n02_purchase_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Purchase Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory analysis of the purchase dataset in order to glean insights into the customers. The idea is to generate a set of assumptions based on what we observe in the data so we can later tests these hypotheses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "segment_colours = ['lightskyblue', 'red', 'gold', 'green']\n",
    "segment_colour_palette = sns.color_palette(segment_colours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_avg_and_std_per_segment(df_data: pd.DataFrame, kind='N_Visits'):\n",
    "    mean_data = df_data.groupby(['SegmentLabel']).mean()[kind]\n",
    "    std_data = df_data.groupby(['SegmentLabel']).std()[kind]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    \n",
    "    sns.barplot(x=mean_data.index, y=mean_data, yerr=std_data, palette=segment_colour_palette, ax=ax)\n",
    "    ax.set_ylabel(f'Average {kind}')\n",
    "    ax.set_xlabel('Segment')\n",
    "    ax.set_title(f'Average {kind} by Segment');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purchase dataset consists of information about the purchases of chocolate candy bars of 500 individuals from a given area when entering a physical FMCG store in a period of 2 years. \n",
    "\n",
    "- Data was collected through the loyalty cards customers use at checkout. \n",
    "- Data was preprocessed and there are no missing values. \n",
    "- The volume of the dataset has been restricted and anonymised to protect the privacy of the customers.\n",
    "- Each row represents a transaction\n",
    "- The ID column correspond to the customer ID. Notice that the customers in this dataset are different from the other dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Variable          | Data type   | Range         | Description                                                                                                        |\n",
    "|-------------------|-------------|---------------|--------------------------------------------------------------------------------------------------------------------|\n",
    "| ID                | numerical   | Integer       | Shows a unique   identifier of a customer.                                                                      |\n",
    "| Day               | numerical   | Integer       | Day when the customer has   visited the store                                                                      |\n",
    "| Incidence         | categorical | {0,1}         | Indicator whether the  customer has purchased an item from the category of interest                              |\n",
    "| Brand             | categorical | {0,1,2,3,4,5} | Shows which brand the   customer has purchased. 0=No brand was purchased                                           |\n",
    "| Quantity          | numerical   | integer       | Number of items bought by   the customer from the product category of interest                                     |\n",
    "| Last_Inc_Brand    | categorical | {0,1,2,3,4,5} | Shows which brand the   customer has purchased on their previous store visit. 0=No brand was   purchased           |\n",
    "| Last_Inc_Quantity | numerical   | integer       | Number of items bought by   the customer from the product category of interest during their previous   store visit |\n",
    "| Price_1           | numerical   | real          | Price of an item from   Brand 1 on a particular day                                                                |\n",
    "| Price_2           | numerical   | real          | Price of an item from   Brand 2 on a particular day                                                                |\n",
    "| Price_3           | numerical   | real          | Price of an item from   Brand 3 on a particular day                                                                |\n",
    "| Price_4           | numerical   | real          | Price of an item from   Brand 4 on a particular day                                                                |\n",
    "| Price_5           | numerical   | real          | Price of an item from   Brand 5 on a particular day                                                                |\n",
    "| Promotion_1       | categorical | {0,1}         | Indicator whether Brand 1   was on promotion or not on a particular day                                            |\n",
    "| Promotion_2       | categorical | {0,1}         | Indicator of whether Brand 2 was on promotion   or not on a particular day                                         |\n",
    "| Promotion_3       | categorical | {0,1}         | Indicator of whether Brand 3 was on promotion   or not on a particular day                                         |\n",
    "| Promotion_4       | categorical | {0,1}         | Indicator of whether Brand 4 was on promotion   or not on a particular day                                         |\n",
    "| Promotion_5       | categorical | {0,1}         | Indicator of whether Brand 5 was on promotion   or not on a particular day                                         |\n",
    "| Sex             | categorical | {0,1}     | Biological sex (gender)   of a customer. In this dataset there are only 2 different options. 0 = Male,   1=Female                                             |   |\n",
    "| Marital status  | categorical | {0,1}     | Marital status of a   customer. 0=Single, 1=non-single (divorced / separated / married / widowed)                                                             |   |\n",
    "| Age             | numerical   | Integer   | The age of the customer   in years, calculated as current year minus the year of birth of the customer   at the time of creation of the dataset               |   |\n",
    "| Education       | categorical | {0,1,2,3} | Level of education of the   customer. 0=other/unknown, 1=high school, 2=university, 3=graduate   school                                                       |   |\n",
    "| Income          | numerical   | Real      | Self-reported annual income in US dollars of the customer.                                                                                                    |   |\n",
    "| Occupation      | categorical | {0,1,2}   | Category of occupation of   the customer. 0=unemployed/unskilled, 1=skilled employee/official,   2=management/self-employed/highly qualified employee/officer |   |\n",
