dscigametrics

Overview

dscigametrics, or Data Science Google Analytics Metrics, is a python package that provides a set of ready-made functions that can help users with minimual coding skills easily digest and analyse advertising data obtained from Google Analytics. While Google Analytics allows users to easily download data as a csv file, the resulting spreadsheet is an intimidating and unituitive block of dense information. Instead of trying to analyse this in excel, users can instead load it into a python script as a pandas dataframe and let this package do the analysis work for them!

Fuctions in the package

1. compute_metrics summarises general performance of campaign based on four metrics.
2. stat_summary summarises variance of campaign performance based on four metrics.
3. daily_plot visualises performance changes of campaign based on four metrics.
4. find_campaigns identifies the best and worst performing campaigns based on a selected metric.

Where this package fits in

The popularity and influence of Google Analytics means that there is already a decent number of related python packages, such as googleanalytics, which can be found here on PyPI. However the majority of these packages provide functionality that allows developers to interact with the Google Analytics API, which presupposes a fairly high level of technical skill. Our package is intended to help users with a novice familiarity with python by operating directly on downloaded GA data sets instead.

Installation

$ pip install dscigametrics

Developer Installation Instruction

Step 1: Clone the Repository

git clone [email protected]:UBC-MDS/Group_9_GA_Metrics.git
cd Group_9_GA_Metrics  # Navigate to the cloned repository directory

Step 2: Create and Activate the Conda Environment

$ conda env create -f environment.yml  # Create Conda environment
$ conda activate ga_package  # Activate the Conda environment

Step 3: Install the Package Using Poetry

Ensure the Conda environment is activated. You should see Group_9_GA_Metrics in the terminal prompt.

$ poetry install  # Install the package using Poetry

Usage

Here is a basic example of how to use this package:

import dscigametrics
import pandas as pd

data = pd.read_csv('where/is/your/data/saved.csv')

Choose Your parameters:

campaign_id = 123851219
start_date = 20220801
end_date = 20220831

Compute metrics:

metrics_dictionary = compute_metrics(data, campaign_id, start_date, end_date)

conversion rate: 0.116
new to return rate: 0.88
total transaction revenue: $14548.0
average transaction revenue: $501.6551724137931

Calculate Summary Statistics:

summary = stat_summary(data, campaign_id, start_date, end_date)

Create daily plot:

plot = daily_plot(data, campaign_id, start_date, end_date, width=300, height=800)

Find the best and worst performance campaign:

campaign_ids = [219011657, 140569061, 215934049, 123851219]
metric = 'conversion_rate'
best_worst_campaigns = find_campaigns(
    data=data,
    start_date=start_date,
    end_date=end_date,
    campaign_ids=campaign_ids,
    metric=metric
)

{'best_campaign': {'id': 123851219, 'value': 0.116}, 'worst_campaign': {'id': 219011657, 'value': 0.056}}

Online Documentation

Documentation for all functions in the package, as well as a demonstration notebook, can be found here on Read the Docs.

Main Contributors

Beth Ou-Yang, Ian MacCarthy, Yili Tang, Weilin Han

Contributing

Contributions are welcome and greatly appreciated! If you're interested in contributing to this project, take a look at the contributor guide.

License

dscigametrics was created by DSCI524 Cohort8 Group9. It is licensed under the terms of the MIT license.

Credits

dscigametrics was created with cookiecutter and the py-pkgs-cookiecutter template.