CLTV Prediction with BG-NBD and Gamma-Gamma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d92a7aa",
   "metadata": {},
   "source": [
    "# CLTV Prediction with BG-NBD and Gamma-Gamma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb7a75d",
   "metadata": {},
   "source": [
    "### Business Problem "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee25ea15",
   "metadata": {},
   "source": [
    "FLO, an online shoe store, wants to segment its customers and determine marketing strategies according to these segments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca71bc3b",
   "metadata": {},
   "source": [
    "### Data Set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f7030e",
   "metadata": {},
   "source": [
    "The dataset consists of the information obtained from the past shopping behaviors of customers who made their last purchases from Flo as OmniChannel (both online and offline shopping) in the years 2020-2021."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b5ed51",
   "metadata": {},
   "source": [
    "##### Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd51c5ea",
   "metadata": {},
   "source": [
    "master_id : Unique number for each customer\n",
    "\n",
    "order_channel : Channel of the shopping platform is used (Android, ios, Desktop, Mobile)\n",
    "\n",
    "last_order_channel : The channel where the most recent purchase was made\n",
    "\n",
    "first_order_date : Date of the customer's first purchase\n",
    "\n",
    "last_order_date : Customer's last purchase date\n",
    "\n",
    "last_order_date_online : The date of the last purchase made by the customer on the online platform\n",
    "\n",
    "last_order_date_offline : The date of the last purchase made by the customer on the offline platform\n",
    "\n",
    "order_num_total_ever_online : The total number of purchases made by the customer on the online platform\n",
    "\n",
    "order_num_total_ever_offline : The total number of purchases made by the customer offline platform\n",
    "\n",
    "customer_value_total_ever_offline : The total fee paid by the customer for offline shopping\n",
    "\n",
    "customer_value_total_ever_online : The total fee paid by the customer for their online shopping\n",
    "\n",
    "interested_in_categories_12 : A list of categories the customer has shopped in the last 12 months"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c8746d",
   "metadata": {},
   "source": [
    "### Understanding and Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ceec790",
   "metadata": {},
   "outputs": [],
   "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",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "pd.options.mode.chained_assignment = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ffaab39",
   "metadata": {},
   "source": [
    "##### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0f2fb15",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = pd.read_csv(\"/Users/cagladeniz/PycharmProjects/Miuul/CRM Analitiği/Dataset/flo_data_20k.csv\")\n",
    "df = df_.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6234c208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>master_id</th>\n",
       "      <th>order_channel</th>\n",
       "      <th>last_order_channel</th>\n",
       "      <th>first_order_date</th>\n",
       "      <th>last_order_date</th>\n",
       "      <th>last_order_date_online</th>\n",
       "      <th>last_order_date_offline</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",
       "      <th>interested_in_categories_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>139.99</td>\n",
       "      <td>799.38</td>\n",
       "      <td>[KADIN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>159.97</td>\n",
       "      <td>1853.58</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>189.97</td>\n",
       "      <td>395.35</td>\n",
       "      <td>[ERKEK, KADIN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>81.98</td>\n",
       "      <td>[AKTIFCOCUK, COCUK]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>159.99</td>\n",
       "      <td>[AKTIFSPOR]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              master_id order_channel last_order_channel  \\\n",
       "0  cc294636-19f0-11eb-8d74-000d3a38a36f   Android App            Offline   \n",
       "1  f431bd5a-ab7b-11e9-a2fc-000d3a38a36f   Android App             Mobile   \n",
       "2  69b69676-1a40-11ea-941b-000d3a38a36f   Android App        Android App   \n",
       "3  1854e56c-491f-11eb-806e-000d3a38a36f   Android App        Android App   \n",
       "4  d6ea1074-f1f5-11e9-9346-000d3a38a36f       Desktop            Desktop   \n",
       "\n",
       "  first_order_date last_order_date last_order_date_online  \\\n",
