SumoPPM is your perfect ally for quickly summarizing and analyzing data sets, providing an initial understanding of patterns and outliers. Here’s how you can leverage this powerful tool to gain valuable insights and make informed decisions.
Classification of the 100 Best Prompts for Data Analysis with GenBI using SumoPPM
To facilitate the understanding and use of these prompts, we have classified them into different categories covering various areas of data analysis. Each category is designed to help you make the most of your data with SumoPPM.
Data Exploration and Evaluation
1. Summary of Dataset Characteristics
- Prompt: Summarize the main characteristics of this dataset, including data types, missing values, and basic statistics.
- Response (practical example): "Our dataset contains numeric and categorical columns, with 5% missing values in the 'sales' column. Basic statistics indicate that the average sales value is 50,000, with a standard deviation of 10,000."
2. Summary for Non-Technical Stakeholders
- Prompt: Generate a concise summary of this dataset for non-technical stakeholders.
- Response (practical example): "The analysis of our dataset reveals that monthly sales average 50,000 units, with slight seasonal variation. Most products are sold in the northern and southern regions."
3. Distribution of Numeric Variables
- Prompt: Provide information about the distribution of numeric variables and identify any skewness or significant outliers.
- Response (practical example): "The distribution of the 'sales' variable is slightly right-skewed, with several outliers in the upper range. This suggests some exceptionally high sales."
4. Detection of Outliers in 'Sales'
- Prompt: Detect outliers in the 'sales' column and explain their impact on the analysis.
- Response (practical example): "Outliers were detected in the 'sales' column with values above 100,000. These outliers can distort trend analysis, so it is recommended to handle them appropriately."
5. Correlation Matrix
- Prompt: Calculate and interpret the correlation matrix for numeric variables, highlighting strong relationships.
- Response (practical example): "The correlation matrix reveals a strong positive relationship (0.85) between 'advertising expenses' and 'sales'. This indicates that an increase in advertising is associated with an increase in sales."
6. Data Quality Assessment
- Prompt: Assess the quality of the data, focusing on missing values, duplicate records, and data entry errors.
- Response (practical example): "The dataset contains 2% missing values in various columns and 1% duplicate records. Additionally, data entry errors were identified in the 'price' column, where some values were negative."
7. Identification of Anomalous Patterns
- Prompt: Identify anomalous patterns in 'user activity' records and suggest possible causes.
- Response (practical example): "Anomalous user activity patterns were detected at night, which could indicate misuse of accounts or automated activity."
8. Usage Frequency Analysis
- Prompt: Analyze the frequency of platform usage by users.
- Response (practical example): "The analysis reveals that active users use the platform an average of three times per week, while inactive users use it less than once a month."
9. Time on Site Analysis
- Prompt: Analyze the average time users spend on the website.
- Response (practical example): "The average time on the site is 5 minutes per visit, with returning users spending up to 10 minutes."
10. Bounce Rate Analysis
- Prompt: Analyze the website's bounce rate and suggest ways to reduce it.
- Response (practical example): "The current bounce rate is 40%. Improving content relevance and site load speed could help reduce this rate."
11. Customer Demographics Analysis
- Prompt: Analyze customer demographics to better understand the target market.
- Response (practical example): "60% of customers are between 25 and 35 years old, with a slight female majority. This information can help tailor marketing strategies."
12. Payment Preferences Analysis
- Prompt: Analyze customer preferences for payment methods.
- Response (practical example): "60% of customers prefer to pay by credit card, followed by 25% using PayPal and 15% opting for bank transfers."
Data Cleaning and Preprocessing
13. Methods for Cleaning Missing Data
- Prompt: Suggest methods and best practices for cleaning and preprocessing this messy dataset.
- Response (practical example): "It is recommended to use mean imputation for missing values in 'sales' and remove duplicate records to ensure data integrity."
14. Handling Outliers in 'Sales'
- Prompt: How can I effectively deal with outliers in the 'sales' column to ensure they don't distort the analysis?
- Response (practical example): "To handle outliers in 'sales', you can apply techniques like winsorization or use the percentile method to limit extreme values."
15. Duplicate Records Removal
- Prompt: Remove duplicate records from the dataset.