    "| Settlement size | categorical | {0,1,2}   | The size of the city that the customer lives in. 0=small,   1=mid-sized, 2=big                                                                                |   |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purchases = pd.read_csv('data/fmcg-store-purchases.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>ID</th>\n",
       "      <th>Day</th>\n",
       "      <th>Incidence</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Last_Inc_Brand</th>\n",
       "      <th>Last_Inc_Quantity</th>\n",
       "      <th>Price_1</th>\n",
       "      <th>Price_2</th>\n",
       "      <th>Price_3</th>\n",
       "      <th>...</th>\n",
       "      <th>Promotion_3</th>\n",
       "      <th>Promotion_4</th>\n",
       "      <th>Promotion_5</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Marital status</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Settlement size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200000001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200000001</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200000001</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200000001</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.98</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200000001</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Day  Incidence  Brand  Quantity  Last_Inc_Brand  \\\n",
       "0  200000001    1          0      0         0               0   \n",
       "1  200000001   11          0      0         0               0   \n",
       "2  200000001   12          0      0         0               0   \n",
       "3  200000001   16          0      0         0               0   \n",
       "4  200000001   18          0      0         0               0   \n",
       "\n",
       "   Last_Inc_Quantity  Price_1  Price_2  Price_3  ...  Promotion_3  \\\n",
       "0                  0     1.59     1.87     2.01  ...            0   \n",
       "1                  0     1.51     1.89     1.99  ...            0   \n",
       "2                  0     1.51     1.89     1.99  ...            0   \n",
       "3                  0     1.52     1.89     1.98  ...            0   \n",
       "4                  0     1.52     1.89     1.99  ...            0   \n",
       "\n",
       "   Promotion_4  Promotion_5  Sex  Marital status  Age  Education  Income  \\\n",
       "0            0            0    0               0   47          1  110866   \n",
       "1            0            0    0               0   47          1  110866   \n",
       "2            0            0    0               0   47          1  110866   \n",
       "3            0            0    0               0   47          1  110866   \n",
       "4            0            0    0               0   47          1  110866   \n",
       "\n",
       "   Occupation  Settlement size  \n",
       "0           1                0  \n",
       "1           1                0  \n",
       "2           1                0  \n",
       "3           1                0  \n",
       "4           1                0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchases.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not have an equal number of records per customer or an equal number of records per day. So descriptive statistics would neither be appropriate nor useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of rows in the purchase data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58693, 24)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchases.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To segment the new customers in the purchases dataset, we apply the saved models from the segmentation notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Standardize the customer columns in the purchase dataset using the saved scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = pickle.load(open('models/segmentation-scaler.pickle', 'rb'))\n",
    "X_feature_names = pickle.load(open('models/feature-names.pickle', 'rb'))\n",
    "X = scaler.transform(df_purchases[X_feature_names])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: Transform the customers from 7 dimensions to 3 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = pickle.load(open('models/segmentation-pca.pickle', 'rb'))\n",
    "X_3d = pca.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: Apply k-Means model to segment the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_pca = pickle.load(open('models/segmentation-kmeans-pca.pickle', 'rb'))\n",
    "segments = kmeans_pca.predict(X_3d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: Consolidate the segments into the original DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purchases['Segment'] = segments\n",
    "\n",
    "# Apply the segment labels\n",
    "segment_labels = pickle.load(open('models/segment-labels.pickle', 'rb'))\n",
    "df_purchases['SegmentLabel'] = df_purchases['Segment'].map(segment_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we want to analyse the purchasing behaviour for customers. \n",
    "For that we need to create a new DataFrame where we gather different sets of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of visits per customer\n",
    "df_customer_n_visits = df_purchases[['ID', 'Incidence']].groupby(['ID']).count()\n",
    "df_customer_n_visits.rename(columns={'Incidence': 'N_Visits'}, inplace=True)\n",
    "# df_customer_n_visits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of purchases per customer\n",
    "df_customer_n_purchases = df_purchases[['ID', 'Incidence']].groupby(['ID']).sum()\n",
    "df_customer_n_purchases.rename(columns={'Incidence': 'N_Purchases'}, inplace=True)\n",
    "# df_customer_n_purchases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new DataFrame which hold all the statistics\n",
    "df_customer_stats = df_customer_n_purchases.join(df_customer_n_visits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the purchase frequency per customer.\n",
    "df_customer_stats['Purchase_Freq'] = df_customer_stats['N_Purchases'] / df_customer_stats['N_Visits']\n",
    "# df_customer_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>N_Purchases</th>\n",
       "      <th>N_Visits</th>\n",
       "      <th>Purchase_Freq</th>\n",
       "      <th>SegmentLabel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200000001</th>\n",
       "      <td>9</td>\n",
       "      <td>101</td>\n",
       "      <td>0.089109</td>\n",
       "      <td>fewer-opportunities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000002</th>\n",
       "      <td>11</td>\n",
       "      <td>87</td>\n",
       "      <td>0.126437</td>\n",
       "      <td>well-off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000003</th>\n",
