CLTV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"flo_data_20k.csv\")\n",
    "df_=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19945 entries, 0 to 19944\n",
      "Data columns (total 12 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   master_id                          19945 non-null  object \n",
      " 1   order_channel                      19945 non-null  object \n",
      " 2   last_order_channel                 19945 non-null  object \n",
      " 3   first_order_date                   19945 non-null  object \n",
      " 4   last_order_date                    19945 non-null  object \n",
      " 5   last_order_date_online             19945 non-null  object \n",
      " 6   last_order_date_offline            19945 non-null  object \n",
      " 7   order_num_total_ever_online        19945 non-null  float64\n",
      " 8   order_num_total_ever_offline       19945 non-null  float64\n",
      " 9   customer_value_total_ever_offline  19945 non-null  float64\n",
      " 10  customer_value_total_ever_online   19945 non-null  float64\n",
      " 11  interested_in_categories_12        19945 non-null  object \n",
      "dtypes: float64(4), object(8)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>19945.000000</td>\n",
       "      <td>19945.000000</td>\n",
       "      <td>19945.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.110855</td>\n",
       "      <td>1.913913</td>\n",
       "      <td>253.922597</td>\n",
       "      <td>497.321690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.225647</td>\n",
       "      <td>2.062880</td>\n",
       "      <td>301.532853</td>\n",
       "      <td>832.601886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.990000</td>\n",
       "      <td>149.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>179.980000</td>\n",
       "      <td>286.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>319.970000</td>\n",
       "      <td>578.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>18119.140000</td>\n",
       "      <td>45220.130000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_num_total_ever_online  order_num_total_ever_offline  \\\n",
       "count                 19945.000000                  19945.000000   \n",
       "mean                      3.110855                      1.913913   \n",
       "std                       4.225647                      2.062880   \n",
       "min                       1.000000                      1.000000   \n",
       "25%                       1.000000                      1.000000   \n",
       "50%                       2.000000                      1.000000   \n",
       "75%                       4.000000                      2.000000   \n",
       "max                     200.000000                    109.000000   \n",
       "\n",
       "       customer_value_total_ever_offline  customer_value_total_ever_online  \n",
       "count                       19945.000000                      19945.000000  \n",
       "mean                          253.922597                        497.321690  \n",
       "std                           301.532853                        832.601886  \n",
       "min                            10.000000                         12.990000  \n",
       "25%                            99.990000                        149.980000  \n",
       "50%                           179.980000                        286.460000  \n",
       "75%                           319.970000                        578.440000  \n",
       "max                         18119.140000                      45220.130000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_thresholds(dataframe, column):\n",
    "    quartile1=dataframe[column].quantile(0.01)\n",
    "    quartile2=dataframe[column].quantile(0.99)\n",
    "    interquantile=quartile2-quartile1\n",
    "    \n",
    "    higher_thresholds= quartile2+ interquantile*1.5\n",
    "    lower_thresholds=quartile1- interquantile*1.5\n",
    "    return (higher_thresholds,lower_thresholds)\n",
    "\n",
    "# Çok detaylı bir outlier elemekten ziyade absürt rakamların atılması için 0.01 ve 0.99 değerlerini kullandık. \n",
    "    \n",
    "def replace_with_thresholds(dataframe,column):\n",
    "    higher,lower =outlier_thresholds(dataframe,column)\n",
    "    dataframe.loc[(dataframe[column]<lower),column]=round(lower,0)\n",
    "    dataframe.loc[(dataframe[column]>higher),column]=round(higher,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_with_thresholds(df,\"order_num_total_ever_online\")\n",
    "replace_with_thresholds(df,\"order_num_total_ever_offline\")\n",
    "replace_with_thresholds(df,\"customer_value_total_ever_offline\")\n",
    "replace_with_thresholds(df,\"customer_value_total_ever_online\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>19945.000000</td>\n",
       "      <td>19945.00000</td>\n",
       "      <td>19945.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.091953</td>\n",
       "      <td>1.886187</td>\n",
       "      <td>251.92132</td>\n",
       "      <td>489.705676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.809541</td>\n",
       "      <td>1.434694</td>\n",
       "      <td>251.02367</td>\n",
       "      <td>632.609844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>12.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.99000</td>\n",
       "      <td>149.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>179.98000</td>\n",
       "      <td>286.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>319.97000</td>\n",
       "      <td>578.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3020.00000</td>\n",
       "      <td>7800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_num_total_ever_online  order_num_total_ever_offline  \\\n",
       "count                 19945.000000                  19945.000000   \n",
       "mean                      3.091953                      1.886187   \n",
       "std                       3.809541                      1.434694   \n",
       "min                       1.000000                      1.000000   \n",
       "25%                       1.000000                      1.000000   \n",
       "50%                       2.000000                      1.000000   \n",
       "75%                       4.000000                      2.000000   \n",
       "max                      48.000000                     16.000000   \n",
       "\n",
       "       customer_value_total_ever_offline  customer_value_total_ever_online  \n",
       "count                        19945.00000                      19945.000000  \n",
