17 ChatGPT Prompts For Data Analysis To Save Time In 2025

Are you wondering how you can prompt ChatGPT for data analysis?

The best data analysis prompts can help you create tables and get recommendations on your data to make better-informed decisions.

But here’s the thing to keep in mind: your output will only be as good as your prompt. 

In this article, I’ll walk you through 17 ChatGPT prompts for data analysis that you can use to save time from the ones that our team has been using.

➡️ Throughout this article, I’ll be using ChatGPT inside of Team-GPT (our collaborative platform) since it allows me to switch between different ChatGPT models to refine results.

Data Consolidation & Cleaning Prompts

My favourite ChatGPT use case for data analysis is the ability to consolidate and clean data so it can help me prepare the data for further accurate and consistent analysis.

The prompts I’m about to share can help you streamline the process of merging datasets, removing errors, and standardizing information to create a reliable foundation for further analysis:

1. Sales Transaction Data Discrepancy & Cleaning

Prompt: “I have a CSV file [insert CSV file] containing sales transaction data from multiple store locations (columns: TransactionID, StoreID, SaleDate, ProductID, Quantity, and Price). Some rows have missing or incorrect StoreIDs, and some of the prices look off. Please outline a step-by-step approach to identify and handle these discrepancies, and provide sample Python code for cleaning tasks like removing or imputing missing StoreIDs and fixing price outliers.”

Pro tip: Type out the column in your CSV file for more accurate analysis (e.g., TransactionID, StoreID, SaleDate).

Output:

I gave ChatGPT example data to help me clean up sales transaction data. 

It helped me identify and correct missing and incorrect StoreIDs by replacing invalid values with the most frequent store ID.

ChatGPT was also able to detect price outliers using the IQR method and replace extreme values with the median price to maintain realistic pricing.

Why this is good: This prompt can help you ensure that the dataset is clean and reliable, to prevent errors in future sales analysis and decision-making.

2. Time Series Data Consolidation & Cleaning

Prompt: “I’m pulling data from several APIs (weather, stock prices, and social media sentiment) to create a unified dataset. However, the timestamps don’t match perfectly, and there are gaps in data collection for some intervals. Please describe how to align these time series data, deal with missing intervals, and combine the sources into a single pandas DataFrame—include best practices for handling edge cases like inconsistent time zones.”

Output:

In this example, ChatGPT helped me align all three datasets by converting timestamps to a common time zone and resampling them to a consistent hourly frequency.

The tool then combined the DataFrames on the shared DateTime index and checked for NaNs or inconsistencies.

Why this is useful: This process helped me unify disparate time series into a consolidated dataset, which made further analysis and insights far smoother and more accurate for my team.

3. Survey Data Consolidation & Validation

Prompt: “I’m trying to consolidate survey results (satisfaction ratings, open-ended responses, and demographic info) gathered through different platforms. Some of the demographic questions were optional, so many users have partial responses. Some numeric ratings include typographical errors. I want you to consolidate and clean the data, correcting typos, and ensuring that the final dataset has no errors.’’

Output:

ChatGPT helped me correct typographical errors in the satisfaction ratings of survey respondents.

The tool also helped me clean and standardize the demographic data, and also converted text-based age errors to numeric values, amongst other things.

Why this is good: This helped me create a fully structured and usable dataset, which makes it easier for my marketing team to analyze satisfaction trends and demographic insights.

Prompts For Analyzing Data Sets

Analyzing data sets is usually a time-consuming process because it involves uncovering patterns, relationships in data, and trends to answer specific questions or resolve problems.

I’ve been using these ChatGPT prompts to help us identify insights in our data and make better decisions: 👇

4. Exploratory Analysis of Customer Purchase Behavior

Prompt: “I have a dataset of purchase transactions containing customer IDs, product categories, transaction timestamps, and payment amounts. I want to understand the distribution of spending, identify repeat customers, and see if certain product categories correlate with higher revenue. Create me a table with your findings using this data: [insert data sets.]

Output:

After providing ChatGPT’s o1 with a random sample of data, it compiled the table to summarize the key metrics – including total transactions, spending distribution, and the highest-spending product categories.

What I particularly like about this prompt is that ChatGPT illustrates each finding, so it’s easier to report on them.

Additionally, ChatGPT helped me identify repeat customers and confirmed that electronics drives a substantial portion of the example company’s earnings.

Why is this good: This prompt can help you quickly spot high-value segments and find out areas to further analyze in your data.

