CustomerSegmentation-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>Customer Segmentation Using RFM Analysis in Python</center></h1>\n",
    "\n",
    "Retail industry, an early adopter of data warehousing, has largely benefited from the capacity and capability of data warehouses such as Teradata, Oracle, etc. to host an exceptionally large amount of data. On the daily basis, colossal amount of data is generated across the stores.\n",
    "The main purpose of storing such a huge amount of data is to analyse the behaviour of customers and make strategies accordingly in order to drive more profits. But the amount of data generated is so huge that it makes any analysis a very difficult task.\n",
    "This project will demonstrate how customers can be segmented in different segment along with the code in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset \n",
    "For my analysis I am using a sample dataset from a retail customer dataset containg sales data for 1 year containing around 541909 observations with 8 features variables.\n",
    "The link to the dataset can be found here:- http://archive.ics.uci.edu/ml/machine-learning-databases/00352/\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importing all the packages required for the data analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# loading the dataset:\n",
    "df = pd.read_excel('/Users/deepanshparab/Downloads/Online Retail.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  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",
      "5    536365     22752         SET 7 BABUSHKA NESTING BOXES         2   \n",
      "6    536365     21730    GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
      "7    536366     22633               HAND WARMER UNION JACK         6   \n",
      "8    536366     22632            HAND WARMER RED POLKA DOT         6   \n",
      "9    536367     84879        ASSORTED COLOUR BIRD ORNAMENT        32   \n",
      "\n",
      "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
      "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
      "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
      "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
      "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
      "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
      "5 2010-12-01 08:26:00       7.65     17850.0  United Kingdom  \n",
      "6 2010-12-01 08:26:00       4.25     17850.0  United Kingdom  \n",
      "7 2010-12-01 08:28:00       1.85     17850.0  United Kingdom  \n",
      "8 2010-12-01 08:28:00       1.85     17850.0  United Kingdom  \n",
      "9 2010-12-01 08:34:00       1.69     13047.0  United Kingdom  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.head(10))\n",
    "# looking at the shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Explorations:\n",
    "Columns Description :\n",
    "* InvoiceNo : Unique ID for every time a consumer visits the store\n",
    "* StockCode: Unique ID for a product\n",
    "* Description : Description of the product\n",
    "* Quantity : No of units bought\n",
    "* InvoiceDate : Date and time of purchase\n",
    "* Unit Price : Price for 1 unit of the product\n",
    "* CustomerId : Unique ID for each customer\n",
    "* Country : Country where transaction happened\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also for our analysis we need to calculate the total sales for each product by multiplying Quantity with Unit Price. Also,  convert Invoice Date in YYYYMM format.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "201012    42481\n",
       "201101    35147\n",
       "201102    27707\n",
       "201103    36748\n",
       "201104    29916\n",
       "201105    37030\n",
       "201106    36874\n",
       "201107    39518\n",
       "201108    35284\n",
       "201109    50226\n",
       "201110    60742\n",
       "201111    84711\n",