       "mean                           251.92132                        489.705676  \n",
       "std                            251.02367                        632.609844  \n",
       "min                             10.00000                         12.990000  \n",
       "25%                             99.99000                        149.980000  \n",
       "50%                            179.98000                        286.460000  \n",
       "75%                            319.97000                        578.440000  \n",
       "max                           3020.00000                       7800.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['master_id', 'order_channel', 'last_order_channel', 'first_order_date',\n",
       "       'last_order_date', 'last_order_date_online', 'last_order_date_offline',\n",
       "       'order_num_total_ever_online', 'order_num_total_ever_offline',\n",
       "       'customer_value_total_ever_offline', 'customer_value_total_ever_online',\n",
       "       'interested_in_categories_12'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df[\"order_num_total_ever\"]=df[\"order_num_total_ever_online\"]+df[\"order_num_total_ever_offline\"]\n",
    "df[\"customer_value_total_ever\"]=df[\"customer_value_total_ever_offline\"]+df[\"customer_value_total_ever_online\"]\n",
    "# Online ve offlinelar toplamı toplam değerlerdir.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19945 entries, 0 to 19944\n",
      "Data columns (total 14 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   master_id                          19945 non-null  object \n",
      " 1   order_channel                      19945 non-null  object \n",
      " 2   last_order_channel                 19945 non-null  object \n",
      " 3   first_order_date                   19945 non-null  object \n",
      " 4   last_order_date                    19945 non-null  object \n",
      " 5   last_order_date_online             19945 non-null  object \n",
      " 6   last_order_date_offline            19945 non-null  object \n",
      " 7   order_num_total_ever_online        19945 non-null  float64\n",
      " 8   order_num_total_ever_offline       19945 non-null  float64\n",
      " 9   customer_value_total_ever_offline  19945 non-null  float64\n",
      " 10  customer_value_total_ever_online   19945 non-null  float64\n",
      " 11  interested_in_categories_12        19945 non-null  object \n",
      " 12  order_num_total_ever               19945 non-null  float64\n",
      " 13  customer_value_total_ever          19945 non-null  float64\n",
      "dtypes: float64(6), object(8)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19945 entries, 0 to 19944\n",
      "Data columns (total 14 columns):\n",
      " #   Column                             Non-Null Count  Dtype         \n",
      "---  ------                             --------------  -----         \n",
      " 0   master_id                          19945 non-null  object        \n",
      " 1   order_channel                      19945 non-null  object        \n",
      " 2   last_order_channel                 19945 non-null  object        \n",
      " 3   first_order_date                   19945 non-null  datetime64[ns]\n",
      " 4   last_order_date                    19945 non-null  datetime64[ns]\n",
      " 5   last_order_date_online             19945 non-null  datetime64[ns]\n",
      " 6   last_order_date_offline            19945 non-null  datetime64[ns]\n",
      " 7   order_num_total_ever_online        19945 non-null  float64       \n",
      " 8   order_num_total_ever_offline       19945 non-null  float64       \n",
      " 9   customer_value_total_ever_offline  19945 non-null  float64       \n",
      " 10  customer_value_total_ever_online   19945 non-null  float64       \n",
      " 11  interested_in_categories_12        19945 non-null  object        \n",
      " 12  order_num_total_ever               19945 non-null  float64       \n",
      " 13  customer_value_total_ever          19945 non-null  float64       \n",
      "dtypes: datetime64[ns](4), float64(6), object(4)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "changes=['first_order_date',\n",
    "       'last_order_date', 'last_order_date_online', 'last_order_date_offline']\n",
    "# Gerekli sütunları date formatına çeviriyoruz\n",
    "for i in changes:\n",
    "    df[i]=pd.to_datetime(df[i])\n",
    "    \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-06-01 00:00:00')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Analyze_Date=pd.to_datetime(max(df[\"last_order_date\"])+pd.Timedelta(days=2))\n",
    "Analyze_Date #Analiz tarihi en büyük trih parametresinden 2 gün sonra olarak kabul edilmiştir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format',lambda x : '%.2f' % x)\n",
    "cltv=pd.DataFrame()\n",
    "cltv[\"customer_id\"]=df[\"master_id\"] #Müşteri ID\n",
    "cltv[\"recency_cltv_weekly\"]=((df[\"last_order_date\"]-df[\"first_order_date\"]).dt.days)/7 # Haftalık olarak son satın almamızdan ne kadar bir süre geçmiştir\n",
    "cltv[\"T_weekly\"]=((Analyze_Date-df[\"first_order_date\"]).dt.days)/7 #Haftalık olrak ilk satın almamızdan ne kadar süre geçmiştir.\n",
    "cltv[\"frequency\"]=df[\"order_num_total_ever\"] # Toplam  tekrar eden satın alma\n",
    "cltv[\"monetary_cltv_avg\"]=df[\"customer_value_total_ever\"]/df[\"order_num_total_ever\"]  # Satın alma başına ortalama kazanç\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifetimes import BetaGeoFitter\n",
    "from lifetimes import GammaGammaFitter\n",
    "from lifetimes.plotting import plot_period_transactions\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>17.00</td>\n",
       "      <td>30.57</td>\n",
       "      <td>5.00</td>\n",
       "      <td>187.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>209.86</td>\n",
       "      <td>224.86</td>\n",
       "      <td>21.00</td>\n",
       "      <td>95.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>52.29</td>\n",
       "      <td>78.86</td>\n",
       "      <td>5.00</td>\n",
       "      <td>117.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>1.57</td>\n",
       "      <td>20.86</td>\n",
       "      <td>2.00</td>\n",
       "      <td>60.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>83.14</td>\n",
       "      <td>95.43</td>\n",
       "      <td>2.00</td>\n",
       "      <td>104.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19940</th>\n",
       "      <td>727e2b6e-ddd4-11e9-a848-000d3a38a36f</td>\n",
       "      <td>41.14</td>\n",
       "      <td>88.43</td>\n",
       "      <td>3.00</td>\n",
