User Prompts

# User Prompts for Julius AI

## Prompt 1: Marketing Campaign Analysis
"I've uploaded my marketing campaign data with columns: campaign_name, channel (email, social, search, display), start_date, end_date, budget_spent, impressions, clicks, conversions, revenue_generated, target_audience. Analyze this data comprehensively: (1) Calculate ROI, CPC, CPA, and ROAS for each campaign, (2) Rank channels by efficiency (best conversions per dollar), (3) Show a scatter plot of budget vs revenue with campaign labels, (4) Identify the top 3 and bottom 3 campaigns with explanations, (5) Create a budget allocation recommendation for next quarter based on these results, (6) Visualize the conversion funnel (impressions → clicks → conversions) by channel."

## Prompt 2: Employee Attrition Analysis
"Here's our HR dataset with employee_id, department, role, tenure_months, salary, performance_rating (1-5), satisfaction_score (1-10), overtime_hours_monthly, promotion_in_last_2_years (yes/no), left_company (yes/no), exit_date. I need a thorough attrition analysis: (1) Overall attrition rate and monthly trend, (2) Attrition risk factors — what predicts whether someone leaves? (3) Attrition by department, role, and tenure bucket, (4) Correlation between satisfaction, salary, and attrition, (5) Build a simple predictive model to flag current employees at high risk of leaving, (6) Recommend 3-5 data-driven retention strategies based on the patterns you find."

## Prompt 3: Financial Performance Report
"I have quarterly financial data for our company: quarter, revenue, cogs (cost of goods sold), operating_expenses, marketing_spend, headcount, new_customers, churned_customers, mrr (monthly recurring revenue), arr (annual recurring revenue). Create a comprehensive financial report: (1) Calculate gross margin, operating margin, and net margin per quarter, (2) Revenue growth rate (QoQ and YoY), (3) Customer acquisition cost (CAC) and customer lifetime value (LTV) trends, (4) CAC payback period, (5) Burn rate and runway if applicable, (6) Generate professional charts for each metric, (7) Write an executive summary highlighting the most important trends and concerns."

## Prompt 4: Product Usage Analytics
"My SaaS product analytics data includes: user_id, signup_date, plan_type (free/starter/pro/enterprise), daily_active (boolean per day), features_used (comma-separated list), session_duration_minutes, pages_visited, support_tickets_opened, upgraded (boolean), upgrade_date. Analyze to find: (1) Daily/weekly/monthly active user trends, (2) Feature adoption rates — which features are most and least used, (3) Engagement correlation with upgrades — what behavior predicts conversion from free to paid, (4) User segments by usage pattern (power users, casual, at-risk), (5) Time-to-upgrade distribution, (6) Churn prediction — which active users are likely to stop using the product. Show charts for each insight."

## Prompt 5: Survey Data Analysis
"I ran a customer satisfaction survey with 2,000 responses. Columns include: respondent_id, age_group, gender, region, product_used, nps_score (0-10), satisfaction_overall (1-5), ease_of_use (1-5), value_for_money (1-5), customer_support (1-5), would_recommend (yes/no), open_feedback (text). Perform a complete survey analysis: (1) NPS calculation (promoters, passives, detractors) with visual breakdown, (2) Satisfaction scores by demographic segment and product, (3) Correlation matrix between all rating dimensions, (4) Key driver analysis — which factors most influence overall satisfaction, (5) Sentiment analysis on the open feedback text — top themes and sentiment distribution, (6) Cross-tabulation of NPS by region and product with chi-square test for significance."