       "201112    25525\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Price'] = df['UnitPrice']*df['Quantity']\n",
    "\n",
    "tst = df.groupby(['InvoiceDate','InvoiceNo'])\n",
    "        \n",
    "tst.size()\n",
    "\n",
    "# converting \n",
    "df['date'] = df['InvoiceDate'].map(lambda x: 100*x.year + x.month)\n",
    "\n",
    "tst = df.groupby(['date'])\n",
    "tst.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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BB9XXBwLnl3QtsG5EbALsC1xZSrm3lPIb4EpgvyldGkmSJGk5mFCf6oiYC+wMXAdsVEq5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\nCQkJCR1EEqoTEhISEhISEhISOoj/ByoYTTtd57aHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2098ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking country-wise sales \n",
    "Cust_country=df[['Country','CustomerID']].drop_duplicates()\n",
    "\n",
    "#Calculating the distinct count of customer for each country\n",
    "Cust_country_count=Cust_country.groupby(['Country'])['CustomerID'].\\\n",
    "aggregate('count').reset_index().sort_values('CustomerID', ascending=False)\n",
    "\n",
    "#Plotting the count of customers\n",
    "country=list(Cust_country_count['Country'])\n",
    "Cust_id=list(Cust_country_count['CustomerID'])\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(country, Cust_id, alpha=0.8, color=color[2])\n",
    "plt.xticks(rotation='60')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen from the graph that around 90% of the customers exist in United Kingdom only followed by Germany and France. Hence, the company should focus on the United Kingdom for it marketing and other strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will start with the RFM analysis. I tagged each customer on a scale of 1 to 5 for each of the criterion where 5 will be the highest and 1 will be the lowest tagging. The customers on the top of all the three criterion will be the most significant customers.\n",
    "### RFM (Recency, Frequency, Monetary) analysis\n",
    "RFM criterion is one of the oldest and most widely used technique for selecting the most significant customers. It supports the selection of customers that are most recent (R), frequent (F), and add a larger monetary value (M) in every transaction.\n",
    "* Recency (R): Recency is the most important predictor of who is more likely to respond to an offer. Customers who have purchased recently are more likely to purchase again when compared to those who did not purchase recently.\n",
    "To tag the customers on the basis of recency flag, we will first filter the customers of United Kingdom and take distinct dates of their purchase.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "Cust_date_UK=df[df['Country']=='United Kingdom']\n",
    "Cust_date_UK=Cust_date_UK[['CustomerID','date']].drop_duplicates()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I tagged the customers from 1 to 5 in steps of 2 months i.e. 5 if customer bought in last 2 months else 4 if made a purchase in last 4 months and so on. Finally for a customer, maximum of the recency flag is taken as final recency flag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>date</th>\n",
       "      <th>Recency_Flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17850.0</td>\n",
       "      <td>201012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13047.0</td>\n",
       "      <td>201012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>13748.0</td>\n",
       "      <td>201012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>15100.0</td>\n",
       "      <td>201012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>15291.0</td>\n",
       "      <td>201012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID    date  Recency_Flag\n",
       "0      17850.0  201012             1\n",
       "9      13047.0  201012             1\n",
       "46     13748.0  201012             1\n",
