2_Starbucks_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target Audience for Direct Marketing in Starbucks Rewards Mobile App"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is part 2 of Starbucks capstone project. In this part, I perform exploratory data analysis (EDA).\n",
    "\n",
    "See `1_Starbucks_cleaning.ipynb` and `3_Starbucks_modeling.ipynb` for more information on other steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pandas.io.json import json_normalize\n",
    "%matplotlib inline\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = pd.read_csv('data/starbucks_customer_level.csv', sep=';', parse_dates=['became_member_on'])\n",
    "offers = pd.read_csv('data/starbucks_offer_level.csv', sep=';')\n",
    "transcript = pd.read_json('data/transcript.json', orient='records', lines=True)\n",
    "profile = pd.read_json('data/profile.json', orient='records', lines=True)\n",
    "profile.became_member_on = pd.to_datetime(profile.became_member_on.astype(str), format=\"%Y/%m/%d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer's Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>person</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>total_rewarded</th>\n",
       "      <th>transactions_num</th>\n",
       "      <th>offers_received</th>\n",
       "      <th>offers_viewed</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_reward_size</th>\n",
       "      <th>avg_bogo_size</th>\n",
       "      <th>avg_discount_size</th>\n",
       "      <th>offers_rr</th>\n",
       "      <th>offers_cvr</th>\n",
       "      <th>bogo_rr</th>\n",
       "      <th>bogo_cvr</th>\n",
       "      <th>discount_rr</th>\n",
       "      <th>discount_cvr</th>\n",
       "      <th>informational_rr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>77.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>75.0</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>159.27</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>66.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender   age                            person became_member_on    income  \\\n",
       "0    NaN   NaN  68be06ca386d4c31939f3a4f0e3dd783       2017-01-01       NaN   \n",
       "1      F  55.0  0610b486422d4921ae7d2bf64640c50b       2017-01-01  112000.0   \n",
       "2    NaN   NaN  38fe809add3b4fcf9315a9694bb96ff5       2018-01-01       NaN   \n",
       "3      F  75.0  78afa995795e4d85b5d9ceeca43f5fef       2017-01-01  100000.0   \n",
       "4    NaN   NaN  a03223e636434f42ac4c3df47e8bac43       2017-01-01       NaN   \n",
       "\n",
       "   total_amount  total_rewarded  transactions_num  offers_received  \\\n",
       "0         20.40             5.0                 9              5.0   \n",
       "1         77.01             0.0                 3              2.0   \n",
       "2         14.30             0.0                 6              2.0   \n",
       "3        159.27            15.0                 7              4.0   \n",
       "4          4.65             0.0                 3              5.0   \n",
       "\n",
       "   offers_viewed  ...  avg_reward_size  avg_bogo_size  avg_discount_size  \\\n",
       "0            5.0  ...              2.5            0.0                2.5   \n",
       "1            0.0  ...              0.0            0.0                0.0   \n",
       "2            1.0  ...              0.0            0.0                0.0   \n",
       "3            4.0  ...              7.5            7.5                0.0   \n",
       "4            3.0  ...              0.0            0.0                0.0   \n",
       "\n",
       "   offers_rr  offers_cvr  bogo_rr  bogo_cvr  discount_rr  discount_cvr  \\\n",
       "0      100.0        40.0      0.0      0.00       100.00          40.0   \n",
       "1        0.0         0.0      0.0      0.00         0.00           0.0   \n",
       "2       50.0         0.0    100.0      0.00         0.00           0.0   \n",
       "3      100.0        50.0    100.0     66.67         0.00           0.0   \n",
       "4       60.0         0.0      0.0      0.00        66.67           0.0   \n",
       "\n",
       "   informational_rr  \n",
       "0               0.0  \n",
       "1               0.0  \n",
       "2               0.0  \n",
       "3             100.0  \n",
       "4              50.0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see distributions of age, gender and income. Note: NaNs values here are not included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20, 4));\n",
    "\n",
    "plt.subplot(1,4,1)\n",
    "profile.became_member_on.hist();\n",
    "plt.title(\"Distribution of Membership Duration\");\n",
    "plt.ylim(0, 6000);\n",
    "\n",
    "plt.subplot(1,4,2)\n",
    "plt.title(\"Age Distribution\")\n",
    "customers.age.hist();\n",
    "plt.ylim(0, 6000);\n",
    "\n",
    "plt.subplot(1,4,3)\n",
    "plt.title(\"Income Distribution\")\n",
    "customers.income.hist();\n",
    "plt.ylim(0, 6000);\n",
    "\n",
    "plt.subplot(1,4,4)\n",
    "plt.title(\"Distribution of Gender\")\n",
    "customers.gender.value_counts().plot.bar();\n",
    "for i, gender_num in enumerate(customers.gender.value_counts()):\n",
    "    #X position just after the end of the bar:\n",
    "    plt.text(i, gender_num, gender_num, ha = 'center', va='bottom');\n",