       "0       2020-10-30      2021-02-26             2021-02-21   \n",
       "1       2017-02-08      2021-02-16             2021-02-16   \n",
       "2       2019-11-27      2020-11-27             2020-11-27   \n",
       "3       2021-01-06      2021-01-17             2021-01-17   \n",
       "4       2019-08-03      2021-03-07             2021-03-07   \n",
       "\n",
       "  last_order_date_offline  order_num_total_ever_online  \\\n",
       "0              2021-02-26                         4.00   \n",
       "1              2020-01-10                        19.00   \n",
       "2              2019-12-01                         3.00   \n",
       "3              2021-01-06                         1.00   \n",
       "4              2019-08-03                         1.00   \n",
       "\n",
       "   order_num_total_ever_offline  customer_value_total_ever_offline  \\\n",
       "0                          1.00                             139.99   \n",
       "1                          2.00                             159.97   \n",
       "2                          2.00                             189.97   \n",
       "3                          1.00                              39.99   \n",
       "4                          1.00                              49.99   \n",
       "\n",
       "   customer_value_total_ever_online       interested_in_categories_12  \n",
       "0                            799.38                           [KADIN]  \n",
       "1                           1853.58  [ERKEK, COCUK, KADIN, AKTIFSPOR]  \n",
       "2                            395.35                    [ERKEK, KADIN]  \n",
       "3                             81.98               [AKTIFCOCUK, COCUK]  \n",
       "4                            159.99                       [AKTIFSPOR]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1115a242",
   "metadata": {},
   "source": [
    "##### Define outlier functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "183f7d76",
   "metadata": {},
   "outputs": [],
   "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",
    "def replace_with_thresholds(dataframe, variable):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, variable)\n",
    "    dataframe.loc[(dataframe[variable] < low_limit), variable] = round(low_limit,0)\n",
    "    dataframe.loc[(dataframe[variable] > up_limit), variable] = round(up_limit,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e233818",
   "metadata": {},
   "source": [
    "##### If there are outliers in the columns, find them and prevent these outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fe5f2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"order_num_total_ever_online\", \"order_num_total_ever_offline\", \"customer_value_total_ever_offline\",\"customer_value_total_ever_online\"]\n",
    "for col in columns:\n",
    "    replace_with_thresholds(df, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb243ee",
   "metadata": {},
   "source": [
    "##### Create new variables for each customer's total purchases and spend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8340053c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"order_num_total\"] = df[\"order_num_total_ever_online\"] + df[\"order_num_total_ever_offline\"]\n",
    "df[\"customer_value_total\"] = df[\"customer_value_total_ever_offline\"] + df[\"customer_value_total_ever_online\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49cc78d",
   "metadata": {},
   "source": [
    "##### Change the type of variables that express date to date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3bd8cf77",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_columns = df.columns[df.columns.str.contains(\"date\")]\n",
    "df[date_columns] = df[date_columns].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79985a7e",
   "metadata": {},
   "source": [
    "### Creating the CLTV Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "222e962c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"last_order_date\"].max() # 2021-05-30\n",
    "analysis_date = dt.datetime(2021,6,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4012bad",
   "metadata": {},
   "source": [
    "##### Create a new cltv dataframe with customer_id, recency_cltv_weekly, T_weekly, frequency and monetary_cltv_avg values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1cdf991",
   "metadata": {},
   "outputs": [],
   "source": [
    "cltv_df = pd.DataFrame()\n",
    "cltv_df[\"customer_id\"] = df[\"master_id\"]\n",
    "cltv_df[\"recency_cltv_weekly\"] = ((df[\"last_order_date\"]- df[\"first_order_date\"]).astype('timedelta64[D]')) / 7\n",
    "cltv_df[\"T_weekly\"] = ((analysis_date - df[\"first_order_date\"]).astype('timedelta64[D]'))/7\n",
    "cltv_df[\"frequency\"] = df[\"order_num_total\"]\n",
