online-retail-ii-rfm-analysis-cltv-prediction.ipynb

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   "source": [
    "## BUSINESS PROBLEM"
   ]
  },
  {
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   "source": [
    "UK-based retail company wants to segment its customers and determine a roadmap for sales and marketing activities. It thinks that marketing activities specific to customer segments that exhibit common behaviors will increase revenue.\n",
    "RFM analysis will be used for segmentation. In addition, in order for the company to make a medium and long term plan, the existing customers\n",
    "estimate the potential value they will provide to the company in the future."
   ]
  },
  {
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   },
   "source": [
    "### DATASET BACKGROUND"
   ]
  },
  {
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     "status": "completed"
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   "source": [
    "The dataset Online Retail II contains online sales transactions of a UK-based retail company between 01/12/2009 and 09/12/2011. The company's product catalog includes souvenirs and most of its customers are wholesalers."
   ]
  },
  {
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   "source": [
    "8 Variable, 541.909 Observation\n",
    "\n",
    "* InvoiceNo : Invoice Number (If this code starts with C, it means that the transaction was canceled)\n",
    "* StockCode : Product Code (Unique for each product)\n",
    "* Description : Product Name\n",
    "* Quantity : Number of Products (How many of the products in the invoices were sold)\n",
    "* InvoiceDate : Invoice Date\n",
    "* UnitPrice : Invoice Price (£)\n",
    "* CustomerID : Unique Customer Number\n",
    "* Country : Country Name"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "### IMPORTINGS"
   ]
  },
  {
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      "Successfully installed autograd-1.6.2 lifetimes-0.11.3\r\n"
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   "source": [
    "!pip install lifetimes"
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   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from lifetimes import BetaGeoFitter\n",
    "from lifetimes import GammaGammaFitter\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "\n",
    "df_ = pd.read_excel(\"/kaggle/input/online-retail-ii-dataset/online_retail_II.xlsx\", sheet_name=\"Year 2010-2011\")\n",
    "df = df_.copy()"
   ]
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    "tags": []
   },
   "source": [
    "### FUNCTIONS"
   ]
  },
  {
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   "source": [
    "def outlier_thresholds(dataframe, variable):\n",
    "    quartile1 = dataframe[variable].quantile(0.01)\n",
    "    quartile3 = dataframe[variable].quantile(0.99)\n",
    "    interquantile_range = quartile3 - quartile1\n",
    "    up_limit = quartile3 + 1.5 * interquantile_range\n",
    "    low_limit = quartile1 - 1.5 * interquantile_range\n",
    "    return low_limit, up_limit\n",
    "\n",
    "\n",
    "def replace_with_thresholds(dataframe, variable):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, variable)\n",
    "    # dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit\n",
    "    dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit"
   ]
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    "tags": []
   },
   "source": [
    "### DATA UNDERSTANDING & PREPARATION"
   ]
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       "<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>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.550</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.390</td>\n",
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       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.750</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.390</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.390</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1  536365     71053                  WHITE METAL LANTERN         6   \n",
       "2  536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3  536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4  536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  Price  Customer ID         Country  \n",
       "0 2010-12-01 08:26:00  2.550    17850.000  United Kingdom  \n",
       "1 2010-12-01 08:26:00  3.390    17850.000  United Kingdom  \n",
       "2 2010-12-01 08:26:00  2.750    17850.000  United Kingdom  \n",
       "3 2010-12-01 08:26:00  3.390    17850.000  United Kingdom  \n",
       "4 2010-12-01 08:26:00  3.390    17850.000  United Kingdom  "
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    "df.head()"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541910 entries, 0 to 541909\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   Invoice      541910 non-null  object        \n",
      " 1   StockCode    541910 non-null  object        \n",
      " 2   Description  540456 non-null  object        \n",
      " 3   Quantity     541910 non-null  int64         \n",
      " 4   InvoiceDate  541910 non-null  datetime64[ns]\n",
      " 5   Price        541910 non-null  float64       \n",
      " 6   Customer ID  406830 non-null  float64       \n",
      " 7   Country      541910 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
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       "Invoice             0\n",
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       "Quantity            0\n",
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       "Invoice        0\n",
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       "Description    0\n",
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    },
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     "exception": false,
     "start_time": "2024-05-26T15:59:33.492268",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3684"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"StockCode\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44302d82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:33.555078Z",
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     "exception": false,
     "start_time": "2024-05-26T15:59:33.541612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4 PURPLE FLOCK DINNER CANDLES</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50'S CHRISTMAS GIFT BAG LARGE</th>\n",
       "      <td>1883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOLLY GIRL BEAKER</th>\n",
       "      <td>2391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I LOVE LONDON MINI BACKPACK</th>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I LOVE LONDON MINI RUCKSACK</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZINC T-LIGHT HOLDER STARS SMALL</th>\n",
       "      <td>4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZINC TOP  2 DOOR WOODEN SHELF</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZINC WILLIE WINKIE  CANDLE STICK</th>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZINC WIRE KITCHEN ORGANISER</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZINC WIRE SWEETHEART LETTER TRAY</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3896 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Quantity\n",
       "Description                               \n",
       " 4 PURPLE FLOCK DINNER CANDLES         140\n",
       " 50'S CHRISTMAS GIFT BAG LARGE        1883\n",
       " DOLLY GIRL BEAKER                    2391\n",
       " I LOVE LONDON MINI BACKPACK           360\n",
       " I LOVE LONDON MINI RUCKSACK             1\n",
       "...                                    ...\n",
       "ZINC T-LIGHT HOLDER STARS SMALL       4850\n",
       "ZINC TOP  2 DOOR WOODEN SHELF            5\n",
       "ZINC WILLIE WINKIE  CANDLE STICK      2595\n",
       "ZINC WIRE KITCHEN ORGANISER             25\n",
       "ZINC WIRE SWEETHEART LETTER TRAY        80\n",
       "\n",
       "[3896 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Description\").agg({\"Quantity\" : \"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d3d3f39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:33.657081Z",
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     "shell.execute_reply": "2024-05-26T15:59:33.737131Z"
    },
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     "duration": 0.098476,
     "end_time": "2024-05-26T15:59:33.740496",
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     "start_time": "2024-05-26T15:59:33.642020",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WHITE HANGING HEART T-LIGHT HOLDER</th>\n",
       "      <td>2070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGENCY CAKESTAND 3 TIER</th>\n",
       "      <td>1905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JUMBO BAG RED RETROSPOT</th>\n",
       "      <td>1662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSORTED COLOUR BIRD ORNAMENT</th>\n",
       "      <td>1418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARTY BUNTING</th>\n",
       "      <td>1416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Invoice\n",
       "Description                                \n",
       "WHITE HANGING HEART T-LIGHT HOLDER     2070\n",
       "REGENCY CAKESTAND 3 TIER               1905\n",
