retail_rfm_analysis.ipynb

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 "cells": [
  {
   "source": [
    "# Retail RFM Analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "This notebook explores RFM (Recency, Frequency, Monetary Value) analysis and customer segmentation via unsupervised clustering. The dataset itself came from a [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Online+Retail+II), and contains nearly three years of UK-based online retail store transactions (01/12/2009 to 09/12/2011). The general overview of steps taken are as follows:\n",
    "\n",
    "    - data cleaning: removing nulls, formatting values, and any other housekeeping\n",
    "    - rfm analysis: three subsections, one for each letter; broken down by customer id\n",
    "        > recency: days since most recent transaction\n",
    "        > frequency: total number of transactions\n",
    "        > monetary value: total revenue amount\n",
    "    - customer segmentation: attaching labels to customer clusters, mitigation steps\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Data Cleaning"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Pretty standard data prep here, the only standout step would be the removal of cancelled transactions (those that begin with 'c')."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global module imports\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "45622278"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# downloading uci dataset\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00502/online_retail_II.xlsx'\n",
    "\n",
    "f = requests.get(url)\n",
    "\n",
    "open('online_retail_ii.xlsx', 'wb').write(f.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       Invoice StockCode                          Description  Quantity  \\\n",
       "0       489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1       489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2       489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3       489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4       489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "...        ...       ...                                  ...       ...   \n",
       "525456  538171     22271                 FELTCRAFT DOLL ROSIE         2   \n",
       "525457  538171     22750         FELTCRAFT PRINCESS LOLA DOLL         1   \n",
       "525458  538171     22751       FELTCRAFT PRINCESS OLIVIA DOLL         1   \n",
       "525459  538171     20970   PINK FLORAL FELTCRAFT SHOULDER BAG         2   \n",
       "525460  538171     21931               JUMBO STORAGE BAG SUKI         2   \n",
       "\n",
       "               InvoiceDate  Price  Customer ID         Country  \n",
       "0      2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
       "1      2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "2      2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "3      2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
       "4      2009-12-01 07:45:00   1.25      13085.0  United Kingdom  \n",
       "...                    ...    ...          ...             ...  \n",
       "525456 2010-12-09 20:01:00   2.95      17530.0  United Kingdom  \n",
       "525457 2010-12-09 20:01:00   3.75      17530.0  United Kingdom  \n",
       "525458 2010-12-09 20:01:00   3.75      17530.0  United Kingdom  \n",
       "525459 2010-12-09 20:01:00   3.75      17530.0  United Kingdom  \n",
       "525460 2010-12-09 20:01:00   1.95      17530.0  United Kingdom  \n",
       "\n",
       "[525461 rows x 8 columns]"
      ],
      "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>Invoice</th>\n      <th>StockCode</th>\n      <th>Description</th>\n      <th>Quantity</th>\n      <th>InvoiceDate</th>\n      <th>Price</th>\n      <th>Customer ID</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>489434</td>\n      <td>85048</td>\n      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n      <td>12</td>\n      <td>2009-12-01 07:45:00</td>\n      <td>6.95</td>\n      <td>13085.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>489434</td>\n      <td>79323P</td>\n      <td>PINK CHERRY LIGHTS</td>\n      <td>12</td>\n      <td>2009-12-01 07:45:00</td>\n      <td>6.75</td>\n      <td>13085.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>489434</td>\n      <td>79323W</td>\n      <td>WHITE CHERRY LIGHTS</td>\n      <td>12</td>\n      <td>2009-12-01 07:45:00</td>\n      <td>6.75</td>\n      <td>13085.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>489434</td>\n      <td>22041</td>\n      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n      <td>48</td>\n      <td>2009-12-01 07:45:00</td>\n      <td>2.10</td>\n      <td>13085.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>489434</td>\n      <td>21232</td>\n      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n      <td>24</td>\n      <td>2009-12-01 07:45:00</td>\n      <td>1.25</td>\n      <td>13085.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>525456</th>\n      <td>538171</td>\n      <td>22271</td>\n      <td>FELTCRAFT DOLL ROSIE</td>\n      <td>2</td>\n      <td>2010-12-09 20:01:00</td>\n      <td>2.95</td>\n      <td>17530.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>525457</th>\n      <td>538171</td>\n      <td>22750</td>\n      <td>FELTCRAFT PRINCESS LOLA DOLL</td>\n      <td>1</td>\n      <td>2010-12-09 20:01:00</td>\n      <td>3.75</td>\n      <td>17530.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>525458</th>\n      <td>538171</td>\n      <td>22751</td>\n      <td>FELTCRAFT PRINCESS OLIVIA DOLL</td>\n      <td>1</td>\n      <td>2010-12-09 20:01:00</td>\n      <td>3.75</td>\n      <td>17530.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>525459</th>\n      <td>538171</td>\n      <td>20970</td>\n      <td>PINK FLORAL FELTCRAFT SHOULDER BAG</td>\n      <td>2</td>\n      <td>2010-12-09 20:01:00</td>\n      <td>3.75</td>\n      <td>17530.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>525460</th>\n      <td>538171</td>\n      <td>21931</td>\n      <td>JUMBO STORAGE BAG SUKI</td>\n      <td>2</td>\n      <td>2010-12-09 20:01:00</td>\n      <td>1.95</td>\n      <td>17530.0</td>\n      <td>United Kingdom</td>\n    </tr>\n  </tbody>\n</table>\n<p>525461 rows × 8 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# importing excel file\n",
