As the Head of Customer Support at Diogo's Delicious Chocolate Company, I've noticed a growing trend in customer complaints regarding slow response times to emails and lengthy waiting times on phone calls. We need to understand these trends better and forecast future complaint volumes to allocate our resources effectively and improve our customer service experience. I'd like you to analyze the time series data related to customer complaints and identify patterns that can help us in predicting and address these issues more efficiently.
To complete this project, you'll need to perform a comprehensive time series analysis using the provided customer complaint datasets. Apply various techniques such as exploratory data analysis, forecasting, and visualization using Python libraries like pandas, statsmodels, and matplotlib. Develop a SARIMAX forecasting model to predict future complaint volumes and resource requirements. Once you have completed your analysis, prepare a detailed report with visualizations, insights, and recommendations that can help us make data-driven decisions to enhance our customer support. Remember, our goal is to improve response times and better serve our customers, so your findings will play a critical role in shaping our support strategies moving forward.
In this lab, you will assume the role of a Data Analyst at "Diogo's Delicious Chocolate Company," a renowned chocolate manufacturer. The company has been receiving a significant number of customer complaints regarding slow response times to emails and long waiting times on phone calls. Your manager, the Head of Customer Support, has assigned you the task of analyzing the time series data related to customer complaints to identify trends, seasonality, and patterns that can help the company forecast future complaint volumes and allocate resources accordingly.
- Conduct time series analysis on a real-world dataset to identify trends, seasonality, and other patterns using Python libraries such as pandas and statsmodels.
- Utilize exploratory data analysis (EDA) methods on time series data to gain insights and identify potential issues before building forecasting models.
- Develop SARIMAX models in Python to capture seasonal and non-seasonal components of time series data for enhanced forecasting accuracy.
- Implement cross-validation techniques and optimize model parameters to improve forecasting performance in time series analysis.
- Time Series Analysis
- Forecasting
- Exploratory Data Analysis for Time Series Data
- Cross-Validation and Parameter Tuning for Forecasting Model