- Response (practical example): "The dataset has been cleaned of duplicate records, reducing the total number of records from 10,000 to 9,850."
16. Date Transformation to Weekdays
- Prompt: Transform dates in the column “X” into weekdays.
- Response (practical example): "The dates in the 'X' column have been transformed into weekdays, allowing for a more detailed analysis of daily sales patterns."
Statistical Analysis and Hypothesis Testing
17. Design of Hypothesis Tests
- Prompt: Help me design a hypothesis test to determine if there is a significant difference in conversion rates between two versions of the website.
- Response (practical example): "A hypothesis test has been designed to compare conversion rates of two website versions. The null hypothesis states that there is no significant difference between the conversion rates."
18. Correlation between 'Advertising Expenses' and 'Sales Revenue'
- Prompt: Calculate the correlation coefficient between 'advertising expenses' and 'sales revenue' in our dataset. Is the relationship significant?
- Response (practical example): "The correlation coefficient between 'advertising expenses' and 'sales revenue' is 0.87, indicating a strong positive relationship. This relationship is significant at the 0.01 level."
19. T-Test for Comparing Means
- Prompt: Perform a t-test to compare the means of two independent groups in our data. What are the conclusions?
- Response (practical example): "The t-test between groups A and B shows a significant difference in means, with a p-value of 0.03. This suggests a statistical difference in average sales between the two groups."
20. Linear Regression Analysis
- Prompt: Perform a linear regression analysis to predict 'sales' based on 'advertising expenses'. Interpret the coefficients.
- Response (practical example): "The linear regression analysis shows that for every 1,000 increase in 'advertising expenses', 'sales' increase by 2,500 units. The coefficient of determination (R²) is 0.75, indicating that the model explains 75% of the variability in sales."
21. Sample Size Calculation
- Prompt: Calculate the sample size needed for a hypothesis test with specified power and effect size.
- Response (practical example): "To achieve a statistical power of 80% and a medium effect size, a sample of at least 150 observations per group is needed."
Marketing and Sales Analysis
22. Time Series Analysis
- Prompt: Examine time series data to identify seasonality or trends and summarize your findings.
- Response (practical example): "The time series analysis shows clear seasonality with sales peaks in summer and winter. A growing trend is also observed over time."
23. Marketing Impact Analysis
- Prompt: Evaluate the impact of the latest marketing campaign on sales.
- Response (practical example): "The marketing campaign increased sales by 20% in the first month, with a 150% return on investment."
24. Competitor Analysis
- Prompt: Perform a competitor analysis to identify competitive advantages.
- Response (practical example): "The competitor analysis reveals that our products are competitively priced, but we need to improve our social media presence to match our competitors."
25. Discount Impact Analysis
- Prompt: Evaluate the impact of discounts on sales.
- Response (practical example): "Discounts increased sales by 25% but reduced the profit margin by 10%. Finding a balance between sales and profitability is necessary."
26. Cost-Benefit Analysis
- Prompt: Perform a cost-benefit analysis for a new project.
- Response (practical example): "The cost-benefit analysis indicates that implementing the new CRM system could save 50,000 euros annually in operational costs."
27. Social Media Advertising ROI Analysis
- Prompt: Calculate the ROI of social media advertising campaigns.
- Response (practical example): "The ROI of Facebook campaigns is 120%, while Instagram campaigns yield 150%, suggesting better performance on Instagram."
28. Expansion Strategy Analysis
- Prompt: Analyze data to evaluate the feasibility of expanding into new markets.
- Response (practical example):
"The analysis suggests that expanding into Asian markets could increase revenues by 30% due to high demand for tech products."
29. Discount Strategy Impact Analysis
- Prompt: Evaluate the impact of different discount strategies on sales.
- Response (practical example): "Volume discounts increased sales by 15%, while time-limited discounts generated a 20% increase."
30. Purchase Trend Analysis
- Prompt: Identify emerging purchase trends among customers.
- Response (practical example): "Data shows a growing trend towards eco-friendly products, with a 25% increase in sales in this category over the past year."
31. New vs. Returning Customer Sales Analysis
- Prompt: Calculate the proportion of sales from new customers versus returning customers.