    "df = pd.read_excel('online_retail_ii.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 525461 entries, 0 to 525460\nData columns (total 8 columns):\n #   Column       Non-Null Count   Dtype         \n---  ------       --------------   -----         \n 0   Invoice      525461 non-null  object        \n 1   StockCode    525461 non-null  object        \n 2   Description  522533 non-null  object        \n 3   Quantity     525461 non-null  int64         \n 4   InvoiceDate  525461 non-null  datetime64[ns]\n 5   Price        525461 non-null  float64       \n 6   Customer ID  417534 non-null  float64       \n 7   Country      525461 non-null  object        \ndtypes: datetime64[ns](1), float64(2), int64(1), object(4)\nmemory usage: 32.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# dataframe info\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Invoice             0\n",
       "StockCode           0\n",
       "Description      2928\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "Price               0\n",
       "Customer ID    107927\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# data integrity checks\n",
    "df.isnull().sum()"
   ]
  },
  {
   "source": [
    "Missing descriptions won't impact our analysis, though the same cannot be said about customer ids. These will need to be dropped, resulting in a loss of roughly 20% of our dataset."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Invoice        0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "Price          0\n",
       "Customer ID    0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "# removing incomplete rows\n",
    "df.dropna(subset = ['Customer ID'], inplace = True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "source": [
    "The UCI repo notes that invoices starting with the letter 'c' indicate a cancellation, so these will also need to be dropped."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pruning cancelled transactions\n",
    "df = df.query('Invoice.str[0] not in [\"c\", \"C\"]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Final dataframe shape: (407695, 4)\n"
     ]
    }
   ],
   "source": [
    "# dropping unnecessary columns\n",
    "df = df.loc[:, ['Customer ID', 'InvoiceDate', 'Price', 'Quantity']]\n",
    "print(f'Final dataframe shape: {df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Customer ID InvoiceDate  Price  Quantity\n",
       "0           13085.0  2009-12-01   6.95        12\n",
       "1           13085.0  2009-12-01   6.75        12\n",
       "2           13085.0  2009-12-01   6.75        12\n",
       "3           13085.0  2009-12-01   2.10        48\n",
       "4           13085.0  2009-12-01   1.25        24\n",
       "...             ...         ...    ...       ...\n",
       "525456      17530.0  2010-12-09   2.95         2\n",
       "525457      17530.0  2010-12-09   3.75         1\n",
       "525458      17530.0  2010-12-09   3.75         1\n",
       "525459      17530.0  2010-12-09   3.75         2\n",
       "525460      17530.0  2010-12-09   1.95         2\n",
       "\n",
       "[407695 rows x 4 columns]"
      ],
      "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>Customer ID</th>\n      <th>InvoiceDate</th>\n      <th>Price</th>\n      <th>Quantity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13085.0</td>\n      <td>2009-12-01</td>\n      <td>6.95</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13085.0</td>\n      <td>2009-12-01</td>\n      <td>6.75</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>13085.0</td>\n      <td>2009-12-01</td>\n      <td>6.75</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>13085.0</td>\n      <td>2009-12-01</td>\n      <td>2.10</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>13085.0</td>\n      <td>2009-12-01</td>\n      <td>1.25</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>525456</th>\n      <td>17530.0</td>\n      <td>2010-12-09</td>\n      <td>2.95</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>525457</th>\n      <td>17530.0</td>\n      <td>2010-12-09</td>\n      <td>3.75</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>525458</th>\n      <td>17530.0</td>\n      <td>2010-12-09</td>\n      <td>3.75</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>525459</th>\n      <td>17530.0</td>\n      <td>2010-12-09</td>\n      <td>3.75</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>525460</th>\n      <td>17530.0</td>\n      <td>2010-12-09</td>\n      <td>1.95</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>407695 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "# reformatting datetime column\n",
    "df.InvoiceDate = df.InvoiceDate.dt.date\n",
    "df"
   ]
  },
  {
   "source": [
