From a253955528bc47076f6bd7947a2ca44c27a45af4 Mon Sep 17 00:00:00 2001 From: Lexical Bits Date: Mon, 2 Feb 2026 16:26:10 -0500 Subject: [PATCH] First round of fiddling --- .gitignore | 46 + 2026-01-02_query-01-booking-people.sql | 146 + 2026-01-02_query-02-email.sql | 83 + 2026-01-02_query-03-sms-appts-checklist.sql | 151 + README.md | 15 + churn-analysis.ipynb | 184 + churn.csv | 12822 ++++++++++++++++++ poetry.lock | 2579 ++++ pyproject.toml | 16 + 9 files changed, 16042 insertions(+) create mode 100644 .gitignore create mode 100644 2026-01-02_query-01-booking-people.sql create mode 100644 2026-01-02_query-02-email.sql create mode 100644 2026-01-02_query-03-sms-appts-checklist.sql create mode 100644 README.md create mode 100644 churn-analysis.ipynb create mode 100644 churn.csv create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0bb1fd7 --- /dev/null +++ b/.gitignore @@ -0,0 +1,46 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual environments +.venv/ +venv/ +ENV/ + +# Jupyter +.ipynb_checkpoints/ + +# IDE +.idea/ +.vscode/ +*.swp +*.swo + +# OS +.DS_Store +Thumbs.db + +# Environment +.env +.python-version + +# Data (uncomment if you want to ignore data files) +# *.csv diff --git a/2026-01-02_query-01-booking-people.sql b/2026-01-02_query-01-booking-people.sql new file mode 100644 index 0000000..aae540d --- /dev/null +++ b/2026-01-02_query-01-booking-people.sql @@ -0,0 +1,146 @@ +-- First establish some base parameters for our users +WITH config AS ( + SELECT + 14 AS short_term, + 90 AS medium_term, + 360 AS long_term, + CAST('2023-01-01' AS timestamp) AS date_cutoff +), +-- Then establish a standard core set of user data +users_with_churn_stats AS ( + SELECT id, active, billing_state, + DATE_TRUNC('month', created_at) AS month_created, + created_at, free_trial_ends_at, updated_at, + updated_at::date - created_at::date AS lifespan + FROM users, config + -- We'll only look at newish users. + WHERE created_at >= config.date_cutoff +), +-- Next use that core data to extract the records we want to analyze +-- and categorize them by date and status +users_to_examine AS ( + -- First the users who exited just after the trial + SELECT 'quick-exit' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan <= config.short_term + + UNION + + -- Next, users who gave the tools some time but still left + SELECT 'fair-trial' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan < config.medium_term + AND lifespan > config.short_term + + UNION + + -- Then some longer-time users who still churned. + -- We'll cut this off after 6 months since afterwards + -- company closures, etc. could factor in. + + SELECT 'short-termer' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan >= config.medium_term + AND lifespan < config.long_term + + UNION + + -- Finally, people who are still kicking. + -- This cohort is going to be a little wrong for older + -- dates since it only includes those who are STILL around. + SELECT 'active-user' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE BILLING_STATE != 'CHURNED' + AND lifespan >= config.long_term +), +-- Well add booking forms created by era +booking_form_counts AS ( + SELECT bf.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT booking_forms.user_id, booking_forms.created_at::date - users.created_at::date AS created_age + FROM config, booking_forms JOIN users ON booking_forms.user_id = users.id + WHERE booking_forms.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) bf + GROUP BY bf.user_id +), +-- Bookings made by era +booking_counts AS ( + SELECT bk.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT booking_forms.user_id, bookings.created_at::date - users.created_at::date AS created_age + FROM config, booking_forms + JOIN users ON booking_forms.user_id = users.id + JOIN bookings ON bookings.booking_form_id = booking_forms.id + WHERE booking_forms.created_at >= config.date_cutoff + AND bookings.