       "65     15100.0  201012             1\n",
       "82     15291.0  201012             1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def recency(row):\n",
    "    if row['date']>201110:\n",
    "        val = 5\n",
    "    elif row['date'] <= 201110 and row['date'] > 201108:\n",
    "        val = 4\n",
    "    elif row['date'] <= 201108 and row['date'] > 201106:\n",
    "        val = 3\n",
    "    elif row['date'] <= 201106 and row['date'] > 201104:\n",
    "        val = 2\n",
    "    else:\n",
    "        val = 1\n",
    "    return val\n",
    "\n",
    "Cust_date_UK['Recency_Flag'] = Cust_date_UK.apply(recency, axis=1)\n",
    "Cust_date_UK.head()   \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Recency_Flag\n",
       "1    4014\n",
       "2    1930\n",
       "3    1768\n",
       "4    2464\n",
       "5    2167\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tst = Cust_date_UK.groupby('Recency_Flag')\n",
    "tst.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that around <b>2200</b> customers out of 3900 customers have purchased in last 2 months. Note that some customers have not visited the store in last 4–8 months. To regain that lost customer base, business should look out for the reasons why these customers stop visiting the stores.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency (F):\n",
    "The second most important factor is how frequently these customers purchase. The higher the frequency, the higher is the chances of these responding to the offers.\n",
    "To tag the customers on the basis of frequency flag, we will take country wise count of distinct invoice number for each customer and then filter out the customers of United Kingdom.\n",
    "After that, invoice count will be split into 5 equal parts to rank the customers on a scale of 1 to 5 where 5 being the most frequent.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cust_freq=df[['Country','InvoiceNo','CustomerID']].drop_duplicates()\n",
    "Cust_freq.head()\n",
    "\n",
    "#Calculating the count of unique purchase for each customer and his buying freq in descending order\n",
    "Cust_freq_count=Cust_freq.groupby(['Country','CustomerID'])['InvoiceNo'].aggregate('count').\\\n",
    "reset_index().sort_values('InvoiceNo', ascending=False)\n",
    "\n",
    "\n",
    "Cust_freq_count_UK=Cust_freq_count[Cust_freq_count['Country']=='United Kingdom']\n",
    "Cust_freq_count_UK.head()\n",
    "unique_invoice=Cust_freq_count_UK[['InvoiceNo']].drop_duplicates()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freqency_Band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>(55.0, 224.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>(38.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>(25.0, 38.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4057</th>\n",
       "      <td>(13.0, 25.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2856</th>\n",
       "      <td>(0.999, 13.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Freqency_Band\n",
       "428   (55.0, 224.0]\n",
       "2017   (38.0, 55.0]\n",
       "2031   (25.0, 38.0]\n",
       "4057   (13.0, 25.0]\n",
       "2856  (0.999, 13.0]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dividing in 5 equal parts\n",
    "unique_invoice['Freqency_Band'] = pd.qcut(unique_invoice['InvoiceNo'], 5)\n",
    "unique_invoice=unique_invoice[['Freqency_Band']].drop_duplicates()\n",
    "unique_invoice\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/deepanshparab/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "def frequency(row):\n",
    "    if row['InvoiceNo'] <= 13:\n",
    "        val = 1\n",
    "    elif row['InvoiceNo'] > 13 and row['InvoiceNo'] <= 25:\n",
    "        val = 2\n",
    "    elif row['InvoiceNo'] > 25 and row['InvoiceNo'] <= 38:\n",
    "        val = 3\n",
    "    elif row['InvoiceNo'] > 38 and row['InvoiceNo'] <= 55:\n",
    "        val = 4\n",
