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Notebook for "How to Measure your E-Commerce Business" at Confoo 2016
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Confoo examples.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to measure your e-commerce business\n",
"### Confoo presentation - Françoise Provencher - Feb 24th 2016"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# For fancy looking graphs\n",
"from matplotlib import pyplot as plt \n",
"plt.style.use('ggplot')\n",
"%matplotlib inline\n",
"\n",
"# For dealing elegantly with unicode and with integer division\n",
"from __future__ import unicode_literals\n",
"from __future__ import division\n",
"\n",
"# Pandas is our favorite library for data exploration\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Q1 - What’s my top 2 popular t-shirt colors?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I sell lots of different t-shirt colors, but to much choice is a bad thing so I want to re-focus my collection to only the best-seller colors. Which are those best-sellers?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's read the data and store it into a pandas dataframe\n",
"orders = pd.read_csv('data/orders_export.csv')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Email</th>\n",
" <th>Financial Status</th>\n",
" <th>Paid at</th>\n",
" <th>Fulfillment Status</th>\n",
" <th>Fulfilled at</th>\n",
" <th>Accepts Marketing</th>\n",
" <th>Currency</th>\n",
" <th>Subtotal</th>\n",
" <th>Shipping</th>\n",
" <th>...</th>\n",
" <th>Tax 1 Name</th>\n",
" <th>Tax 1 Value</th>\n",
" <th>Tax 2 Name</th>\n",
" <th>Tax 2 Value</th>\n",
" <th>Tax 3 Name</th>\n",
" <th>Tax 3 Value</th>\n",
" <th>Tax 4 Name</th>\n",
" <th>Tax 4 Value</th>\n",
" <th>Tax 5 Name</th>\n",
" <th>Tax 5 Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>#1017</td>\n",
" <td>stanley.Kubrick@gmail.com</td>\n",
" <td>paid</td>\n",
" <td>2016-01-29 16:06:01 -0500</td>\n",
" <td>fulfilled</td>\n",
" <td>2016-02-03 02:54:26 -0500</td>\n",
" <td>no</td>\n",
" <td>CAD</td>\n",
" <td>35</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>GST 5%</td>\n",
" <td>1.75</td>\n",
" <td>QST 9.975%</td>\n",
" <td>3.49</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>#1016</td>\n",
" <td>alfred.Hitchcock@gmail.com</td>\n",
" <td>paid</td>\n",
" <td>2016-01-29 15:31:15 -0500</td>\n",
" <td>fulfilled</td>\n",
" <td>2016-02-02 15:58:06 -0500</td>\n",
" <td>no</td>\n",
" <td>CAD</td>\n",
" <td>40</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>GST 5%</td>\n",
" <td>1.75</td>\n",
" <td>QST 9.975%</td>\n",
" <td>3.49</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>#1015</td>\n",
" <td>alejandro.Inaritu@gmail.com</td>\n",
" <td>paid</td>\n",
" <td>2016-01-29 14:55:48 -0500</td>\n",
" <td>fulfilled</td>\n",
" <td>2016-02-01 18:03:12 -0500</td>\n",
" <td>no</td>\n",
" <td>CAD</td>\n",
" <td>40</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>GST 5%</td>\n",
" <td>1.75</td>\n",
" <td>QST 9.975%</td>\n",
" <td>3.49</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>#1014</td>\n",
" <td>ginger.Rogers@gmail.com</td>\n",
" <td>paid</td>\n",
" <td>NaN</td>\n",
" <td>fulfilled</td>\n",
" <td>2016-01-19 13:21:23 -0500</td>\n",
" <td>no</td>\n",
" <td>CAD</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>GST 5%</td>\n",
" <td>1.75</td>\n",
" <td>QST 9.975%</td>\n",
" <td>3.49</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>#1013</td>\n",
" <td>takashi.Miike@gmail.com</td>\n",
" <td>paid</td>\n",
" <td>2015-12-17 15:54:02 -0500</td>\n",
" <td>fulfilled</td>\n",
" <td>2016-01-07 15:21:15 -0500</td>\n",
" <td>no</td>\n",
" <td>CAD</td>\n",
" <td>35</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>GST 5%</td>\n",
" <td>1.75</td>\n",
" <td>QST 9.975%</td>\n",
" <td>3.49</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 66 columns</p>\n",
"</div>"
],
"text/plain": [
" Name Email Financial Status \\\n",
"0 #1017 stanley.Kubrick@gmail.com paid \n",
"1 #1016 alfred.Hitchcock@gmail.com paid \n",
"2 #1015 alejandro.Inaritu@gmail.com paid \n",
"3 #1014 ginger.Rogers@gmail.com paid \n",
"4 #1013 takashi.Miike@gmail.com paid \n",
"\n",
" Paid at Fulfillment Status Fulfilled at \\\n",
"0 2016-01-29 16:06:01 -0500 fulfilled 2016-02-03 02:54:26 -0500 \n",
"1 2016-01-29 15:31:15 -0500 fulfilled 2016-02-02 15:58:06 -0500 \n",
"2 2016-01-29 14:55:48 -0500 fulfilled 2016-02-01 18:03:12 -0500 \n",