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) bk + GROUP BY bk.user_id +), +-- Employees added by era +employee_counts AS ( + SELECT empl.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT employees.user_id, employees.created_at::date - users.created_at::date AS created_age + FROM config, employees JOIN users ON employees.user_id = users.id + WHERE employees.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) empl + GROUP BY empl.user_id +), +-- Contacts created by era +contact_counts AS ( + SELECT cust.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT customers.user_id, customers.created_at::date - users.created_at::date AS created_age + FROM config, customers JOIN users ON customers.user_id = users.id + WHERE customers.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) cust + GROUP BY cust.user_id +), +-- Finally, we'll flatten it all out into a repurposable flat table +SELECT users_to_examine.id, + users_to_examine.category, + COALESCE(booking_form_counts.short_term, 0) AS booking_forms_short_term, + COALESCE(booking_form_counts.medium_term, 0) AS booking_forms_medium_term, + COALESCE(booking_form_counts.long_term, 0) AS booking_forms_long_term, + COALESCE(booking_form_counts.total, 0) AS booking_forms, + COALESCE(booking_counts.short_term, 0) AS bookings_short_term, + COALESCE(booking_counts.medium_term, 0) AS bookings_medium_term, + COALESCE(booking_counts.long_term, 0) AS bookings_long_term, + COALESCE(booking_counts.total, 0) AS bookings, + COALESCE(employee_counts.short_term, 0) AS employees_short_term, + COALESCE(employee_counts.medium_term, 0) AS employees_medium_term, + COALESCE(employee_counts.long_term, 0) AS employees_long_term, + COALESCE(employee_counts.total, 0) AS employees, + COALESCE(contact_counts.short_term, 0) AS contacts_short_term, + COALESCE(contact_counts.medium_term, 0) AS contacts_medium_term, + COALESCE(contact_counts.long_term, 0) AS contacts_long_term, + COALESCE(contact_counts.total, 0) AS contacts +FROM users_to_examine + LEFT OUTER JOIN booking_form_counts ON booking_form_counts.user_id = users_to_examine.id + LEFT OUTER JOIN booking_counts ON booking_counts.user_id = users_to_examine.id + LEFT OUTER JOIN employee_counts ON employee_counts.user_id = users_to_examine.id + LEFT OUTER JOIN contact_counts ON contact_counts.user_id = users_to_examine.id +; diff --git a/2026-01-02_query-02-email.sql b/2026-01-02_query-02-email.sql new file mode 100644 index 0000000..2717f71 --- /dev/null +++ b/2026-01-02_query-02-email.sql @@ -0,0 +1,83 @@ +-- First establish some base parameters for our users +WITH config AS ( + SELECT + 14 AS short_term, + 90 AS medium_term, + 360 AS long_term, + CAST('2023-01-01' AS timestamp) AS date_cutoff +), +-- Then establish a standard core set of user data +users_with_churn_stats AS ( + SELECT id, active, billing_state, + DATE_TRUNC('month', created_at) AS month_created, + created_at, free_trial_ends_at, updated_at, + updated_at::date - created_at::date AS lifespan + FROM users, config + -- We'll only look at newish users. + WHERE created_at >= config.date_cutoff +), +-- Next use that core data to extract the records we want to analyze +-- and categorize them by date and status +users_to_examine AS ( + -- First the users who exited just after the trial + SELECT 'quick-exit' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan <= config.short_term + + UNION + + -- Next, users who gave the tools some time but still left + SELECT 'fair-trial' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan < config.medium_term + AND lifespan > config.short_term + + UNION + + -- Then some longer-time users who still churned. + -- We'll cut this off after 6 months since afterwards + -- company closures, etc. could factor in. + + SELECT 'short-termer' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan >= config.medium_term + AND lifespan < config.long_term + + UNION + + -- Finally, people who are still kicking. + -- This cohort is going to be a little wrong for older + -- dates since it only includes those who are STILL around. + SELECT 'active-user' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE BILLING_STATE != 'CHURNED' + AND lifespan >= config.long_term +), +-- Emails sent by era +email_counts AS ( + SELECT email.