    "    else:\n",
    "        val = 5\n",
    "    return val\n",
    "\n",
    "Cust_freq_count_UK['Freq_Flag'] = Cust_freq_count_UK.apply(frequency, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Freq_Flag\n",
       "1    3669\n",
       "2     192\n",
       "3      51\n",
       "4      25\n",
       "5      13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us check the distribution of Frequency flags:\n",
    "Cust_freq_count_UK.groupby(['Freq_Flag']).size()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kVfWaJA9OckBVbczsbibPT3JWVT0xyWVJHjN1PyfJ8Uk2JPlmkickSXdfW1V/\nkuT8qd+p3b30wk4AANitDQng3f1rW9j00GX6dpInb+E4ZyQ5YwVLAwCAoXbHizABAGDNEsABAGAg\nARwAAAYSwAEAYCABHAAABhLAAQBgIAEcAAAGEsABAGAgARwAAAYSwAEAYCABHAAABhLAAQBgIAEc\nAAAGEsABAGAgARwAAAYSwAEAYCABHAAABhLAAQBgIAEcAAAGEsABAGAgARwAAAYSwAEAYCABHAAA\nBhLAAQBgIAEcAAAGEsABAGAgARwAAAYSwAEAYCABHAAABhLAAQBgIAEcAAAGEsABAGAgARwAAAYS\nwAEAYCABHAAABhLAAQBgIAEcAAAGEsABAGAgARwAAAYSwAEAYCABHAAABhLAAQBgIAEcAAAGEsAB\nAGAgARwAAAYSwAEAYCABHAAABhLAAQBgoIUH8Kr6fFV9sqouqKr1U9udquq8qrp0et1/aq+qelFV\nbaiqC6vq/outHgAAdszCA/jkP3T3kd191LT+9CTv6e4jkrxnWk+Shyc5YlpOTvKS4ZUCAMCtsLsE\n8KVOSHLm9P7MJI+ca39lz3woyX5VdeAiCgQAgJ2xOwTwTvKuqvpoVZ08td2lu69Mkun1zlP7QUku\nn9t349R2C1V1clWtr6r1mzZt2oWlAwDAjtln0QUkeVB3X1FVd05yXlV9eit9a5m2/r6G7tOTnJ4k\nRx111PdtBwCARVn4CHh3XzG9XpPkzUmOTnL15qkl0+s1U/eNSQ6Z2/3gJFeMqxYAAG6dhQbwqvqB\nqvrBze+THJPkU0nOTnLS1O2kJG+d3p+d5PHT3VAemOT6zVNVAABgNVj0FJS7JHlzVW2u5dXd/c6q\nOj/JWVX1xCSXJXnM1P+cJMcn2ZDkm0meML5kAADYeQsN4N392ST3Xab9y0keukx7J3nygNIAAGCX\nWPgccAAA2JMI4AAAMJAADgAAAwngAAAwkAAOAAADCeAAADCQAA4AAAMJ4AAAMJAADgAAAwngAAAw\nkAAOAAADCeAAADCQAA4AAAMJ4AAAMJAADgAAAwngAAAwkAAOAAADCeAAADCQAA4AAAMJ4AAAMJAA\nDgAAAwngAAAwkAAOAAADCeAAADCQAA4AAAMJ4AAAMJAADgAAAwngAAAwkAAOAAAD7bPoAmA1uezU\nn1p0CWynQ5/1yUWXAADLMgIOAAADCeAAADCQAA4AAAMJ4AAAMJAADgAAAwngAAAwkAAOAAADCeAA\nADCQAA4AAAMJ4AAAMJAADgAAAwngAAAwkAAOAAADCeAAADCQAA4AAAPts+gCAFa7y079qUWXwHY6\n9FmfXHQJAEbAAQBgpFUZwKtQlPQ/AAAGf0lEQVTquKr6TFVtqKqnL7oeAADYXqtuCkpV7Z3kxUl+\nMcnGJOdX1dndffFiKwOA7zE1afUwNYnRVl0AT3J0kg3d/dkkqarXJjkhiQAOAOzW/GG2euzKP8yq\nu3fZwXeFqnp0kuO6+zen9ccleUB3P2Wuz8lJTp5W75nkM8ML3T0dkORLiy6C3Y7vBcvxvWA5vhcs\nx/fie+7e3eu21Wk1joDXMm23+Cuiu09PcvqYclaPqlrf3Uctug52L74XLMf3guX4XrAc34sdtxov\nwtyY5JC59YOTXLGgWgAAYIesxgB+fpIjqurwqrpNkhOTnL3gmgAAYLusuiko3X1jVT0lyblJ9k5y\nRndftOCyVgvTcliO7wXL8b1gOb4XLMf3YgetuoswAQBgNVuNU1AAAGDVEsABAGAgARwAAAYSwGEP\nU1U/UVUPrao7Lmk/blE1sXhVdXRV/ez0/t5V9d+r6vhF18Xupapeuega2L1U1c9N/744ZtG1rCYu\nwtxDVdUTuvvli66Dsarqd5I8OcklSY5M8tTufuu07WPdff9F1sdiVNUpSR6e2Z2xzkvygCTvT/Kw\nJOd292mLq45Fqaqlt/itJP8hyXuTpLt/aXhRLFxVfaS7j57e/1Zm/015c5Jjkrytu5+/yPpWCwF8\nD1VVl3X3oYuug7Gq6pNJ/l13f72qDkvyhiR/191/VVUf7+77LbRAFmL6XhyZ5LZJrkpycHd/tapu\nn+TD3f3TCy2QhaiqjyW5OMnfZvbE6Urymsyev5Hu/sfFVceizP+3oqrOT3J8d2+qqh9I8qHu/qnF\nVrg6rLr7gLP9qurCLW1KcpeRtbDb2Lu7v54k3f35qnpwkjdU1d0z+16wZ7qxu29K8s2q+tfu/mqS\ndPe3qurmBdfG4hyV5KlJ/jDJ73f3BVX1LcF7j7dXVe2f2TTm6u5NSdLd36iqGxdb2uohgK9td0ly\nbJLrlrRXkn8ZXw67gauq6sjuviBJppHw/5jkjCRGLfZc36mqO3T3N5P8zObGqvrhJAL4Hqq7b07y\nwqp6/fR6deQGkh9O8tHMskRX1V27+6rpuiIDOdvJP0hr29uT3HFz2JpXVe8fXw67gccnucUIRXff\nmOTxVfU3iymJ3cC/7+4bku+Grs32TXLSYkpid9HdG5M8pqr+3yRfXXQ9LFZ3H7aFTTcnedTAUlY1\nc8ABAGAgtyEEAICBBHAAABhIAAdgp1XVf6qqDy66DoDVRAAHWAWq6vNV9a2q+vrccrdB5z6sqnrJ\nuT8x4twAa5G7oACsHo/o7ndvaWNV7TPd1WZX2W8XHx9gj2AEHGCVmhuZfmJVXZbpEeFV9cCq+peq\n+kpVfWJ64NLmfQ6vqn+sqq9V1XlV9b+r6lUrWNNfVdXlVfXVqvpoVf383LbbV9WZVXVdVV1SVf+j\nqjau1LkBVgsBHGD1+4Uk90pybFUdlOQdSZ6b5E5Jfi/JG6tq3dT31Zk9ROOAJH+Slb/P9/mZPdb+\nTtO5Xl9Vt5u2nZLksCQ/muQXk/zGCp8bYFUQwAFWj7dMo9pfqaq3zLU/u7u/0d3fyizUntPd53T3\nzd19XpL1SY6vqkOT/GySP+7uG7r7A0netgPn/9Lc+X9vuQ7d/aru/nJ339jdL0hy2yT3nDb/apLn\ndfd108NdXrRjHx9gbTAHHGD1eOT8HPCqOmx6e/lcn7tn9tTCR8y17ZvkfUnuluS67v7G3LYvJDlk\nO89/wLbmgFfV05L85nSuTvJDmY22Z2qbr/XyAOyBBHCA1W/+kcaXJ/m77v6tpZ2q6u5J9q+qH5gL\n4Ycu2X+nTfO9/yDJQ5Nc1N03V9V1SWrqcmWSg5NcPK1vb/AHWFNMQQFYW16V5BFVdWxV7V1Vt6uq\nB1fVwd39hcymozynqm5TVT+X5BFbP9wO+cEkNybZlGSfqnpWZiPgm52V5BlVtf80V/