       "      <td>133.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19941</th>\n",
       "      <td>25cd53d4-61bf-11ea-8dd8-000d3a38a36f</td>\n",
       "      <td>42.29</td>\n",
       "      <td>65.29</td>\n",
       "      <td>2.00</td>\n",
       "      <td>195.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19942</th>\n",
       "      <td>8aea4c2a-d6fc-11e9-93bc-000d3a38a36f</td>\n",
       "      <td>88.71</td>\n",
       "      <td>89.86</td>\n",
       "      <td>3.00</td>\n",
       "      <td>210.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19943</th>\n",
       "      <td>e50bb46c-ff30-11e9-a5e8-000d3a38a36f</td>\n",
       "      <td>98.43</td>\n",
       "      <td>113.86</td>\n",
       "      <td>6.00</td>\n",
       "      <td>168.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19944</th>\n",
       "      <td>740998d2-b1f7-11e9-89fa-000d3a38a36f</td>\n",
       "      <td>39.57</td>\n",
       "      <td>91.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>130.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19945 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                customer_id  recency_cltv_weekly  T_weekly  \\\n",
       "0      cc294636-19f0-11eb-8d74-000d3a38a36f                17.00     30.57   \n",
       "1      f431bd5a-ab7b-11e9-a2fc-000d3a38a36f               209.86    224.86   \n",
       "2      69b69676-1a40-11ea-941b-000d3a38a36f                52.29     78.86   \n",
       "3      1854e56c-491f-11eb-806e-000d3a38a36f                 1.57     20.86   \n",
       "4      d6ea1074-f1f5-11e9-9346-000d3a38a36f                83.14     95.43   \n",
       "...                                     ...                  ...       ...   \n",
       "19940  727e2b6e-ddd4-11e9-a848-000d3a38a36f                41.14     88.43   \n",
       "19941  25cd53d4-61bf-11ea-8dd8-000d3a38a36f                42.29     65.29   \n",
       "19942  8aea4c2a-d6fc-11e9-93bc-000d3a38a36f                88.71     89.86   \n",
       "19943  e50bb46c-ff30-11e9-a5e8-000d3a38a36f                98.43    113.86   \n",
       "19944  740998d2-b1f7-11e9-89fa-000d3a38a36f                39.57     91.00   \n",
       "\n",
       "       frequency  monetary_cltv_avg  \n",
       "0           5.00             187.87  \n",
       "1          21.00              95.88  \n",
       "2           5.00             117.06  \n",
       "3           2.00              60.98  \n",
       "4           2.00             104.99  \n",
       "...          ...                ...  \n",
       "19940       3.00             133.99  \n",
       "19941       2.00             195.24  \n",
       "19942       3.00             210.98  \n",
       "19943       6.00             168.29  \n",
       "19944       2.00             130.98  \n",
       "\n",
       "[19945 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.BetaGeoFitter: fitted with 19945 subjects, a: 0.00, alpha: 76.17, b: 0.00, r: 3.66>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgf=BetaGeoFitter(penalizer_coef=0.001)\n",
    "bgf.fit(cltv[\"frequency\"],cltv[\"recency_cltv_weekly\"],cltv[\"T_weekly\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.97\n",
       "1       0.98\n",
       "2       0.67\n",
       "3       0.70\n",
       "4       0.40\n",
       "        ... \n",
       "19940   0.49\n",
       "19941   0.48\n",
       "19942   0.48\n",
       "19943   0.61\n",
       "19944   0.41\n",
       "Name: exp_sales_3_month, Length: 19945, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv[\"exp_sales_3_month\"] =bgf.predict(12,cltv[\"frequency\"],cltv[\"recency_cltv_weekly\"],cltv[\"T_weekly\"]) # 3 aylık yani 12 haftalık beklenen satın almaları\n",
    "cltv[\"exp_sales_3_month\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.95\n",
       "1       1.97\n",
       "2       1.34\n",
       "3       1.40\n",
       "4       0.79\n",
       "        ... \n",
       "19940   0.97\n",
       "19941   0.96\n",
       "19942   0.96\n",
       "19943   1.22\n",
       "19944   0.81\n",
       "Name: exp_sales_6_month, Length: 19945, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv[\"exp_sales_6_month\"]  =bgf.predict(6*4,cltv[\"frequency\"],cltv[\"recency_cltv_weekly\"],cltv[\"T_weekly\"])# 3 aylık yani 12 haftalık beklenen satın almaları\n",
    "cltv[\"exp_sales_6_month\"]  # 6*4 --- 6 sayısı haftayı  4 ise her ay içerisinde bulunan haftayı temsil etmektedir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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>customer_id</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_3_month</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>17.00</td>\n",
       "      <td>30.57</td>\n",
       "      <td>5.00</td>\n",
       "      <td>187.87</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>209.86</td>\n",
       "      <td>224.86</td>\n",
       "      <td>21.00</td>\n",
       "      <td>95.88</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>52.29</td>\n",
       "      <td>78.86</td>\n",
       "      <td>5.00</td>\n",
       "      <td>117.06</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>1.57</td>\n",
       "      <td>20.86</td>\n",
       "      <td>2.00</td>\n",
       "      <td>60.98</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>83.14</td>\n",
       "      <td>95.43</td>\n",
       "      <td>2.00</td>\n",
       "      <td>104.99</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19940</th>\n",
       "      <td>727e2b6e-ddd4-11e9-a848-000d3a38a36f</td>\n",
       "      <td>41.14</td>\n",
       "      <td>88.43</td>\n",
       "      <td>3.00</td>\n",
       "      <td>133.99</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19941</th>\n",
       "      <td>25cd53d4-61bf-11ea-8dd8-000d3a38a36f</td>\n",
       "      <td>42.29</td>\n",
       "      <td>65.29</td>\n",
       "      <td>2.00</td>\n",
       "      <td>195.24</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19942</th>\n",
       "      <td>8aea4c2a-d6fc-11e9-93bc-000d3a38a36f</td>\n",
       "      <td>88.71</td>\n",
       "      <td>89.86</td>\n",
       "      <td>3.00</td>\n",
       "      <td>210.98</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19943</th>\n",
       "      <td>e50bb46c-ff30-11e9-a5e8-000d3a38a36f</td>\n",
       "      <td>98.43</td>\n",
       "      <td>113.86</td>\n",
       "      <td>6.00</td>\n",
       "      <td>168.29</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19944</th>\n",
       "      <td>740998d2-b1f7-11e9-89fa-000d3a38a36f</td>\n",
       "      <td>39.57</td>\n",