- Response (practical example): "40% of sales come from new customers, while 60% come from returning customers, indicating a loyal customer base."
32. Purchase Frequency Analysis
- Prompt: Analyze customer purchase frequency to identify patterns.
- Response (practical example): "The analysis reveals that loyal customers purchase on average every 45 days, while new customers make their second purchase after 90 days."
33. Customer Reviews Impact Analysis
- Prompt: Analyze how customer reviews affect sales.
- Response (practical example): "Products with positive reviews have 20% more sales than products with negative or no reviews."
34. Video Advertising Effectiveness Analysis
- Prompt: Analyze the effectiveness of video advertising compared to other formats.
- Response (practical example): "Video ads have a conversion rate of 5%, higher than the 3% rate of graphic ads."
35. Click-Through Rate (CTR) Analysis in Ad Campaigns
- Prompt: Evaluate the CTR of different advertising campaigns and suggest improvements.
- Response (practical example): "The average CTR is 2%. Improving ad design and targeting could increase this rate."
36. Social Media Advertising ROI Analysis
- Prompt: Calculate the ROI of social media advertising campaigns.
- Response (practical example): "The ROI of Facebook campaigns is 120%, while Instagram campaigns yield 150%, suggesting better performance on Instagram."
37. Loyalty Program Return Analysis
- Prompt: Evaluate the return of loyalty programs in terms of retention and sales.
- Response (practical example): "Loyalty programs have increased customer retention by 20% and sales by 15%."
38. Discount Strategy Effectiveness Analysis
- Prompt: Evaluate the impact of different discount strategies on sales.
- Response (practical example): "Volume discounts increased sales by 15%, while time-limited discounts generated a 20% increase."
39. Dynamic Pricing Strategy Impact Analysis
- Prompt: Evaluate the effectiveness of dynamic pricing strategies.
- Response (practical example): "Implementing dynamic pricing has increased sales by 10% and optimized profit margins."
40. Discount Impact Analysis
- Prompt: Evaluate the impact of discounts on sales and profitability.
- Response (practical example): "Discounts increased sales by 25% but reduced the profit margin by 10%. Finding a balance between sales and profitability is necessary."
Customer and User Behavior Analysis
41. Churn Analysis
- Prompt: Identify characteristics of users who have left the service in the past six months.
- Response (practical example): "Users who have left the service typically have lower interaction with the platform and come from lower-income regions."
42. Customer Segmentation
- Prompt: Perform customer segmentation based on their purchasing habits.
- Response (practical example): "Customer segmentation has identified three main groups: frequent buyers, occasional buyers, and new customers. Each group has different product preferences."
43. Shopping Basket Analysis
- Prompt: Analyze joint purchase patterns to identify products that sell together.
- Response (practical example): "Basket analysis reveals that customers who buy laptops also tend to buy mice and keyboards in the same transaction."
44. New Features Adoption Analysis
- Prompt: Evaluate the adoption of new product features by users.
- Response (practical example): "The new live chat feature has been adopted by 30% of active users in the first month, with high reported satisfaction."
45. Mobile vs. Desktop Traffic Analysis
- Prompt: Compare website traffic from mobile devices versus desktop devices.
- Response (practical example): "60% of traffic comes from mobile devices, with a 3% conversion rate, while 40% comes from desktop, with a 5% conversion rate."
46. Customer Loyalty Analysis
- Prompt: Analyze customer loyalty rates and suggest strategies for improvement.
- Response (practical example): "The loyalty rate is 40%. Implementing a rewards program and improving after-sales service could increase this rate."
47. Customer Return Rate Analysis
- Prompt: Calculate the customer return rate and suggest ways to increase it.
- Response (practical example): "The customer return rate is 25%. Implementing a loyalty program could increase this rate by 10%."
48. Customer Satisfaction Analysis
- Prompt: Analyze survey data to evaluate customer satisfaction.
- Response (practical example): "Survey results indicate an overall satisfaction of 4.2 out of 5, with high scores for product quality and delivery times."
49. Customer Reviews Impact Analysis
- Prompt: Analyze how customer reviews affect sales.
- Response (practical example): "Products with positive reviews have 20% more sales than products with negative or no reviews."