"3 NaN fulfilled 2016-01-19 13:21:23 -0500 \n",
"4 2015-12-17 15:54:02 -0500 fulfilled 2016-01-07 15:21:15 -0500 \n",
"\n",
" Accepts Marketing Currency Subtotal Shipping ... Tax 1 Name \\\n",
"0 no CAD 35 5 ... GST 5% \n",
"1 no CAD 40 5 ... GST 5% \n",
"2 no CAD 40 5 ... GST 5% \n",
"3 no CAD 0 0 ... GST 5% \n",
"4 no CAD 35 5 ... GST 5% \n",
"\n",
" Tax 1 Value Tax 2 Name Tax 2 Value Tax 3 Name Tax 3 Value Tax 4 Name \\\n",
"0 1.75 QST 9.975% 3.49 NaN NaN NaN \n",
"1 1.75 QST 9.975% 3.49 NaN NaN NaN \n",
"2 1.75 QST 9.975% 3.49 NaN NaN NaN \n",
"3 1.75 QST 9.975% 3.49 NaN NaN NaN \n",
"4 1.75 QST 9.975% 3.49 NaN NaN NaN \n",
"\n",
" Tax 4 Value Tax 5 Name Tax 5 Value \n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
"[5 rows x 66 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let'S look at the first 5 rows of the dataframe to have an idea what it looks like\n",
"orders.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'Name', u'Email', u'Financial Status', u'Paid at',\n",
" u'Fulfillment Status', u'Fulfilled at', u'Accepts Marketing',\n",
" u'Currency', u'Subtotal', u'Shipping', u'Taxes', u'Total',\n",
" u'Discount Code', u'Discount Amount', u'Shipping Method', u'Created at',\n",
" u'Lineitem quantity', u'Lineitem name', u'Lineitem price',\n",
" u'Lineitem compare at price', u'Lineitem sku',\n",
" u'Lineitem requires shipping', u'Lineitem taxable',\n",
" u'Lineitem fulfillment status', u'Billing Name', u'Billing Street',\n",
" u'Billing Address1', u'Billing Address2', u'Billing Company',\n",
" u'Billing City', u'Billing Zip', u'Billing Province',\n",
" u'Billing Country', u'Billing Phone', u'Shipping Name',\n",
" u'Shipping Street', u'Shipping Address1', u'Shipping Address2',\n",
" u'Shipping Company', u'Shipping City', u'Shipping Zip',\n",
" u'Shipping Province', u'Shipping Country', u'Shipping Phone', u'Notes',\n",
" u'Note Attributes', u'Cancelled at', u'Payment Method',\n",
" u'Payment Reference', u'Refunded Amount', u'Vendor', u'Id', u'Tags',\n",
" u'Risk Level', u'Source', u'Lineitem discount', u'Tax 1 Name',\n",
" u'Tax 1 Value', u'Tax 2 Name', u'Tax 2 Value', u'Tax 3 Name',\n",
" u'Tax 3 Value', u'Tax 4 Name', u'Tax 4 Value', u'Tax 5 Name',\n",
" u'Tax 5 Value'],\n",
" dtype='object')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's look at all the columns\n",
"orders.columns"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 p is for momentum - 6-12 months\n",
"1 Reciprocate - L / Eggplant / Men\n",
"2 Above Average - XL / Sea foam / Men\n",
"3 Test\n",
"4 Shine on you crazy diamond - Women / Tri-Cranb...\n",
"5 Ailleurs - Men / Truffle / S\n",
"6 Special request\n",
"7 Special request\n",
"8 Ailleurs - Men / Eggplant / M\n",
"9 Shine on you crazy diamond - M / Slate / Men\n",
"10 Ailleurs - Women / Eggplant / M\n",
"11 Shine on you crazy diamond - M / Cream / Men\n",
"12 Shine on you crazy diamond - M / Slate / Men\n",
"13 Quark & Laser - Medium / Sea Foam / Men\n",
"14 Shine on you crazy diamond - M / Sea foam / Men\n",
"15 Shine on you crazy diamond - S / Mint / Women\n",
"16 Reciprocate - S / Eggplant / Women\n",
"17 Mainstream - Large / Asphalt\n",
"18 Above Average - M / Sea foam / Men\n",
"19 Quark & Laser - Medium / Cream\n",
"20 Shine on you crazy diamond Bravais lattice - S...\n",
"21 Standing on the shoulders of giants - L / Crea...\n",
"Name: Lineitem name, dtype: object"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's look at the data of the column 'Lineitem name'\n",
"orders['Lineitem name']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### At this point, I know I want\n",
"\n",
" **1 - A function that takes in the text of `Lineitem name` and returns a list of variants (if any)**\n",
" \n",
" **2 - A function that takes in a list of variants and returns the color (if there is one)**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Let's define the first function\n",
"def get_variants(lineitem):\n",
" try:\n",
" return [variant.strip() \n",
" for variant \n",
" in lineitem.split('-', 1)[1].split('/')]\n",
" except:\n",
" return []"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 [6-12 months]\n",
"1 [L, Eggplant, Men]\n",
"2 [XL, Sea foam, Men]\n",
"3 []\n",
"4 [Women, Tri-Cranberry, S]\n",
"5 [Men, Truffle, S]\n",
"6 []\n",
"7 []\n",
"8 [Men, Eggplant, M]\n",
"9 [M, Slate, Men]\n",
"10 [Women, Eggplant, M]\n",
"11 [M, Cream, Men]\n",