       "JUMBO BAG RED RETROSPOT                1662\n",
       "ASSORTED COLOUR BIRD ORNAMENT          1418\n",
       "PARTY BUNTING                          1416"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Description\").agg({\"Invoice\" : \"count\"}).sort_values(\"Invoice\", ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d842ecb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:33.766099Z",
     "iopub.status.busy": "2024-05-26T15:59:33.765735Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df[~df[\"Invoice\"].str.contains(\"C\", na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8cdabdbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:34.284542Z",
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     "start_time": "2024-05-26T15:59:34.270497",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.550</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.390</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.750</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.390</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.390</td>\n",
       "      <td>17850.000</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1  536365     71053                  WHITE METAL LANTERN         6   \n",
       "2  536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3  536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4  536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  Price  Customer ID         Country  TotalPrice  \n",
       "0 2010-12-01 08:26:00  2.550    17850.000  United Kingdom      15.300  \n",
       "1 2010-12-01 08:26:00  3.390    17850.000  United Kingdom      20.340  \n",
       "2 2010-12-01 08:26:00  2.750    17850.000  United Kingdom      22.000  \n",
       "3 2010-12-01 08:26:00  3.390    17850.000  United Kingdom      20.340  \n",
       "4 2010-12-01 08:26:00  3.390    17850.000  United Kingdom      20.340  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TotalPrice\"] = df[\"Quantity\"] * df[\"Price\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d28642f",
   "metadata": {
    "papermill": {
     "duration": 0.076253,
     "end_time": "2024-05-26T15:59:34.393161",
     "exception": false,
     "start_time": "2024-05-26T15:59:34.316908",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### CALCULATING RFM METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f18239e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:34.420765Z",
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    },
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     "start_time": "2024-05-26T15:59:34.405671",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "today_date = df[\"InvoiceDate\"].max() + dt.timedelta(days=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ddac8a1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:34.463373Z",
     "iopub.status.busy": "2024-05-26T15:59:34.462975Z",
     "iopub.status.idle": "2024-05-26T15:59:35.428745Z",
     "shell.execute_reply": "2024-05-26T15:59:35.427641Z"
    },
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     "duration": 0.981948,
     "end_time": "2024-05-26T15:59:35.431220",
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     "start_time": "2024-05-26T15:59:34.449272",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Customer ID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.000</td>\n",
       "      <td>327</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.000</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.000</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.000</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.000</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>334.400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  recency  frequency  monetary\n",
       "0    12346.000      327          1 77183.600\n",
       "1    12347.000        3          7  4310.000\n",
       "2    12348.000       76          4  1797.240\n",
       "3    12349.000       20          1  1757.550\n",
       "4    12350.000      311          1   334.400"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm = df.groupby(\"Customer ID\").agg({\"InvoiceDate\" : lambda InvoiceDate: (today_date - InvoiceDate.max()).days,\n",
    "                                     \"Invoice\" : lambda Invoice: Invoice.nunique(),\n",
    "                                     \"TotalPrice\" : lambda TotalPrice: TotalPrice.sum()})\n",
    "\n",
    "rfm.columns = [\"recency\", \"frequency\", \"monetary\"]\n",
    "rfm.reset_index(inplace=True)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c840fe36",
   "metadata": {
    "papermill": {
     "duration": 0.012693,
     "end_time": "2024-05-26T15:59:35.456482",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.443789",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### CALCULATING RFM SCORES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d02571e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:35.483003Z",
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     "iopub.status.idle": "2024-05-26T15:59:35.516263Z",
     "shell.execute_reply": "2024-05-26T15:59:35.515250Z"
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     "duration": 0.049945,
     "end_time": "2024-05-26T15:59:35.518853",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.468908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "      <th>frequency_score</th>\n",
       "      <th>monetary_score</th>\n",
       "      <th>RF_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.000</td>\n",
       "      <td>327</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.600</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.000</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.000</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.000</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.240</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.000</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.550</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.000</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>334.400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  recency  frequency  monetary recency_score frequency_score  \\\n",
       "0    12346.000      327          1 77183.600             1               1   \n",
       "1    12347.000        3          7  4310.000             5               5   \n",
       "2    12348.000       76          4  1797.240             2               4   \n",
       "3    12349.000       20          1  1757.550             4               1   \n",
       "4    12350.000      311          1   334.400             1               1   \n",
       "\n",
       "  monetary_score RF_Score  \n",
       "0              5       11  \n",
       "1              5       55  \n",
       "2              4       24  \n",
       "3              4       41  \n",
       "4              2       11  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm[\"recency_score\"] = pd.qcut(rfm[\"recency\"], 5, [5, 4, 3, 2, 1])\n",
    "rfm[\"frequency_score\"] = pd.qcut(rfm[\"frequency\"].rank(method=\"first\"), 5, [1, 2, 3, 4, 5])\n",
    "rfm[\"monetary_score\"] = pd.qcut(rfm[\"monetary\"], 5, [1, 2, 3, 4, 5])\n",
    "\n",
    "rfm[\"RF_Score\"] = rfm[\"recency_score\"].astype(str) + rfm[\"frequency_score\"].astype(str)\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac8c2c0",
   "metadata": {
    "papermill": {
     "duration": 0.012426,
     "end_time": "2024-05-26T15:59:35.544242",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.531816",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### CREATING & ANALYSING RFM SEGMENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4a2c75f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:35.572712Z",
     "iopub.status.busy": "2024-05-26T15:59:35.570900Z",
     "iopub.status.idle": "2024-05-26T15:59:35.642413Z",
     "shell.execute_reply": "2024-05-26T15:59:35.641651Z"
    },
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     "duration": 0.087397,
     "end_time": "2024-05-26T15:59:35.644401",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.557004",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Customer ID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "      <th>frequency_score</th>\n",
       "      <th>monetary_score</th>\n",
       "      <th>RF_Score</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.000</td>\n",
       "      <td>327</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.600</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.000</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.000</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>champions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.000</td>\n",
       "      <td>76</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.240</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>at_Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.000</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.550</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>promising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350.000</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>334.400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  recency  frequency  monetary recency_score frequency_score  \\\n",
