Customer_segentation_with_RFM-solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Segmentation using RFM analysis\n",
    "\n",
    "This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.\n",
    "\n",
    "We will create cutomer segments as per the Recency,Frequency and Monetary analysis by analyzing the data to know our customer base. This knowlwdge can then be used to target customers to retain customers, pitch offers etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import time, warnings\n",
    "import datetime as dt\n",
    "\n",
    "#visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>545220</td>\n",
       "      <td>21955</td>\n",
       "      <td>DOORMAT UNION JACK GUNS AND ROSES</td>\n",
       "      <td>2</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>7.95</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>545220</td>\n",
       "      <td>48194</td>\n",
       "      <td>DOORMAT HEARTS</td>\n",
       "      <td>2</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>7.95</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>545220</td>\n",
       "      <td>22556</td>\n",
       "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
       "      <td>12</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>1.65</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>545220</td>\n",
       "      <td>22139</td>\n",
       "      <td>RETROSPOT TEA SET CERAMIC 11 PC</td>\n",
       "      <td>3</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>4.95</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>545220</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>4</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>3.75</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    545220     21955    DOORMAT UNION JACK GUNS AND ROSES         2   \n",
       "1    545220     48194                       DOORMAT HEARTS         2   \n",
       "2    545220     22556       PLASTERS IN TIN CIRCUS PARADE         12   \n",
       "3    545220     22139     RETROSPOT TEA SET CERAMIC 11 PC          3   \n",
       "4    545220    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         4   \n",
       "\n",
       "     InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  3/1/2011 8:30       7.95     14620.0  United Kingdom  \n",
       "1  3/1/2011 8:30       7.95     14620.0  United Kingdom  \n",
       "2  3/1/2011 8:30       1.65     14620.0  United Kingdom  \n",
       "3  3/1/2011 8:30       4.95     14620.0  United Kingdom  \n",
       "4  3/1/2011 8:30       3.75     14620.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../data/commercial_data.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(236079, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove rows where customerID are NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(176137, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(subset=['CustomerID'],how='all',inplace=True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RFM Analysis\n",
    "RFM (Recency, Frequency, Monetary) analysis is a customer segmentation technique that uses past purchase behavior to divide customers into groups. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promotions and also for future personalization services.\n",
    "\n",
    "**RECENCY (R)**: Days since last purchase\n",
    "\n",
    "**FREQUENCY (F):** Total number of purchases\n",
    "\n",
    "**MONETARY VALUE (M):** Total money this customer spent.\n",
    "\n",
    "We will create those 3 customer attributes for each customer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recency\n",
    "To calculate recency, we need to choose a date point from which we evaluate how many days ago was the customer's last purchase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find out the latest date in the data to use it as for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9/9/2011 9:52'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#last date available in our dataset\n",
    "data['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-12-09\n"
     ]
    }
   ],
   "source": [
    "now = dt.date(2011,12,9)\n",
    "print(now)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new column called date which contains the date of invoice only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date'] = pd.DatetimeIndex(data['InvoiceDate']).date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <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",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>545220</td>\n",
       "      <td>21955</td>\n",
       "      <td>DOORMAT UNION JACK GUNS AND ROSES</td>\n",
       "      <td>2</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>7.95</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2011-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>545220</td>\n",
       "      <td>48194</td>\n",
       "      <td>DOORMAT HEARTS</td>\n",
       "      <td>2</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>7.95</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2011-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>545220</td>\n",
       "      <td>22556</td>\n",
       "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
       "      <td>12</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>1.65</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2011-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>545220</td>\n",
       "      <td>22139</td>\n",
       "      <td>RETROSPOT TEA SET CERAMIC 11 PC</td>\n",
       "      <td>3</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>4.95</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2011-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>545220</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>4</td>\n",
       "      <td>3/1/2011 8:30</td>\n",
       "      <td>3.75</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2011-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    545220     21955    DOORMAT UNION JACK GUNS AND ROSES         2   \n",
       "1    545220     48194                       DOORMAT HEARTS         2   \n",
       "2    545220     22556       PLASTERS IN TIN CIRCUS PARADE         12   \n",
       "3    545220     22139     RETROSPOT TEA SET CERAMIC 11 PC          3   \n",