       "      <td>91.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>130.98</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19945 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                customer_id  recency_cltv_weekly  T_weekly  \\\n",
       "0      cc294636-19f0-11eb-8d74-000d3a38a36f                17.00     30.57   \n",
       "1      f431bd5a-ab7b-11e9-a2fc-000d3a38a36f               209.86    224.86   \n",
       "2      69b69676-1a40-11ea-941b-000d3a38a36f                52.29     78.86   \n",
       "3      1854e56c-491f-11eb-806e-000d3a38a36f                 1.57     20.86   \n",
       "4      d6ea1074-f1f5-11e9-9346-000d3a38a36f                83.14     95.43   \n",
       "...                                     ...                  ...       ...   \n",
       "19940  727e2b6e-ddd4-11e9-a848-000d3a38a36f                41.14     88.43   \n",
       "19941  25cd53d4-61bf-11ea-8dd8-000d3a38a36f                42.29     65.29   \n",
       "19942  8aea4c2a-d6fc-11e9-93bc-000d3a38a36f                88.71     89.86   \n",
       "19943  e50bb46c-ff30-11e9-a5e8-000d3a38a36f                98.43    113.86   \n",
       "19944  740998d2-b1f7-11e9-89fa-000d3a38a36f                39.57     91.00   \n",
       "\n",
       "       frequency  monetary_cltv_avg  exp_sales_3_month  exp_sales_6_month  \n",
       "0           5.00             187.87               0.97               1.95  \n",
       "1          21.00              95.88               0.98               1.97  \n",
       "2           5.00             117.06               0.67               1.34  \n",
       "3           2.00              60.98               0.70               1.40  \n",
       "4           2.00             104.99               0.40               0.79  \n",
       "...          ...                ...                ...                ...  \n",
       "19940       3.00             133.99               0.49               0.97  \n",
       "19941       2.00             195.24               0.48               0.96  \n",
       "19942       3.00             210.98               0.48               0.96  \n",
       "19943       6.00             168.29               0.61               1.22  \n",
       "19944       2.00             130.98               0.41               0.81  \n",
       "\n",
       "[19945 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggf=GammaGammaFitter(penalizer_coef=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.GammaGammaFitter: fitted with 19945 subjects, p: 4.15, q: 0.47, v: 4.08>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggf.fit(cltv[\"frequency\"],cltv[\"monetary_cltv_avg\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       193.63\n",
       "1        96.67\n",
       "2       120.97\n",
       "3        67.32\n",
       "4       114.33\n",
       "         ...  \n",
       "19940   141.36\n",
       "19941   210.72\n",
       "19942   221.78\n",
       "19943   172.65\n",
       "19944   142.09\n",
       "Name: exp_average_value, Length: 19945, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv[\"exp_average_value\"]=ggf.conditional_expected_average_profit(cltv[\"frequency\"],cltv[\"monetary_cltv_avg\"])\n",
    "cltv[\"exp_average_value\"] #Modelimizi fit edip sütünumuza ekledik."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'recency_cltv_weekly', 'T_weekly', 'frequency',\n",
       "       'monetary_cltv_avg', 'exp_sales_3_month', 'exp_sales_6_month',\n",
       "       'exp_average_value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cltv[\"cltv\"]=ggf.customer_lifetime_value(bgf,cltv[\"frequency\"],cltv[\"recency_cltv_weekly\"],cltv[\"T_weekly\"],cltv[\"monetary_cltv_avg\"],time=6,freq='W',discount_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>index</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_cltv_avg</th>\n",
       "      <th>exp_sales_3_month</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>cltv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9055</td>\n",
       "      <td>47a642fe-975b-11eb-8c2a-000d3a38a36f</td>\n",
       "      <td>2.86</td>\n",
       "      <td>7.86</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1401.80</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1449.06</td>\n",
       "      <td>3327.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13880</td>\n",
       "      <td>7137a5c0-7aad-11ea-8f20-000d3a38a36f</td>\n",
       "      <td>6.14</td>\n",
       "      <td>13.14</td>\n",
       "      <td>11.00</td>\n",
       "      <td>758.09</td>\n",
       "      <td>1.97</td>\n",
       "      <td>3.94</td>\n",
       "      <td>767.36</td>\n",
       "      <td>3172.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17323</td>\n",
       "      <td>f59053e2-a503-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>51.71</td>\n",
       "      <td>101.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1127.61</td>\n",
       "      <td>1708.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12438</td>\n",
       "      <td>625f40a2-5bd2-11ea-98b0-000d3a38a36f</td>\n",
       "      <td>74.29</td>\n",
       "      <td>74.57</td>\n",
       "      <td>16.00</td>\n",
       "      <td>501.87</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.13</td>\n",
       "      <td>506.17</td>\n",
       "      <td>1662.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7330</td>\n",
       "      <td>a4d534a2-5b1b-11eb-8dbd-000d3a38a36f</td>\n",
       "      <td>62.71</td>\n",
       "      <td>67.29</td>\n",
       "      <td>52.00</td>\n",
       "      <td>166.22</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.31</td>\n",
       "      <td>166.71</td>\n",
       "      <td>1628.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8868</td>\n",
       "      <td>9ce6e520-89b0-11ea-a6e7-000d3a38a36f</td>\n",
       "      <td>3.43</td>\n",
       "      <td>34.43</td>\n",
       "      <td>8.00</td>\n",
       "      <td>601.23</td>\n",
       "      <td>1.27</td>\n",
       "      <td>2.53</td>\n",
       "      <td>611.49</td>\n",
       "      <td>1623.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6402</td>\n",
       "      <td>851de3b4-8f0c-11eb-8cb8-000d3a38a36f</td>\n",
       "      <td>8.29</td>\n",
       "      <td>9.43</td>\n",
       "      <td>2.00</td>\n",
       "      <td>862.69</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.59</td>\n",
       "      <td>923.68</td>\n",
       "      <td>1538.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6666</td>\n",
       "      <td>53fe00d4-7b7a-11eb-960b-000d3a38a36f</td>\n",
       "      <td>9.71</td>\n",
       "      <td>13.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>259.87</td>\n",