5. Summarize dataset statistics

Prompt: “Provide a comprehensive summary of the key statistics for [dataset name], including measures of central tendency, dispersion, and distribution for each variable. Create a table using [dataset].’’

Output:

After providing ChatGPT with example data, the tool calculated the mean, median, mode, min, max, standard deviation, and IQR for each variable from the data.

The output notes which variables showed wider dispersion (e.g., higher standard deviation or IQR) and which had clear modes.

Why this is good: This would make it easier for data scientists to compare the data’s trends and ranges side-by-side. It can also point you toward potential areas for deeper investigation or targeted data cleaning.

Analyze data with Team-GPT by inserting a sheet

Before I proceed with more ChatGPT prompts, I just wanted to show you Team-GPT’s new feature that makes it one of the best AI tools for data analysis: Spreadsheet Analyzer.

The way it works is that you can upload a sheet, and you will be able to summarize it and compare it to other sheets.

Here is the analysis that I was provided with for this sample sheet:

Learn more about how data analysts are using Team-GPT.

6. Evaluating Customer Feedback and Sentiment

Prompt: “This CSV file [insert CSV file] holds customer feedback, ratings, and textual comments. I need to assess overall satisfaction, sentiment polarity, and common complaint themes. Visualize the results and draw actionable insights.”

Output:

ChatGPT helped me calculate the average satisfaction rating after using the sample data on customer feedback and sentiment that I provided it with in CSV format.

It also checked how often each rating was used, and generated polarity scores for each comment so I can see a numeric value (1-5) of how positive or negative the comment is.

Why this is good: This prompt can help you prioritize the biggest pain points that negatively impact customers.

7. Help design a dashboard

Prompt: “Outline the structure and components of an interactive dashboard to present key insights from [dataset name], focusing on user interactivity and data storytelling.”

Output:

If you are wondering how to build a dashboard using your available data – ChatGPT can help you with this as well.

It helped me design (roughly) a dashboard layout featuring KPI cards, dynamic charts, a map, and a data table to help my executive team explore sales insights.

ChatGPT also used cross-filtering and storytelling elements in the mock dashboard, which is why this is one of my favourite prompts.

Why it’s good: This can help you visualize what a dashboard can look like with your data and also show which data points to prioritize.

Statistical Analysis Prompts

Statistical analysis applies mathematical techniques to help data scientists quantify relationships, test hypotheses, and draw conclusions from their data.

These ChatGPT prompts can help you save time from doing statistical analysis manually: ⬇️

8. Perform hypothesis testing

Prompt: “Formulate and conduct appropriate hypothesis tests for [specific question] using [dataset name]. Interpret the results and their practical significance.”

Output:

ChatGPT helped me conduct a hypothesis test to determine whether customers who used discounts spent less on average.

The tool compared their purchase amounts using the sample data I provided it with, and found a statistically significant difference.

In the context of this example, it helped me find that discount users spent significantly less, with a p-value far below 0.05 – helping me reject the null hypothesis.

Why this is good: This prompt can be used to assess the impact of factors like discounts to test out your hypothesis.

9. Develop regression models

Prompt: “Suggest suitable regression models for predicting [target variable] in [dataset name]. Include model selection criteria and validation methods.”

Output: 

ChatGPT helped me predict monthly customer spending after I asked it to use regression models with sample data.

The platform selected features based on correlation analysis and evaluating models using RMSE and R².

Why this is good: This prompt helped me identify the best approach for forecasting customer spending (from this example), enabling better business decisions for targeted marketing and personalized recommendations.

10. Conduct time series analysis

Prompt: “Outline a comprehensive approach to analyze the time series data in [dataset name], including trend analysis, seasonality detection, and forecasting methods.”

Output:

ChatGPT helped me conduct a time series analysis on our sales data. The tool helped me by:

• Identifying recurring trends.
• Detecting seasonality.
• Applying forecasting models, such as ARIMA, to predict future sales.

The main takeaway from the sample data was that the sales are increasing over time with a clear annual seasonal pattern.

Why this was good: In the context of this example, this prompt helped me build a reliable sales forecast for better inventory planning and revenue projections.

11. Generate insightful plots

Prompt: “Create a list of the most informative visualizations for [dataset name], including the type of plot, variables to use, and insights we can gain from each.”

Output:

I prompted ChatGPT to generate insightful plots using the sample data that I provided to the platform.

It analyzed sales data using multiple visualizations, including:

• Trends over time.
• Store performance.
• Product category breakdowns.
• The relationship between volume and revenue.