       "4    545220    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         4   \n",
       "\n",
       "     InvoiceDate  UnitPrice  CustomerID         Country        date  \n",
       "0  3/1/2011 8:30       7.95     14620.0  United Kingdom  2011-03-01  \n",
       "1  3/1/2011 8:30       7.95     14620.0  United Kingdom  2011-03-01  \n",
       "2  3/1/2011 8:30       1.65     14620.0  United Kingdom  2011-03-01  \n",
       "3  3/1/2011 8:30       4.95     14620.0  United Kingdom  2011-03-01  \n",
       "4  3/1/2011 8:30       3.75     14620.0  United Kingdom  2011-03-01  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the last date of purchase with respect to CustomerID and calculate the RECENCY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>LastPurshaceDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>2011-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12748.0</td>\n",
       "      <td>2011-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12749.0</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12820.0</td>\n",
       "      <td>2011-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12821.0</td>\n",
       "      <td>2011-05-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   CustomerID LastPurshaceDate\n",
       "0     12747.0       2011-08-22\n",
       "1     12748.0       2011-09-30\n",
       "2     12749.0       2011-08-01\n",
       "3     12820.0       2011-09-26\n",
       "4     12821.0       2011-05-09"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by customers and check last date of purshace\n",
    "recency_df = data.groupby(by='CustomerID', as_index=False)['date'].max()\n",
    "recency_df.columns = ['CustomerID','LastPurshaceDate']\n",
    "recency_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate recency\n",
    "recency_df['Recency'] = recency_df['LastPurshaceDate'].apply(lambda x: (now - x).days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>LastPurshaceDate</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>2011-08-22</td>\n",
       "      <td>109</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12748.0</td>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12749.0</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12820.0</td>\n",
       "      <td>2011-09-26</td>\n",
       "      <td>74</td>\n",
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       "      <th>4</th>\n",
       "      <td>12821.0</td>\n",
       "      <td>2011-05-09</td>\n",
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      ],
      "text/plain": [
       "   CustomerID LastPurshaceDate  Recency\n",
       "0     12747.0       2011-08-22      109\n",
       "1     12748.0       2011-09-30       70\n",
       "2     12749.0       2011-08-01      130\n",
       "3     12820.0       2011-09-26       74\n",
       "4     12821.0       2011-05-09      214"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recency_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency\n",
    "Frequency helps us to know how many times a customer purchased from us. To do that we need to check how many invoices are registered by the same customer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop duplicate data from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop duplicates\n",
    "retail_uk_copy = data\n",
    "retail_uk_copy.drop_duplicates(subset=['InvoiceNo', 'CustomerID'], keep=\"first\", inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the frequency of purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Frequency</th>\n",
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       "      <td>5</td>\n",
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       "      <td>12748.0</td>\n",
       "      <td>96</td>\n",
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       "      <th>2</th>\n",
       "      <td>12749.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12820.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12821.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Frequency\n",
       "0     12747.0          5\n",
       "1     12748.0         96\n",
       "2     12749.0          3\n",
       "3     12820.0          1\n",
       "4     12821.0          1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate frequency of purchases\n",
    "frequency_df = retail_uk_copy.groupby(by=['CustomerID'], as_index=False)['InvoiceNo'].count()\n",
    "frequency_df.columns = ['CustomerID','Frequency']\n",
    "frequency_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monetary\n",
    "\n",
    "**Monetary attribute answers the question: How much money did the customer spent over time?**\n",
    "\n",
    "### To do that, first, we will create a new column total cost to have the total price per invoice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create column total cost\n",
    "data['TotalCost'] = data['Quantity'] * data['UnitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>CustomerID</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>191.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12748.0</td>\n",
       "      <td>1054.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12749.0</td>\n",
       "      <td>67.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12820.0</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12821.0</td>\n",
       "      <td>19.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Monetary\n",
       "0     12747.0    191.85\n",
       "1     12748.0   1054.43\n",
       "2     12749.0     67.00\n",
       "3     12820.0     15.00\n",
       "4     12821.0     19.92"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monetary_df = data.groupby(by='CustomerID',as_index=False).agg({'TotalCost': 'sum'})\n",
    "monetary_df.columns = ['CustomerID','Monetary']\n",
    "monetary_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create RFM Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>LastPurshaceDate</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>2011-08-22</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12748.0</td>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12749.0</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12820.0</td>\n",
