How to perform conversion funnel analysis with r?

@elmo.conroy 

Performing conversion funnel analysis in R involves several steps. Here is a general approach:

1. 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.
2. Data Cleaning: Clean your data by removing duplicates, missing values, and outliers.
3. 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.
4. 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."
5. 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.
6. 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.
7. 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.

@elmo.conroy 

Additionally, here is a step-by-step example of how to perform conversion funnel analysis in R:

Step 1: Load Data Start by loading your data into R. You can use functions like read.csv() or read_excel() from the readr or openxlsx packages to read the data from a file.

Step 2: Clean and Prepare Data Clean your data by removing duplicates, fixing missing values, and handling outliers. You can use functions from the dplyr package such as distinct(), na.omit(), and filter() for data cleaning.

Next, prepare your data for funnel analysis. Create new variables or aggregate the data as needed. For example, you may want to calculate the time spent in each step, or the number of unique users in each step.

Step 3: Define Funnel Stages Define the stages of your conversion funnel. This involves determining the key events or actions that users take as they progress through the funnel. For example, in an e-commerce funnel, the stages might be "visited site," "viewed product," "added to cart," and "completed purchase".

Step 4: Funnel Visualization Visualize the conversion funnel to gain a better understanding of the user journey. Use the ggplot2 package to create visualizations such as stacked bar charts or line plots. Each stage of the funnel can be represented as a separate bar or point on the plot.

Step 5: Funnel Analysis Analyze the funnel to measure the conversion rates and identify drop-off points. Calculate conversion rates by dividing the number of users in each successive stage by the number of users in the previous stage. Additionally, segment your data to analyze different user groups separately.

You can use functions from the dplyr package, such as group_by() and summarize(), to calculate conversion rates and segment the data.

Step 6: Actionable Insights Based on the analysis, draw actionable insights to improve your conversion funnel. Identify areas where users drop off the most and investigate potential reasons. Use the insights to make data-driven decisions and optimize your conversion process.

In conclusion, performing conversion funnel analysis in R involves loading and preparing the data, defining the funnel stages, visualizing the funnel, analyzing the conversion rates, and drawing actionable insights. R offers a wide range of packages and functions to help you with each step of the analysis.