       "0    12346.000      327          1 77183.600             1               1   \n",
       "1    12347.000        3          7  4310.000             5               5   \n",
       "2    12348.000       76          4  1797.240             2               4   \n",
       "3    12349.000       20          1  1757.550             4               1   \n",
       "4    12350.000      311          1   334.400             1               1   \n",
       "\n",
       "  monetary_score RF_Score      Segment  \n",
       "0              5       11  hibernating  \n",
       "1              5       55    champions  \n",
       "2              4       24      at_Risk  \n",
       "3              4       41    promising  \n",
       "4              2       11  hibernating  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_map = {r'[1-2][1-2]': 'hibernating',\n",
    "           r'[1-2][3-4]': 'at_Risk',\n",
    "           r'[1-2]5': 'cant_loose',\n",
    "           r'3[1-2]': 'about_to_sleep',\n",
    "           r'33': 'need_attention',\n",
    "           r'[3-4][4-5]': 'loyal_customers',\n",
    "           r'41': 'promising',\n",
    "           r'51': 'new_customers',\n",
    "           r'[4-5][2-3]': 'potential_loyalists',\n",
    "           r'5[4-5]': 'champions'}\n",
    "\n",
    "rfm[\"Segment\"] = rfm[\"RF_Score\"].replace(seg_map, regex=True)\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2dfcf57",
   "metadata": {
    "papermill": {
     "duration": 0.012919,
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     "start_time": "2024-05-26T15:59:35.657532",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### SELECTING IMPORTANT SEGMENTS & ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf5c69f6",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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    },
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     "duration": 0.048761,
     "end_time": "2024-05-26T15:59:35.731967",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.683206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"4\" halign=\"left\">frequency</th>\n",
       "      <th colspan=\"4\" halign=\"left\">monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>at_Risk</th>\n",
       "      <td>374</td>\n",
       "      <td>74</td>\n",
       "      <td>580</td>\n",
       "      <td>156.062</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>580</td>\n",
       "      <td>2.866</td>\n",
       "      <td>44534.300</td>\n",
       "      <td>52.000</td>\n",
       "      <td>580</td>\n",
       "      <td>1076.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>champions</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>633</td>\n",
       "      <td>6.877</td>\n",
       "      <td>210</td>\n",
       "      <td>3</td>\n",
       "      <td>633</td>\n",
       "      <td>12.417</td>\n",
       "      <td>280206.020</td>\n",
       "      <td>201.120</td>\n",
       "      <td>633</td>\n",
       "      <td>6857.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hibernating</th>\n",
       "      <td>375</td>\n",
       "      <td>74</td>\n",
       "      <td>1065</td>\n",
       "      <td>218.898</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1065</td>\n",
       "      <td>1.101</td>\n",
       "      <td>77183.600</td>\n",
       "      <td>3.750</td>\n",
       "      <td>1065</td>\n",
       "      <td>487.708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency                   frequency                    monetary  \\\n",
       "                max min count    mean       max min count   mean        max   \n",
       "Segment                                                                       \n",
       "at_Risk         374  74   580 156.062         6   2   580  2.866  44534.300   \n",
       "champions        14   2   633   6.877       210   3   633 12.417 280206.020   \n",
       "hibernating     375  74  1065 218.898         2   1  1065  1.101  77183.600   \n",
       "\n",
       "                                    \n",
       "                min count     mean  \n",
       "Segment                             \n",
       "at_Risk      52.000   580 1076.506  \n",
       "champions   201.120   633 6857.964  \n",
       "hibernating   3.750  1065  487.708  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_selected = rfm[rfm[\"Segment\"].isin([\"hibernating\", \"champions\", \"at_Risk\"])]\n",
    "\n",
    "rfm_selected.groupby(\"Segment\").agg({\"recency\" : [\"max\", \"min\", \"count\", \"mean\"],\n",
    "                                     \"frequency\" : [\"max\", \"min\", \"count\", \"mean\"],\n",
    "                                     \"monetary\" : [\"max\", \"min\", \"count\", \"mean\"]})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be5d3c8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:35.760176Z",
     "iopub.status.busy": "2024-05-26T15:59:35.759788Z",
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     "shell.execute_reply": "2024-05-26T15:59:35.768570Z"
    },
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     "duration": 0.02652,
     "end_time": "2024-05-26T15:59:35.772048",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.745528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12352\n",
       "1    12359\n",
       "2    12370\n",
       "3    12380\n",
       "4    12388\n",
       "Name: Customer ID, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_loyal = rfm[rfm[\"Segment\"].isin([\"loyal_customers\"])][\"Customer ID\"].astype(\"int\").reset_index(drop=True)\n",
    "rfm_loyal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4fc8797",
   "metadata": {
    "papermill": {
     "duration": 0.012958,
     "end_time": "2024-05-26T15:59:35.798493",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.785535",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### CLTV PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd38c7a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:35.826853Z",
     "iopub.status.busy": "2024-05-26T15:59:35.826473Z",
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     "shell.execute_reply": "2024-05-26T15:59:35.931408Z"
    },
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     "start_time": "2024-05-26T15:59:35.811514",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df[df[\"Quantity\"] > 0]\n",
    "df = df[df[\"Price\"] > 0]\n",
    "\n",
    "replace_with_thresholds(df, \"Quantity\")\n",
    "replace_with_thresholds(df, \"Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c32cc4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:35.964143Z",
     "iopub.status.busy": "2024-05-26T15:59:35.963766Z",
     "iopub.status.idle": "2024-05-26T15:59:35.970075Z",
     "shell.execute_reply": "2024-05-26T15:59:35.968837Z"
    },
    "papermill": {
     "duration": 0.023524,
     "end_time": "2024-05-26T15:59:35.972609",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.949085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "today_date = df[\"InvoiceDate\"].max() + dt.timedelta(days=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82577aba",
   "metadata": {
    "papermill": {
     "duration": 0.013009,
     "end_time": "2024-05-26T15:59:35.999180",
     "exception": false,
     "start_time": "2024-05-26T15:59:35.986171",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### PREPARATION of DATA STRUCTURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aee90a92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:36.027960Z",
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    },
    "tags": []
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</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>12347.000</th>\n",
       "      <td>52.143</td>\n",
       "      <td>52.571</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.000</td>\n",
       "      <td>615.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.000</th>\n",
       "      <td>40.286</td>\n",
       "      <td>51.286</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.240</td>\n",
       "      <td>449.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.000</th>\n",
       "      <td>37.143</td>\n",
       "      <td>42.571</td>\n",
       "      <td>8</td>\n",
       "      <td>2506.040</td>\n",
       "      <td>313.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356.000</th>\n",
       "      <td>43.143</td>\n",
       "      <td>46.714</td>\n",
       "      <td>3</td>\n",
       "      <td>2811.430</td>\n",
       "      <td>937.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12358.000</th>\n",
       "      <td>21.286</td>\n",
       "      <td>21.714</td>\n",
       "      <td>2</td>\n",
       "      <td>1168.060</td>\n",
       "      <td>584.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency_cltv_weekly  T_weekly  frequency  monetary  \\\n",
       "Customer ID                                                       \n",
       "12347.000                 52.143    52.571          7  4310.000   \n",
       "12348.000                 40.286    51.286          4  1797.240   \n",
       "12352.000                 37.143    42.571          8  2506.040   \n",
       "12356.000                 43.143    46.714          3  2811.430   \n",
