Best Jasper prompts for Market Research Analysts and Marketing Specialists

Design an ETL pipeline using Python and Snowflake to extract customer demographic data from our CRM, transform it into a suitable format for analysis, and load it into our data warehouse. The pipeline should handle missing values, data type conversions, and ensure data quality checks are in place. Provide a step-by-step implementation plan, including code snippets and a list of required dependencies. Assume the CRM data is stored in a PostgreSQL database and the data warehouse is a Snowflake instance.

Develop a Python script using scikit-learn and Pandas to compare the performance of three different regression models (linear, decision tree, and random forest) on a dataset containing customer purchase history and demographic information. The script should preprocess the data, split it into training and testing sets, train each model, and evaluate their performance using metrics such as mean squared error and R-squared. Provide a concise report detailing the results, including visualizations and recommendations for the best model to use for predicting customer lifetime value.

Create a set of 10 variations of a marketing email promoting a new product launch, targeting different customer segments based on their purchase history and demographic characteristics. Use a combination of natural language processing and machine learning techniques to generate copy that is both personalized and optimized for conversion. Provide a CSV file containing the email copy, subject lines, and target audience characteristics for each variation, as well as a Python script to automate the generation process using a template engine.

Conduct a thorough data quality audit on our customer feedback dataset, which is stored in a Tableau dashboard. Identify and document any data inconsistencies, missing values, or formatting issues, and provide recommendations for data cleansing and normalization. Develop a concise report detailing the findings, including data visualizations and statistical summaries, and outline a step-by-step plan for implementing the recommended changes. Assume the dataset contains 10,000 records and 20 variables, including customer demographics, feedback ratings, and comment text.