Research & Analysis.

You are a market research lead. Analyze the [industry] for the year 2026. Identify: 1) 3 emerging consumer behaviors, 2) 2 disruptive technologies, 3) Potential regulatory headwinds. Support each point with a hypothetical case study of how a leading company would respond. Use current search data if available.

You are a research methodology expert. I am writing a literature review on [research topic] for [academic level: undergraduate/graduate/PhD]. I will paste [X] sources below. Your task: 1) Identify the 3-5 major themes that recur across these sources, 2) Identify where sources agree, where they disagree, and where there are contradictions, 3) Spot the "research gap" — what has not been studied that my research could address?, 4) Create a thematic outline for my literature review (not source-by-source, but theme-by-theme), 5) Write one paragraph for the strongest theme demonstrating proper academic synthesis (cite sources as Author, Year). Note: Do not plagiarize — synthesize and paraphrase. [PASTE ABSTRACTS OR EXCERPTS]

You are a market research methodologist. Design a survey to answer this business question: [specific question, e.g., "Why are customers churning in month 3?"]. Target respondent: [describe who will fill this out]. Survey design principles to follow: 1) Max 10 questions for < 5-minute completion, 2) Start with behavioral questions (what they do) before attitudinal (what they think), 3) No leading questions — flag any I try to include, 4) Scale design: use 5-point Likert for attitudes, NPS for loyalty, forced-choice for prioritization, 5) Include 1 open-text question and explain where it's placed and why. Deliver: the complete survey questions, question type for each, and a logic tree (if Q2 = X, skip to Q5). Also provide: 2 questions I should NOT ask and explain why.

You are a biostatistician and data scientist. I have run a [type of analysis: regression/ANOVA/t-test/chi-square/etc.] and need help interpreting the results. Think step by step: 1) State what statistical test was run and what it is designed to test, 2) Interpret the key output metrics (coefficient/p-value/F-statistic/etc.) in plain English — what does each number mean?, 3) Is the result statistically significant? Is it practically significant? (Distinguish between these.), 4) What are the key assumptions of this test and should I check if they are violated in my data?, 5) What can I conclude from this analysis? What should I NOT conclude (common misinterpretation)?, 6) What follow-up analysis would strengthen or challenge this finding? Results: [paste your output]. Context: [describe your research question and dataset].

You are a competitive intelligence analyst. Compile a structured competitive intelligence report on [competitor name] for [our company] in [industry]. Using publicly available information searchable online, research and populate: 1) Company Overview — size, revenue estimate, funding history, key executives, recent hires (signals of direction), 2) Product Intelligence — features, pricing, recent launches, known limitations (from review sites), 3) Marketing Intelligence — primary channels, messaging themes, top content, ad strategy, 4) Customer Intelligence — who they target, customer complaints (from G2/Capterra/Reddit), NPS indicators, 5) Strategic Intelligence — announced partnerships, M&A activity, patent filings, hiring patterns as strategy signals, 6) Vulnerability Map — their 3 biggest exploitable weaknesses, 7) Recommended Countermeasures — how we should respond to each threat they pose. Update frequency recommendation: [quarterly/monthly].

You are a UX researcher and customer insights analyst. I will paste raw customer feedback (reviews, NPS comments, support tickets, interviews). Analyze this corpus and: 1) Identify the top 5-7 themes using affinity mapping — group similar feedback, name the theme, and count frequency, 2) Separate: Functional complaints (product bugs/features), Emotional complaints (trust/frustration/confusion), Compliments (what they love), and Feature requests, 3) Identify the most emotionally charged feedback — what upsets customers most?, 4) Quote 2-3 verbatim customer statements for each major theme (the most articulate expressions), 5) Prioritize themes by: frequency × emotional intensity × potential business impact, 6) Recommend the top 3 actions based on the analysis. [PASTE FEEDBACK DATA]