strategy and product vision? - **Feasibility**: Can we realistically address this? (Technical feasibility, resource availability, time to impact) ### Presenting Opportunity Sizing - Be transparent about assumptions and confidence levels - Show the math: "Based on support ticket volume, approximately 2,000 users per month encounter this issue. Interview data suggests 60% of them consider it a significant blocker." - Use ranges rather than false precision: "This affects 1,500-2,500 users monthly" not "This affects 2,137 users monthly" - Compare opportunities against each other to create a relative ranking, not just absolute scores ## Output Format Use clear headers and structured formatting. Each finding should stand on its own — a reader should be able to read any single finding and understand it without reading the rest. ## Tips - Let the data speak. Do not force findings into a predetermined narrative. - Distinguish between what users say and what they do. Behavioral data is stronger than stated preferences. - Quotes are powerful evidence. Include them generously, with attribution to participant type (not name). - Be explicit about confidence levels. A finding from 2 interviews is a hypothesis, not a conclusion. - Contradictions in the data are interesting, not inconvenient. They often reveal distinct user segments. - Recommendations should be specific enough to act on. "Improve onboarding" is not actionable. "Add a progress indicator to the setup flow" is. - Resist the temptation to synthesize too many themes. 5-8 strong findings are better than 20 weak ones. --- Attribution added by YourAIKit. It is not part of the upstream file. - Source: https://github.com/anthropics/knowledge-work-plugins/tree/main/product-management/skills/synthesize-research - Credit: Anthropic, Knowledge Work Plugins (product-management) - Upstream copy: anthropics/knowledge-work-plugins at commit 33d3dea41a341b341d4f5da2e8801ae0c89be2b8, product-management/skills/synthesize-research/SKILL.md - Copyright: held by the upstream authors credited above - License: Apache-2.0. Full text: https://youraikit.com/licenses/anthropics-knowledge-work-plugins-product-management/LICENSE - The full upstream license text (LICENSE) follows. License (LICENSE): ```text Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ```