customer-relationship-management.ipynb

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     "text": [
      "Collecting Lifetimes\r\n",
      "  Downloading Lifetimes-0.11.3-py3-none-any.whl (584 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m584.2/584.2 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: pandas>=0.24.0 in /opt/conda/lib/python3.7/site-packages (from Lifetimes) (1.3.5)\r\n",
      "Requirement already satisfied: dill>=0.2.6 in /opt/conda/lib/python3.7/site-packages (from Lifetimes) (0.3.5.1)\r\n",
      "Collecting autograd>=1.2.0\r\n",
      "  Downloading autograd-1.5-py3-none-any.whl (48 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.9/48.9 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from Lifetimes) (1.21.6)\r\n",
      "Requirement already satisfied: scipy>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from Lifetimes) (1.7.3)\r\n",
      "Requirement already satisfied: future>=0.15.2 in /opt/conda/lib/python3.7/site-packages (from autograd>=1.2.0->Lifetimes) (0.18.2)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24.0->Lifetimes) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24.0->Lifetimes) (2022.1)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->Lifetimes) (1.15.0)\r\n",
      "Installing collected packages: autograd, Lifetimes\r\n",
      "Successfully installed Lifetimes-0.11.3 autograd-1.5\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
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   "source": [
    "!pip install Lifetimes"
   ]
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   "source": [
    "# import os\n",
    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "\n",
    "import os\n",
    "import datetime\n",
    "import squarify\n",
    "import warnings\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from operator import attrgetter\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot\n",
    "from sklearn.metrics import (silhouette_score,\n",
    "                             calinski_harabasz_score,\n",
    "                             davies_bouldin_score)\n",
    "from lifetimes import BetaGeoFitter, GammaGammaFitter\n",
    "from lifetimes.plotting import plot_period_transactions\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "palette = 'Set2'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe2d52f",
   "metadata": {
    "execution": {
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   "source": [
    "# Analysis Approach:\n",
    "1. RFM \n",
    "2. Cohort Analysis\n",
    "3. Customer LTV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f69e30",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "source": [
    "# EDA"
   ]
  },
  {
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   "source": [
    "# Data Definitions\n",
    "\n",
    "* InvoiceNo: Invoice number that consists 6 digits. If this code starts with letter 'c', it indicates a cancellation.\n",
    "* StockCode: Product code that consists 5 digits.\n",
    "* Description: Product name.\n",
    "* Quantity: The quantities of each product per transaction.\n",
    "* InvoiceDate: Represents the day and time when each transaction was generated.\n",
    "* UnitPrice: Product price per unit.\n",
    "* CustomerID: Customer number that consists 5 digits. Each customer has a unique customer ID.\n",
    "* Country: Name of the country where each customer resides."
   ]
  },
  {
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     "status": "completed"
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   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55      17850  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39      17850  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75      17850  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39      17850  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39      17850  United Kingdom  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecommerce_path = '/kaggle/input/ecommerce-data/'\n",
    "\n",
    "df = pd.read_csv(os.path.join(ecommerce_path, 'data.csv'),\n",
    "                 header = 0,\n",
    "                 encoding = 'unicode_escape',\n",
    "                 dtype = {'CustomerID': str,\n",
    "                          'InvoiceID': str},\n",
    "                 parse_dates = ['InvoiceDate'], \n",
    "                 infer_datetime_format = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1ce706",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "Customer can order multiple items with the same Invoice Date/Invoice No."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "488a31ee",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Integrity Check\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "5ce6f425",
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     "data": {
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       "5268"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "#Number of duplicated items\n",
    "df.duplicated().sum() "
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.926694334288598"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "df['CustomerID'].isnull().sum()/df.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f67009",
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   },
   "source": [
    "A quarter of CustomerID has nulls. Yikes."
   ]
  },
  {
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   "execution_count": 7,
   "id": "f86e035e",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo              object\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID             object\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df.dtypes"
   ]
  },
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    },
    "tags": []
   },
   "source": [
    "Where do most of our customer reside? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.14.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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of Orders by Countries\",\"x\":0.5,\"xanchor\":\"center\",\"y\":0.9,\"yanchor\":\"top\"},\"width\":1000},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "world_map = df[['CustomerID', 'InvoiceNo', 'Country']\n",
    "              ].groupby(['CustomerID', 'InvoiceNo', 'Country']\n",
    "                       ).count().reset_index(drop = False)\n",
    "countries = world_map['Country'].value_counts()\n",
    "data = dict(type='choropleth',\n",
    "            locations = countries.index,\n",
    "            locationmode = 'country names',\n",
    "            z = countries,\n",
    "            text = countries.index,\n",
    "            colorbar = {'title':'Orders'},\n",
    "            colorscale='Viridis',\n",
    "            reversescale = False)\n",
    "\n",
    "layout = dict(title={'text': \"Number of Orders by Countries\",\n",
    "                     'y':0.9,\n",
    "                     'x':0.5,\n",
    "                     'xanchor': 'center',\n",
    "                     'yanchor': 'top'},\n",
    "              geo = dict(resolution = 50,\n",
    "                         showocean = True,\n",
    "                         oceancolor = \"LightBlue\",\n",
    "                         showland = True,\n",
    "                         landcolor = \"whitesmoke\",\n",
    "                         showframe = True),\n",
    "             template = 'plotly_white',\n",
    "             height = 600,\n",
    "             width = 1000)\n",
    "\n",
    "choromap = go.Figure(data = [data], layout = layout)\n",
    "iplot(choromap, validate = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d76412",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "Looks like customers are spread across major countries in most continents: North and South America, Europe, Australia. However, presence in Asia could be more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41fa6a85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:02.376114Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice\n",
       "count  541909.000000  541909.000000\n",
       "mean        9.552250       4.611114\n",
       "std       218.081158      96.759853\n",
       "min    -80995.000000  -11062.060000\n",
       "25%         1.000000       1.250000\n",
       "50%         3.000000       2.080000\n",
       "75%        10.000000       4.130000\n",
       "max     80995.000000   38970.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11d3aef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-31T18:45:21.178606Z",
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     "shell.execute_reply": "2022-10-31T18:45:21.230439Z",
     "shell.execute_reply.started": "2022-10-31T18:45:21.178559Z"
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are outliers. Negative values in Quantity and UnitPrice reflect cancelled orders. There are also missing value in Description and CustomerID. \n",
    "\n",
    "Next step would be:\n",
    "1. take out NULL values\n",
    "1. only keep non-cancelled orders and item quantities greater than 0\n",
    "1. after taking out outliers, reband quantiles\n",
    "1. multiply Quantity and UnitPrice to get total spent per order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "947973b4",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "def replace_with_thresholds(dataframe, variable, q1 = 0.25, q3 = 0.75):\n",
    "    \n",
    "    '''\n",
    "    Detects outliers with IQR method and replaces with thresholds \n",
    "    \n",
    "    '''\n",
    "    \n",
    "    df_ = dataframe.copy()\n",
    "    quartile1 = df_[variable].quantile(q1)\n",
    "    quartile3 = df_[variable].quantile(q3)\n",
    "    iqr = quartile3 - quartile1\n",
    "    \n",
    "    up_limit = quartile3 + 1.5 * iqr\n",
    "    low_limit = quartile1 - 1.5 * iqr\n",
    "    df_.loc[(df_[variable] < low_limit), variable] = low_limit\n",
    "    df_.loc[(df_[variable] > up_limit), variable] = up_limit\n",
    "    \n",
    "    return df_\n",
    "\n",
    "def ecommerce_preprocess(dataframe):\n",
    "    df_ = dataframe.copy()\n",
    "    \n",
    "    #Missing Values\n",
    "    df_ = df_.dropna()\n",
    "    \n",
    "    #Cancelled Orders & Quantity\n",
    "    df_ = df_[~df_['InvoiceNo'].str.contains('C', na = False)]\n",
    "    df_ = df_[df_['Quantity'] > 0]\n",
    "    \n",
    "    #Replacing Outliers\n",
    "    df_ = replace_with_thresholds(df_, \"Quantity\", q1 = 0.01, q3 = 0.99)\n",
    "    df_ = replace_with_thresholds(df_, \"UnitPrice\", q1 = 0.01, q3 = 0.99)\n",
    "    \n",
    "    #Total Price\n",
    "    df_[\"TotalPrice\"] = df_[\"Quantity\"] * df_[\"UnitPrice\"]\n",
    "    \n",
    "    return df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a4186da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:02.542850Z",
     "iopub.status.busy": "2022-11-08T01:38:02.541749Z",
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     "shell.execute_reply": "2022-11-08T01:38:03.201070Z"
    },
    "papermill": {
     "duration": 0.677047,
     "end_time": "2022-11-08T01:38:03.205224",
     "exception": false,
     "start_time": "2022-11-08T01:38:02.528177",
     "status": "completed"
    },
    "tags": []
   },
   "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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>397924.000000</td>\n",
       "      <td>397924.000000</td>\n",
       "      <td>397924.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.833736</td>\n",
       "      <td>2.893163</td>\n",
       "      <td>20.627751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.534512</td>\n",
       "      <td>3.227059</td>\n",
       "      <td>51.826400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>4.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>11.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>19.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>298.500000</td>\n",
       "      <td>37.060000</td>\n",
       "      <td>3268.575000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     TotalPrice\n",
       "count  397924.000000  397924.000000  397924.000000\n",
       "mean       11.833736       2.893163      20.627751\n",
       "std        25.534512       3.227059      51.826400\n",
       "min         1.000000       0.000000       0.000000\n",
       "25%         2.000000       1.250000       4.680000\n",
       "50%         6.000000       1.950000      11.800000\n",
       "75%        12.000000       3.750000      19.800000\n",
       "max       298.500000      37.060000    3268.575000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ecommerce_preprocess(df)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417cd892",
   "metadata": {
    "papermill": {
     "duration": 0.012269,
     "end_time": "2022-11-08T01:38:03.230346",
     "exception": false,
     "start_time": "2022-11-08T01:38:03.218077",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# RFM (Recency, Frequency, Monetary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d62cdea0",
   "metadata": {
    "papermill": {
     "duration": 0.012249,
     "end_time": "2022-11-08T01:38:03.255051",
     "exception": false,
     "start_time": "2022-11-08T01:38:03.242802",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "RFM is a good first pass to see what sort of breakdown is possible across whole user base. If it is too crude, can make come up with more user behavior metrics.\n",
    "\n",
    "Recency help us judge customers' retention/engagement whereas Frequency and Monetary help us judge their lifetime value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8fced56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:03.282165Z",
     "iopub.status.busy": "2022-11-08T01:38:03.281763Z",
     "iopub.status.idle": "2022-11-08T01:38:03.291077Z",
     "shell.execute_reply": "2022-11-08T01:38:03.290102Z"
    },
    "papermill": {
     "duration": 0.025817,
     "end_time": "2022-11-08T01:38:03.293431",
     "exception": false,
     "start_time": "2022-11-08T01:38:03.267614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:50:00')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the max date in order to determine which date to subtract from when determining Recency grades\n",
    "df['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1cd1eccd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:03.321283Z",
     "iopub.status.busy": "2022-11-08T01:38:03.320594Z",
     "iopub.status.idle": "2022-11-08T01:38:03.327662Z",
     "shell.execute_reply": "2022-11-08T01:38:03.326620Z"
    },
    "papermill": {
     "duration": 0.023745,
     "end_time": "2022-11-08T01:38:03.329914",
     "exception": false,
     "start_time": "2022-11-08T01:38:03.306169",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 12, 11, 0, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_date = dt.datetime(2011,12,11)\n",
    "today_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4829135f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:03.357982Z",
     "iopub.status.busy": "2022-11-08T01:38:03.357260Z",
     "iopub.status.idle": "2022-11-08T01:38:04.630093Z",
     "shell.execute_reply": "2022-11-08T01:38:04.629203Z"
    },
    "papermill": {
     "duration": 1.290956,
     "end_time": "2022-11-08T01:38:04.633518",
     "exception": false,
     "start_time": "2022-11-08T01:38:03.342562",
     "status": "completed"
    },
    "tags": []
   },
   "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>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>310.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>1770.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1491.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>331.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CustomerID  recency  frequency  monetary\n",
       "0      12346      326          1    310.44\n",
       "1      12347        3          7   4310.00\n",
       "2      12348       76          4   1770.78\n",
       "3      12349       19          1   1491.72\n",
       "4      12350      311          1    331.46"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#at customer level aggregates\n",
    "rfm = df.groupby('CustomerID').agg({'InvoiceDate': lambda x: (today_date - x.max()).days,\n",
    "                                    'InvoiceNo': lambda x: x.nunique(),\n",
    "                                    'TotalPrice': lambda x: x.sum()})\n",
    "\n",
    "rfm.columns = ['recency', 'frequency', 'monetary']\n",
    "\n",
    "rfm = rfm.loc[rfm['monetary'] > 0] #only want customers who spend more than $0\n",
    "rfm = rfm.reset_index()\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1271642",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-01T17:54:16.792001Z",
     "iopub.status.busy": "2022-11-01T17:54:16.791421Z",
     "iopub.status.idle": "2022-11-01T17:54:16.829961Z",
     "shell.execute_reply": "2022-11-01T17:54:16.828215Z",
     "shell.execute_reply.started": "2022-11-01T17:54:16.791933Z"
    },
    "papermill": {
     "duration": 0.012716,
     "end_time": "2022-11-08T01:38:04.660905",
     "exception": false,
     "start_time": "2022-11-08T01:38:04.648189",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# RFM Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f51f52fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:04.690160Z",
     "iopub.status.busy": "2022-11-08T01:38:04.688910Z",
     "iopub.status.idle": "2022-11-08T01:38:04.711940Z",
     "shell.execute_reply": "2022-11-08T01:38:04.710652Z"
    },
    "papermill": {
     "duration": 0.040481,
     "end_time": "2022-11-08T01:38:04.714726",
     "exception": false,
     "start_time": "2022-11-08T01:38:04.674245",
     "status": "completed"
    },
    "tags": []
   },
   "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>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>310.44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>1770.78</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1491.72</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>331.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>18280</td>\n",
       "      <td>278</td>\n",
       "      <td>1</td>\n",
       "      <td>180.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>18281</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>80.82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>18282</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>178.05</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18283</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>18287</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4338 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  recency  frequency  monetary recency_score\n",
       "0         12346      326          1    310.44             1\n",
       "1         12347        3          7   4310.00             5\n",
       "2         12348       76          4   1770.78             2\n",
       "3         12349       19          1   1491.72             4\n",
       "4         12350      311          1    331.46             1\n",
       "...         ...      ...        ...       ...           ...\n",
       "4333      18280      278          1    180.60             1\n",
       "4334      18281      181          1     80.82             1\n",
       "4335      18282        8          2    178.05             5\n",
       "4336      18283        4         16   2094.88             5\n",
       "4337      18287       43          3   1837.28             3\n",
       "\n",
       "[4338 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm2 = rfm.copy()\n",
    "rfm2['recency_score'] = pd.qcut(rfm2['recency'],5,labels = [5, 4, 3, 2, 1])\n",
    "rfm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f5d5550",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:04.745431Z",
     "iopub.status.busy": "2022-11-08T01:38:04.744272Z",
     "iopub.status.idle": "2022-11-08T01:38:04.767050Z",
     "shell.execute_reply": "2022-11-08T01:38:04.765815Z"
    },
    "papermill": {
     "duration": 0.040999,
     "end_time": "2022-11-08T01:38:04.769945",
     "exception": false,
     "start_time": "2022-11-08T01:38:04.728946",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#For now, we are only combining R and F. Monetary will not be included in overall RFM score. 5 is highest ranking.\n",
    "def get_rfm_scores(dataframe):\n",
    "    \n",
    "    df_ = dataframe.copy()\n",
    "    df_['recency_score'] = pd.qcut(df_['recency'],5,labels = [5, 4, 3, 2, 1])\n",
    "    df_['frequency_score'] = pd.qcut(df_['frequency'].rank(method = \"first\"), 5, labels = [1, 2, 3, 4, 5])\n",
    "    df_['monetary_score'] = pd.qcut(df_['monetary'], 5, labels = [1, 2, 3, 4, 5])\n",
    "    df_['RFM_SCORE'] = (df_['recency_score'].astype(str) + df_['frequency_score'].astype(str))\n",
    "    \n",
    "    return df_\n",
    "\n",
    "rfm = get_rfm_scores(rfm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e9e48f",
   "metadata": {
    "papermill": {
     "duration": 0.013683,
     "end_time": "2022-11-08T01:38:04.797688",
     "exception": false,
     "start_time": "2022-11-08T01:38:04.784005",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9278344",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:04.827305Z",
     "iopub.status.busy": "2022-11-08T01:38:04.826816Z",
     "iopub.status.idle": "2022-11-08T01:38:04.922829Z",
     "shell.execute_reply": "2022-11-08T01:38:04.921548Z"
    },
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     "duration": 0.113626,
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     "exception": false,
     "start_time": "2022-11-08T01:38:04.811674",
     "status": "completed"
    },
    "tags": []
   },
   "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>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "      <th>frequency_score</th>\n",
       "      <th>monetary_score</th>\n",
       "      <th>RFM_SCORE</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>310.44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>champions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>1770.78</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>at_Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1491.72</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>promising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>331.46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CustomerID  recency  frequency  monetary recency_score frequency_score  \\\n",
       "0      12346      326          1    310.44             1               1   \n",
       "1      12347        3          7   4310.00             5               5   \n",
       "2      12348       76          4   1770.78             2               4   \n",
       "3      12349       19          1   1491.72             4               1   \n",
       "4      12350      311          1    331.46             1               1   \n",
       "\n",
       "  monetary_score RFM_SCORE      segment  \n",
       "0              2        11  hibernating  \n",
       "1              5        55    champions  \n",
       "2              4        24      at_Risk  \n",
       "3              4        41    promising  \n",
       "4              2        11  hibernating  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_map = {r'[1-2][1-2]': 'hibernating',\n",
    "           r'[1-2][3-4]': 'at_Risk',\n",
    "           r'[1-2]5': 'cant_loose',\n",
    "           r'3[1-2]': 'about_to_sleep',\n",
    "           r'33': 'need_attention',\n",
    "           r'[3-4][4-5]': 'loyal_customers',\n",
    "           r'41': 'promising',\n",
    "           r'51': 'new_customers',\n",
    "           r'[4-5][2-3]': 'potential_loyalists',\n",
    "           r'5[4-5]': 'champions'}\n",
    "\n",
    "rfm['segment'] = rfm['RFM_SCORE'].replace(seg_map, regex = True)\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1eba0cf",
   "metadata": {
    "papermill": {
     "duration": 0.014077,
     "end_time": "2022-11-08T01:38:04.953770",
     "exception": false,
     "start_time": "2022-11-08T01:38:04.939693",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Segmentation Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf414ccb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:04.985681Z",
     "iopub.status.busy": "2022-11-08T01:38:04.985155Z",
     "iopub.status.idle": "2022-11-08T01:38:04.996955Z",
     "shell.execute_reply": "2022-11-08T01:38:04.995675Z"
    },
    "papermill": {
     "duration": 0.031262,
     "end_time": "2022-11-08T01:38:05.000343",
     "exception": false,
     "start_time": "2022-11-08T01:38:04.969081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hibernating            0.246888\n",
       "loyal_customers        0.188797\n",
       "champions              0.145920\n",
       "at_Risk                0.136699\n",
       "potential_loyalists    0.111572\n",
       "about_to_sleep         0.081143\n",
       "need_attention         0.043107\n",
       "promising              0.021669\n",
       "cant_loose             0.014523\n",
       "new_customers          0.009682\n",
       "Name: segment, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.segment.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b156d719",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:05.031067Z",
     "iopub.status.busy": "2022-11-08T01:38:05.030296Z",
     "iopub.status.idle": "2022-11-08T01:38:05.404079Z",
     "shell.execute_reply": "2022-11-08T01:38:05.403075Z"
    },
    "papermill": {
     "duration": 0.392508,
     "end_time": "2022-11-08T01:38:05.407071",
     "exception": false,
     "start_time": "2022-11-08T01:38:05.014563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18, 8))\n",
    "ax = sns.countplot(data = rfm,\n",
    "                   x = 'segment',\n",
    "                   palette = palette)\n",
    "total = len(rfm.segment)\n",
    "for patch in ax.patches:\n",
    "    percentage = '{:.1f}%'.format(100 * patch.get_height()/total)\n",
    "    x = patch.get_x() + patch.get_width() / 2 - 0.17\n",
    "    y = patch.get_y() + patch.get_height() * 1.005\n",
    "    ax.annotate(percentage, (x, y), size = 14)\n",
    "plt.title('Number of Customers by Segments', size = 16)\n",
    "plt.xlabel('Segment', size = 14)\n",
    "plt.ylabel('Count', size = 14)\n",
    "plt.xticks(size = 10)\n",
    "plt.yticks(size = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d400bb",
   "metadata": {
    "papermill": {
     "duration": 0.015066,
     "end_time": "2022-11-08T01:38:05.438369",
     "exception": false,
     "start_time": "2022-11-08T01:38:05.423303",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can easily see from this percentage break down that top 3 of the user base is hibernating, loyal customers or champions. This is optimistic since it shows that even though many customers are dormant there are a bunch that are activity engaging."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab03690",
   "metadata": {
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     "end_time": "2022-11-08T01:38:05.467783",
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     "start_time": "2022-11-08T01:38:05.453755",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# RFM Segments' Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6910b95",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-11-08T01:38:05.538035Z",
     "shell.execute_reply": "2022-11-08T01:38:05.536786Z"
    },
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     "duration": 0.05885,
     "end_time": "2022-11-08T01:38:05.540857",
     "exception": false,
     "start_time": "2022-11-08T01:38:05.482007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"4\" halign=\"left\">monetary</th>\n",
       "      <th colspan=\"4\" halign=\"left\">frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>about_to_sleep</th>\n",
       "      <td>10.943474</td>\n",
       "      <td>34</td>\n",
       "      <td>72</td>\n",
       "      <td>53.312500</td>\n",
       "      <td>533.161343</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6207.670</td>\n",
       "      <td>469.058097</td>\n",
       "      <td>0.368913</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.161932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>at_Risk</th>\n",
       "      <td>68.618828</td>\n",
       "      <td>73</td>\n",
       "      <td>374</td>\n",
       "      <td>153.785835</td>\n",
       "      <td>943.170824</td>\n",
       "      <td>52.00</td>\n",
       "      <td>11072.670</td>\n",
       "      <td>938.458341</td>\n",
       "      <td>0.954415</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2.878583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cant_loose</th>\n",
       "      <td>65.250378</td>\n",
       "      <td>73</td>\n",
       "      <td>373</td>\n",
       "      <td>132.968254</td>\n",
       "      <td>2008.352354</td>\n",
       "      <td>70.02</td>\n",
       "      <td>10254.180</td>\n",
       "      <td>2646.822540</td>\n",
       "      <td>4.289630</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>8.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>champions</th>\n",
       "      <td>3.683300</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6.361769</td>\n",
       "      <td>18366.105822</td>\n",
       "      <td>198.23</td>\n",
       "      <td>266163.525</td>\n",
       "      <td>6498.612978</td>\n",
       "      <td>16.476484</td>\n",
       "      <td>3</td>\n",
       "      <td>210</td>\n",
       "      <td>12.417062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hibernating</th>\n",
       "      <td>92.013560</td>\n",
       "      <td>73</td>\n",
       "      <td>374</td>\n",
       "      <td>217.605042</td>\n",
       "      <td>553.714975</td>\n",
       "      <td>3.75</td>\n",
       "      <td>9182.345</td>\n",
       "      <td>398.573036</td>\n",
       "      <td>0.302492</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.101774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loyal_customers</th>\n",
       "      <td>15.577050</td>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "      <td>33.608059</td>\n",
       "      <td>5405.787839</td>\n",
       "      <td>36.56</td>\n",
       "      <td>120210.355</td>\n",
       "      <td>2752.519574</td>\n",
       "      <td>4.545669</td>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>6.479853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>need_attention</th>\n",
       "      <td>11.552074</td>\n",
       "      <td>34</td>\n",
       "      <td>72</td>\n",
       "      <td>52.427807</td>\n",
       "      <td>732.298552</td>\n",
       "      <td>6.90</td>\n",
       "      <td>4353.830</td>\n",
       "      <td>847.657086</td>\n",
       "      <td>0.470081</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2.326203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_customers</th>\n",
       "      <td>3.902112</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>7.428571</td>\n",
       "      <td>174.620952</td>\n",
       "      <td>89.94</td>\n",
       "      <td>848.550</td>\n",
       "      <td>314.883690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential_loyalists</th>\n",
       "      <td>9.338808</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>17.398760</td>\n",
       "      <td>601.746293</td>\n",
       "      <td>20.80</td>\n",
       "      <td>4628.750</td>\n",
       "      <td>674.628357</td>\n",
       "      <td>0.651401</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.010331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promising</th>\n",
       "      <td>5.192519</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>23.510638</td>\n",
       "      <td>223.634736</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1491.720</td>\n",
       "      <td>285.623723</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       recency                           monetary          \\\n",
       "                           std min  max        mean           std     min   \n",
       "segment                                                                     \n",
       "about_to_sleep       10.943474  34   72   53.312500    533.161343    6.20   \n",
       "at_Risk              68.618828  73  374  153.785835    943.170824   52.00   \n",
       "cant_loose           65.250378  73  373  132.968254   2008.352354   70.02   \n",
       "champions             3.683300   1   13    6.361769  18366.105822  198.23   \n",
       "hibernating          92.013560  73  374  217.605042    553.714975    3.75   \n",
       "loyal_customers      15.577050  15   72   33.608059   5405.787839   36.56   \n",
       "need_attention       11.552074  34   72   52.427807    732.298552    6.90   \n",
       "new_customers         3.902112   1   13    7.428571    174.620952   89.94   \n",
       "potential_loyalists   9.338808   1   33   17.398760    601.746293   20.80   \n",
       "promising             5.192519  15   33   23.510638    223.634736   30.00   \n",
       "\n",
       "                                              frequency                      \n",
       "                            max         mean        std min  max       mean  \n",
       "segment                                                                      \n",
       "about_to_sleep         6207.670   469.058097   0.368913   1    2   1.161932  \n",
       "at_Risk               11072.670   938.458341   0.954415   2    6   2.878583  \n",
       "cant_loose            10254.180  2646.822540   4.289630   6   34   8.380952  \n",
       "champions            266163.525  6498.612978  16.476484   3  210  12.417062  \n",
       "hibernating            9182.345   398.573036   0.302492   1    2   1.101774  \n",
       "loyal_customers      120210.355  2752.519574   4.545669   3   63   6.479853  \n",
       "need_attention         4353.830   847.657086   0.470081   2    3   2.326203  \n",
       "new_customers           848.550   314.883690   0.000000   1    1   1.000000  \n",
       "potential_loyalists    4628.750   674.628357   0.651401   1    3   2.010331  \n",
       "promising              1491.720   285.623723   0.000000   1    1   1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm[['recency','monetary','frequency','segment']]\\\n",
    ".groupby('segment')\\\n",
    ".agg({'mean','std','max','min'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8649aca",
   "metadata": {
    "papermill": {
     "duration": 0.015088,
     "end_time": "2022-11-08T01:38:05.571423",
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     "start_time": "2022-11-08T01:38:05.556335",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Based on the customer segment, we can see where each segment stands with respect to recency, frequency and monetary. We can see that the champions and loyal customers have the highest monetary and frequency values within their distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0e9beeb",