    "cltv_df[\"monetary_cltv_avg\"] = df[\"customer_value_total\"] / df[\"order_num_total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eedec2d3",
   "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",
       "  </tbody>\n",
       "</table>\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",
       "   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  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b664b8",
   "metadata": {},
   "source": [
    "### Establishment of BG/NBD, Gamma-Gamma Models,\n",
    "### Calculation of 6-month CLTV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0cff3e",
   "metadata": {},
   "source": [
    "##### Establish the BG/NBD model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "357222c3",
   "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": 12,
     "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": "markdown",
   "id": "bff7a3d9",
   "metadata": {},
   "source": [
    "##### Estimate expected purchases from customers in 3 months and add them as exp_sales_3_month to cltv dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7965bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "cltv_df[\"exp_sales_3_month\"] = bgf.predict(4*3,\n",
    "                                       cltv_df['frequency'],\n",
    "                                       cltv_df['recency_cltv_weekly'],\n",
    "                                       cltv_df['T_weekly'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac807c8e",
   "metadata": {},
   "source": [
    "##### Estimate expected purchases from customers in 6 months and add them as exp_sales_6_month to cltv dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "847b58f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cltv_df[\"exp_sales_6_month\"] = bgf.predict(4*6,\n",
    "                                       cltv_df['frequency'],\n",
    "                                       cltv_df['recency_cltv_weekly'],\n",
    "                                       cltv_df['T_weekly'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f94c21",
   "metadata": {},
   "source": [
    "##### Please review the 10 people who will make the most purchases in the 3rd and 6th months. Check if there is any difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8124f551",
   "metadata": {},
   "outputs": [
    {
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       "      <td>147.14</td>\n",
       "      <td>49.00</td>\n",
       "      <td>161.85</td>\n",
       "      <td>2.83</td>\n",
       "      <td>5.66</td>\n",
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       "    <tr>\n",
       "      <th>6756</th>\n",
       "      <td>27310582-6362-11ea-a6dc-000d3a38a36f</td>\n",
       "      <td>62.71</td>\n",
       "      <td>64.14</td>\n",
       "      <td>29.00</td>\n",
       "      <td>168.88</td>\n",
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       "      <td>13.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>259.87</td>\n",
       "      <td>2.78</td>\n",
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       "      <td>176.20</td>\n",
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       "                                customer_id  recency_cltv_weekly  T_weekly  \\\n",
       "7330   a4d534a2-5b1b-11eb-8dbd-000d3a38a36f                62.71     67.29   \n",
       "15611  4a7e875e-e6ce-11ea-8f44-000d3a38a36f                39.71     40.00   \n",
       "8328   1902bf80-0035-11eb-8341-000d3a38a36f                28.86     33.29   \n",
       "19538  55d54d9e-8ac7-11ea-8ec0-000d3a38a36f                52.57     58.71   \n",
       "14373  f00ad516-c4f4-11ea-98f7-000d3a38a36f                38.00     46.43   \n",
       "10489  7af5cd16-b100-11e9-9757-000d3a38a36f               103.14    111.86   \n",
       "4315   d5ef8058-a5c6-11e9-a2fc-000d3a38a36f               133.14    147.14   \n",
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       "10536  e143b6fa-d6f8-11e9-93bc-000d3a38a36f               104.57    113.43   \n",
       "\n",
       "       frequency  monetary_cltv_avg  exp_sales_3_month  exp_sales_6_month  \n",
       "7330       52.00             166.22               4.66               9.31  \n",
       "15611      29.00             165.30               3.37               6.75  \n",
       "8328       25.00              97.44               3.14               6.28  \n",
       "19538      31.00             228.53               3.08               6.17  \n",
       "14373      27.00             141.35               3.00               6.00  \n",
       "10489      43.00             157.11               2.98               5.96  \n",
       "4315       49.00             161.85               2.83               5.66  \n",
       "6756       29.00             168.88               2.79               5.59  \n",