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT sent_emails.user_id, sent_emails.created_at::date - users.created_at::date AS created_age + FROM config, sent_emails JOIN users ON sent_emails.user_id = users.id + WHERE sent_emails.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) email + GROUP BY email.user_id +) +-- Finally, we'll flatten it all out into a repurposable flat table +SELECT users_to_examine.id, + users_to_examine.category, + COALESCE(email_counts.short_term, 0) AS emails_short_term, + COALESCE(email_counts.medium_term, 0) AS emails_medium_term, + COALESCE(email_counts.long_term, 0) AS emails_long_term, + COALESCE(email_counts.total, 0) AS emails +FROM users_to_examine + LEFT OUTER JOIN email_counts ON email_counts.user_id = users_to_examine.id +; diff --git a/2026-01-02_query-03-sms-appts-checklist.sql b/2026-01-02_query-03-sms-appts-checklist.sql new file mode 100644 index 0000000..6f679dc --- /dev/null +++ b/2026-01-02_query-03-sms-appts-checklist.sql @@ -0,0 +1,151 @@ +-- First establish some base parameters for our users +WITH config AS ( + SELECT + 14 AS short_term, + 90 AS medium_term, + 360 AS long_term, + CAST('2023-01-01' AS timestamp) AS date_cutoff +), +-- Then establish a standard core set of user data +users_with_churn_stats AS ( + SELECT id, active, billing_state, + DATE_TRUNC('month', created_at) AS month_created, + created_at, free_trial_ends_at, updated_at, + updated_at::date - created_at::date AS lifespan + FROM users, config + -- We'll only look at newish users. + WHERE created_at >= config.date_cutoff +), +-- Next use that core data to extract the records we want to analyze +-- and categorize them by date and status +users_to_examine AS ( + -- First the users who exited just after the trial + SELECT 'quick-exit' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan <= config.short_term + + UNION + + -- Next, users who gave the tools some time but still left + SELECT 'fair-trial' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan < config.medium_term + AND lifespan > config.short_term + + UNION + + -- Then some longer-time users who still churned. + -- We'll cut this off after 6 months since afterwards + -- company closures, etc. could factor in. + + SELECT 'short-termer' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE billing_state = 'CHURNED' + AND lifespan >= config.medium_term + AND lifespan < config.long_term + + UNION + + -- Finally, people who are still kicking. + -- This cohort is going to be a little wrong for older + -- dates since it only includes those who are STILL around. + SELECT 'active-user' AS category, uwcs.* + FROM users_with_churn_stats uwcs, config + WHERE BILLING_STATE != 'CHURNED' + AND lifespan >= config.long_term +), +-- Messages sent by era +sms_counts AS ( + SELECT texts.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT sent_texts.user_id, sent_texts.created_at::date - users.created_at::date AS created_age + FROM config, sent_texts JOIN users ON sent_texts.user_id = users.id + WHERE sent_texts.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) texts + GROUP BY texts.user_id +), +-- Appointments by era +appointment_counts AS ( + SELECT appts.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT appointments.user_id, appointments.created_at::date - users.created_at::date AS created_age + FROM config, appointments JOIN users ON appointments.user_id = users.id + WHERE appointments.