    "## RFM Analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Each of the three components of RFM will be calcuated and clustered individually, then compiled into a final cluster for customer segmentation. Frequency might be the only component that isn't quite as straightforward, as there are multiple decent options of re-encoding 'InvoiceDate' into a meaningful feature."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Recency"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Best defined as the time between each customer's last transaction time and the last date of this dataset, this ranges from 0 downwards to -373 days."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recency calculation\n",
    "df['Recency'] = df.InvoiceDate - df.InvoiceDate.max()\n",
    "temp = df.groupby('Customer ID')['Recency'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Customer ID  Recency\n",
       "0         13085.0     -314\n",
       "1         13078.0       -1\n",
       "2         15362.0      -83\n",
       "3         18102.0        0\n",
       "4         12682.0        0\n",
       "...           ...      ...\n",
       "4309      18269.0       -1\n",
       "4310      13270.0       -1\n",
       "4311      12942.0        0\n",
       "4312      13369.0        0\n",
       "4313      15211.0        0\n",
       "\n",
       "[4314 rows x 2 columns]"
      ],
      "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>Customer ID</th>\n      <th>Recency</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13085.0</td>\n      <td>-314</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13078.0</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15362.0</td>\n      <td>-83</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18102.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>12682.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4309</th>\n      <td>18269.0</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>4310</th>\n      <td>13270.0</td>\n      <td>-1</td>\n    </tr>\n    <tr>\n      <th>4311</th>\n      <td>12942.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4312</th>\n      <td>13369.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4313</th>\n      <td>15211.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4314 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# merging with unique customer ids\n",
    "df_rfm = pd.DataFrame(df['Customer ID'].unique(), columns = ['Customer ID'])\n",
    "df_rfm = df_rfm.merge(temp, how = 'left', left_on = 'Customer ID', right_index = True)\n",
    "df_rfm.Recency = df_rfm.Recency.apply(lambda x: x.days)\n",
    "df_rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ordered clustering function\n",
    "def ordered_clusters(series, clusters = 5, ascending = True):\n",
    "    '''\n",
    "    Clusters passed values then re-orders labels based on ascending flag.\n",
    "\n",
    "        Arguments:\n",
    "            series (list-like): values to cluster\n",
    "            clusters (int): number of clusters\n",
    "            ascending (bool): order to sort in\n",
    "        \n",
    "        Returns:\n",
    "            sorted (list-like): ordered labels for series\n",
    "    '''\n",
    "\n",
    "    labels = KMeans(n_clusters = clusters, random_state = 42).fit(np.array(series).reshape(-1, 1)).labels_\n",
    "    df_rfm['labels'] = labels\n",
    "    val = 1 if ascending else -1\n",
    "    order = np.argsort(val*df_rfm.groupby('labels')['Recency'].mean())\n",
    "    df_rfm.drop('labels', inplace = True, axis = 1)\n",
    "    reps = {j:i for i, j in zip(order.index, order.values)}\n",
    "    labels = [reps[i] for i in labels]\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Customer ID  Recency  RecencyScore\n",
       "0         13085.0     -314             0\n",
       "1         13078.0       -1             4\n",
       "2         15362.0      -83             3\n",
       "3         18102.0        0             4\n",
       "4         12682.0        0             4\n",
       "...           ...      ...           ...\n",
       "4309      18269.0       -1             4\n",
       "4310      13270.0       -1             4\n",
       "4311      12942.0        0             4\n",
       "4312      13369.0        0             4\n",
       "4313      15211.0        0             4\n",
       "\n",
       "[4314 rows x 3 columns]"
      ],
      "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>Customer ID</th>\n      <th>Recency</th>\n      <th>RecencyScore</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13085.0</td>\n      <td>-314</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13078.0</td>\n      <td>-1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15362.0</td>\n      <td>-83</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18102.0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>12682.0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4309</th>\n      <td>18269.0</td>\n      <td>-1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4310</th>\n      <td>13270.0</td>\n      <td>-1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4311</th>\n      <td>12942.0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4312</th>\n      <td>13369.0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4313</th>\n      <td>15211.0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>4314 rows × 3 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "# adding labels to df\n",
    "df_rfm['RecencyScore'] = ordered_clusters(df_rfm.Recency)\n",
    "df_rfm"
   ]
  },
  {
   "source": [