       "6666       17.00             259.87               2.78               5.56  \n",
       "10536      40.00             176.20               2.76               5.53  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df.sort_values(\"exp_sales_3_month\",ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a5174f4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>161.85</td>\n",
       "      <td>2.83</td>\n",
       "      <td>5.66</td>\n",
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       "      <td>27310582-6362-11ea-a6dc-000d3a38a36f</td>\n",
       "      <td>62.71</td>\n",
       "      <td>64.14</td>\n",
       "      <td>29.00</td>\n",
       "      <td>168.88</td>\n",
       "      <td>2.79</td>\n",
       "      <td>5.59</td>\n",
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       "      <td>2.76</td>\n",
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      "text/plain": [
       "                                customer_id  recency_cltv_weekly  T_weekly  \\\n",
       "7330   a4d534a2-5b1b-11eb-8dbd-000d3a38a36f                62.71     67.29   \n",
       "15611  4a7e875e-e6ce-11ea-8f44-000d3a38a36f                39.71     40.00   \n",
       "8328   1902bf80-0035-11eb-8341-000d3a38a36f                28.86     33.29   \n",
       "19538  55d54d9e-8ac7-11ea-8ec0-000d3a38a36f                52.57     58.71   \n",
       "14373  f00ad516-c4f4-11ea-98f7-000d3a38a36f                38.00     46.43   \n",
       "10489  7af5cd16-b100-11e9-9757-000d3a38a36f               103.14    111.86   \n",
       "4315   d5ef8058-a5c6-11e9-a2fc-000d3a38a36f               133.14    147.14   \n",
       "6756   27310582-6362-11ea-a6dc-000d3a38a36f                62.71     64.14   \n",
       "6666   53fe00d4-7b7a-11eb-960b-000d3a38a36f                 9.71     13.00   \n",
       "10536  e143b6fa-d6f8-11e9-93bc-000d3a38a36f               104.57    113.43   \n",
       "\n",
       "       frequency  monetary_cltv_avg  exp_sales_3_month  exp_sales_6_month  \n",
       "7330       52.00             166.22               4.66               9.31  \n",
       "15611      29.00             165.30               3.37               6.75  \n",
       "8328       25.00              97.44               3.14               6.28  \n",
       "19538      31.00             228.53               3.08               6.17  \n",
       "14373      27.00             141.35               3.00               6.00  \n",
       "10489      43.00             157.11               2.98               5.96  \n",
       "4315       49.00             161.85               2.83               5.66  \n",
       "6756       29.00             168.88               2.79               5.59  \n",
       "6666       17.00             259.87               2.78               5.56  \n",
       "10536      40.00             176.20               2.76               5.53  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df.sort_values(\"exp_sales_6_month\",ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379cec0d",
   "metadata": {},
   "source": [
    "##### Fit the Gamma-Gamma model. Estimate the average value of the customers and add it to the cltv dataframe as exp_average_value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85a81053",
   "metadata": {},
   "outputs": [
    {
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       "      <th>customer_id</th>\n",
       "      <th>recency_cltv_weekly</th>\n",
       "      <th>T_weekly</th>\n",
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       "      <th>exp_average_value</th>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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",
       "   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",
       "   exp_average_value  \n",
       "0             193.63  \n",
       "1              96.67  \n",
       "2             120.97  \n",
       "3              67.32  \n",
       "4             114.33  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggf = GammaGammaFitter(penalizer_coef=0.01)\n",
    "ggf.fit(cltv_df['frequency'], cltv_df['monetary_cltv_avg'])\n",
    "cltv_df[\"exp_average_value\"] = ggf.conditional_expected_average_profit(cltv_df['frequency'],\n",
    "                                                                cltv_df['monetary_cltv_avg'])\n",
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7e276f",
   "metadata": {},
   "source": [
    "##### Calculate the CLTV for 6 months and add it to the dataframe as cltv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fde12919",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "cltv_df[\"cltv\"] = cltv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4787ed",
   "metadata": {},
   "source": [