0pK3hugFVD\nAAdYQ7r78iQnJHlmZkH48iS/n+/9+/6xSR6Q5NrMLop85Qqe/twk/5Dk/2Y2teXbueU0k1OTbEzy\nuSTvTvKGJDes4PkBVoXqXpH/8wjAKlRVz05yj+4efkeSqvovSU7s7l8YfW6ARTICDsAQVXVgVT2o\nqvaqqnsmeVqSNy+6LoDRBHAAUlW/vuRR85uXi1bwNLdJ8jdJvpbZQ4PemuSvV/D4AKuCKSgAADCQ\nEXAAABhIAAcAgIEEcAAAGEgABwCAgQRwAAAYSAAHAICB/n+arHKVgI5RIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2219b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x='Freq_Flag', data=Cust_freq_count_UK, color=color[1])\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Freq_Flag', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title('Frequency of Freq_Flag', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monetary Value (M): \n",
    "The third factor is the amount of money these customers have spent on purchases. Customers who have spent higher contribute more value to the business as compared to those who have spent less.\n",
    "To tag the customers on the basis of monetary flag, we will take country wise sum of Total price for each customer and then filter out the customers of United Kingdom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating the Sum of total monetary purchase for each customer\n",
    "\n",
    "Cust_monetary = df.groupby(['Country','CustomerID'])['Total_Price'].aggregate('sum').\\\n",
    "reset_index().sort_values('Total_Price', ascending=False)\n",
    "Cust_monetary_UK=Cust_monetary[Cust_monetary['Country']=='United Kingdom']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that there are some negative values in the total price column. This is the case because when a customer returns the product it purchased, it is stored as a negative value in the quantity column.\n",
    "Before splitting Total price in 5 parts, we will remove these negative quantities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monetary_Band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4237</th>\n",
       "      <td>(1932.504, 256438.49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>(892.136, 1932.504]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>(463.248, 892.136]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>(243.046, 463.248]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>(-0.000999999999988, 243.046]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      monetary_Band\n",
       "4237          (1932.504, 256438.49]\n",
       "1959            (892.136, 1932.504]\n",
       "930              (463.248, 892.136]\n",
       "1877             (243.046, 463.248]\n",
       "2879  (-0.000999999999988, 243.046]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_price=Cust_monetary_UK[['Total_Price']].drop_duplicates()\n",
    "unique_price=unique_price[unique_price['Total_Price'] > 0]\n",
    "unique_price['monetary_Band'] = pd.qcut(unique_price['Total_Price'], 5)\n",
    "unique_price=unique_price[['monetary_Band']].drop_duplicates()\n",
    "unique_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/deepanshparab/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "def monetary(row):\n",
    "    if row['Total_Price'] <= 243:\n",
    "        val = 1\n",
    "    elif row['Total_Price'] > 243 and row['Total_Price'] <= 463:\n",
    "        val = 2\n",
    "    elif row['Total_Price'] > 463 and row['Total_Price'] <= 892:\n",
    "        val = 3\n",
    "    elif row['Total_Price'] > 892 and row['Total_Price'] <= 1932:\n",
    "        val = 4\n",
    "    else:\n",
    "        val = 5\n",
    "    return val\n",
    "Cust_monetary_UK['Monetary_Flag'] = Cust_monetary_UK.apply(monetary, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Monetary_Flag\n",
       "1    842\n",
       "2    784\n",
       "3    778\n",
       "4    773\n",
       "5    773\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us check the distribution of Monetary flags:\n",