"12 [M, Slate, Men]\n",
"13 [Medium, Sea Foam, Men]\n",
"14 [M, Sea foam, Men]\n",
"15 [S, Mint, Women]\n",
"16 [S, Eggplant, Women]\n",
"17 [Large, Asphalt]\n",
"18 [M, Sea foam, Men]\n",
"19 [Medium, Cream]\n",
"20 [S, Sea foam, Men]\n",
"21 [L, Cream, Women]\n",
"Name: Lineitem name, dtype: object"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's apply (map) it to every lineitem and make sure it works as expected\n",
"orders['Lineitem name'].map(get_variants)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Let's define the 2nd function\n",
"def get_color(variants):\n",
" sizes_and_models = ['6-12 months', 'Small', \n",
" 'Medium', 'Large', 'S', \n",
" 'M', 'L', 'XL', '2XL', \n",
" 'Men', 'Women']\n",
" color = [variant.lower() \n",
" for variant in variants\n",
" if variant not in sizes_and_models]\n",
" try:\n",
" return color[0]\n",
" except:\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 None\n",
"1 eggplant\n",
"2 sea foam\n",
"3 None\n",
"4 tri-cranberry\n",
"5 truffle\n",
"6 None\n",
"7 None\n",
"8 eggplant\n",
"9 slate\n",
"10 eggplant\n",
"11 cream\n",
"12 slate\n",
"13 sea foam\n",
"14 sea foam\n",
"15 mint\n",
"16 eggplant\n",
"17 asphalt\n",
"18 sea foam\n",
"19 cream\n",
"20 sea foam\n",
"21 cream\n",
"Name: Lineitem name, dtype: object"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's apply (map) it to every variant list and make sure it works as expected\n",
"orders['Lineitem name'].map(get_variants)\\\n",
" .map(get_color)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"sea foam 5\n",
"eggplant 4\n",
"cream 3\n",
"slate 2\n",
"mint 1\n",
"truffle 1\n",
"asphalt 1\n",
"tri-cranberry 1\n",
"dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's count these up to get he most popular colors\n",
"orders['Lineitem name'].map(get_variants)\\\n",
" .map(get_color)\\\n",
" .value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10b2611d0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10b0c91d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# We can even visualize the counts as a bar graph\n",
"orders['Lineitem name'].map(get_variants)\\\n",
" .map(get_color)\\\n",
" .value_counts()\\\n",
" .plot(kind=\"bar\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Q2 -Do people drop out of the checkout funnel at the shipping page?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The main reason why people abandon they online carts is because shipping fees are too high. Other shipping turn-off include not offering shipping to the shopper's country. A good number to diagnose those kinds of problems is the drop-off rate during checkout at the step where customers calculate the shipping rates.\n",
"\n",
"This is actually easier to do in Google Analytics, but I want to show how to parse unruly CSV and a simple formula for the drop-off rate."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# These are the steps I care about for the purchase funnel \n",
"funnel_steps = ['Add to cart',\n",
" 'Contact info',\n",
" 'Shipping info',\n",
" 'Payment info',\n",
" 'Place an order']"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Let's parse the unruly CSV we downloaded from Google Analytics. \n",
"# We don't care about most numbers in there, I just want to get the essential\n",
"funnel_data = dict()\n",
"with open('data/Goal_Funnel.csv') as f:\n",
" for line in f:\n",
" cells = line.split(',')\n",
" try:\n",
" if cells[3] in funnel_steps:\n",
" funnel_data[cells[3]] = int(cells[4].strip())\n",
" except:\n",
" pass\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{u'Add to cart': 100,\n",
" u'Contact info': 40,\n",
" u'Payment info': 13,\n",
" u'Place an order': 12,\n",
" u'Shipping info': 35}"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This dictionary contains the number of people who reached each page\n",
"funnel_data"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"62.8571428571 %\n"
]
}
],
"source": [
"# Let's compute the abandonment rate, or drop-off rate, at the shipping step\n",
"shipping_abandonment_rate = \\\n",
" (funnel_data['Shipping info'] - funnel_data['Payment info'])\\\n",
" / funnel_data['Shipping info'] \\\n",
" * 100.\n",
" \n",
"print shipping_abandonment_rate, \"%\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That is enormous! Next step would be to figure out how I can offer free shipping."
]
}
],
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