50. Loyalty Program Return Analysis
- Prompt: Evaluate the return of loyalty programs in terms of retention and sales.
- Response (practical example): "Loyalty programs have increased customer retention by 20% and sales by 15%."
Operational Analysis
51. Employee Turnover Analysis
- Prompt: Analyze employee turnover rates and their causes.
- Response (practical example): "The turnover rate is 15% annually, with the main reasons for leaving being lack of growth opportunities and competitors offering better benefits."
52. Delivery Times Analysis
- Prompt: Evaluate product delivery times to identify delays and improve logistics.
- Response (practical example): "The average delivery time is 5 days, with 10% of orders delayed due to supply chain issues."
53. Product Quality Analysis
- Prompt: Analyze returns and complaints to evaluate product quality.
- Response (practical example): "5% of products sold are returned, with the main complaints related to manufacturing defects and product description discrepancies."
54. Employee Productivity Analysis
- Prompt: Evaluate employee productivity using performance data.
- Response (practical example): "The evaluation shows that sales department employees exceed their monthly targets by 10%, while the technical support department is below target by 5%."
55. Support Response Time Analysis
- Prompt: Evaluate customer support team response times.
- Response (practical example): "The average support response time is 2 hours, but reducing it to 1 hour is recommended to improve customer satisfaction."
56. Customer Feedback Analysis
- Prompt: Analyze customer feedback to identify improvement areas.
- Response (practical example): "Customer feedback suggests improving packaging quality and reducing delivery times to increase customer satisfaction."
57. Coupons and Discounts Usage Analysis
- Prompt: Evaluate the impact of coupons and discounts on sales and profitability.
- Response (practical example): "Coupons have increased sales by 20%, but reduced profit margins by 5%. Optimizing offered discounts could improve profitability."
58. Operational Efficiency Analysis
- Prompt: Analyze the operational efficiency of internal processes.
- Response (practical example): "Process automation has reduced production time by 20% and operational costs by 15%."
59. Product Performance Analysis in Different Markets
- Prompt: Evaluate product performance in different geographic markets.
- Response (practical example): "Product 'X' performs best in Europe with 50% of global sales, while in Asia it only represents 10%. Adapting the product to local preferences could improve sales in Asia."
60. Sales Channel Efficiency Analysis
- Prompt: Analyze the efficiency of different sales channels.
- Response (practical example): "The online sales channel generates 60% of total revenue with a lower customer acquisition cost compared to physical stores."
Financial Analysis
61. Cost Analysis
- Prompt: Perform a cost analysis to identify areas for expense reduction.
- Response (practical example): "The cost analysis suggests reducing logistics expenses by optimizing shipping routes and negotiating better rates with suppliers."
62. Return on Investment (ROI) Analysis
- Prompt: Calculate the ROI of advertising campaigns.
- Response (practical example): "The ROI of the last advertising campaign was 200%, indicating that for every euro invested, 2 euros in additional sales were generated."
63. Profitability by Product Category Analysis
- Prompt: Evaluate the profitability of different product categories.
- Response (practical example): "The 'Electronics' category has the highest profit margin at 25%, while 'Clothing' has the lowest margin at 10%."
64. Financial Performance Analysis
- Prompt: Perform a financial performance analysis of the company.
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Response (practical example): "The financial analysis shows an annual revenue growth of 10% and a net profit margin of 8%. Continuing the innovation investment strategy is recommended."
Data Visualization and Presentation
65. Chart Type Selection
- Prompt: What type of chart is most suitable for showing these data?
- Response (practical example): "To visualize the relationship between customer age and purchase frequency, a scatter plot showing each data point is recommended."
66. Indicator Chart for Sales
- Prompt: Generate an indicator chart to visualize the % of total sales for June relative to the sales target of 1,000,000.
- Response (practical example): "The indicator chart shows that June sales reached 85% of the 1,000,000 sales target, highlighting areas for improvement next month."
67. Bar Chart for Sales by Category
- Prompt: Generate a bar chart to visualize product sales distribution by category.
- Response (practical example): "The bar chart shows that the 'Electronics' category leads sales with 40,000 units, followed by 'Clothing' with 25,000 units."
