@elmo.conroy

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.

#funnel