These insights would have been useful for me if I used our real data because they highlighted top-performing stores, seasonal fluctuations in sales volume and revenue-driving product categories.

Why this prompt is useful: This prompt can help you pinpoint areas for improvement, such as focusing on high-contributing product categories and addressing underperforming product lines.

Data Interpretation & Reporting Prompts

Interpreting and reporting data usually involves transforming complex findings into clear, actionable insights for your stakeholders. 

This is a process that can take hours, if not days.

Luckily, there are ChatGPT’s powerful AI models that can help you with summarizing results effectively and presenting them to your leadership team:

12. Generate automated insights

Prompt: “Analyze [dataset name] and provide a concise report of the top 5 most significant insights, including supporting visualizations and statistical evidence.”

Output:

I fed ChatGPT’s engine purchase patterns across 50,000 customer transactions.

The generative AI tools helped me identify key revenue drivers and growth opportunities after conducting statistical testing and data visualization techniques.

It helped me uncover seasonal patterns, and an interesting finding – that the top 15% of customers generate 68% of the total revenue for the business.

How you can use this: You can use this prompt to help you prioritize your quarterly budget allocation (e.g., focusing more budget on high-value customer retention).

13. Explain model predictions

Prompt: “For the [specific model] trained on [dataset name], develop a method to interpret and explain individual predictions, focusing on feature importance and local explanations.”

ChatGPT helped me develop a multi-faceted approach to model interpretation using SHAP values, counterfactual explanations, and feature interaction analysis.

The tool created a dashboard that (from the sample data) loan officers can use to:

• Explore model predictions.
• Understand key risk factors.
• Identify what changes would be needed for a loan application to be approved.

Why is this useful: You can use this prompt for risk management via probability and making predictions based on your data.

14. Create an executive summary

Prompt: “Synthesize the key findings from our analysis of [dataset name] into a concise executive summary. Highlight the most impactful insights, their business implications, and prioritize 3-5 data-driven recommendations for immediate action. Include relevant visualizations that effectively communicate the main points to non-technical stakeholders.”

Output:

I was able to generate an executive summary with ChatGPT, as the tool summarized key insights from the dataset that I provided it with.

It typed out a summary going over revenue-driving customer segments, seasonal trends, and performance by marketing channel, with email marketing yielding the highest ROI.

Why is this good: This prompt can help you translate raw data into a clear executive summary for your leadership team.

Decision-Making From Data Prompts

Decision-making from data requires translating analytical insights into practical actions or strategies, and it’s no easy feat.

These ChatGPT prompts can help you make data-driven decisions:

15. Identify key influencing factors

Prompt: “Analyze the [specific dataset] and identify the top 3 factors influencing [target variable]. Provide actionable recommendations based on these insights to improve [business objective].”

Output:

ChatGPT helped me analyse the customer churn data I provided it with, and it identified the top 3 predictive factors, including:

• Subscription length.
• Customer support interactions.
• Product engagement.

The tool helped me uncover the pain points in the customer experience from the data it was provided.

Why this is good: You can not only spot the key influencing factors in data, but also provide you with recommendations on next steps, which I found useful for more junior employees.

16. Forecast and strategize

Prompt: “Based on the historical data from [dataset name], forecast the next quarter’s [key performance indicator] and suggest strategic adjustments to optimize our performance. Include potential risks and mitigation strategies.”

Output:

I used ChatGPT to help me forecast Q2 revenue using a SAMIRA model.

The tool predicted a 7.4% YoY increase by using the sample data that I provided to its engine.

What stood out to me was that it outlined three strategic adjustments, including a dynamic pricing strategy, to promote harder in May, and not to expand inventory for certain categories.

Why this is useful: Such prompts can help you develop a data-driven strategy to capitalize on seasonal trends and to help you mitigate operational risks, such as placing orders 30 days earlier and securing secondary suppliers to reduce stockouts and lost sales.

17. Evaluate marketing campaign effectiveness

Prompt: “Evaluate the effectiveness of our last two [marketing/product] campaigns using the data from [insert data with specific timeframe]. Compare their performance across key metrics and recommend which approach we should continue or modify for better results.”

Output:

I asked ChatGPT to help me compare the performance of our email retargeting and social media campaigns using example data.

The tool performed analysis on the campaigns’ conversion rate, CAC, and ROI to figure out which campaign performed better.

Even though this might look a bit basic from my example, you can combine the performance of 5-6 different campaigns and compare performance data across different ad groups or campaigns.