    "##### Observe the 20 people with the highest CLTV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c93609e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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_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>9055</th>\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>13880</th>\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>17323</th>\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>12438</th>\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>7330</th>\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>8868</th>\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>6402</th>\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>6666</th>\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>19538</th>\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>14858</th>\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>17963</th>\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>15516</th>\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>6717</th>\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>4157</th>\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>4735</th>\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>11694</th>\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>11179</th>\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>1853</th>\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>5775</th>\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>7312</th>\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": [
       "                                customer_id  recency_cltv_weekly  T_weekly  \\\n",
       "9055   47a642fe-975b-11eb-8c2a-000d3a38a36f                 2.86      7.86   \n",
       "13880  7137a5c0-7aad-11ea-8f20-000d3a38a36f                 6.14     13.14   \n",
       "17323  f59053e2-a503-11e9-a2fc-000d3a38a36f                51.71    101.00   \n",
       "12438  625f40a2-5bd2-11ea-98b0-000d3a38a36f                74.29     74.57   \n",
       "7330   a4d534a2-5b1b-11eb-8dbd-000d3a38a36f                62.71     67.29   \n",
       "8868   9ce6e520-89b0-11ea-a6e7-000d3a38a36f                 3.43     34.43   \n",
       "6402   851de3b4-8f0c-11eb-8cb8-000d3a38a36f                 8.29      9.43   \n",
       "6666   53fe00d4-7b7a-11eb-960b-000d3a38a36f                 9.71     13.00   \n",
       "19538  55d54d9e-8ac7-11ea-8ec0-000d3a38a36f                52.57     58.71   \n",
       "14858  031b2954-6d28-11eb-99c4-000d3a38a36f                14.86     15.57   \n",
       "17963  8fd88976-6708-11ea-9d38-000d3a38a36f                50.29     63.29   \n",
       "15516  9083981a-f59e-11e9-841e-000d3a38a36f                63.57     83.86   \n",
       "6717   40b4f318-9dfb-11eb-9c47-000d3a38a36f                27.14     33.86   \n",
       "4157   7eed6468-4540-11ea-acaf-000d3a38a36f                89.14     90.00   \n",
       "4735   dbabb58e-6312-11ea-a6dc-000d3a38a36f                61.29     64.29   \n",
       "11694  90f1b7f2-bbad-11ea-a0c9-000d3a38a36f                47.29     48.00   \n",
       "11179  d2e74a36-3228-11eb-860c-000d3a38a36f                 1.14     26.29   \n",
       "1853   f02473b0-43c3-11eb-806e-000d3a38a36f                17.29     23.14   \n",
       "5775   e31293ac-d63a-11e9-93bc-000d3a38a36f                91.71     93.14   \n",
       "7312   90befc98-925a-11eb-b584-000d3a38a36f                 4.14      8.86   \n",
       "\n",
       "       frequency  monetary_cltv_avg  exp_sales_3_month  exp_sales_6_month  \\\n",
       "9055        4.00            1401.80               1.09               2.19   \n",
       "13880      11.00             758.09               1.97               3.94   \n",
       "17323       7.00            1106.47               0.72               1.44   \n",
       "12438      16.00             501.87               1.57               3.13   \n",
       "7330       52.00             166.22               4.66               9.31   \n",
       "8868        8.00             601.23               1.27               2.53   \n",
       "6402        2.00             862.69               0.79               1.59   \n",
       "6666       17.00             259.87               2.78               5.56   \n",
       "19538      31.00             228.53               3.08               6.17   \n",
       "14858       3.00             743.59               0.87               1.74   \n",
       "17963       7.00             694.20               0.92               1.84   \n",
       "15516       4.00            1090.36               0.57               1.15   \n",
       "6717        7.00             544.70               1.16               2.33   \n",
       "4157       27.00             289.76               2.21               4.43   \n",
       "4735       13.00             442.12               1.42               2.85   \n",