       "12358.000                 21.286    21.714          2  1168.060   \n",
       "\n",
       "             monetary_cltv_avg  \n",
       "Customer ID                     \n",
       "12347.000              615.714  \n",
       "12348.000              449.310  \n",
       "12352.000              313.255  \n",
       "12356.000              937.143  \n",
       "12358.000              584.030  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df = pd.DataFrame()\n",
    "\n",
    "cltv_df[\"recency_cltv_weekly\"] = df.groupby(\"Customer ID\").agg({\"InvoiceDate\" : lambda InvoiceDate: (InvoiceDate.max() - InvoiceDate.min()).days}) / 7\n",
    "cltv_df[\"T_weekly\"] = df.groupby(\"Customer ID\").agg({\"InvoiceDate\" : lambda InvoiceDate: (today_date - InvoiceDate.min()).days}) / 7\n",
    "cltv_df[\"frequency\"] = df.groupby(\"Customer ID\").agg({\"Invoice\" : lambda Invoice: Invoice.nunique()})\n",
    "cltv_df[\"monetary\"] = df.groupby(\"Customer ID\").agg({\"TotalPrice\": lambda TotalPrice: TotalPrice.sum()})\n",
    "cltv_df[\"monetary_cltv_avg\"] = cltv_df[\"monetary\"] / cltv_df[\"frequency\"]\n",
    "cltv_df.reset_index()\n",
    "\n",
    "cltv_df = cltv_df[(cltv_df['frequency'] > 1)]\n",
    "\n",
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33ba6211",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:37.753504Z",
     "iopub.status.busy": "2024-05-26T15:59:37.752567Z",
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     "shell.execute_reply": "2024-05-26T15:59:37.762442Z"
    },
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     "exception": false,
     "start_time": "2024-05-26T15:59:37.737087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2845 entries, 12347.0 to 18287.0\n",
      "Data columns (total 5 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   recency_cltv_weekly  2845 non-null   float64\n",
      " 1   T_weekly             2845 non-null   float64\n",
      " 2   frequency            2845 non-null   int64  \n",
      " 3   monetary             2845 non-null   float64\n",
      " 4   monetary_cltv_avg    2845 non-null   float64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 133.4 KB\n"
     ]
    }
   ],
   "source": [
    "cltv_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d230a8d8",
   "metadata": {
    "papermill": {
     "duration": 0.013846,
     "end_time": "2024-05-26T15:59:37.794369",
     "exception": false,
     "start_time": "2024-05-26T15:59:37.780523",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### FITTING BG-NBD MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "489e530d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:37.823074Z",
     "iopub.status.busy": "2024-05-26T15:59:37.822664Z",
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     "shell.execute_reply": "2024-05-26T15:59:37.954941Z"
    },
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     "duration": 0.151501,
     "end_time": "2024-05-26T15:59:37.959430",
     "exception": false,
     "start_time": "2024-05-26T15:59:37.807929",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.BetaGeoFitter: fitted with 2845 subjects, a: 0.12, alpha: 11.44, b: 2.50, r: 2.18>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgf = BetaGeoFitter(penalizer_coef=0.001)\n",
    "bgf.fit(cltv_df[\"frequency\"],\n",
    "        cltv_df[\"recency_cltv_weekly\"],\n",
    "        cltv_df[\"T_weekly\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f1e581f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:38.010113Z",
     "iopub.status.busy": "2024-05-26T15:59:38.009612Z",
     "iopub.status.idle": "2024-05-26T15:59:38.043681Z",
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     "start_time": "2024-05-26T15:59:37.983685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</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>12347.000</th>\n",
       "      <td>52.143</td>\n",
       "      <td>52.571</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.000</td>\n",
       "      <td>615.714</td>\n",
       "      <td>3.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.000</th>\n",
       "      <td>40.286</td>\n",
       "      <td>51.286</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.240</td>\n",
       "      <td>449.310</td>\n",
       "      <td>2.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.000</th>\n",
       "      <td>37.143</td>\n",
       "      <td>42.571</td>\n",
       "      <td>8</td>\n",
       "      <td>2506.040</td>\n",
       "      <td>313.255</td>\n",
       "      <td>4.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356.000</th>\n",
       "      <td>43.143</td>\n",
       "      <td>46.714</td>\n",
       "      <td>3</td>\n",
       "      <td>2811.430</td>\n",
       "      <td>937.143</td>\n",
       "      <td>2.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12358.000</th>\n",
       "      <td>21.286</td>\n",
       "      <td>21.714</td>\n",
       "      <td>2</td>\n",
       "      <td>1168.060</td>\n",
       "      <td>584.030</td>\n",
       "      <td>2.811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency_cltv_weekly  T_weekly  frequency  monetary  \\\n",
       "Customer ID                                                       \n",
       "12347.000                 52.143    52.571          7  4310.000   \n",
       "12348.000                 40.286    51.286          4  1797.240   \n",
       "12352.000                 37.143    42.571          8  2506.040   \n",
       "12356.000                 43.143    46.714          3  2811.430   \n",
       "12358.000                 21.286    21.714          2  1168.060   \n",
       "\n",
       "             monetary_cltv_avg  exp_sales_6_month  \n",
       "Customer ID                                        \n",
       "12347.000              615.714              3.322  \n",
       "12348.000              449.310              2.160  \n",
       "12352.000              313.255              4.258  \n",
       "12356.000              937.143              2.015  \n",
       "12358.000              584.030              2.811  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df[\"exp_sales_6_month\"] = bgf.predict(4 * 6,\n",
    "                                           frequency = cltv_df[\"frequency\"],\n",
    "                                           recency = cltv_df[\"recency_cltv_weekly\"],\n",
    "                                           T = cltv_df[\"T_weekly\"])\n",
    "\n",
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8141f111",
   "metadata": {
    "papermill": {
     "duration": 0.013703,
     "end_time": "2024-05-26T15:59:38.084690",
     "exception": false,
     "start_time": "2024-05-26T15:59:38.070987",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### FITTING GAMMA-GAMMA MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8d69d194",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:38.115044Z",
     "iopub.status.busy": "2024-05-26T15:59:38.114652Z",
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    },
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     "start_time": "2024-05-26T15:59:38.098771",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.GammaGammaFitter: fitted with 2845 subjects, p: 5.62, q: 0.44, v: 5.54>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggf = GammaGammaFitter(penalizer_coef=0.005)\n",
    "ggf.fit(cltv_df[\"frequency\"],\n",
    "        cltv_df[\"monetary_cltv_avg\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4bfa982a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:38.219111Z",
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     "start_time": "2024-05-26T15:59:38.193106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>12347.000</th>\n",
       "      <td>52.143</td>\n",
       "      <td>52.571</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.000</td>\n",
       "      <td>615.714</td>\n",
       "      <td>3.322</td>\n",
       "      <td>625.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.000</th>\n",
       "      <td>40.286</td>\n",
       "      <td>51.286</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.240</td>\n",
       "      <td>449.310</td>\n",
       "      <td>2.160</td>\n",
       "      <td>462.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.000</th>\n",
       "      <td>37.143</td>\n",
       "      <td>42.571</td>\n",
       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356.000</th>\n",
       "      <td>43.143</td>\n",
       "      <td>46.714</td>\n",
       "      <td>3</td>\n",
       "      <td>2811.430</td>\n",
       "      <td>937.143</td>\n",
       "      <td>2.015</td>\n",
       "      <td>971.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12358.000</th>\n",
       "      <td>21.286</td>\n",
       "      <td>21.714</td>\n",
       "      <td>2</td>\n",
       "      <td>1168.060</td>\n",
       "      <td>584.030</td>\n",
       "      <td>2.811</td>\n",
       "      <td>617.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency_cltv_weekly  T_weekly  frequency  monetary  \\\n",
       "Customer ID                                                       \n",
       "12347.000                 52.143    52.571          7  4310.000   \n",
       "12348.000                 40.286    51.286          4  1797.240   \n",