68. Line Chart for Traffic Trends
- Prompt: Create a line chart to show website traffic trends over the past year.
- Response (practical example): "The line chart indicates a steady increase in website traffic, with significant peaks during spring and fall marketing campaigns."
69. Scatter Plot
- Prompt: Suggest the most appropriate type of chart to represent the relationship between customer age and purchase frequency.
- Response (practical example): "The scatter plot reveals a trend where customers between 30 and 40 years tend to make purchases more frequently."
70. Geographic Data Visualization
- Prompt: What visualization method is best to show geographic distribution data?
- Response (practical example): "To show geographic distribution data, a heat map highlighting areas with the most activity or sales is recommended."
71. Correlation Heatmap
- Prompt: Generate a heatmap to visualize the correlation matrix of numeric variables in our dataset.
- Response (practical example): "The correlation heatmap shows that 'income' and 'sales' have a strong positive correlation, while 'expenses' and 'customer satisfaction' have a moderate negative relationship."
72. Sales by Category Chart
- Prompt: Generate a chart to visualize product sales by category.
- Response (practical example): "The sales by category chart reveals that 'Electronics' is the top-selling category, followed by 'Fashion' and 'Home'."
73. Scatter Plot for Numeric Variables
- Prompt: Create a scatter plot to visualize the relationship between two numeric variables.
- Response (practical example): "The scatter plot between 'income' and 'number of visits' shows a positive relationship, suggesting that more visits lead to higher income."
74. Box Plot for Salary Distribution
- Prompt: Produce a box plot to show the salary distribution of employees in our organization.
- Response (practical example): "The salary box plot shows a median of 50,000 euros, with an interquartile range of 40,000 to 60,000 euros. There are some outliers on the upper end."
75. Histogram of Customer Ages
- Prompt: Visualize the age distribution of customers using a histogram.
- Response (practical example): "The histogram of customer ages indicates that most are between 25 and 35 years old, with a peak at 30 years."
76. Website Clicks Heatmap
- Prompt: Create a heatmap to show click concentration on the website during different periods.
- Response (practical example): "The heatmap shows a high concentration of clicks during peak afternoon hours and a significant drop-off during the early morning."
77. Stock Prices Time Series Chart
- Prompt: Generate a time series chart to show daily stock prices of a selected company over the past year.
- Response (practical example): "The time series chart of stock prices shows an upward trend with significant fluctuations during quarterly earnings announcements."
78. Attendance Patterns Heatmap
- Prompt: Create a heatmap to visualize employee attendance patterns.
- Response (practical example): "The attendance heatmap shows the lowest attendance on Mondays and Fridays, suggesting a need to review flexible work policies."
79. Marketing Expenses Pie Chart
- Prompt: Illustrate the division of marketing campaign expenses by channel using a pie chart.
- Response (practical example): "The pie chart shows that 40% of the marketing budget is spent on digital advertising, 30% on events, 20% on print advertising, and 10% on other channels."
80. Traffic Sources Donut Chart
- Prompt: Compare the distribution of website traffic sources with a donut chart.
- Response (practical example): "The donut chart indicates that 50% of web traffic comes from organic searches, 30% from direct traffic, 15% from referrals, and 5% from social media."
81. Organic vs. Paid Traffic Analysis
- Prompt: Compare the performance of organic traffic versus paid traffic.
- Response (practical example): "Organic traffic has a conversion rate of 4%, while paid traffic has a conversion rate of 2%, suggesting higher effectiveness of organic traffic."
Retention and Satisfaction Analysis
82. Mobile App User Engagement Analysis
- Prompt: Evaluate user engagement on mobile applications.
- Response (practical example): "Mobile app engagement increased by 30% following the introduction of new features and UI improvements."
83. Customer Retention Strategy Analysis
- Prompt: Evaluate the effectiveness of customer retention strategies.
- Response (practical example): "Retention strategies, such as loyalty programs, increased customer retention by 25% over the past year."
84. Post-Purchase Satisfaction Rate Analysis
- Prompt: Evaluate the post-purchase customer satisfaction rate.
- Response (practical example): "Post-purchase satisfaction rate is 85%, with customers positively rating delivery speed and product quality."