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) appts + GROUP BY appts.user_id +), +-- Checklists created by era +checklist_counts AS ( + SELECT checks.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT checklist_templates.user_id, checklists.created_at::date - users.created_at::date AS created_age + FROM config, checklist_templates + JOIN users ON checklist_templates.user_id = users.id + JOIN checklists ON checklists.checklist_template_id = checklist_templates.id + WHERE checklist_templates.created_at >= config.date_cutoff + AND checklists.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) checks + GROUP BY checks.user_id +), +-- Checklist events created by era +checklist_filled_counts AS ( + SELECT events.user_id, + SUM(CASE WHEN created_age < config.short_term THEN 1 ELSE 0 END) AS short_term, + SUM(CASE WHEN created_age >= config.short_term AND created_age < config.medium_term THEN 1 ELSE 0 END) AS medium_term, + SUM(CASE WHEN created_age >= config.medium_term AND created_age < config.long_term THEN 1 ELSE 0 END) AS long_term, + COUNT(*) AS total + FROM config, ( + SELECT employees.user_id, checklist_item_events.created_at::date - users.created_at::date AS created_age + FROM config, employees + JOIN users ON employees.user_id = users.id + JOIN checklist_item_events ON checklist_item_events.employee_id = employees.id + WHERE checklist_item_events.created_at >= config.date_cutoff + AND checklist_item_events.created_at >= config.date_cutoff + AND users.created_at >= config.date_cutoff + ) events + GROUP BY events.user_id +) +-- Finally, we'll flatten it all out into a repurposable flat table +SELECT users_to_examine.id, + users_to_examine.category, + users_to_examine.month_created, + users_to_examine.lifespan, + COALESCE(sms_counts.short_term, 0) AS sms_short_term, + COALESCE(sms_counts.medium_term, 0) AS sms_medium_term, + COALESCE(sms_counts.long_term, 0) AS sms_long_term, + COALESCE(sms_counts.total, 0) AS sms, + COALESCE(appointment_counts.short_term, 0) AS appointments_short_term, + COALESCE(appointment_counts.medium_term, 0) AS appointments_medium_term, + COALESCE(appointment_counts.long_term, 0) AS appointments_long_term, + COALESCE(appointment_counts.total, 0) AS appointments, + COALESCE(checklist_counts.short_term, 0) AS checklists_short_term, + COALESCE(checklist_counts.medium_term, 0) AS checklists_medium_term, + COALESCE(checklist_counts.long_term, 0) AS checklists_long_term, + COALESCE(checklist_counts.total, 0) AS checklists, + COALESCE(checklist_filled_counts.short_term, 0) AS checklists_filled_short_term, + COALESCE(checklist_filled_counts.medium_term, 0) AS checklists_filled_medium_term, + COALESCE(checklist_filled_counts.long_term, 0) AS checklists_filled_long_term, + COALESCE(checklist_filled_counts.total, 0) AS checklists_filled +FROM users_to_examine + LEFT OUTER JOIN sms_counts ON sms_counts.user_id = users_to_examine.id + LEFT OUTER JOIN appointment_counts ON appointment_counts.user_id = users_to_examine.id + LEFT OUTER JOIN checklist_counts ON checklist_counts.user_id = users_to_examine.id + LEFT OUTER JOIN checklist_filled_counts ON checklist_filled_counts.user_id = users_to_examine.id +; diff --git a/README.md b/README.md new file mode 100644 index 0000000..0463bbb --- /dev/null +++ b/README.md @@ -0,0 +1,15 @@ +# Jupyter Analysis + +## Setup + +```bash +poetry install +``` + +## Run + +```bash +poetry run jupyter notebook +# or +poetry run jupyter lab +``` diff --git a/churn-analysis.ipynb b/churn-analysis.ipynb new file mode 100644 index 0000000..d0a23a9 --- /dev/null +++ b/churn-analysis.