       "12352.000                 37.143    42.571          8  2506.040   \n",
       "12356.000                 43.143    46.714          3  2811.430   \n",
       "12358.000                 21.286    21.714          2  1168.060   \n",
       "\n",
       "             monetary_cltv_avg  exp_sales_6_month  exp_average_value  \n",
       "Customer ID                                                           \n",
       "12347.000              615.714              3.322            625.367  \n",
       "12348.000              449.310              2.160            462.156  \n",
       "12352.000              313.255              4.258            317.889  \n",
       "12356.000              937.143              2.015            971.098  \n",
       "12358.000              584.030              2.811            617.422  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df[\"exp_average_value\"] = ggf.conditional_expected_average_profit(frequency = cltv_df[\"frequency\"],\n",
    "                                                                       monetary_value = cltv_df[\"monetary_cltv_avg\"])\n",
    "\n",
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218d39bd",
   "metadata": {
    "papermill": {
     "duration": 0.021262,
     "end_time": "2024-05-26T15:59:38.286793",
     "exception": false,
     "start_time": "2024-05-26T15:59:38.265531",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### CALCULATION of CLTV with BG-NBD and GG MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cea76679",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:38.317210Z",
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     "start_time": "2024-05-26T15:59:38.300922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "        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_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>6m_cltv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16446.000</td>\n",
       "      <td>29.143</td>\n",
       "      <td>29.571</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.500</td>\n",
       "      <td>84236.250</td>\n",
       "      <td>2.288</td>\n",
       "      <td>88634.665</td>\n",
       "      <td>212336.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14646.000</td>\n",
       "      <td>50.429</td>\n",
       "      <td>50.857</td>\n",
       "      <td>73</td>\n",
       "      <td>280206.020</td>\n",
       "      <td>3838.439</td>\n",
       "      <td>28.296</td>\n",
       "      <td>3843.737</td>\n",
       "      <td>113960.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18102.000</td>\n",
       "      <td>52.286</td>\n",
       "      <td>52.571</td>\n",
       "      <td>60</td>\n",
       "      <td>259657.300</td>\n",
       "      <td>4327.622</td>\n",
       "      <td>22.791</td>\n",
       "      <td>4334.880</td>\n",
       "      <td>103517.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17450.000</td>\n",
       "      <td>51.286</td>\n",
       "      <td>52.714</td>\n",
       "      <td>46</td>\n",
       "      <td>194550.790</td>\n",
       "      <td>4229.365</td>\n",
       "      <td>17.534</td>\n",
       "      <td>4238.625</td>\n",
       "      <td>77874.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14096.000</td>\n",
       "      <td>13.857</td>\n",
       "      <td>14.714</td>\n",
       "      <td>17</td>\n",
       "      <td>65164.790</td>\n",
       "      <td>3833.223</td>\n",
       "      <td>16.643</td>\n",
       "      <td>3856.047</td>\n",
       "      <td>67162.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12415.000</td>\n",
       "      <td>44.714</td>\n",
       "      <td>48.429</td>\n",
       "      <td>21</td>\n",
       "      <td>124914.530</td>\n",
       "      <td>5948.311</td>\n",
       "      <td>8.885</td>\n",
       "      <td>5976.804</td>\n",
       "      <td>55639.508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14911.000</td>\n",
       "      <td>53.143</td>\n",
       "      <td>53.429</td>\n",
       "      <td>201</td>\n",
       "      <td>143825.060</td>\n",
       "      <td>715.548</td>\n",
       "      <td>73.592</td>\n",
       "      <td>715.928</td>\n",
       "      <td>55206.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14156.000</td>\n",
       "      <td>51.571</td>\n",
       "      <td>53.286</td>\n",
       "      <td>55</td>\n",
       "      <td>117379.630</td>\n",
       "      <td>2134.175</td>\n",
       "      <td>20.581</td>\n",
       "      <td>2138.132</td>\n",
       "      <td>46109.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.571</td>\n",
       "      <td>3</td>\n",
       "      <td>12393.700</td>\n",
       "      <td>4131.233</td>\n",
       "      <td>9.224</td>\n",
       "      <td>4274.401</td>\n",
       "      <td>41201.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17511.000</td>\n",
       "      <td>52.857</td>\n",
       "      <td>53.571</td>\n",
       "      <td>31</td>\n",
       "      <td>91062.380</td>\n",
       "      <td>2937.496</td>\n",
       "      <td>11.944</td>\n",
       "      <td>2947.104</td>\n",
       "      <td>36882.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16684.000</td>\n",
       "      <td>50.429</td>\n",
       "      <td>51.286</td>\n",
       "      <td>28</td>\n",
       "      <td>66653.560</td>\n",
       "      <td>2380.484</td>\n",
       "      <td>11.243</td>\n",
       "      <td>2389.145</td>\n",
       "      <td>28144.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13694.000</td>\n",
       "      <td>52.714</td>\n",
       "      <td>53.571</td>\n",
       "      <td>50</td>\n",
       "      <td>65039.620</td>\n",
       "      <td>1300.792</td>\n",
       "      <td>18.797</td>\n",
       "      <td>1303.489</td>\n",
       "      <td>25673.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14088.000</td>\n",
       "      <td>44.571</td>\n",
       "      <td>46.143</td>\n",
       "      <td>13</td>\n",
       "      <td>50491.810</td>\n",
       "      <td>3883.985</td>\n",
       "      <td>6.110</td>\n",
       "      <td>3914.276</td>\n",
       "      <td>25054.403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15311.000</td>\n",
       "      <td>53.286</td>\n",
       "      <td>53.571</td>\n",
       "      <td>91</td>\n",
       "      <td>60767.900</td>\n",
       "      <td>667.779</td>\n",
       "      <td>33.659</td>\n",
       "      <td>668.569</td>\n",
       "      <td>23579.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17949.000</td>\n",
       "      <td>52.857</td>\n",
       "      <td>53.143</td>\n",
       "      <td>45</td>\n",
       "      <td>58510.480</td>\n",
       "      <td>1300.233</td>\n",
       "      <td>17.132</td>\n",
       "      <td>1303.229</td>\n",
       "      <td>23393.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15769.000</td>\n",
       "      <td>51.857</td>\n",
       "      <td>53.143</td>\n",
       "      <td>26</td>\n",
       "      <td>56252.720</td>\n",
       "      <td>2163.566</td>\n",
       "      <td>10.186</td>\n",
       "      <td>2172.065</td>\n",
       "      <td>23182.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13089.000</td>\n",
       "      <td>52.286</td>\n",
       "      <td>53.000</td>\n",
       "      <td>97</td>\n",
       "      <td>58825.830</td>\n",
       "      <td>606.452</td>\n",
       "      <td>36.076</td>\n",
       "      <td>607.130</td>\n",
       "      <td>22950.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12536.000</td>\n",
       "      <td>2.143</td>\n",
       "      <td>8.571</td>\n",
       "      <td>3</td>\n",
       "      <td>12601.830</td>\n",
       "      <td>4200.610</td>\n",
       "      <td>4.898</td>\n",
       "      <td>4346.150</td>\n",
       "      <td>22266.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15061.000</td>\n",
       "      <td>52.571</td>\n",
       "      <td>53.286</td>\n",
       "      <td>48</td>\n",
       "      <td>54534.140</td>\n",
       "      <td>1136.128</td>\n",
       "      <td>18.163</td>\n",
       "      <td>1138.596</td>\n",
       "      <td>21668.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14298.000</td>\n",
       "      <td>50.286</td>\n",
       "      <td>51.571</td>\n",
       "      <td>44</td>\n",
       "      <td>51527.300</td>\n",
       "      <td>1171.075</td>\n",
       "      <td>17.117</td>\n",
       "      <td>1173.847</td>\n",
       "      <td>21052.392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer ID  recency_cltv_weekly  T_weekly  frequency   monetary  \\\n",
       "0     16446.000               29.143    29.571          2 168472.500   \n",
       "1     14646.000               50.429    50.857         73 280206.020   \n",
       "2     18102.000               52.286    52.571         60 259657.300   \n",
       "3     17450.000               51.286    52.714         46 194550.790   \n",
       "4     14096.000               13.857    14.714         17  65164.790   \n",
       "5     12415.000               44.714    48.429         21 124914.530   \n",
       "6     14911.000               53.143    53.429        201 143825.060   \n",
       "7     14156.000               51.571    53.286         55 117379.630   \n",
       "8     16000.000                0.000     0.571          3  12393.700   \n",