       "      <td>2.78</td>\n",
       "      <td>5.56</td>\n",
       "      <td>262.07</td>\n",
       "      <td>1529.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19538</td>\n",
       "      <td>55d54d9e-8ac7-11ea-8ec0-000d3a38a36f</td>\n",
       "      <td>52.57</td>\n",
       "      <td>58.71</td>\n",
       "      <td>31.00</td>\n",
       "      <td>228.53</td>\n",
       "      <td>3.08</td>\n",
       "      <td>6.17</td>\n",
       "      <td>229.61</td>\n",
       "      <td>1485.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14858</td>\n",
       "      <td>031b2954-6d28-11eb-99c4-000d3a38a36f</td>\n",
       "      <td>14.86</td>\n",
       "      <td>15.57</td>\n",
       "      <td>3.00</td>\n",
       "      <td>743.59</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.74</td>\n",
       "      <td>778.05</td>\n",
       "      <td>1423.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17963</td>\n",
       "      <td>8fd88976-6708-11ea-9d38-000d3a38a36f</td>\n",
       "      <td>50.29</td>\n",
       "      <td>63.29</td>\n",
       "      <td>7.00</td>\n",
       "      <td>694.20</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.84</td>\n",
       "      <td>707.69</td>\n",
       "      <td>1362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15516</td>\n",
       "      <td>9083981a-f59e-11e9-841e-000d3a38a36f</td>\n",
       "      <td>63.57</td>\n",
       "      <td>83.86</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1090.36</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1127.35</td>\n",
       "      <td>1359.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6717</td>\n",
       "      <td>40b4f318-9dfb-11eb-9c47-000d3a38a36f</td>\n",
       "      <td>27.14</td>\n",
       "      <td>33.86</td>\n",
       "      <td>7.00</td>\n",
       "      <td>544.70</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2.33</td>\n",
       "      <td>555.41</td>\n",
       "      <td>1355.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4157</td>\n",
       "      <td>7eed6468-4540-11ea-acaf-000d3a38a36f</td>\n",
       "      <td>89.14</td>\n",
       "      <td>90.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>289.76</td>\n",
       "      <td>2.21</td>\n",
       "      <td>4.43</td>\n",
       "      <td>291.29</td>\n",
       "      <td>1353.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4735</td>\n",
       "      <td>dbabb58e-6312-11ea-a6dc-000d3a38a36f</td>\n",
       "      <td>61.29</td>\n",
       "      <td>64.29</td>\n",
       "      <td>13.00</td>\n",
       "      <td>442.12</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.85</td>\n",
       "      <td>446.82</td>\n",
       "      <td>1334.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11694</td>\n",
       "      <td>90f1b7f2-bbad-11ea-a0c9-000d3a38a36f</td>\n",
       "      <td>47.29</td>\n",
       "      <td>48.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>647.34</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.87</td>\n",
       "      <td>662.11</td>\n",
       "      <td>1297.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11179</td>\n",
       "      <td>d2e74a36-3228-11eb-860c-000d3a38a36f</td>\n",
       "      <td>1.14</td>\n",
       "      <td>26.29</td>\n",
       "      <td>3.00</td>\n",
       "      <td>750.57</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.56</td>\n",
       "      <td>785.34</td>\n",
       "      <td>1286.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1853</td>\n",
       "      <td>f02473b0-43c3-11eb-806e-000d3a38a36f</td>\n",
       "      <td>17.29</td>\n",
       "      <td>23.14</td>\n",
       "      <td>2.00</td>\n",
       "      <td>835.88</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.37</td>\n",
       "      <td>895.04</td>\n",
       "      <td>1285.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5775</td>\n",
       "      <td>e31293ac-d63a-11e9-93bc-000d3a38a36f</td>\n",
       "      <td>91.71</td>\n",
       "      <td>93.14</td>\n",
       "      <td>8.00</td>\n",
       "      <td>727.09</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.65</td>\n",
       "      <td>739.39</td>\n",
       "      <td>1282.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7312</td>\n",
       "      <td>90befc98-925a-11eb-b584-000d3a38a36f</td>\n",
       "      <td>4.14</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.00</td>\n",
       "      <td>431.33</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.73</td>\n",
       "      <td>441.40</td>\n",
       "      <td>1263.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index                           customer_id  recency_cltv_weekly  \\\n",
       "0    9055  47a642fe-975b-11eb-8c2a-000d3a38a36f                 2.86   \n",
       "1   13880  7137a5c0-7aad-11ea-8f20-000d3a38a36f                 6.14   \n",
       "2   17323  f59053e2-a503-11e9-a2fc-000d3a38a36f                51.71   \n",
       "3   12438  625f40a2-5bd2-11ea-98b0-000d3a38a36f                74.29   \n",
       "4    7330  a4d534a2-5b1b-11eb-8dbd-000d3a38a36f                62.71   \n",
       "5    8868  9ce6e520-89b0-11ea-a6e7-000d3a38a36f                 3.43   \n",
       "6    6402  851de3b4-8f0c-11eb-8cb8-000d3a38a36f                 8.29   \n",
       "7    6666  53fe00d4-7b7a-11eb-960b-000d3a38a36f                 9.71   \n",
       "8   19538  55d54d9e-8ac7-11ea-8ec0-000d3a38a36f                52.57   \n",
       "9   14858  031b2954-6d28-11eb-99c4-000d3a38a36f                14.86   \n",
       "10  17963  8fd88976-6708-11ea-9d38-000d3a38a36f                50.29   \n",
       "11  15516  9083981a-f59e-11e9-841e-000d3a38a36f                63.57   \n",
       "12   6717  40b4f318-9dfb-11eb-9c47-000d3a38a36f                27.14   \n",
       "13   4157  7eed6468-4540-11ea-acaf-000d3a38a36f                89.14   \n",
       "14   4735  dbabb58e-6312-11ea-a6dc-000d3a38a36f                61.29   \n",
       "15  11694  90f1b7f2-bbad-11ea-a0c9-000d3a38a36f                47.29   \n",
       "16  11179  d2e74a36-3228-11eb-860c-000d3a38a36f                 1.14   \n",
       "17   1853  f02473b0-43c3-11eb-806e-000d3a38a36f                17.29   \n",
       "18   5775  e31293ac-d63a-11e9-93bc-000d3a38a36f                91.71   \n",
       "19   7312  90befc98-925a-11eb-b584-000d3a38a36f                 4.14   \n",
       "\n",
       "    T_weekly  frequency  monetary_cltv_avg  exp_sales_3_month  \\\n",
       "0       7.86       4.00            1401.80               1.09   \n",
       "1      13.14      11.00             758.09               1.97   \n",
