Data AnalysisPerplexity
Perplexity Prompts for Data Analysis
Perplexity is valuable for contextualising data analysis with current market benchmarks — finding industry averages, recent research, and current data to compare your analysis against. Always request source citations for any benchmark data.
8 copy-paste ready prompts — optimised for Perplexity.
8 Perplexity Data Analysis Prompts
Data Summary Narrative
Analyse this dataset and write a 300-word narrative summary for a non-technical [executive/stakeholder] audience. Data: [paste data or description]. Cover: the key trend, any anomalies or outliers, comparison to [benchmark/previous period], and 2-3 actionable recommendations. Avoid technical jargon.
SQL Query
Write a [database: PostgreSQL/MySQL/BigQuery] SQL query to [describe what you need]. Tables available: [table names and brief descriptions]. Relevant columns: [column names and types]. Requirements: [any specific conditions, joins, groupings]. Include comments explaining each step and an index recommendation if applicable.
Python Data Analysis
Write a Python script using pandas to analyse this dataset: [describe data structure]. Tasks: 1. Load from [CSV/JSON/database]. 2. Clean: handle nulls, remove duplicates, fix types. 3. Compute: [specific metrics or aggregations]. 4. Output: [format: print/CSV/chart]. Include comments and print intermediate results for debugging.
A/B Test Analysis
Analyse the results of this A/B test: [paste results including sample sizes, conversion rates, and confidence level]. Answer: 1. Is the result statistically significant? (show calculation). 2. What is the practical significance (effect size)? 3. Which variant should we ship and why? 4. Any caveats or confounding factors to note?
Dashboard KPI Brief
Design a KPI dashboard brief for a [type of team: marketing/product/sales/operations] team. Business goal: [goal]. Include: 5-7 key metrics with definitions, how each is calculated, data source, target/benchmark, update frequency, and the 1-2 most important metrics that should be prominently displayed.
Data Visualisation Recommendations
Recommend the best chart types to visualise the following data relationships: [describe your data and what you want to show]. For each recommendation: chart type, reason for choosing it, key design considerations (axes, colours, labels), and any pitfalls to avoid. Data: [paste or describe data].
Cohort Analysis Explanation
Analyse this cohort retention data and explain the key findings in plain English: [paste cohort table]. Identify: the strongest and weakest performing cohorts, whether retention is improving or declining over time, the period with the steepest drop-off, and 2 hypotheses for why certain cohorts perform better.
Forecast Summary
Given this historical data: [paste data], generate a 12-month forecast narrative. Include: the forecasting method used and why, key assumptions, the central forecast with a high/low range, factors that could cause the forecast to miss, and how to monitor performance against forecast monthly.
Tips for using Perplexity for Data Analysis
- Ask 'what are current industry benchmarks for [metric] in [industry]? Cite sources' to contextualise your data
- Use Perplexity to find 'recent studies or reports on [topic] published in the last 6 months'
- Always request 'include publication dates' for benchmark data — recency matters for comparative analysis
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