       "9     17511.000               52.857    53.571         31  91062.380   \n",
       "10    16684.000               50.429    51.286         28  66653.560   \n",
       "11    13694.000               52.714    53.571         50  65039.620   \n",
       "12    14088.000               44.571    46.143         13  50491.810   \n",
       "13    15311.000               53.286    53.571         91  60767.900   \n",
       "14    17949.000               52.857    53.143         45  58510.480   \n",
       "15    15769.000               51.857    53.143         26  56252.720   \n",
       "16    13089.000               52.286    53.000         97  58825.830   \n",
       "17    12536.000                2.143     8.571          3  12601.830   \n",
       "18    15061.000               52.571    53.286         48  54534.140   \n",
       "19    14298.000               50.286    51.571         44  51527.300   \n",
       "\n",
       "    monetary_cltv_avg  exp_sales_6_month  exp_average_value    6m_cltv  \n",
       "0           84236.250              2.288          88634.665 212336.646  \n",
       "1            3838.439             28.296           3843.737 113960.646  \n",
       "2            4327.622             22.791           4334.880 103517.482  \n",
       "3            4229.365             17.534           4238.625  77874.797  \n",
       "4            3833.223             16.643           3856.047  67162.312  \n",
       "5            5948.311              8.885           5976.804  55639.508  \n",
       "6             715.548             73.592            715.928  55206.114  \n",
       "7            2134.175             20.581           2138.132  46109.395  \n",
       "8            4131.233              9.224           4274.401  41201.923  \n",
       "9            2937.496             11.944           2947.104  36882.990  \n",
       "10           2380.484             11.243           2389.145  28144.362  \n",
       "11           1300.792             18.797           1303.489  25673.452  \n",
       "12           3883.985              6.110           3914.276  25054.403  \n",
       "13            667.779             33.659            668.569  23579.484  \n",
       "14           1300.233             17.132           1303.229  23393.911  \n",
       "15           2163.566             10.186           2172.065  23182.024  \n",
       "16            606.452             36.076            607.130  22950.244  \n",
       "17           4200.610              4.898           4346.150  22266.083  \n",
       "18           1136.128             18.163           1138.596  21668.673  \n",
       "19           1171.075             17.117           1173.847  21052.392  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df[\"6m_cltv\"] = ggf.customer_lifetime_value(bgf,\n",
    "                                   cltv_df[\"frequency\"],\n",
    "                                   cltv_df[\"recency_cltv_weekly\"],\n",
    "                                   cltv_df[\"T_weekly\"],\n",
    "                                   cltv_df[\"monetary_cltv_avg\"],\n",
    "                                   time=6,\n",
    "                                   freq=\"W\",\n",
    "                                   discount_rate=0.01)\n",
    "\n",
    "cltv_df = cltv_df.sort_values(\"6m_cltv\", ascending=False).reset_index()\n",
    "cltv_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d28824",
   "metadata": {
    "papermill": {
     "duration": 0.015218,
     "end_time": "2024-05-26T15:59:38.447963",
     "exception": false,
     "start_time": "2024-05-26T15:59:38.432745",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 1M-12M CLTV VALUES COMPARISON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2d7f70c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:38.479689Z",
     "iopub.status.busy": "2024-05-26T15:59:38.479251Z",
     "iopub.status.idle": "2024-05-26T15:59:38.662583Z",
     "shell.execute_reply": "2024-05-26T15:59:38.661073Z"
    },
    "papermill": {
     "duration": 0.20244,
     "end_time": "2024-05-26T15:59:38.665331",
     "exception": false,
     "start_time": "2024-05-26T15:59:38.462891",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cltv_df[\"1m_cltv\"] = ggf.customer_lifetime_value(bgf,\n",
    "                                   cltv_df[\"frequency\"],\n",
    "                                   cltv_df[\"recency_cltv_weekly\"],\n",
    "                                   cltv_df[\"T_weekly\"],\n",
    "                                   cltv_df[\"monetary_cltv_avg\"],\n",
    "                                   time=1,\n",
    "                                   freq=\"W\",\n",
    "                                   discount_rate=0.01)\n",
    "\n",
    "cltv_df[\"12m_cltv\"] = ggf.customer_lifetime_value(bgf,\n",
    "                                   cltv_df[\"frequency\"],\n",
    "                                   cltv_df[\"recency_cltv_weekly\"],\n",
    "                                   cltv_df[\"T_weekly\"],\n",
    "                                   cltv_df[\"monetary_cltv_avg\"],\n",
    "                                   time=12,\n",
    "                                   freq=\"W\",\n",
    "                                   discount_rate=0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d81939b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:38.697731Z",
     "iopub.status.busy": "2024-05-26T15:59:38.697090Z",
     "iopub.status.idle": "2024-05-26T15:59:38.713644Z",
     "shell.execute_reply": "2024-05-26T15:59:38.712554Z"
    },
    "papermill": {
     "duration": 0.03582,
     "end_time": "2024-05-26T15:59:38.716337",
     "exception": false,
     "start_time": "2024-05-26T15:59:38.680517",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>6m_cltv</th>\n",
       "      <th>1m_cltv</th>\n",
       "      <th>12m_cltv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16446.000</td>\n",
       "      <td>29.143</td>\n",
       "      <td>29.571</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.500</td>\n",
       "      <td>84236.250</td>\n",
       "      <td>2.288</td>\n",
       "      <td>88634.665</td>\n",
       "      <td>212336.646</td>\n",
       "      <td>37304.052</td>\n",
       "      <td>402225.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14646.000</td>\n",
       "      <td>50.429</td>\n",
       "      <td>50.857</td>\n",
       "      <td>73</td>\n",
       "      <td>280206.020</td>\n",
       "      <td>3838.439</td>\n",
       "      <td>28.296</td>\n",
       "      <td>3843.737</td>\n",
       "      <td>113960.646</td>\n",
       "      <td>19821.753</td>\n",
       "      <td>217496.656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18102.000</td>\n",
       "      <td>52.286</td>\n",
       "      <td>52.571</td>\n",
       "      <td>60</td>\n",
       "      <td>259657.300</td>\n",
       "      <td>4327.622</td>\n",
       "      <td>22.791</td>\n",
       "      <td>4334.880</td>\n",
       "      <td>103517.482</td>\n",
       "      <td>17998.215</td>\n",
       "      <td>197628.737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17450.000</td>\n",
       "      <td>51.286</td>\n",
       "      <td>52.714</td>\n",
       "      <td>46</td>\n",
       "      <td>194550.790</td>\n",
       "      <td>4229.365</td>\n",
       "      <td>17.534</td>\n",
       "      <td>4238.625</td>\n",
       "      <td>77874.797</td>\n",
       "      <td>13539.885</td>\n",
       "      <td>148672.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14096.000</td>\n",
       "      <td>13.857</td>\n",
       "      <td>14.714</td>\n",
       "      <td>17</td>\n",
       "      <td>65164.790</td>\n",
       "      <td>3833.223</td>\n",
       "      <td>16.643</td>\n",
       "      <td>3856.047</td>\n",
       "      <td>67162.312</td>\n",
       "      <td>11905.526</td>\n",
       "      <td>126505.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12415.000</td>\n",
       "      <td>44.714</td>\n",
       "      <td>48.429</td>\n",
       "      <td>21</td>\n",
       "      <td>124914.530</td>\n",
       "      <td>5948.311</td>\n",
       "      <td>8.885</td>\n",
       "      <td>5976.804</td>\n",
       "      <td>55639.508</td>\n",
       "      <td>9686.284</td>\n",
       "      <td>106113.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14911.000</td>\n",
       "      <td>53.143</td>\n",
       "      <td>53.429</td>\n",
       "      <td>201</td>\n",
       "      <td>143825.060</td>\n",
       "      <td>715.548</td>\n",
       "      <td>73.592</td>\n",
       "      <td>715.928</td>\n",
       "      <td>55206.114</td>\n",
       "      <td>9595.651</td>\n",
       "      <td>105421.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14156.000</td>\n",
       "      <td>51.571</td>\n",
       "      <td>53.286</td>\n",
       "      <td>55</td>\n",
       "      <td>117379.630</td>\n",
       "      <td>2134.175</td>\n",
       "      <td>20.581</td>\n",
       "      <td>2138.132</td>\n",
       "      <td>46109.395</td>\n",
       "      <td>8015.618</td>\n",
       "      <td>88040.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.571</td>\n",
       "      <td>3</td>\n",
       "      <td>12393.700</td>\n",
       "      <td>4131.233</td>\n",