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "import numpy as np\n", + "\n", + "# Load CSV file\n", + "df = pd.read_csv('churn.csv')\n", + "\n", + "category_labels = {\n", + " 'quick-exit': 'Free trial only',\n", + " 'fair-trial': '74 day churn',\n", + " 'short-termer': '6 month churn',\n", + " 'active-user': 'No churn'\n", + "}\n", + "timeframe_labels = {\n", + " 'short term': 'During free trial',\n", + " 'medium term': 'After trial, before 90 days',\n", + " 'long term': 'After 90 days, first 6 months'\n", + "}\n", + "\n", + "def fix_dataset_label(k):\n", + " parts = k.split('_')\n", + " if \"term\" in parts:\n", + " timeframe = timeframe_labels[parts[-2:].join(\" \")]\n", + " name = parts[0:-2].join(\" \").title()\n", + " else:\n", + " timeframe = 'Lifetime'\n", + " name = parts.join(\" \").title()\n", + " return f\"{name}: {timeframe}\"\n", + "\n", + "def x_labels(categories):\n", + " return [category_labels[cat] for cat in categories]\n", + "\n", + "def dataset_labels(columns):\n", + " return [fix_dataset_label(col) for col in columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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booking_forms_short_termbooking_forms_medium_termbooking_forms_long_term
category
active-user0.0467010.0415710.040686
fair-trial0.2680020.0203030.000271
quick-exit0.2326730.0000000.000000
short-termer0.2909540.0822130.029034
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" + ], + "text/plain": [ + " booking_forms_short_term booking_forms_medium_term \\\n", + "category \n", + "active-user 0.046701 0.041571 \n", + "fair-trial 0.268002 0.020303 \n", + "quick-exit 0.232673 0.000000 \n", + "short-termer 0.290954 0.082213 \n", + "\n", + " booking_forms_long_term \n", + "category \n", + "active-user 0.040686 \n", + "fair-trial 0.000271 \n", + "quick-exit 0.000000 \n", + "short-termer 0.029034 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns = ['booking_forms_short_term', 'booking_forms_medium_term', 'booking_forms_long_term']\n", + "categories = category_labels.keys() # All\n", + "\n", + "# Group by category and make sure they're in the correct order\n", + "df.groupby('category')[columns].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "# Order categories as defined in category_labels\ncategory_order = [category_labels[k] for k in category_labels.keys()]" + }, + { + "cell_type": "code", + "source": "# Generate charts for all metrics\nmetrics = [\n 'booking_forms',\n 'bookings',\n 'employees', \n 'contacts',\n 'emails',\n 'sms',\n 'appointments',\n 'checklists',\n 'checklists_filled'\n]\n\n# Get total users per category for percentage calculations\ncategory_totals = df.groupby('category').size()\n\ndef create_charts_for_metric(metric_name):\n cols = [f'{metric_name}_short_term', f'{metric_name}_medium_term', f'{metric_name}_long_term']\n title_name = metric_name.replace('_', ' ').title()\n \n # Chart 1: Average usage\n grouped = df.groupby('category')[cols].mean().reset_index()\n melted = grouped.melt(id_vars='category', var_name='timeframe', value_name='value')\n melted['category_label'] = melted['category'].map(category_labels)\n \n fig1 = px.bar(\n melted,\n x='category_label',\n y='value',\n color='timeframe',\n barmode='group',\n title=f'{title_name} Usage by Churn Category and Timeframe',\n category_orders={'category_label': category_order}\n )\n fig1.update_layout(\n xaxis_title='Category',\n yaxis_title='Average Usage',\n legend_title='Timeframe'\n )\n fig1.show()\n \n # Chart 2: Percentage of users with at least one\n counts = df.groupby('category')[cols].apply(lambda x: (x > 0).sum()).reset_index()\n counts_melted = counts.melt(id_vars='category', var_name='timeframe', value_name='count')\n counts_melted['total'] = counts_melted['category'].map(category_totals)\n counts_melted['percentage'] = (counts_melted['count'] / counts_melted['total']) * 100\n counts_melted['category_label'] = counts_melted['category'].map(category_labels)\n \n