    "Since clusters are unsupervised, labels are assigned somewhat haphazardly. To bake in some ordinal meaning to our cluster labels, labels are modified based on within-cluster mean values; higher labels signify a more desirable customer / better score."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Frequency"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "As mentioned above, there are a couple ways of quantifying customer frequency. While this section relies on pure transaction count, a good alternative would be to assess average days between customer transactions."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Customer ID  Recency  RecencyScore  Frequency\n",
       "0         13085.0     -314             0         62\n",
       "1         13078.0       -1             4        443\n",
       "2         15362.0      -83             3         40\n",
       "3         18102.0        0             4        627\n",
       "4         12682.0        0             4        514\n",
       "...           ...      ...           ...        ...\n",
       "4309      18269.0       -1             4          7\n",
       "4310      13270.0       -1             4          1\n",
       "4311      12942.0        0             4         16\n",
       "4312      13369.0        0             4         14\n",
       "4313      15211.0        0             4         23\n",
       "\n",
       "[4314 rows x 4 columns]"
      ],
      "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>Customer ID</th>\n      <th>Recency</th>\n      <th>RecencyScore</th>\n      <th>Frequency</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13085.0</td>\n      <td>-314</td>\n      <td>0</td>\n      <td>62</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13078.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>443</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15362.0</td>\n      <td>-83</td>\n      <td>3</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18102.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>627</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>12682.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>514</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4309</th>\n      <td>18269.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>4310</th>\n      <td>13270.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4311</th>\n      <td>12942.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>4312</th>\n      <td>13369.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>4313</th>\n      <td>15211.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n<p>4314 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# frequency calculation\n",
    "temp = df.groupby('Customer ID').InvoiceDate.count()\n",
    "temp = temp.rename('Frequency')\n",
    "df_rfm = df_rfm.merge(temp, how = 'left', left_on = 'Customer ID', right_index = True)\n",
    "df_rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Customer ID  Recency  RecencyScore  Frequency  FrequencyScore\n",
       "0         13085.0     -314             0         62               0\n",
       "1         13078.0       -1             4        443               2\n",
       "2         15362.0      -83             3         40               0\n",
       "3         18102.0        0             4        627               2\n",
       "4         12682.0        0             4        514               2\n",
       "...           ...      ...           ...        ...             ...\n",
       "4309      18269.0       -1             4          7               0\n",
       "4310      13270.0       -1             4          1               0\n",
       "4311      12942.0        0             4         16               0\n",
       "4312      13369.0        0             4         14               0\n",
       "4313      15211.0        0             4         23               0\n",
       "\n",
       "[4314 rows x 5 columns]"
      ],
      "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>Customer ID</th>\n      <th>Recency</th>\n      <th>RecencyScore</th>\n      <th>Frequency</th>\n      <th>FrequencyScore</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13085.0</td>\n      <td>-314</td>\n      <td>0</td>\n      <td>62</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13078.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>443</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15362.0</td>\n      <td>-83</td>\n      <td>3</td>\n      <td>40</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18102.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>627</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>12682.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>514</td>\n      <td>2</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>4309</th>\n      <td>18269.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4310</th>\n      <td>13270.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4311</th>\n      <td>12942.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>16</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4312</th>\n      <td>13369.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>14</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4313</th>\n      <td>15211.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>23</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4314 rows × 5 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# frequency ordered clusters\n",
    "df_rfm['FrequencyScore'] = ordered_clusters(df_rfm.Frequency)\n",
    "df_rfm"
   ]
  },
  {
   "source": [
    "### Monetary Value"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Since no data on company profits per good was provided in this dataset, monetary value is only being looked at from an overall revenue standpoint. Profit margins per product may not all align, leading to some customers who may score high marks in overall revenue while turning a small (or even negative) profit."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Customer ID  Recency  RecencyScore  Frequency  FrequencyScore  \\\n",