       "      <td>9.224</td>\n",
       "      <td>4274.401</td>\n",
       "      <td>41201.923</td>\n",
       "      <td>7514.395</td>\n",
       "      <td>76498.587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17511.000</td>\n",
       "      <td>52.857</td>\n",
       "      <td>53.571</td>\n",
       "      <td>31</td>\n",
       "      <td>91062.380</td>\n",
       "      <td>2937.496</td>\n",
       "      <td>11.944</td>\n",
       "      <td>2947.104</td>\n",
       "      <td>36882.990</td>\n",
       "      <td>6411.953</td>\n",
       "      <td>70421.314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  recency_cltv_weekly  T_weekly  frequency   monetary  \\\n",
       "0    16446.000               29.143    29.571          2 168472.500   \n",
       "1    14646.000               50.429    50.857         73 280206.020   \n",
       "2    18102.000               52.286    52.571         60 259657.300   \n",
       "3    17450.000               51.286    52.714         46 194550.790   \n",
       "4    14096.000               13.857    14.714         17  65164.790   \n",
       "5    12415.000               44.714    48.429         21 124914.530   \n",
       "6    14911.000               53.143    53.429        201 143825.060   \n",
       "7    14156.000               51.571    53.286         55 117379.630   \n",
       "8    16000.000                0.000     0.571          3  12393.700   \n",
       "9    17511.000               52.857    53.571         31  91062.380   \n",
       "\n",
       "   monetary_cltv_avg  exp_sales_6_month  exp_average_value    6m_cltv  \\\n",
       "0          84236.250              2.288          88634.665 212336.646   \n",
       "1           3838.439             28.296           3843.737 113960.646   \n",
       "2           4327.622             22.791           4334.880 103517.482   \n",
       "3           4229.365             17.534           4238.625  77874.797   \n",
       "4           3833.223             16.643           3856.047  67162.312   \n",
       "5           5948.311              8.885           5976.804  55639.508   \n",
       "6            715.548             73.592            715.928  55206.114   \n",
       "7           2134.175             20.581           2138.132  46109.395   \n",
       "8           4131.233              9.224           4274.401  41201.923   \n",
       "9           2937.496             11.944           2947.104  36882.990   \n",
       "\n",
       "    1m_cltv   12m_cltv  \n",
       "0 37304.052 402225.954  \n",
       "1 19821.753 217496.656  \n",
       "2 17998.215 197628.737  \n",
       "3 13539.885 148672.750  \n",
       "4 11905.526 126505.647  \n",
       "5  9686.284 106113.512  \n",
       "6  9595.651 105421.283  \n",
       "7  8015.618  88040.247  \n",
       "8  7514.395  76498.587  \n",
       "9  6411.953  70421.314  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df.sort_values(\"1m_cltv\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7de443fe",
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    "execution": {
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     "status": "completed"
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   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>6m_cltv</th>\n",
       "      <th>1m_cltv</th>\n",
       "      <th>12m_cltv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16446.000</td>\n",
       "      <td>29.143</td>\n",
       "      <td>29.571</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.500</td>\n",
       "      <td>84236.250</td>\n",
       "      <td>2.288</td>\n",
       "      <td>88634.665</td>\n",
       "      <td>212336.646</td>\n",
       "      <td>37304.052</td>\n",
       "      <td>402225.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14646.000</td>\n",
       "      <td>50.429</td>\n",
       "      <td>50.857</td>\n",
       "      <td>73</td>\n",
       "      <td>280206.020</td>\n",
       "      <td>3838.439</td>\n",
       "      <td>28.296</td>\n",
       "      <td>3843.737</td>\n",
       "      <td>113960.646</td>\n",
       "      <td>19821.753</td>\n",
       "      <td>217496.656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18102.000</td>\n",
       "      <td>52.286</td>\n",
       "      <td>52.571</td>\n",
       "      <td>60</td>\n",
       "      <td>259657.300</td>\n",
       "      <td>4327.622</td>\n",
       "      <td>22.791</td>\n",
       "      <td>4334.880</td>\n",
       "      <td>103517.482</td>\n",
       "      <td>17998.215</td>\n",
       "      <td>197628.737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17450.000</td>\n",
       "      <td>51.286</td>\n",
       "      <td>52.714</td>\n",
       "      <td>46</td>\n",
       "      <td>194550.790</td>\n",
       "      <td>4229.365</td>\n",
       "      <td>17.534</td>\n",
       "      <td>4238.625</td>\n",
       "      <td>77874.797</td>\n",
       "      <td>13539.885</td>\n",
       "      <td>148672.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14096.000</td>\n",
       "      <td>13.857</td>\n",
       "      <td>14.714</td>\n",
       "      <td>17</td>\n",
       "      <td>65164.790</td>\n",
       "      <td>3833.223</td>\n",
       "      <td>16.643</td>\n",
       "      <td>3856.047</td>\n",
       "      <td>67162.312</td>\n",
       "      <td>11905.526</td>\n",
       "      <td>126505.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12415.000</td>\n",
       "      <td>44.714</td>\n",
       "      <td>48.429</td>\n",
       "      <td>21</td>\n",
       "      <td>124914.530</td>\n",
       "      <td>5948.311</td>\n",
       "      <td>8.885</td>\n",
       "      <td>5976.804</td>\n",
       "      <td>55639.508</td>\n",
       "      <td>9686.284</td>\n",
       "      <td>106113.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14911.000</td>\n",
       "      <td>53.143</td>\n",
       "      <td>53.429</td>\n",
       "      <td>201</td>\n",
       "      <td>143825.060</td>\n",
       "      <td>715.548</td>\n",
       "      <td>73.592</td>\n",
       "      <td>715.928</td>\n",
       "      <td>55206.114</td>\n",
       "      <td>9595.651</td>\n",
       "      <td>105421.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14156.000</td>\n",
       "      <td>51.571</td>\n",
       "      <td>53.286</td>\n",
       "      <td>55</td>\n",
       "      <td>117379.630</td>\n",
       "      <td>2134.175</td>\n",
       "      <td>20.581</td>\n",
       "      <td>2138.132</td>\n",
       "      <td>46109.395</td>\n",
       "      <td>8015.618</td>\n",
       "      <td>88040.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.571</td>\n",
       "      <td>3</td>\n",
       "      <td>12393.700</td>\n",
       "      <td>4131.233</td>\n",
       "      <td>9.224</td>\n",
       "      <td>4274.401</td>\n",
       "      <td>41201.923</td>\n",
       "      <td>7514.395</td>\n",
       "      <td>76498.587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17511.000</td>\n",
       "      <td>52.857</td>\n",
       "      <td>53.571</td>\n",
       "      <td>31</td>\n",
       "      <td>91062.380</td>\n",
       "      <td>2937.496</td>\n",
       "      <td>11.944</td>\n",
       "      <td>2947.104</td>\n",
       "      <td>36882.990</td>\n",
       "      <td>6411.953</td>\n",
       "      <td>70421.314</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Customer ID  recency_cltv_weekly  T_weekly  frequency   monetary  \\\n",
       "0    16446.000               29.143    29.571          2 168472.500   \n",
       "1    14646.000               50.429    50.857         73 280206.020   \n",
       "2    18102.000               52.286    52.571         60 259657.300   \n",
       "3    17450.000               51.286    52.714         46 194550.790   \n",
       "4    14096.000               13.857    14.714         17  65164.790   \n",
       "5    12415.000               44.714    48.429         21 124914.530   \n",
       "6    14911.000               53.143    53.429        201 143825.060   \n",
       "7    14156.000               51.571    53.286         55 117379.630   \n",
       "8    16000.000                0.000     0.571          3  12393.700   \n",
       "9    17511.000               52.857    53.571         31  91062.380   \n",
       "\n",
       "   monetary_cltv_avg  exp_sales_6_month  exp_average_value    6m_cltv  \\\n",
       "0          84236.250              2.288          88634.665 212336.646   \n",
       "1           3838.439             28.296           3843.737 113960.646   \n",
       "2           4327.622             22.791           4334.880 103517.482   \n",
       "3           4229.365             17.534           4238.625  77874.797   \n",
       "4           3833.223             16.643           3856.047  67162.312   \n",
       "5           5948.311              8.885           5976.804  55639.508   \n",
       "6            715.548             73.592            715.928  55206.114   \n",
       "7           2134.175             20.581           2138.132  46109.395   \n",
       "8           4131.233              9.224           4274.401  41201.923   \n",
       "9           2937.496             11.944           2947.104  36882.990   \n",
       "\n",
       "    1m_cltv   12m_cltv  \n",