       "11694       6.00             647.34               0.93               1.87   \n",
       "11179       3.00             750.57               0.78               1.56   \n",
       "1853        2.00             835.88               0.68               1.37   \n",
       "5775        8.00             727.09               0.83               1.65   \n",
       "7312        6.00             431.33               1.36               2.73   \n",
       "\n",
       "       exp_average_value    cltv  \n",
       "9055             1449.06 3327.78  \n",
       "13880             767.36 3172.39  \n",
       "17323            1127.61 1708.98  \n",
       "12438             506.17 1662.61  \n",
       "7330              166.71 1628.89  \n",
       "8868              611.49 1623.81  \n",
       "6402              923.68 1538.86  \n",
       "6666              262.07 1529.23  \n",
       "19538             229.61 1485.82  \n",
       "14858             778.05 1423.00  \n",
       "17963             707.69 1362.61  \n",
       "15516            1127.35 1359.44  \n",
       "6717              555.41 1355.44  \n",
       "4157              291.29 1353.53  \n",
       "4735              446.82 1334.83  \n",
       "11694             662.11 1297.52  \n",
       "11179             785.34 1286.14  \n",
       "1853              895.04 1285.23  \n",
       "5775              739.39 1282.58  \n",
       "7312              441.40 1263.19  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df.sort_values(\"cltv\",ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d14cd9",
   "metadata": {},
   "source": [
    "### Creating Segments by CLTV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b582f007",
   "metadata": {},
   "source": [
    "##### Creating Segments with qcut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5897bf18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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",
       "      <th>exp_average_value</th>\n",
       "      <th>cltv</th>\n",
       "      <th>cltv_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",
       "  </tbody>\n",
       "</table>\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",
       "   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",
       "   exp_average_value   cltv 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  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cltv_df[\"cltv_segment\"] = pd.qcut(cltv_df[\"cltv\"], 4, labels=[\"D\", \"C\", \"B\", \"A\"])\n",
    "cltv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b529b14f",
   "metadata": {},
   "source": [
    "### Functionalization of the whole process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "975991f7",
   "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",
       "      <th>exp_average_value</th>\n",
       "      <th>cltv</th>\n",
       "      <th>cltv_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>5</th>\n",
       "      <td>e585280e-aae1-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>120.86</td>\n",
       "      <td>132.29</td>\n",
       "      <td>3.00</td>\n",
       "      <td>66.95</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.77</td>\n",
       "      <td>71.35</td>\n",
       "      <td>57.43</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c445e4ee-6242-11ea-9d1a-000d3a38a36f</td>\n",
       "      <td>32.57</td>\n",
       "      <td>64.86</td>\n",
       "      <td>4.00</td>\n",
       "      <td>93.98</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.30</td>\n",
       "      <td>98.13</td>\n",
       "      <td>134.28</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3f1b4dc8-8a7d-11ea-8ec0-000d3a38a36f</td>\n",
       "      <td>12.71</td>\n",
       "      <td>54.57</td>\n",
       "      <td>2.00</td>\n",
       "      <td>81.81</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.04</td>\n",
       "      <td>89.57</td>\n",
       "      <td>97.70</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cfbda69e-5b4f-11ea-aca7-000d3a38a36f</td>\n",
       "      <td>58.43</td>\n",
       "      <td>70.71</td>\n",
       "      <td>5.00</td>\n",
       "      <td>210.94</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.42</td>\n",
       "      <td>217.30</td>\n",
       "      <td>322.73</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1143f032-440d-11ea-8b43-000d3a38a36f</td>\n",
       "      <td>61.71</td>\n",
       "      <td>96.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>82.98</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.79</td>\n",
       "      <td>90.81</td>\n",
       "      <td>75.22</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "5  e585280e-aae1-11e9-a2fc-000d3a38a36f               120.86    132.29   \n",