       "2     101.00       7.00            1106.47               0.72   \n",
       "3      74.57      16.00             501.87               1.57   \n",
       "4      67.29      52.00             166.22               4.66   \n",
       "5      34.43       8.00             601.23               1.27   \n",
       "6       9.43       2.00             862.69               0.79   \n",
       "7      13.00      17.00             259.87               2.78   \n",
       "8      58.71      31.00             228.53               3.08   \n",
       "9      15.57       3.00             743.59               0.87   \n",
       "10     63.29       7.00             694.20               0.92   \n",
       "11     83.86       4.00            1090.36               0.57   \n",
       "12     33.86       7.00             544.70               1.16   \n",
       "13     90.00      27.00             289.76               2.21   \n",
       "14     64.29      13.00             442.12               1.42   \n",
       "15     48.00       6.00             647.34               0.93   \n",
       "16     26.29       3.00             750.57               0.78   \n",
       "17     23.14       2.00             835.88               0.68   \n",
       "18     93.14       8.00             727.09               0.83   \n",
       "19      8.86       6.00             431.33               1.36   \n",
       "\n",
       "    exp_sales_6_month  exp_average_value    cltv  \n",
       "0                2.19            1449.06 3327.78  \n",
       "1                3.94             767.36 3172.39  \n",
       "2                1.44            1127.61 1708.98  \n",
       "3                3.13             506.17 1662.61  \n",
       "4                9.31             166.71 1628.89  \n",
       "5                2.53             611.49 1623.81  \n",
       "6                1.59             923.68 1538.86  \n",
       "7                5.56             262.07 1529.23  \n",
       "8                6.17             229.61 1485.82  \n",
       "9                1.74             778.05 1423.00  \n",
       "10               1.84             707.69 1362.61  \n",
       "11               1.15            1127.35 1359.44  \n",
       "12               2.33             555.41 1355.44  \n",
       "13               4.43             291.29 1353.53  \n",
       "14               2.85             446.82 1334.83  \n",
       "15               1.87             662.11 1297.52  \n",
       "16               1.56             785.34 1286.14  \n",
       "17               1.37             895.04 1285.23  \n",
       "18               1.65             739.39 1282.58  \n",
       "19               2.73             441.40 1263.19  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv.sort_values(ascending=False,by=\"cltv\").head(20).reset_index() # 6 aylık customer life time value a göre sıralanmıştır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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_cltv_avg</th>\n",
       "      <th>exp_sales_3_month</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>cltv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19945.00</td>\n",
       "      <td>19945.00</td>\n",
       "      <td>19945.00</td>\n",
       "      <td>19945.00</td>\n",
       "      <td>19945.00</td>\n",
       "      <td>19945.00</td>\n",
       "      <td>19945.00</td>\n",
       "      <td>19945.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95.26</td>\n",
       "      <td>114.47</td>\n",
       "      <td>4.98</td>\n",
       "      <td>152.10</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.15</td>\n",
       "      <td>159.24</td>\n",
       "      <td>195.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>74.59</td>\n",
       "      <td>74.77</td>\n",
       "      <td>4.12</td>\n",
       "      <td>73.52</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.46</td>\n",
       "      <td>77.02</td>\n",
       "      <td>132.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.00</td>\n",
       "      <td>22.49</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>25.08</td>\n",
       "      <td>12.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.43</td>\n",
       "      <td>73.86</td>\n",
       "      <td>3.00</td>\n",
       "      <td>103.65</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.87</td>\n",
       "      <td>108.77</td>\n",
       "      <td>112.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.57</td>\n",
       "      <td>93.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>136.91</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.07</td>\n",
       "      <td>142.92</td>\n",
       "      <td>165.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>109.43</td>\n",
       "      <td>119.43</td>\n",
       "      <td>6.00</td>\n",
       "      <td>182.45</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.34</td>\n",
       "      <td>190.45</td>\n",
       "      <td>240.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>433.43</td>\n",
       "      <td>437.14</td>\n",
       "      <td>57.00</td>\n",
       "      <td>1401.80</td>\n",
       "      <td>4.66</td>\n",
       "      <td>9.31</td>\n",
       "      <td>1449.06</td>\n",
       "      <td>3327.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       recency_cltv_weekly  T_weekly  frequency  monetary_cltv_avg  \\\n",
       "count             19945.00  19945.00   19945.00           19945.00   \n",
       "mean                 95.26    114.47       4.98             152.10   \n",
       "std                  74.59     74.77       4.12              73.52   \n",
       "min                   0.00      0.71       2.00              22.49   \n",
       "25%                  50.43     73.86       3.00             103.65   \n",
       "50%                  76.57     93.00       4.00             136.91   \n",
       "75%                 109.43    119.43       6.00             182.45   \n",
       "max                 433.43    437.14      57.00            1401.80   \n",
       "\n",
       "       exp_sales_3_month  exp_sales_6_month  exp_average_value     cltv  \n",
       "count           19945.00           19945.00           19945.00 19945.00  \n",
       "mean                0.58               1.15             159.24   195.12  \n",
       "std                 0.23               0.46              77.02   132.88  \n",
       "min                 0.15               0.30              25.08    12.11  \n",
       "25%                 0.43               0.87             108.77   112.25  \n",
       "50%                 0.54               1.07             142.92   165.47  \n",