       "      <td>2011-09-26</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12821.0</td>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID LastPurshaceDate  Recency  Frequency\n",
       "0     12747.0       2011-08-22      109          5\n",
       "1     12748.0       2011-09-30       70         96\n",
       "2     12749.0       2011-08-01      130          3\n",
       "3     12820.0       2011-09-26       74          1\n",
       "4     12821.0       2011-05-09      214          1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge recency dataframe with frequency dataframe\n",
    "temp_df = recency_df.merge(frequency_df,on='CustomerID')\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>LastPurshaceDate</th>\n",
       "      <th>Recency</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>12747.0</th>\n",
       "      <td>2011-08-22</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "      <td>191.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
       "      <td>1054.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>67.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>2011-09-26</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821.0</th>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>19.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LastPurshaceDate  Recency  Frequency  Monetary\n",
       "CustomerID                                               \n",
       "12747.0          2011-08-22      109          5    191.85\n",
       "12748.0          2011-09-30       70         96   1054.43\n",
       "12749.0          2011-08-01      130          3     67.00\n",
       "12820.0          2011-09-26       74          1     15.00\n",
       "12821.0          2011-05-09      214          1     19.92"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge with monetary dataframe to get a table with the 3 columns\n",
    "rfm_df = temp_df.merge(monetary_df,on='CustomerID')\n",
    "#use CustomerID as index\n",
    "rfm_df.set_index('CustomerID',inplace=True)\n",
    "#check the head\n",
    "rfm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer segments with RFM Model\n",
    "\n",
    "**The simplest way to create customers segments from RFM Model is to use Quartiles. We assign a score from 1 to 4 to Recency, Frequency and Monetary. Four is the best/highest value, and one is the lowest/worst value. A final RFM score is calculated simply by combining individual RFM score numbers.**\n",
    "\n",
    "Note: Quintiles (score from 1-5) offer better granularity, in case the business needs that but it will be more challenging to create segments since we will have 555 possible combinations. So, we will use quartiles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find RFM quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>119.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>183.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>92.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Recency  Frequency  Monetary\n",
       "0.25     85.0        1.0     16.35\n",
       "0.50    119.0        2.0     35.40\n",
       "0.75    183.0        3.0     92.42"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles = rfm_df.quantile(q=[0.25,0.5,0.75])\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Recency': {0.25: 85.0, 0.5: 119.0, 0.75: 183.0},\n",
       " 'Frequency': {0.25: 1.0, 0.5: 2.0, 0.75: 3.0},\n",
       " 'Monetary': {0.25: 16.35, 0.5: 35.400000000000006, 0.75: 92.42000000000002}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of RFM Segments\n",
    "\n",
    "We will create two segmentation classes since, high recency is bad, while high frequency and monetary value is good.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create functions as per the appropriate quaritle values and apply them to create segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arguments (x = value, p = recency, monetary_value, frequency, d = quartiles dict)\n",
    "def RScore(x,p,d):\n",
    "    '''Function suiting the requirement of high recency being bad'''\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "def FMScore(x,p,d):\n",
    "    '''Function suiting the requirement of high frequency and monetary value being good'''\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create rfm segmentation table\n",
    "rfm_segmentation = rfm_df\n",
    "rfm_segmentation['R_Quartile'] = rfm_segmentation['Recency'].apply(RScore, args=('Recency',quantiles,))\n",
    "rfm_segmentation['F_Quartile'] = rfm_segmentation['Frequency'].apply(FMScore, args=('Frequency',quantiles,))\n",
    "rfm_segmentation['M_Quartile'] = rfm_segmentation['Monetary'].apply(FMScore, args=('Monetary',quantiles,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LastPurshaceDate</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12747.0</th>\n",
       "      <td>2011-08-22</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "      <td>191.85</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
       "      <td>1054.43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>67.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>2011-09-26</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821.0</th>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>19.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LastPurshaceDate  Recency  Frequency  Monetary  R_Quartile  \\\n",
       "CustomerID                                                              \n",
       "12747.0          2011-08-22      109          5    191.85           3   \n",
       "12748.0          2011-09-30       70         96   1054.43           4   \n",
       "12749.0          2011-08-01      130          3     67.00           2   \n",
       "12820.0          2011-09-26       74          1     15.00           4   \n",
       "12821.0          2011-05-09      214          1     19.92           1   \n",
       "\n",
       "            F_Quartile  M_Quartile  \n",
       "CustomerID                          \n",
       "12747.0              4           4  \n",
       "12748.0              4           4  \n",
       "12749.0              3           3  \n",
       "12820.0              1           1  \n",