fig2 = px.bar(\n counts_melted,\n x='category_label',\n y='percentage',\n color='timeframe',\n barmode='group',\n title=f'% of Users with At Least One {title_name} by Churn Category and Timeframe',\n category_orders={'category_label': category_order}\n )\n fig2.update_layout(\n xaxis_title='Category',\n yaxis_title='% of Users',\n legend_title='Timeframe'\n )\n fig2.show()\n\n# Generate all charts\nfor metric in metrics:\n create_charts_for_metric(metric)", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": "# Generate cumulative charts for all metrics\ndef create_cumulative_charts_for_metric(metric_name):\n cols = [f'{metric_name}_short_term', f'{metric_name}_medium_term', f'{metric_name}_long_term']\n title_name = metric_name.replace('_', ' ').title()\n \n # Chart 1: Cumulative average usage\n grouped = df.groupby('category')[cols].mean().reset_index()\n # Make cumulative\n grouped['cumulative_short'] = grouped[cols[0]]\n grouped['cumulative_medium'] = grouped[cols[0]] + grouped[cols[1]]\n grouped['cumulative_long'] = grouped[cols[0]] + grouped[cols[1]] + grouped[cols[2]]\n \n cumulative_cols = ['cumulative_short', 'cumulative_medium', 'cumulative_long']\n melted = grouped.melt(id_vars='category', value_vars=cumulative_cols, var_name='timeframe', value_name='value')\n melted['category_label'] = melted['category'].map(category_labels)\n \n timeframe_labels_cumulative = {\n 'cumulative_short': 'Through trial',\n 'cumulative_medium': 'Through 90 days',\n 'cumulative_long': 'Through 6 months'\n }\n melted['timeframe_label'] = melted['timeframe'].map(timeframe_labels_cumulative)\n \n fig1 = px.bar(\n melted,\n x='category_label',\n y='value',\n color='timeframe_label',\n barmode='group',\n title=f'{title_name} Cumulative Usage by Churn Category',\n category_orders={\n 'category_label': category_order,\n 'timeframe_label': ['Through trial', 'Through 90 days', 'Through 6 months']\n }\n )\n fig1.update_layout(\n xaxis_title='Category',\n yaxis_title='Cumulative Average Usage',\n legend_title='Timeframe'\n )\n fig1.show()\n \n # Chart 2: Cumulative percentage of users with at least one\n # Check if user used feature in ANY period up to that point (not sum of counts)\n df['_cumul_short'] = df[cols[0]] > 0\n df['_cumul_medium'] = (df[cols[0]] > 0) | (df[cols[1]] > 0)\n df['_cumul_long'] = (df[cols[0]] > 0) | (df[cols[1]] > 0) | (df[cols[2]] > 0)\n \n counts = df.groupby('category')[['_cumul_short', '_cumul_medium', '_cumul_long']].sum().reset_index()\n counts.columns = ['category', 'cumulative_short', 'cumulative_medium', 'cumulative_long']\n \n counts_melted = counts.melt(id_vars='category', value_vars=cumulative_cols, var_name='timeframe', value_name='count')\n counts_melted['total'] = counts_melted['category'].map(category_totals)\n counts_melted['percentage'] = (counts_melted['count'] / counts_melted['total']) * 100\n counts_melted['category_label'] = counts_melted['category'].map(category_labels)\n counts_melted['timeframe_label'] = counts_melted['timeframe'].map(timeframe_labels_cumulative)\n \n fig2 = px.bar(\n counts_melted,\n x='category_label',\n y='percentage',\n color='timeframe_label',\n barmode='group',\n title=f'Cumulative % of Users with At Least One {title_name} by Churn Category',\n category_orders={\n 'category_label': category_order,\n 'timeframe_label': ['Through trial', 'Through 90 days', 'Through 6 months']\n }\n )\n fig2.update_layout(\n xaxis_title='Category',\n yaxis_title='Cumulative % of Users',\n legend_title='Timeframe'\n )\n fig2.show()\n\n# Generate all cumulative charts\nfor metric in metrics:\n create_cumulative_charts_for_metric(metric)", + "metadata": {}, + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/churn.csv b/churn.csv new file mode 100644 index 0000000..3e0eeee --- /dev/null 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