    "plt.ylim(0, 9000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "earliest member since: 2013-01-01 00:00:00, latest member since: 2018-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(f\"earliest member since: {customers.became_member_on.min()}, latest member since: {customers.became_member_on.max()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer's Spending Habits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>14825.0</td>\n",
       "      <td>54.393524</td>\n",
       "      <td>17.383705</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>55.000</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>101.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>14825.0</td>\n",
       "      <td>65404.991568</td>\n",
       "      <td>21598.299410</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>49000.00</td>\n",
       "      <td>64000.000</td>\n",
       "      <td>80000.0000</td>\n",
       "      <td>120000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amount</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>104.438351</td>\n",
       "      <td>125.922619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.82</td>\n",
       "      <td>69.405</td>\n",
       "      <td>148.7825</td>\n",
       "      <td>1608.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rewarded</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>5.581941</td>\n",
       "      <td>6.752801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transactions_num</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>8.173706</td>\n",
       "      <td>5.116250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>36.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offers_received</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>4.486882</td>\n",
       "      <td>1.076165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offers_viewed</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>3.266706</td>\n",
       "      <td>1.354569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offers_completed</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>1.156235</td>\n",
       "      <td>1.170933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_received</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>1.794059</td>\n",
       "      <td>1.120485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_viewed</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>1.465059</td>\n",
       "      <td>1.059699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_completed</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>0.520176</td>\n",
       "      <td>0.776632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_received</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>1.796647</td>\n",
       "      <td>1.131248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_viewed</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>1.239706</td>\n",
       "      <td>1.028684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_completed</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>0.636059</td>\n",
       "      <td>0.824121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>informational_received</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>0.896176</td>\n",
       "      <td>0.874963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>informational_viewed</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>0.561941</td>\n",
       "      <td>0.735380</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bogo</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>3.882647</td>\n",
       "      <td>6.144766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_discount</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>1.699294</td>\n",
       "      <td>2.351574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_order_size</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>13.340649</td>\n",
       "      <td>15.998453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.04</td>\n",
       "      <td>11.410</td>\n",
       "      <td>20.2400</td>\n",
       "      <td>451.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_reward_size</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>2.951226</td>\n",
       "      <td>3.002326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.500</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_bogo_size</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>2.786979</td>\n",
       "      <td>3.855307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_discount_size</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>1.205397</td>\n",
       "      <td>1.433173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offers_rr</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>73.068568</td>\n",
       "      <td>25.465248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>75.000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offers_cvr</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>34.452880</td>\n",
       "      <td>32.993342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.330</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_rr</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>72.503930</td>\n",
       "      <td>39.292697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>100.000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bogo_cvr</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>28.781861</td>\n",
       "      <td>40.440557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_rr</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>61.110842</td>\n",
       "      <td>42.101023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>66.670</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_cvr</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>38.222179</td>\n",
       "      <td>44.886484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>informational_rr</th>\n",
       "      <td>17000.0</td>\n",
       "      <td>39.309119</td>\n",
       "      <td>46.689329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count          mean           std      min  \\\n",
       "age                     14825.0     54.393524     17.383705     18.0   \n",
       "income                  14825.0  65404.991568  21598.299410  30000.0   \n",
       "total_amount            17000.0    104.438351    125.922619      0.0   \n",
       "total_rewarded          17000.0      5.581941      6.752801      0.0   \n",
       "transactions_num        17000.0      8.173706      5.116250      0.0   \n",
       "offers_received         17000.0      4.486882      1.076165      0.0   \n",
       "offers_viewed           17000.0      3.266706      1.354569      0.0   \n",
       "offers_completed        17000.0      1.156235      1.170933      0.0   \n",
       "bogo_received           17000.0      1.794059      1.120485      0.0   \n",
       "bogo_viewed             17000.0      1.465059      1.059699      0.0   \n",
       "bogo_completed          17000.0      0.520176      0.776632      0.0   \n",
       "discount_received       17000.0      1.796647      1.131248      0.0   \n",
       "discount_viewed         17000.0      1.239706      1.028684      0.0   \n",
       "discount_completed      17000.0      0.636059      0.824121      0.0   \n",
       "informational_received  17000.0      0.896176      0.874963      0.0   \n",
       "informational_viewed    17000.0      0.561941      0.735380      0.0   \n",
       "total_bogo              17000.0      3.882647      6.144766      0.0   \n",
       "total_discount          17000.0      1.699294      2.351574      0.0   \n",
       "avg_order_size          17000.0     13.340649     15.998453      0.0   \n",
       "avg_reward_size         17000.0      2.951226      3.002326      0.0   \n",
       "avg_bogo_size           17000.0      2.786979      3.855307      0.0   \n",
       "avg_discount_size       17000.0      1.205397      1.433173      0.0   \n",
       "offers_rr               17000.0     73.068568     25.465248      0.0   \n",
       "offers_cvr              17000.0     34.452880     32.993342      0.0   \n",
       "bogo_rr                 17000.0     72.503930     39.292697      0.0   \n",
       "bogo_cvr                17000.0     28.781861     40.440557      0.0   \n",
       "discount_rr             17000.0     61.110842     42.101023      0.0   \n",
       "discount_cvr            17000.0     38.222179     44.886484      0.0   \n",
       "informational_rr        17000.0     39.309119     46.689329      0.0   \n",
       "\n",
       "                             25%        50%         75%        max  \n",
       "age                        42.00     55.000     66.0000     101.00  \n",
       "income                  49000.00  64000.000  80000.0000  120000.00  \n",
       "total_amount               21.82     69.405    148.7825    1608.69  \n",
       "total_rewarded              0.00      3.000     10.0000      55.00  \n",
       "transactions_num            4.00      7.000     11.0000      36.00  \n",
       "offers_received             4.00      5.000      5.0000       6.00  \n",
       "offers_viewed               2.00      3.000      4.0000       6.00  \n",
       "offers_completed            0.00      1.000      2.0000       6.00  \n",
       "bogo_received               1.00      2.000      3.0000       6.00  \n",
       "bogo_viewed                 1.00      1.000      2.0000       6.00  \n",
       "bogo_completed              0.00      0.000      1.0000       5.00  \n",
       "discount_received           1.00      2.000      3.0000       6.00  \n",
       "discount_viewed             0.00      1.000      2.0000       6.00  \n",
       "discount_completed          0.00      0.000      1.0000       5.00  \n",
       "informational_received      0.00      1.000      1.0000       5.00  \n",
       "informational_viewed        0.00      0.000      1.0000       5.00  \n",
       "total_bogo                  0.00      0.000      5.0000      50.00  \n",
       "total_discount              0.00      0.000      3.0000      19.00  \n",
       "avg_order_size              3.04     11.410     20.2400     451.47  \n",
       "avg_reward_size             0.00      2.500      5.0000      10.00  \n",
       "avg_bogo_size               0.00      0.000      5.0000      10.00  \n",
       "avg_discount_size           0.00      0.000      2.0000       5.00  \n",
       "offers_rr                  50.00     75.000    100.0000     100.00  \n",
       "offers_cvr                  0.00     33.330     60.0000     100.00  \n",
       "bogo_rr                    50.00    100.000    100.0000     100.00  \n",
       "bogo_cvr                    0.00      0.000     50.0000     100.00  \n",
       "discount_rr                 0.00     66.670    100.0000     100.00  \n",
       "discount_cvr                0.00      0.000    100.0000     100.00  \n",
       "informational_rr            0.00      0.000    100.0000     100.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** \n",
    " - **Customers**: the typical Stabucks mobile app customer is middle-aged (median - 55 years) and has income of about \\\\$64000). During the experiment, the customers spent on average \\\\$104.44 (min - \\\\$0, max - \\\\$1608.7) and got \\\\$5.6 (min - \\\\$0, max - \\\\$55) rewarded. Furthermore, customers would make on average 8 transactions with the average order size being \\\\$13.34.\n",