       "75%                 0.67               1.34             190.45   240.06  \n",
       "max                 4.66               9.31            1449.06  3327.78  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segmentlere ayıracağız. A,B,C ve D olarak 4 farklı gruba ayıracağız\n",
    "cltv.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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_cltv_avg</th>\n",
       "      <th>exp_sales_3_month</th>\n",
       "      <th>exp_sales_6_month</th>\n",
       "      <th>exp_average_value</th>\n",
       "      <th>cltv</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>17.00</td>\n",
       "      <td>30.57</td>\n",
       "      <td>5.00</td>\n",
       "      <td>187.87</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.95</td>\n",
       "      <td>193.63</td>\n",
       "      <td>395.73</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>209.86</td>\n",
       "      <td>224.86</td>\n",
       "      <td>21.00</td>\n",
       "      <td>95.88</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.97</td>\n",
       "      <td>96.67</td>\n",
       "      <td>199.43</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>52.29</td>\n",
       "      <td>78.86</td>\n",
       "      <td>5.00</td>\n",
       "      <td>117.06</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.34</td>\n",
       "      <td>120.97</td>\n",
       "      <td>170.22</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>1.57</td>\n",
       "      <td>20.86</td>\n",
       "      <td>2.00</td>\n",
       "      <td>60.98</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.40</td>\n",
       "      <td>67.32</td>\n",
       "      <td>98.95</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>83.14</td>\n",
       "      <td>95.43</td>\n",
       "      <td>2.00</td>\n",
       "      <td>104.99</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.79</td>\n",
       "      <td>114.33</td>\n",
       "      <td>95.01</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19940</th>\n",
       "      <td>727e2b6e-ddd4-11e9-a848-000d3a38a36f</td>\n",
       "      <td>41.14</td>\n",
       "      <td>88.43</td>\n",
       "      <td>3.00</td>\n",
       "      <td>133.99</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.97</td>\n",
       "      <td>141.36</td>\n",
       "      <td>144.10</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19941</th>\n",
       "      <td>25cd53d4-61bf-11ea-8dd8-000d3a38a36f</td>\n",
       "      <td>42.29</td>\n",
       "      <td>65.29</td>\n",
       "      <td>2.00</td>\n",
       "      <td>195.24</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.96</td>\n",
       "      <td>210.72</td>\n",
       "      <td>212.44</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19942</th>\n",
       "      <td>8aea4c2a-d6fc-11e9-93bc-000d3a38a36f</td>\n",
       "      <td>88.71</td>\n",
       "      <td>89.86</td>\n",
       "      <td>3.00</td>\n",
       "      <td>210.98</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.96</td>\n",
       "      <td>221.78</td>\n",
       "      <td>224.13</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19943</th>\n",
       "      <td>e50bb46c-ff30-11e9-a5e8-000d3a38a36f</td>\n",
       "      <td>98.43</td>\n",
       "      <td>113.86</td>\n",
       "      <td>6.00</td>\n",
       "      <td>168.29</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.22</td>\n",
       "      <td>172.65</td>\n",
       "      <td>221.08</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19944</th>\n",
       "      <td>740998d2-b1f7-11e9-89fa-000d3a38a36f</td>\n",
       "      <td>39.57</td>\n",
       "      <td>91.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>130.98</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.81</td>\n",
       "      <td>142.09</td>\n",
       "      <td>121.22</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19945 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                customer_id  recency_cltv_weekly  T_weekly  \\\n",
       "0      cc294636-19f0-11eb-8d74-000d3a38a36f                17.00     30.57   \n",
       "1      f431bd5a-ab7b-11e9-a2fc-000d3a38a36f               209.86    224.86   \n",
       "2      69b69676-1a40-11ea-941b-000d3a38a36f                52.29     78.86   \n",
       "3      1854e56c-491f-11eb-806e-000d3a38a36f                 1.57     20.86   \n",
       "4      d6ea1074-f1f5-11e9-9346-000d3a38a36f                83.14     95.43   \n",
       "...                                     ...                  ...       ...   \n",
       "19940  727e2b6e-ddd4-11e9-a848-000d3a38a36f                41.14     88.43   \n",
       "19941  25cd53d4-61bf-11ea-8dd8-000d3a38a36f                42.29     65.29   \n",
       "19942  8aea4c2a-d6fc-11e9-93bc-000d3a38a36f                88.71     89.86   \n",
       "19943  e50bb46c-ff30-11e9-a5e8-000d3a38a36f                98.43    113.86   \n",
       "19944  740998d2-b1f7-11e9-89fa-000d3a38a36f                39.57     91.00   \n",
       "\n",
       "       frequency  monetary_cltv_avg  exp_sales_3_month  exp_sales_6_month  \\\n",
       "0           5.00             187.87               0.97               1.95   \n",
       "1          21.00              95.88               0.98               1.97   \n",
       "2           5.00             117.06               0.67               1.34   \n",
       "3           2.00              60.98               0.70               1.40   \n",
       "4           2.00             104.99               0.40               0.79   \n",
       "...          ...                ...                ...                ...   \n",
       "19940       3.00             133.99               0.49               0.97   \n",
       "19941       2.00             195.24               0.48               0.96   \n",
       "19942       3.00             210.98               0.48               0.96   \n",
       "19943       6.00             168.29               0.61               1.22   \n",
       "19944       2.00             130.98               0.41               0.81   \n",
       "\n",
       "       exp_average_value   cltv Segment  \n",
       "0                 193.63 395.73       A  \n",
       "1                  96.67 199.43       B  \n",
       "2                 120.97 170.22       B  \n",
       "3                  67.32  98.95       D  \n",
       "4                 114.33  95.01       D  \n",
       "...                  ...    ...     ...  \n",
       "19940             141.36 144.10       C  \n",
       "19941             210.72 212.44       B  \n",