       "0 37304.052 402225.954  \n",
       "1 19821.753 217496.656  \n",
       "2 17998.215 197628.737  \n",
       "3 13539.885 148672.750  \n",
       "4 11905.526 126505.647  \n",
       "5  9686.284 106113.512  \n",
       "6  9595.651 105421.283  \n",
       "7  8015.618  88040.247  \n",
       "8  7514.395  76498.587  \n",
       "9  6411.953  70421.314  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df.sort_values(\"12m_cltv\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2514a9",
   "metadata": {
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     "start_time": "2024-05-26T15:59:38.784201",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### COUNTRY BASED CLTV SEGMENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0ba80bbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:38.833863Z",
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     "start_time": "2024-05-26T15:59:38.816128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>6m_cltv</th>\n",
       "      <th>1m_cltv</th>\n",
       "      <th>12m_cltv</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16446.000</td>\n",
       "      <td>29.143</td>\n",
       "      <td>29.571</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.500</td>\n",
       "      <td>84236.250</td>\n",
       "      <td>2.288</td>\n",
       "      <td>88634.665</td>\n",
       "      <td>212336.646</td>\n",
       "      <td>37304.052</td>\n",
       "      <td>402225.954</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2079</th>\n",
       "      <td>18102.000</td>\n",
       "      <td>52.286</td>\n",
       "      <td>52.571</td>\n",
       "      <td>60</td>\n",
       "      <td>259657.300</td>\n",
       "      <td>4327.622</td>\n",
       "      <td>22.791</td>\n",
       "      <td>4334.880</td>\n",
       "      <td>103517.482</td>\n",
       "      <td>17998.215</td>\n",
       "      <td>197628.737</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>17450.000</td>\n",
       "      <td>51.286</td>\n",
       "      <td>52.714</td>\n",
       "      <td>46</td>\n",
       "      <td>194550.790</td>\n",
       "      <td>4229.365</td>\n",
       "      <td>17.534</td>\n",
       "      <td>4238.625</td>\n",
       "      <td>77874.797</td>\n",
       "      <td>13539.885</td>\n",
       "      <td>148672.750</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2847</th>\n",
       "      <td>14096.000</td>\n",
       "      <td>13.857</td>\n",
       "      <td>14.714</td>\n",
       "      <td>17</td>\n",
       "      <td>65164.790</td>\n",
       "      <td>3833.223</td>\n",
       "      <td>16.643</td>\n",
       "      <td>3856.047</td>\n",
       "      <td>67162.312</td>\n",
       "      <td>11905.526</td>\n",
       "      <td>126505.647</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15747</th>\n",
       "      <td>16000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.571</td>\n",
       "      <td>3</td>\n",
       "      <td>12393.700</td>\n",
       "      <td>4131.233</td>\n",
       "      <td>9.224</td>\n",
       "      <td>4274.401</td>\n",
       "      <td>41201.923</td>\n",
       "      <td>7514.395</td>\n",
       "      <td>76498.587</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer ID  recency_cltv_weekly  T_weekly  frequency   monetary  \\\n",
       "0        16446.000               29.143    29.571          2 168472.500   \n",
       "2079     18102.000               52.286    52.571         60 259657.300   \n",
       "2510     17450.000               51.286    52.714         46 194550.790   \n",
       "2847     14096.000               13.857    14.714         17  65164.790   \n",
       "15747    16000.000                0.000     0.571          3  12393.700   \n",
       "\n",
       "       monetary_cltv_avg  exp_sales_6_month  exp_average_value    6m_cltv  \\\n",
       "0              84236.250              2.288          88634.665 212336.646   \n",
       "2079            4327.622             22.791           4334.880 103517.482   \n",
       "2510            4229.365             17.534           4238.625  77874.797   \n",
       "2847            3833.223             16.643           3856.047  67162.312   \n",
       "15747           4131.233              9.224           4274.401  41201.923   \n",
       "\n",
       "        1m_cltv   12m_cltv         Country  \n",
       "0     37304.052 402225.954  United Kingdom  \n",
       "2079  17998.215 197628.737  United Kingdom  \n",
       "2510  13539.885 148672.750  United Kingdom  \n",
       "2847  11905.526 126505.647  United Kingdom  \n",
       "15747  7514.395  76498.587  United Kingdom  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df = pd.merge(cltv_df, df[[\"Customer ID\", \"Country\"]], on=\"Customer ID\", how=\"inner\").drop_duplicates()\n",
    "cltv_df = cltv_df[cltv_df[\"Country\"].isin([\"United Kingdom\"])]\n",
    "\n",
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aabdc5f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-26T15:59:39.132797Z",
     "iopub.status.busy": "2024-05-26T15:59:39.132390Z",
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     "shell.execute_reply": "2024-05-26T15:59:39.151056Z"
    },
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Customer ID</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>6m_cltv</th>\n",
       "      <th>1m_cltv</th>\n",
       "      <th>12m_cltv</th>\n",
       "      <th>Country</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16446.000</td>\n",
       "      <td>29.143</td>\n",
       "      <td>29.571</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.500</td>\n",
       "      <td>84236.250</td>\n",
       "      <td>2.288</td>\n",
       "      <td>88634.665</td>\n",
       "      <td>212336.646</td>\n",
       "      <td>37304.052</td>\n",
       "      <td>402225.954</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2079</th>\n",
       "      <td>18102.000</td>\n",
       "      <td>52.286</td>\n",
       "      <td>52.571</td>\n",
       "      <td>60</td>\n",
       "      <td>259657.300</td>\n",
       "      <td>4327.622</td>\n",
       "      <td>22.791</td>\n",
       "      <td>4334.880</td>\n",
       "      <td>103517.482</td>\n",
       "      <td>17998.215</td>\n",
       "      <td>197628.737</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>17450.000</td>\n",
       "      <td>51.286</td>\n",
       "      <td>52.714</td>\n",
       "      <td>46</td>\n",
       "      <td>194550.790</td>\n",
       "      <td>4229.365</td>\n",
       "      <td>17.534</td>\n",
       "      <td>4238.625</td>\n",
       "      <td>77874.797</td>\n",
       "      <td>13539.885</td>\n",
       "      <td>148672.750</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2847</th>\n",
       "      <td>14096.000</td>\n",
       "      <td>13.857</td>\n",
       "      <td>14.714</td>\n",
       "      <td>17</td>\n",
       "      <td>65164.790</td>\n",
       "      <td>3833.223</td>\n",
       "      <td>16.643</td>\n",
       "      <td>3856.047</td>\n",
       "      <td>67162.312</td>\n",
       "      <td>11905.526</td>\n",
       "      <td>126505.647</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15747</th>\n",
       "      <td>16000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.571</td>\n",
       "      <td>3</td>\n",
       "      <td>12393.700</td>\n",
       "      <td>4131.233</td>\n",
       "      <td>9.224</td>\n",
       "      <td>4274.401</td>\n",
       "      <td>41201.923</td>\n",
       "      <td>7514.395</td>\n",
       "      <td>76498.587</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer ID  recency_cltv_weekly  T_weekly  frequency   monetary  \\\n",
       "0        16446.000               29.143    29.571          2 168472.500   \n",
       "2079     18102.000               52.286    52.571         60 259657.300   \n",
       "2510     17450.000               51.286    52.714         46 194550.790   \n",
       "2847     14096.000               13.857    14.714         17  65164.790   \n",
       "15747    16000.000                0.000     0.571          3  12393.700   \n",
       "\n",
       "       monetary_cltv_avg  exp_sales_6_month  exp_average_value    6m_cltv  \\\n",
       "0              84236.250              2.288          88634.665 212336.646   \n",
       "2079            4327.622             22.791           4334.880 103517.482   \n",
       "2510            4229.365             17.534           4238.625  77874.797   \n",
       "2847            3833.223             16.643           3856.047  67162.312   \n",
       "15747           4131.233              9.224           4274.401  41201.923   \n",
       "\n",
       "        1m_cltv   12m_cltv         Country Segment  \n",
       "0     37304.052 402225.954  United Kingdom       A  \n",
       "2079  17998.215 197628.737  United Kingdom       A  \n",
       "2510  13539.885 148672.750  United Kingdom       A  \n",
       "2847  11905.526 126505.647  United Kingdom       A  \n",
       "15747  7514.395  76498.587  United Kingdom       A  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df[\"Segment\"] = pd.qcut(cltv_df[\"6m_cltv\"], 4, [\"D\", \"C\", \"B\", \"A\"])\n",
    "\n",
    "cltv_df.head()"
   ]
  }
 ],
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