Why this is good: This can be used by marketing teams, especially ones running campaigns on Meta or Google Ads, since you can see which ad groups perform the best and allocate more of your budget there.

How To Write Better AI Prompts For Data Analysis

The thing about generative AI tools like ChatGPT is that they work best when you provide them with clear and structured instructions.

There are 3 key elements of a good prompt for data analysis:

1. Goal – Define your desired outcome from the prompt

Your prompts for data analysis should clearly state what you need ChatGPT to do. I have found that actionable verbs are ideal to guide the response.

Examples:

• “Create a detailed project plan for conducting a competitor analysis.’’
• “Summarize the key takeaways from this report.’’
• “Analyze this data set and show me how much revenue I made this year in comparison to last year.’’

2. Context – Provide the data that ChatGPT should be working with

The data sets are the ‘’context’’ for ChatGPT in your prompts, which shape the response provided by ChatGPT.

The rule of thumb is that the more data and details you provide to the platform, the better the AI can tailor its answer to your needs.

Pro tip: I’ve found that directly uploading CSV files works best inside ChatGPT for data analysis.

3. Persona – Set the right tone of voice

A persona tells ChatGPT and other AI engines how they should act to improve the relevance of their response.

Example personas for data analysis:

• A data scientist is conducting time series analysis.
• A marketing manager is looking for results from a marketing campaign.

Pro tip: You can use Team-GPT’s pre-made prompts and Personas instead of always building your own from scratch every time.

Team-GPT provides over 100 ready-made prompts and Personas for various use cases, including data analysis.

Here’s a video of how you can use Team-GPT’s prompt library:

These Prompts and personas are useful for several reasons, as they:

• Save time—no need to craft new prompts every time.
• Ensures consistency—every piece of research will be tailored to your organization.
• Are easy to customize—you can adjust details while keeping core instructions intact.

You can store custom-made best-performing prompts, so they’re always ready when you need them.

Want to learn more about prompting? Check out our video on Prompting Methodology to see how structured prompts can improve your results:

Here’s An Example of a Bad and a Good AI Prompt For Data Analysis

As you might have already experienced, not all ChatGPT prompts deliver useful results for analyzing data sets. 

A vague prompt can lead to generic responses, while a well-structured prompt provides actionable and relevant output. Here’s an example:

An example of a bad prompt

“Why are our sales going down?”

Why it’s bad:

• Too vague—ChatGPT doesn’t know what kind of sales data you have.
• Lacks details around your industry.

An example of a good prompt

‘’I am a data analyst working for a mid-sized e-commerce company specializing in health and wellness products. We are experiencing a decline in conversion rates on our website, particularly during the checkout process. You are an expert data analyst and statistical modeler with deep knowledge of e-commerce metrics, including customer journey analysis, A/B testing, and statistical significance. Help me identify potential reasons for the decline in conversion rates, suggest relevant data analysis methods, and provide step-by-step guidance to conduct this analysis. Also, suggest data visualization techniques to clearly communicate findings to stakeholders.’’

Why it’s good:

• Specifies the problem and industry (a decline in conversion rates during the checkout process).
• Defines how you want ChatGPT to act.
• Adds a request (identify potential reasons and suggest data visualization techniques).

💡 Pro tip: To get the best results from ChatGPT, you should include context, details, data points, if possible, and a clear objective in your prompt.

Next Steps: Analyze Data Alongside Your Team With ChatGPT on Team-GPT

Team-GPT helps enterprises customize an AI model for data analysis and visualization of data (for deeper data understanding and finding new insights).

You can also use our brand new spreadsheet analyzer that can help you get findings from your Excel files.

Our collaborative AI solution strives to make AI an integral part of your organization and provides you with features that will help your team operate more efficiently. 

Lastly, you can import your ChatGPT chat history directly into Team-GPT in a matter of seconds.

Apart from that, you can access:

• A comprehensive pre-made prompt library and personas to create efficient workflows.
• Detailed usage analytics to track employee engagement.
• Enterprise-grade security ensures data privacy and the ability to host the platform on your servers.

Sounds interesting? Book a demo with one of our AI adoption experts to help you learn more about our platform.

Read More:

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• 10 Best AI Tools for Product Managers in 2025
• 5 Best ChatGPT Use Cases in 2025 [Prompts Included]
• ChatGPT Pricing: Is it Good Value for Money
• 15 Best ChatGPT Plugins You Should Have
• 10 Best AI Marketing Tools You Should Have
• 10 Best ChatGPT Alternatives You Must Try