85. Usage Frequency Analysis
- Prompt: Analyze the frequency of platform usage by users.
- Response (practical example): "The analysis reveals that active users use the platform an average of three times per week, while inactive users use it less than once a month."
86. Social Media Engagement Analysis
- Prompt: Evaluate brand engagement on social media and its impact on brand image.
- Response (practical example): "Instagram posts average 500 interactions, while Facebook posts average 300. Increasing the frequency of Instagram posts could boost overall engagement."
87. Brand Perception Analysis
- Prompt: Analyze brand perception among customers and on social media.
- Response (practical example): "Brand perception is mostly positive, with 70% favorable mentions on social media. Criticisms focus on pricing, suggesting a need to review pricing strategy."
Events and Seasons Analysis
88. Seasonal Events Impact Analysis
- Prompt: Analyze how seasonal events affect sales.
- Response (practical example): "Sales increase by 30% during the holiday season and decrease by 15% in summer. Planning specific campaigns for these seasons can optimize results."
89. Purchase Behavior by Time of Day Analysis
- Prompt: Analyze customer purchase behavior at different times of the day.
- Response (practical example): "Most purchases occur between 6 p.m. and 9 p.m., suggesting that promotions during this period could be more effective."
Training and Productivity Analysis
90. Training Effectiveness Analysis
- Prompt: Evaluate the impact of training on employee performance.
- Response (practical example): "Training has improved employee productivity by 20% and reduced errors by 15%."
91. Team Performance Analysis
- Prompt: Evaluate the performance of different teams within the organization.
- Response (practical example): "The sales team has exceeded their goals by 10%, while the support team needs improvement, with an 80% problem resolution rate."
New Features and Products Analysis
92. New Features Adoption Analysis
- Prompt: Evaluate the adoption of new product features by users.
- Response (practical example): "The new live chat feature has been adopted by 30% of active users in the first month, with high reported satisfaction."
93. Product Personalization Effectiveness Analysis
- Prompt: Evaluate the effectiveness of product personalization strategies.
- Response (practical example): "Personalized products have increased customer satisfaction by 20% and repeat purchase rates by 15%."
94. New Product Performance Analysis
- Prompt: Evaluate the performance of new products launched in the market.
- Response (practical example): "The new product 'Y' has captured 10% of the market in its first quarter, with positive reviews highlighting its innovation and ease of use."
95. New Product Market Performance Analysis
- Prompt: Evaluate the market performance of new products in terms of sales and customer acceptance.
- Response (practical example): "The new product 'Z' achieved a 15% adoption rate in its first month, with a 2% return rate, indicating good acceptance and quality."
96. Product Features Impact on Customer Satisfaction Analysis
- Prompt: Analyze how different product features affect customer satisfaction.
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Response (practical example): "The most valued product features include ease of use and durability, which correlate positively with 90% customer satisfaction."
97. Seasonal Product Demand Analysis
- Prompt: Evaluate how product demand varies across different seasons of the year.
- Response (practical example): "Winter product demand increases by 40% from November to February, while summer products peak in demand in June and July."
98. New Product Marketing Strategies Analysis
- Prompt: Evaluate the effectiveness of marketing strategies used to launch new products.
- Response (practical example): "Digital marketing campaigns for the new product 'W' resulted in a 30% increase in sales in the first two weeks, highlighting the high effectiveness of social media ads."
99. New Product Pricing Analysis
- Prompt: Evaluate customer perception of new product prices.
- Response (practical example): "70% of customers perceive new product prices as fair, although 15% consider them slightly high. Adjusting prices or offering initial discounts could improve acceptance."
100. New Product Positioning Analysis
- Prompt: Analyze the positioning of new products compared to competitors.
- Response (practical example): "The new product 'X' is well-positioned in terms of innovation and quality, surpassing direct competitors in these areas, but its slightly higher price could be a barrier for some customer segments."
Conclusion
These 100 prompts, classified into specific categories, will enable you to maximize the analytical capabilities of SumoPPM and obtain valuable and actionable insights for your business. From initial exploration to advanced visualization, these prompts will guide you through each stage of data analysis. Start using SumoPPM today and take your data analysis to the next level!
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