       "0         13085.0     -314             0         62               0   \n",
       "1         13078.0       -1             4        443               2   \n",
       "2         15362.0      -83             3         40               0   \n",
       "3         18102.0        0             4        627               2   \n",
       "4         12682.0        0             4        514               2   \n",
       "...           ...      ...           ...        ...             ...   \n",
       "4309      18269.0       -1             4          7               0   \n",
       "4310      13270.0       -1             4          1               0   \n",
       "4311      12942.0        0             4         16               0   \n",
       "4312      13369.0        0             4         14               0   \n",
       "4313      15211.0        0             4         23               0   \n",
       "\n",
       "      MonetaryValue  \n",
       "0           2017.20  \n",
       "1          16904.51  \n",
       "2            613.08  \n",
       "3         349164.35  \n",
       "4          11745.69  \n",
       "...             ...  \n",
       "4309         168.60  \n",
       "4310         590.00  \n",
       "4311         258.75  \n",
       "4312         308.28  \n",
       "4313         383.07  \n",
       "\n",
       "[4314 rows x 6 columns]"
      ],
      "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>Customer ID</th>\n      <th>Recency</th>\n      <th>RecencyScore</th>\n      <th>Frequency</th>\n      <th>FrequencyScore</th>\n      <th>MonetaryValue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13085.0</td>\n      <td>-314</td>\n      <td>0</td>\n      <td>62</td>\n      <td>0</td>\n      <td>2017.20</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13078.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>443</td>\n      <td>2</td>\n      <td>16904.51</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15362.0</td>\n      <td>-83</td>\n      <td>3</td>\n      <td>40</td>\n      <td>0</td>\n      <td>613.08</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18102.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>627</td>\n      <td>2</td>\n      <td>349164.35</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>12682.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>514</td>\n      <td>2</td>\n      <td>11745.69</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4309</th>\n      <td>18269.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>7</td>\n      <td>0</td>\n      <td>168.60</td>\n    </tr>\n    <tr>\n      <th>4310</th>\n      <td>13270.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>590.00</td>\n    </tr>\n    <tr>\n      <th>4311</th>\n      <td>12942.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>16</td>\n      <td>0</td>\n      <td>258.75</td>\n    </tr>\n    <tr>\n      <th>4312</th>\n      <td>13369.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>14</td>\n      <td>0</td>\n      <td>308.28</td>\n    </tr>\n    <tr>\n      <th>4313</th>\n      <td>15211.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>23</td>\n      <td>0</td>\n      <td>383.07</td>\n    </tr>\n  </tbody>\n</table>\n<p>4314 rows × 6 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# monetary value calculation\n",
    "df['OrderTotal'] = df.Price * df.Quantity\n",
    "temp = df.groupby('Customer ID')['OrderTotal'].sum()\n",
    "temp = temp.rename('MonetaryValue')\n",
    "df_rfm = df_rfm.merge(temp, how = 'left', left_on = 'Customer ID', right_index = True)\n",
    "df_rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Customer ID  Recency  RecencyScore  Frequency  FrequencyScore  \\\n",
       "0         13085.0     -314             0         62               0   \n",
       "1         13078.0       -1             4        443               2   \n",
       "2         15362.0      -83             3         40               0   \n",
       "3         18102.0        0             4        627               2   \n",
       "4         12682.0        0             4        514               2   \n",
       "...           ...      ...           ...        ...             ...   \n",
       "4309      18269.0       -1             4          7               0   \n",
       "4310      13270.0       -1             4          1               0   \n",
       "4311      12942.0        0             4         16               0   \n",
       "4312      13369.0        0             4         14               0   \n",
       "4313      15211.0        0             4         23               0   \n",
       "\n",
       "      MonetaryValue  MonetaryValueScore  \n",
       "0           2017.20                   0  \n",
       "1          16904.51                   1  \n",
       "2            613.08                   0  \n",
       "3         349164.35                   4  \n",
       "4          11745.69                   1  \n",
       "...             ...                 ...  \n",
       "4309         168.60                   0  \n",
       "4310         590.00                   0  \n",
       "4311         258.75                   0  \n",
       "4312         308.28                   0  \n",
       "4313         383.07                   0  \n",
       "\n",
       "[4314 rows x 7 columns]"
      ],