    " \n",
    " \n",
    " - **Offers**: Customers would receive on average 4-5 offers, would view about 3 offers and would complete about 1 offer. The average return on bogo offers is higher than on discount offers - \\\\$3.8 vs $1.7. Each customer viewed on average 73% of offers he/she received (Response Rate), with bogo offers being viewed more often (73%) than discounts (61%) or informational offers (39%). Each customer reacted upon (CVR) about 34% of offers he/she viewed, with CVR for discounts being higher (38%) than for bogo (29%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Offers - received, viewed, completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4));\n",
    "plt.title(\"Frequency of each Event\");\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "transcript.event.value_counts()[1:].plot.bar();\n",
    "plt.title(\"Original\")\n",
    "for i, event_num in enumerate(transcript.event.value_counts()[1:]):\n",
    "    #X position just after the end of the bar:\n",
    "    plt.text(i, event_num, event_num, ha = 'center', va = 'bottom');\n",
    "    \n",
    "plt.subplot(1,2,2)\n",
    "frequency = customers[['offers_received','offers_viewed', 'offers_completed']].sum().astype(int)\n",
    "frequency.plot.bar(color='orange');\n",
    "plt.title(\"After Correction\");\n",
    "for i, event_num in enumerate(frequency):\n",
    "    #X position just after the end of the bar:\n",
    "    plt.text(i, event_num, event_num, ha = 'center', va = 'bottom');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20, 4));\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "received_by_offer_type = offers['offer_type'].value_counts()\n",
    "received_by_offer_type.plot.bar();\n",
    "plt.title(\"Received Offers\");\n",
    "for i, event_num in enumerate(received_by_offer_type):\n",
    "    #X position just after the end of the bar:\n",
    "    plt.text(i, event_num, event_num, ha = 'center', va = 'bottom');\n",
    "plt.ylim(0, 33000);\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "viewed_by_offer_type = offers[offers.viewed_on_time == 1].groupby('offer_type').viewed_on_time.count()\n",
    "viewed_by_offer_type.plot.bar();\n",
    "plt.title(\"Viewed Offers\");\n",
    "for i, event_num in enumerate(viewed_by_offer_type):\n",
    "    #X position just after the end of the bar:\n",
    "    plt.text(i, event_num, event_num, ha = 'center', va = 'bottom');\n",
    "plt.xlabel(\"\");\n",
    "plt.ylim(0, 33000);\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "completed_by_offer_type = offers[offers.completed_after_viewing == 1].groupby('offer_type').completed_after_viewing.count()\n",
    "completed_by_offer_type.plot.bar();\n",
    "plt.title(\"Completed Offers\");\n",
    "for i, event_num in enumerate(completed_by_offer_type):\n",
    "    #X position just after the end of the bar:\n",
    "    plt.text(i, event_num, event_num, ha = 'center', va = 'bottom');\n",
    "plt.xlabel(\"\");\n",
    "plt.ylim(0, 33000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rewards = offers[offers.completed_after_viewing == 1].groupby(['offer_type'])['reward'].sum()\n",
    "plt.title(\"Total Amount Rewarded by Offer Type, $\");\n",
    "rewards.plot.bar();\n",
    "for i, event_num in enumerate(rewards):\n",
    "    #X position just after the end of the bar:\n",
    "    plt.text(i, event_num, event_num, ha = 'center', va = 'bottom');\n",
    "plt.xlabel(\"\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customers Engagement with Offers - RR and CVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['offers_rr', 'bogo_rr', 'discount_rr', 'informational_rr', 'offers_cvr', 'bogo_cvr', 'discount_cvr',]\n",
    "plt.figure(figsize=(15, 5));\n",
    "\n",
    "for i, each in enumerate(customers[cols]):\n",
    "    plt.subplot(2,4,i+1)\n",
    "    customers[each].hist();\n",
    "    plt.title(f\"{each}, %\");\n",
    "    plt.ylim(0, 12000);\n",
    "    plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: people view offers much more often than complete. Bogos viewed ofter than discounts or informational. However, customers complete discount offers more often than bogos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['became_member_on', 'age', 'income', 'total_amount', 'avg_order_size', 'transactions_num', 'offers_cvr', 'bogo_cvr', 'discount_cvr']\n",
    "sns.heatmap(customers[cols].corr(), cmap = 'rocket_r', annot = True, fmt = '.2f');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: not profile, but spending habits (like number of transactions and total amount) seem to correlate more with the conversion rates. For bogo_cvr total amount spent seems to be more defining, while for discount_cvr - transactions frequency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customers-Offers Interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16994 customers received offers, while 6 did not\n"
     ]
    }
   ],
   "source": [
    "print(f\"{(customers.offers_received != 0).sum()} customers received offers, while {(customers.offers_received == 0).sum()} did not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of those who received, 16735 customers viewed offers, while 259 did not\n"
     ]
    }
   ],
   "source": [
    "print(f\"Out of those who received, {((customers.offers_received != 0) & (customers.offers_viewed != 0)).sum()} customers viewed offers, while {((customers.offers_received != 0) & (customers.offers_viewed == 0)).sum()} did not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of those who received & viewed, 10640 customers completed the offers, while 6095 did not\n"