       "19942             221.78 224.13       B  \n",
       "19943             172.65 221.08       B  \n",
       "19944             142.09 121.22       C  \n",
       "\n",
       "[19945 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv[\"Segment\"]=pd.qcut(cltv[\"cltv\"],4,labels=['D','C','B','A'])\n",
    "cltv\n",
    "# 4 segmente ayırdık. Şimdi segmentlere göre percentile ve ortalama gibi değerlere bakacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">recency_cltv_weekly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T_weekly</th>\n",
       "      <th colspan=\"2\" halign=\"left\">frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">monetary_cltv_avg</th>\n",
       "      <th colspan=\"2\" halign=\"left\">exp_sales_3_month</th>\n",
       "      <th colspan=\"2\" halign=\"left\">exp_sales_6_month</th>\n",
       "      <th colspan=\"2\" halign=\"left\">exp_average_value</th>\n",
       "      <th colspan=\"2\" halign=\"left\">cltv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>&lt;lambda_0&gt;</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>139.00</td>\n",
       "      <td>301.43</td>\n",
       "      <td>162.18</td>\n",
       "      <td>324.86</td>\n",
       "      <td>3.77</td>\n",
       "      <td>6.00</td>\n",
       "      <td>93.15</td>\n",
       "      <td>130.44</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.11</td>\n",
       "      <td>98.69</td>\n",
       "      <td>137.39</td>\n",
       "      <td>80.34</td>\n",
       "      <td>106.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>92.63</td>\n",
       "      <td>177.86</td>\n",
       "      <td>112.82</td>\n",
       "      <td>199.07</td>\n",
       "      <td>4.40</td>\n",
       "      <td>8.00</td>\n",
       "      <td>125.79</td>\n",
       "      <td>163.63</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.38</td>\n",
       "      <td>132.25</td>\n",
       "      <td>173.07</td>\n",
       "      <td>138.31</td>\n",
       "      <td>159.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>81.99</td>\n",
       "      <td>142.71</td>\n",
       "      <td>100.33</td>\n",
       "      <td>160.14</td>\n",
       "      <td>5.09</td>\n",
       "      <td>9.00</td>\n",
       "      <td>160.64</td>\n",
       "      <td>212.99</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.61</td>\n",
       "      <td>168.00</td>\n",
       "      <td>225.88</td>\n",
       "      <td>199.53</td>\n",
       "      <td>230.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>67.43</td>\n",
       "      <td>114.50</td>\n",
       "      <td>82.55</td>\n",
       "      <td>122.00</td>\n",
       "      <td>6.65</td>\n",
       "      <td>13.00</td>\n",
       "      <td>228.83</td>\n",
       "      <td>332.36</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.26</td>\n",
       "      <td>238.02</td>\n",
       "      <td>349.79</td>\n",
       "      <td>362.32</td>\n",
       "      <td>531.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        recency_cltv_weekly            T_weekly            frequency  \\\n",
       "                       mean <lambda_0>     mean <lambda_0>      mean   \n",
       "Segment                                                                \n",
       "D                    139.00     301.43   162.18     324.86      3.77   \n",
       "C                     92.63     177.86   112.82     199.07      4.40   \n",
       "B                     81.99     142.71   100.33     160.14      5.09   \n",
       "A                     67.43     114.50    82.55     122.00      6.65   \n",
       "\n",
       "                   monetary_cltv_avg            exp_sales_3_month             \\\n",
       "        <lambda_0>              mean <lambda_0>              mean <lambda_0>   \n",
       "Segment                                                                        \n",
       "D             6.00             93.15     130.44              0.41       0.56   \n",
       "C             8.00            125.79     163.63              0.53       0.69   \n",
       "B             9.00            160.64     212.99              0.60       0.81   \n",
       "A            13.00            228.83     332.36              0.77       1.13   \n",
       "\n",
       "        exp_sales_6_month            exp_average_value              cltv  \\\n",
       "                     mean <lambda_0>              mean <lambda_0>   mean   \n",
       "Segment                                                                    \n",
       "D                    0.82       1.11             98.69     137.39  80.34   \n",
       "C                    1.05       1.38            132.25     173.07 138.31   \n",
       "B                    1.20       1.61            168.00     225.88 199.53   \n",
       "A                    1.55       2.26            238.02     349.79 362.32   \n",
       "\n",
       "                    \n",
       "        <lambda_0>  \n",
       "Segment             \n",
       "D           106.83  \n",
       "C           159.88  \n",
       "B           230.24  \n",
       "A           531.62  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv[[ 'recency_cltv_weekly', 'T_weekly', 'frequency',\n",
    "       'monetary_cltv_avg', 'exp_sales_3_month', 'exp_sales_6_month',\n",
    "       'exp_average_value','cltv']\n",
    "      ].groupby(cltv[\"Segment\"]).agg({'recency_cltv_weekly': ['mean',lambda x :x.quantile(0.9)]\n",
    "                                      ,'T_weekly': ['mean',lambda x :x.quantile(0.9)]\n",
    "                                      ,'frequency': ['mean',lambda x :x.quantile(0.9)]\n",
    "                                      ,'monetary_cltv_avg': ['mean',lambda x :x.quantile(0.9)]\n",
    "                                      ,'exp_sales_3_month': ['mean',lambda x :x.quantile(0.9)]\n",
    "                                      ,'exp_sales_6_month': ['mean',lambda x :x.quantile(0.9)]\n",
    "                                      ,'exp_average_value': ['mean',lambda x :x.quantile(0.9)]\n",
    "                                      ,'cltv': ['mean',lambda x :x.quantile(0.9)]})\n",
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A segmentinin içerisinde bulunan bir kısım müşteri şirket için çok önemlidir. Yaşam boyu değeri diğerlerine kıyasla daha yüksek orandadır. Bu insanlar için ayrı ayrı çalışabilir ve farklı etkinlikler düzenlenebilir. D segmenti için ise kampanyalar indirimler uygulayarak gerekli çalışmalar yapılabilir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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