      "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>Customer ID</th>\n      <th>Recency</th>\n      <th>RecencyScore</th>\n      <th>Frequency</th>\n      <th>FrequencyScore</th>\n      <th>MonetaryValue</th>\n      <th>MonetaryValueScore</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13085.0</td>\n      <td>-314</td>\n      <td>0</td>\n      <td>62</td>\n      <td>0</td>\n      <td>2017.20</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13078.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>443</td>\n      <td>2</td>\n      <td>16904.51</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>15362.0</td>\n      <td>-83</td>\n      <td>3</td>\n      <td>40</td>\n      <td>0</td>\n      <td>613.08</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18102.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>627</td>\n      <td>2</td>\n      <td>349164.35</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>12682.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>514</td>\n      <td>2</td>\n      <td>11745.69</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      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4309</th>\n      <td>18269.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>7</td>\n      <td>0</td>\n      <td>168.60</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4310</th>\n      <td>13270.0</td>\n      <td>-1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>590.00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4311</th>\n      <td>12942.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>16</td>\n      <td>0</td>\n      <td>258.75</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4312</th>\n      <td>13369.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>14</td>\n      <td>0</td>\n      <td>308.28</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4313</th>\n      <td>15211.0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>23</td>\n      <td>0</td>\n      <td>383.07</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4314 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "# monetary value ordered clusters\n",
    "df_rfm['MonetaryValueScore'] = ordered_clusters(df_rfm.MonetaryValue)\n",
    "df_rfm"
   ]
  },
  {
   "source": [
    "## Customer Segmentation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1008x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# tuning cluster count\n",
    "wcss = []\n",
    "X_train = df_rfm.loc[:, ['RecencyScore', 'FrequencyScore', 'MonetaryValueScore']].to_numpy(dtype = 'int')\n",
    "for i in range(1, 15):\n",
    "    wcss.append(KMeans(n_clusters = i, random_state = 42).fit(X_train).inertia_)\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(14, 6)\n",
    "ax.plot(range(1, 15), wcss)\n",
    "ax.set_title('WCSS Elbow Method')\n",
    "ax.set_xlabel('Clusters')\n",
    "ax.set_ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "This section is a bit more loosely defined, since the number of clusters here would likely be defined in the business requirements. Nonetheless, a quick elbow method highlights an optimal cluster count of 4. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-19-a71baed3d0cb>:4: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n  df_rfm.groupby('Segment')['RecencyScore', 'FrequencyScore', 'MonetaryValueScore'].mean()\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         RecencyScore  FrequencyScore  MonetaryValueScore\n",
       "Segment                                                  \n",
       "0            3.971388        1.246066            0.257511\n",
       "1            2.693099        0.105932            0.010896\n",
       "2            0.619174        0.019973            0.011984\n",
       "3            4.000000        0.000000            0.019802"
      ],
      "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>RecencyScore</th>\n      <th>FrequencyScore</th>\n      <th>MonetaryValueScore</th>\n    </tr>\n    <tr>\n      <th>Segment</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.971388</td>\n      <td>1.246066</td>\n      <td>0.257511</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.693099</td>\n      <td>0.105932</td>\n      <td>0.010896</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.619174</td>\n      <td>0.019973</td>\n      <td>0.011984</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.000000</td>\n      <td>0.000000</td>\n      <td>0.019802</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# differentiating clusters by features\n",
    "labels = KMeans(n_clusters = 4, random_state = 42).fit(X_train).labels_\n",
    "df_rfm['Segment'] = labels\n",
    "df_rfm.groupby('Segment')['RecencyScore', 'FrequencyScore', 'MonetaryValueScore'].mean()"
   ]
  },
  {
   "source": [
    "Labels for each of the above four segments could be as follows:\n",
    "\n",
    "    0. Ole reliable: your most reliable and largest revenue producing crowd; those most worth keeping happy (also likely highest lifetime value customers)\n",
    "    1. Needs encouragement: recent shoppers who are semi-reliable; lower lifetime value than above but still potentially worth chasing\n",
    "    2. Lost cause: likely one-and-done shoppers from a while back, the least likely to become valuable customers; lowest lifetime value\n",
    "    3. The backup's backup: recent new shoppers, high risk and potentially high reward; second lowest lifetime value"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Keep in mind that while the above descriptions are subjective, they do stand in as reasonable interpretations of the customer segmentation results. They help to paint a picture of the four largest customer segments and their likely purchasing behaviour. They also imply a rough order of lifetime value, signaling which segments are worth spending more money to retain."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}