Performing conversion funnel analysis in R involves several steps. Here is a general approach:
- Load data: Load your data into R, whether it is from a CSV, Excel file or database. Make sure it contains the necessary columns for analysis.
- Data Cleaning: Clean your data by removing duplicates, missing values, and outliers.
- Data Preparation: Prepare your data for analysis by creating new variables or aggregating data. For example, you might want to group data by time, user, or event type.
- Funnel Creation: Create a funnel by defining the stages of the conversion process. This can be done by grouping events into steps, such as "visited the site," "added to cart," and "completed purchase."
- Funnel Visualization: Visualize the funnel using a chart, such as a bar chart or line graph. You can use R packages such as "ggplot2" or "plotly" for this purpose.
- Funnel Analysis: Analyze the funnel using statistical techniques such as conversion rates, drop-off rates, and segmentation analysis. You can use R packages such as "dplyr" and "tidyr" to perform these analyses.
- Insights: Finally, draw insights from your analysis and make data-driven decisions based on the results. You can use R packages such as "shiny" to create interactive dashboards and share your findings with others.
Overall, the process of conversion funnel analysis in R involves data cleaning, preparation, funnel creation, visualization, analysis, and insights. There are many R packages available that can help with each step of the process, and the specific tools you use will depend on your data and analysis goals.