   "metadata": {
    "papermill": {
     "duration": 0.014674,
     "end_time": "2022-11-08T01:38:05.601040",
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     "start_time": "2022-11-08T01:38:05.586366",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Cohort Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51994b93",
   "metadata": {
    "papermill": {
     "duration": 0.015035,
     "end_time": "2022-11-08T01:38:05.631942",
     "exception": false,
     "start_time": "2022-11-08T01:38:05.616907",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A cohort is a group of customers that share the same customer qualities, such as acquisition channel, registration of app, first purchase date. Retention lowers in percentage across periods because customers churn over time. And there are less customers at the beginning of a cohort because takes time for customers to grow in a certain cohort group. In the below analysis, we break up groups based on order date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9eb6905b",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-11-08T01:38:07.173201Z"
    },
    "papermill": {
     "duration": 1.530462,
     "end_time": "2022-11-08T01:38:07.177152",
     "exception": false,
     "start_time": "2022-11-08T01:38:05.646690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def CohortAnalysis(dataframe):\n",
    "    \n",
    "    data = dataframe.copy()\n",
    "    data = data[['CustomerID', 'InvoiceNo', 'InvoiceDate']].drop_duplicates()\n",
    "    data['order_month'] = data['InvoiceDate'].dt.to_period('M')\n",
    "    data['cohort'] = data.groupby('CustomerID')['InvoiceDate']\\\n",
    "    .transform('min').dt.to_period('M')\n",
    "    cohort_data = data.groupby(['cohort', 'order_month'])\\\n",
    "    .agg(n_customers=('CustomerID', 'nunique')).reset_index(drop=False)\n",
    "    cohort_data['period_number'] = (cohort_data.order_month - cohort_data.cohort)\\\n",
    "    .apply(attrgetter('n'))\n",
    "    cohort_pivot = cohort_data.pivot_table(index = 'cohort',\n",
    "                                           columns = 'period_number',\n",
    "                                           values = 'n_customers')\n",
    "    cohort_size = cohort_pivot.iloc[:,0]\n",
    "    retention_matrix = cohort_pivot.divide(cohort_size, axis = 0)\n",
    "    with sns.axes_style(\"white\"):\n",
    "        fig, ax = plt.subplots(1, 2, figsize=(12, 8),\n",
    "                        sharey=True,\n",
    "                        gridspec_kw={'width_ratios': [1, 11]})\n",
    "        sns.heatmap(retention_matrix, \n",
    "                    mask = retention_matrix.isnull(), \n",
    "                    annot = True,\n",
    "                    cbar = False,\n",
    "                    fmt='.0%', \n",
    "                    cmap='coolwarm', ax=ax[1])\n",
    "        ax[1].set_title('Monthly Cohorts: User Retention', fontsize=14)\n",
    "        ax[1].set(xlabel='# of periods',\n",
    "                  ylabel='')\n",
    "        white_cmap = mcolors.ListedColormap(['white'])\n",
    "        sns.heatmap(pd.DataFrame(cohort_size).rename(columns={0: 'cohort_size'}), \n",
    "                    annot=True, \n",
    "                    cbar = False,\n",
    "                    fmt='g',\n",
    "                    cmap=white_cmap,\n",
    "                    ax=ax[0])\n",
    "        fig.tight_layout()\n",
    "    \n",
    "CohortAnalysis(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0d7ca7",
   "metadata": {
    "papermill": {
     "duration": 0.016781,
     "end_time": "2022-11-08T01:38:07.211302",
     "exception": false,
     "start_time": "2022-11-08T01:38:07.194521",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The ideal type of cohort analysis is based on broader scope of event data where there are more qualities to describe a customer and therefore have more distinct groups. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b2b26d",
   "metadata": {
    "papermill": {
     "duration": 0.016135,
     "end_time": "2022-11-08T01:38:07.244217",
     "exception": false,
     "start_time": "2022-11-08T01:38:07.228082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Customer Lifetime Value\n",
    "The amount of revenue a customer will bring during its lifetime as a customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7080c0df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:07.279600Z",
     "iopub.status.busy": "2022-11-08T01:38:07.278866Z",
     "iopub.status.idle": "2022-11-08T01:38:08.712778Z",
     "shell.execute_reply": "2022-11-08T01:38:08.711574Z"
    },
    "papermill": {
     "duration": 1.454401,
     "end_time": "2022-11-08T01:38:08.715268",
     "exception": false,
     "start_time": "2022-11-08T01:38:07.260867",
     "status": "completed"
    },
    "tags": []
   },
   "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>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346</th>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>310.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>365</td>\n",
       "      <td>368</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>282</td>\n",
       "      <td>359</td>\n",
       "      <td>4</td>\n",
       "      <td>1770.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1491.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>0</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>331.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency    T  frequency  monetary\n",
       "CustomerID                                   \n",
       "12346             0  326          1    310.44\n",
       "12347           365  368          7   4310.00\n",
       "12348           282  359          4   1770.78\n",
       "12349             0   19          1   1491.72\n",