       "6  c445e4ee-6242-11ea-9d1a-000d3a38a36f                32.57     64.86   \n",
       "7  3f1b4dc8-8a7d-11ea-8ec0-000d3a38a36f                12.71     54.57   \n",
       "8  cfbda69e-5b4f-11ea-aca7-000d3a38a36f                58.43     70.71   \n",
       "9  1143f032-440d-11ea-8b43-000d3a38a36f                61.71     96.00   \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",
       "5       3.00              66.95               0.38               0.77   \n",
       "6       4.00              93.98               0.65               1.30   \n",
       "7       2.00              81.81               0.52               1.04   \n",
       "8       5.00             210.94               0.71               1.42   \n",
       "9       2.00              82.98               0.39               0.79   \n",
       "\n",
       "   exp_average_value   cltv 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",
       "5              71.35  57.43            D  \n",
       "6              98.13 134.28            C  \n",
       "7              89.57  97.70            D  \n",
       "8             217.30 322.73            A  \n",
       "9              90.81  75.22            D  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def create_cltv_df(dataframe):\n",
    "\n",
    "    # Preparing Data\n",
    "    columns = [\"order_num_total_ever_online\", \"order_num_total_ever_offline\", \"customer_value_total_ever_offline\",\"customer_value_total_ever_online\"]\n",
    "    for col in columns:\n",
    "        replace_with_thresholds(dataframe, col)\n",
    "\n",
    "    dataframe[\"order_num_total\"] = dataframe[\"order_num_total_ever_online\"] + dataframe[\"order_num_total_ever_offline\"]\n",
    "    dataframe[\"customer_value_total\"] = dataframe[\"customer_value_total_ever_offline\"] + dataframe[\"customer_value_total_ever_online\"]\n",
    "    dataframe = dataframe[~(dataframe[\"customer_value_total\"] == 0) | (dataframe[\"order_num_total\"] == 0)]\n",
    "    date_columns = dataframe.columns[dataframe.columns.str.contains(\"date\")]\n",
    "    dataframe[date_columns] = dataframe[date_columns].apply(pd.to_datetime)\n",
    "\n",
    "    # Creating the CLTV Data Structure\n",
    "    dataframe[\"last_order_date\"].max()  # 2021-05-30\n",
    "    analysis_date = dt.datetime(2021, 6, 1)\n",
    "    cltv_df = pd.DataFrame()\n",
    "    cltv_df[\"customer_id\"] = dataframe[\"master_id\"]\n",
    "    cltv_df[\"recency_cltv_weekly\"] = ((dataframe[\"last_order_date\"] - dataframe[\"first_order_date\"]).astype('timedelta64[D]')) / 7\n",
    "    cltv_df[\"T_weekly\"] = ((analysis_date - dataframe[\"first_order_date\"]).astype('timedelta64[D]')) / 7\n",
    "    cltv_df[\"frequency\"] = dataframe[\"order_num_total\"]\n",
    "    cltv_df[\"monetary_cltv_avg\"] = dataframe[\"customer_value_total\"] / dataframe[\"order_num_total\"]\n",
    "    cltv_df = cltv_df[(cltv_df['frequency'] > 1)]\n",
    "\n",
    "    # Establish the BG/NBD model\n",
    "    bgf = BetaGeoFitter(penalizer_coef=0.001)\n",
    "    bgf.fit(cltv_df['frequency'],\n",
    "            cltv_df['recency_cltv_weekly'],\n",
    "            cltv_df['T_weekly'])\n",
    "    cltv_df[\"exp_sales_3_month\"] = bgf.predict(4 * 3,\n",
    "                                               cltv_df['frequency'],\n",
    "                                               cltv_df['recency_cltv_weekly'],\n",
    "                                               cltv_df['T_weekly'])\n",
    "    cltv_df[\"exp_sales_6_month\"] = bgf.predict(4 * 6,\n",
    "                                               cltv_df['frequency'],\n",
    "                                               cltv_df['recency_cltv_weekly'],\n",
    "                                               cltv_df['T_weekly'])\n",
    "\n",
    "    # Establish the Gamma Gamma model\n",
    "    ggf = GammaGammaFitter(penalizer_coef=0.01)\n",
    "    ggf.fit(cltv_df['frequency'], cltv_df['monetary_cltv_avg'])\n",
    "    cltv_df[\"exp_average_value\"] = ggf.conditional_expected_average_profit(cltv_df['frequency'],\n",
    "                                                                           cltv_df['monetary_cltv_avg'])\n",
    "\n",
    "    # Prediction of Cltv \n",
    "    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",
    "    cltv_df[\"cltv\"] = cltv\n",
    "\n",
    "    # CLTV segmentation\n",
    "    cltv_df[\"cltv_segment\"] = pd.qcut(cltv_df[\"cltv\"], 4, labels=[\"D\", \"C\", \"B\", \"A\"])\n",
    "\n",
    "    return cltv_df\n",
    "\n",
    "cltv_df = create_cltv_df(df)\n",
    "\n",
    "\n",
    "cltv_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52a76963",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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