     ]
    }
   ],
   "source": [
    "print(f\"Out of those who received & viewed, {((customers.offers_received != 0) & (customers.offers_viewed != 0) & (customers.offers_completed != 0)).sum()} customers completed the offers, while {((customers.offers_received != 0) & (customers.offers_viewed != 0) & (customers.offers_completed == 0)).sum()} did not\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.4% of customers viewed offers on time\n"
     ]
    }
   ],
   "source": [
    "# total viewing rate - % customers who viewed the offers on time\n",
    "viewing_rate = (customers.offers_viewed > 0).sum()\n",
    "print(f\"{viewing_rate/customers.shape[0]*100:.1f}% of customers viewed offers on time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rewarded Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.6% of customers completed offers after viewing\n"
     ]
    }
   ],
   "source": [
    "# total rewarded rate - % customers who completed the offers \n",
    "rewarded_rate = (customers.offers_completed > 0).sum()\n",
    "print(f\"{rewarded_rate/customers.shape[0]*100:.1f}% of customers completed offers after viewing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revenue, Campaign Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_json_in_transcript_dataset(transcript):\n",
    "    '''\n",
    "    Input:\n",
    "        transcript: original transcript dataset \n",
    "    Output:\n",
    "        transcript_clean: normalized transcript dataset\n",
    "    '''    \n",
    "    # normalize json in value column\n",
    "    value_normalized = json_normalize(transcript['value'])\n",
    "    # merging the two columns 'offer id' and 'offer_id' into new one 'id'\n",
    "    value_normalized['id'] = value_normalized['offer id'].fillna(value_normalized['offer_id'])\n",
    "    # dropping the redundant columns 'offer id', 'offer_id'\n",
    "    value_normalized = value_normalized.drop(['offer id', 'offer_id'], axis=1)\n",
    "    #rename the column 'reward' to 'rewarded'\n",
    "    value_normalized.rename(columns={value_normalized.columns[1]:'rewarded'}, inplace=True)\n",
    "    # join value_normalized with transcript \n",
    "    transcript_clean = transcript.join(value_normalized)    \n",
    "    # dropping 'value', 'event' columns\n",
    "    transcript_clean = transcript_clean.drop(['value'], axis=1)\n",
    "    \n",
    "    return transcript_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript_clean = normalize_json_in_transcript_dataset(transcript)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total revenue during test period - $1775451.97\n",
      "campaign cost based on true customer behavior - $94893.0\n",
      "actual campaign cost - $164676.0\n"
     ]
    }
   ],
   "source": [
    "revenue = customers.total_amount.sum()\n",
    "print(f\"total revenue during test period - ${revenue:.2f}\")\n",
    "true_campaign_cost = customers.total_rewarded.sum()\n",
    "actual_campaign_cost = transcript_clean.rewarded.sum()\n",
    "print(f\"campaign cost based on true customer behavior - ${true_campaign_cost}\")\n",
    "print(f\"actual campaign cost - ${actual_campaign_cost}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cost per Acquisition (CPA) -  \"also known as cost per order or lead – tells you how much each new customer cost to obtain.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actual cost per acquisition - $1.19\n",
      "cost per acquisition based on true customer behavior - $0.68\n"
     ]
    }
   ],
   "source": [
    "# campaign cost per transaction\n",
    "actual_cost_per_acquisition = actual_campaign_cost/customers.transactions_num.sum()\n",
    "true_cost_per_acquisition = true_campaign_cost/customers.transactions_num.sum()\n",
    "print(f\"actual cost per acquisition - ${actual_cost_per_acquisition:.2f}\")\n",
    "print(f\"cost per acquisition based on true customer behavior - ${true_cost_per_acquisition:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average Order Size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average order size - $12.78\n"
     ]
    }
   ],
   "source": [
    "# revenue per transaction\n",
    "average_order_size = revenue/customers.transactions_num.sum()\n",
    "print(f\"average order size - ${average_order_size:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revenue per Order - how much profit the company made from each order on average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actual revenue per order - $11.59\n",
      "true revenue per order - $12.09\n"
     ]
    }
   ],
   "source": [
    "actual_revenue_per_order = average_order_size-actual_cost_per_acquisition\n",
    "true_revenue_per_order = average_order_size-true_cost_per_acquisition\n",
    "print(f\"actual revenue per order - ${actual_revenue_per_order:.2f}\")\n",
    "print(f\"true revenue per order - ${true_revenue_per_order:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return on Investment (ROI), %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actual ROI - 978%\n",
      "correct ROI - 1771%\n"
     ]
    }
   ],
   "source": [
    "actual_roi = (revenue-actual_campaign_cost)/actual_campaign_cost * 100\n",
    "true_roi = (revenue-true_campaign_cost)/true_campaign_cost * 100\n",
    "print(f\"actual ROI - {actual_roi:.0f}%\")\n",
    "print(f\"correct ROI - {true_roi:.0f}%\")"
   ]
  }
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