       "12350             0  311          1    331.46"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df = df.groupby('CustomerID').agg({'InvoiceDate': [lambda x: (x.max() - x.min()).days,\n",
    "                                                        lambda x: (today_date - x.min()).days], \n",
    "                                        'InvoiceNo': 'nunique',\n",
    "                                        'TotalPrice': 'sum'})\n",
    "\n",
    "cltv_df.columns = cltv_df.columns.droplevel(0)\n",
    "cltv_df.columns = ['recency', 'T', 'frequency', 'monetary']\n",
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cd24a18a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-08T01:38:08.750430Z",
     "iopub.status.busy": "2022-11-08T01:38:08.749293Z",
     "iopub.status.idle": "2022-11-08T01:38:08.758897Z",
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   "source": [
    "#Average Order Value\n",
    "cltv_df[\"monetary\"] = cltv_df[\"monetary\"] / cltv_df[\"frequency\"]\n",
    "\n",
    "#Recency & Tenure\n",
    "cltv_df[\"recency\"] = cltv_df[\"recency\"] / 7\n",
    "cltv_df[\"T\"] = cltv_df[\"T\"] / 7\n",
    "\n",
    "#Frequency\n",
    "cltv_df = cltv_df[(cltv_df['frequency'] > 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce00833f",
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    "tags": []
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   "source": [
    "# Beta Geometric / Negative Binomial Distribution (BG/NBD) Model\n",
    "\n",
    "Beta Geometric / Negative Binomial Distribution models assume that the number of transactions made by each customer follows a Poisson process with a heterogeneity in transaction rates across customers following a gamma distribution. These assumptions give us a Negative binomial distribution (NBD) for modeling the number of transactions made by the customer while he is alive. We can build BG/NBD model by using BetaGeoFitter and lifetimes packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "baf0d866",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.BetaGeoFitter: fitted with 2845 subjects, a: 0.12, alpha: 11.40, b: 2.49, r: 2.18>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BGF = BetaGeoFitter(penalizer_coef = 0.001) #avoid overfitting\n",
    "\n",
    "BGF.fit(cltv_df['frequency'],\n",
    "        cltv_df['recency'],\n",
    "        cltv_df['T'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f2d580",
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     "start_time": "2022-11-08T01:38:09.001865",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Top 10 Expected Number of Transaction (1 Week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d5c8c2dd",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Expected Number of Transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>12748</td>\n",
       "      <td>3.265158</td>\n",
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       "    <tr>\n",
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       "    </tr>\n",
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       "  CustomerID  Expected Number of Transactions\n",
       "0      12748                         3.265158\n",
       "1      14911                         3.126645\n",
       "2      17841                         1.940290\n",
       "3      13089                         1.537528\n",
       "4      14606                         1.463999\n",
       "5      15311                         1.433717\n",
       "6      12971                         1.357024\n",
       "7      14646                         1.222517\n",
       "8      13408                         0.986249\n",
       "9      18102                         0.968607"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "BGF.conditional_expected_number_of_purchases_up_to_time(1,\n",
    "                                                        cltv_df['frequency'],\n",
    "                                                        cltv_df['recency'],\n",
    "                                                        cltv_df['T']\n",
    "                                                       ).sort_values(ascending = False)\\\n",
    ".head(10)\\\n",
    ".to_frame('Expected Number of Transactions')\\\n",
    ".reset_index() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e508ae6",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Top 10 Expected Number of Transaction (1 Month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb8fe90f",
   "metadata": {
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Expected Number of Transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "  CustomerID  Expected Number of Transactions\n",
       "0      12748                        13.025670\n",
       "1      14911                        12.473095\n",
       "2      17841                         7.740345\n",
       "3      13089                         6.133456\n",
       "4      14606                         5.840269\n",
       "5      15311                         5.719467\n",
       "6      12971                         5.413481\n",
       "7      14646                         4.876360\n",
       "8      13408                         3.934373\n",
       "9      18102                         3.863856"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BGF.conditional_expected_number_of_purchases_up_to_time(4,\n",
    "                                                        cltv_df['frequency'],\n",
    "                                                        cltv_df['recency'],\n",
    "                                                        cltv_df['T']\n",
    "                                                       ).sort_values(ascending = False)\\\n",
    ".head(10)\\\n",
    ".to_frame('Expected Number of Transactions')\\\n",
    ".reset_index()"
   ]
  },
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