    "Cust_monetary_UK.groupby(['Monetary_Flag']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAA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\nbv8kkKsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25201048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x='Monetary_Flag', data=Cust_monetary_UK, color=color[1])\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Monetary_Flag', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title('Frequency of Monetary_Flag', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an almost equal distribution of customers as far as monetary value is concerned.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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_Flag</th>\n",
       "      <th>Freq_Flag</th>\n",
       "      <th>Monetary_Flag_x</th>\n",
       "      <th>Monetary_Flag_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12748.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12749.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12820.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12821.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12822.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12823.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12824.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12826.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Recency_Flag  Freq_Flag  Monetary_Flag_x  Monetary_Flag_y\n",
       "0     12346.0             1          1                1                1\n",
       "1     12747.0             5          1                5                5\n",
       "2     12748.0             5          5                5                5\n",
       "3     12749.0             5          1                5                5\n",
       "4     12820.0             5          1                4                4\n",
       "5     12821.0             2          1                1                1\n",
       "6     12822.0             4          1                4                4\n",
       "7     12823.0             4          1                4                4\n",
       "8     12824.0             4          1                2                2\n",
       "9     12826.0             5          1                4                4"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Combining all the three flags :\n",
    "# Cust_UK_All=pd.merge(Cust_date_UK,Cust_freq_count_UK[['CustomerID','Freq_Flag']],\\\n",
    "# on=['CustomerID'],how='left')\n",
    "Cust_UK_All=pd.merge(Cust_UK_All,Cust_monetary_UK[['CustomerID','Monetary_Flag']],\\\n",
    "on=['CustomerID'],how='left')\n",
    "\n",
    "\n",
    "Cust_UK_All.head(10)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis Report\n",
    "Based on business requirements, various cut-offs are imposed on each of the three parameters. After applying the cut-offs, customers can be classified mainly in three segments:\n",
    "* <b>Customers clearing all the three cut-offs</b> are the best and the most reliable customers. Business should focus on making customised promotional strategies and loyalty schemes for these customers in order to retain this valuable customer base.\n",
    "* <b>Customers failing the recency criterion only </b>are those customers who have stopped visiting the store. Business should focus on these customers and look out for the reason why they abandoned visiting the stores.\n",
    "* <b>Customers clearing the recency criterion but failing frequency criterion</b> are the new customers. Business should provide more incentives and offers to these customers and try to retain these new customers.\n",
    "Apart from segmenting customers, business can also use RFM criterion to filter out a reliable customer base and perform analysis like Market Basket Analysis to see customer buying pattern or assess the success of marketing strategies by analysing the response of these customers."
   ]
  },
  {
   "cell_type": "code",
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