       "12821.0              1           2  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now that we have the score of each customer, we can represent our customer segmentation, combine the scores (R_Quartile, F_Quartile,M_Quartile) together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</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>12747.0</th>\n",
       "      <td>2011-08-22</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "      <td>191.85</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
       "      <td>96</td>\n",
       "      <td>1054.43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>67.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>2011-09-26</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821.0</th>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>19.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LastPurshaceDate  Recency  Frequency  Monetary  R_Quartile  \\\n",
       "CustomerID                                                              \n",
       "12747.0          2011-08-22      109          5    191.85           3   \n",
       "12748.0          2011-09-30       70         96   1054.43           4   \n",
       "12749.0          2011-08-01      130          3     67.00           2   \n",
       "12820.0          2011-09-26       74          1     15.00           4   \n",
       "12821.0          2011-05-09      214          1     19.92           1   \n",
       "\n",
       "            F_Quartile  M_Quartile RFMScore  \n",
       "CustomerID                                   \n",
       "12747.0              4           4      344  \n",
       "12748.0              4           4      444  \n",
       "12749.0              3           3      233  \n",
       "12820.0              1           1      411  \n",
       "12821.0              1           2      112  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation['RFMScore'] = rfm_segmentation.R_Quartile.map(str) \\\n",
    "                            + rfm_segmentation.F_Quartile.map(str) \\\n",
    "                            + rfm_segmentation.M_Quartile.map(str)\n",
    "rfm_segmentation.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best Recency score = 4: most recently purchase. Best Frequency score = 4: most quantity purchase. Best Monetary score = 4: spent the most."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FInd out the best customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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>LastPurshaceDate</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</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>18102.0</th>\n",
       "      <td>2011-09-28</td>\n",
       "      <td>72</td>\n",
       "      <td>34</td>\n",
       "      <td>26632.62</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17949.0</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
       "      <td>32</td>\n",
       "      <td>22504.73</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17450.0</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
       "      <td>28</td>\n",
       "      <td>18009.06</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16029.0</th>\n",
       "      <td>2011-09-20</td>\n",
       "      <td>80</td>\n",
       "      <td>39</td>\n",
       "      <td>15119.49</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16013.0</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>70</td>\n",
       "      <td>24</td>\n",
       "      <td>10402.34</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12901.0</th>\n",
       "      <td>2011-09-19</td>\n",
       "      <td>81</td>\n",
       "      <td>20</td>\n",
       "      <td>5915.66</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13798.0</th>\n",
       "      <td>2011-09-28</td>\n",
       "      <td>72</td>\n",
       "      <td>34</td>\n",
       "      <td>4648.80</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17857.0</th>\n",
       "      <td>2011-09-28</td>\n",
       "      <td>72</td>\n",
       "      <td>12</td>\n",
       "      <td>4644.68</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13694.0</th>\n",
       "      <td>2011-09-29</td>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>4472.68</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15061.0</th>\n",
       "      <td>2011-09-27</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>3417.70</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LastPurshaceDate  Recency  Frequency  Monetary  R_Quartile  \\\n",
       "CustomerID                                                              \n",
       "18102.0          2011-09-28       72         34  26632.62           4   \n",
       "17949.0          2011-09-30       70         32  22504.73           4   \n",
       "17450.0          2011-09-30       70         28  18009.06           4   \n",
       "16029.0          2011-09-20       80         39  15119.49           4   \n",
       "16013.0          2011-09-30       70         24  10402.34           4   \n",
       "12901.0          2011-09-19       81         20   5915.66           4   \n",
       "13798.0          2011-09-28       72         34   4648.80           4   \n",
       "17857.0          2011-09-28       72         12   4644.68           4   \n",
       "13694.0          2011-09-29       71         32   4472.68           4   \n",
       "15061.0          2011-09-27       73         23   3417.70           4   \n",
       "\n",
       "            F_Quartile  M_Quartile RFMScore  \n",
       "CustomerID                                   \n",
       "18102.0              4           4      444  \n",
       "17949.0              4           4      444  \n",
       "17450.0              4           4      444  \n",
       "16029.0              4           4      444  \n",
       "16013.0              4           4      444  \n",
       "12901.0              4           4      444  \n",
       "13798.0              4           4      444  \n",
       "17857.0              4           4      444  \n",
       "13694.0              4           4      444  \n",
       "15061.0              4           4      444  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation[rfm_segmentation['RFMScore']=='444'].sort_values('Monetary', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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