       "      <td>10</td>\n",
       "      <td>97</td>\n",
       "      <td>0.103093</td>\n",
       "      <td>fewer-opportunities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000004</th>\n",
       "      <td>11</td>\n",
       "      <td>85</td>\n",
       "      <td>0.129412</td>\n",
       "      <td>fewer-opportunities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000005</th>\n",
       "      <td>13</td>\n",
       "      <td>111</td>\n",
       "      <td>0.117117</td>\n",
       "      <td>standard</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_Purchases  N_Visits  Purchase_Freq         SegmentLabel\n",
       "ID                                                                  \n",
       "200000001            9       101       0.089109  fewer-opportunities\n",
       "200000002           11        87       0.126437             well-off\n",
       "200000003           10        97       0.103093  fewer-opportunities\n",
       "200000004           11        85       0.129412  fewer-opportunities\n",
       "200000005           13       111       0.117117             standard"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since each customer has a unique SegmentLabel, we can pick the first one.\n",
    "df_customer_segment = df_purchases[['ID', 'SegmentLabel']].groupby(['ID']).first()\n",
    "\n",
    "df_customer_stats = df_customer_stats.join(df_customer_segment)\n",
    "df_customer_stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visits per Segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SegmentLabel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>career-focused</th>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fewer-opportunities</th>\n",
       "      <td>0.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard</th>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>well-off</th>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Proportion\n",
       "SegmentLabel                   \n",
       "career-focused            0.208\n",
       "fewer-opportunities       0.376\n",
       "standard                  0.220\n",
       "well-off                  0.196"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_customers = df_customer_stats.shape[0]\n",
    "\n",
    "df_segment_proportions = df_customer_stats[['N_Visits', 'SegmentLabel']].groupby(['SegmentLabel']).count() / N_customers\n",
    "df_segment_proportions = df_segment_proportions.rename(columns={'N_Visits': 'Proportion'})\n",
    "df_segment_proportions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "ax.pie(\n",
    "    x=df_segment_proportions['Proportion'], \n",
    "    labels=df_segment_proportions.index.values,\n",
    "    autopct='%1.1f%%',\n",
    "    colors=segment_colours\n",
    ")\n",
    "ax.set_title('Proportions of Store Visits by Segment');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pie charts gives us an idea of how store visitors are distributed across segments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Purchase Occasion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to find a quantitative measure to describe the purchase behaviours by segments.\n",
    "In order words, we would like to answer two questions:\n",
    "\n",
    "1. How often do people from different segment visit the store?\n",
    "2. How often do they buy chocolate candy relative to the store visits?\n",
    "\n",
    "To answer these questions, we can calculate the mean and standard deviations by segments. \n",
    "The mean of a segment indicates the average behaviour of that segment\n",
    "whereas the standard deviation show how homogeneous the segment is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_avg_and_std_per_segment(df_customer_stats, 'N_Visits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spread amount customers in the standard segment is high implying that these customers are least alike when it comes to how often they visit the store. The other segments are very similar in terms of their store visits. This makes more comparable with respect to our future analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Purchase Incidence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second question would like to know is how often do people from different segment buy chocolate candy relative to the store visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_avg_and_std_per_segment(df_customer_stats, 'N_Purchases')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standard segment buys on average more chocolate candy more often. \n",
    "Again, the standard deviation is the highest. \n",
    "One explanation could be that a part of the segment buys the product more frequently than other parts.\n",
    "Although consumers in this segment may have similar income, they might differ in what they spend their money on.\n",
    "\n",
    "The most homogeneous segment appears to be consumers in the Fewer-Opportunities segment. This is signified by the segment having the lowest standard deviation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Purchase Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_avg_and_std_per_segment(df_customer_stats, 'Purchase_Freq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brand Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purchase_incidents = df_purchases[df_purchases['Incidence'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Brand_1</th>\n",
       "      <th>Brand_2</th>\n",
       "      <th>Brand_3</th>\n",
       "      <th>Brand_4</th>\n",
       "      <th>Brand_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Brand_1  Brand_2  Brand_3  Brand_4  Brand_5\n",
       "6         0        1        0        0        0\n",
       "11        0        0        0        0        1\n",
       "19        1        0        0        0        0\n",
       "24        0        0        0        1        0\n",
       "29        0        1        0        0        0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_brand_features = pd.get_dummies(df_purchase_incidents['Brand'], prefix='Brand', prefix_sep='_')\n",
    "df_brand_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Brand_1</th>\n",
       "      <th>Brand_2</th>\n",
       "      <th>Brand_3</th>\n",
       "      <th>Brand_4</th>\n",
       "      <th>Brand_5</th>\n",
       "      <th>Segment</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>200000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200000001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Brand_1  Brand_2  Brand_3  Brand_4  Brand_5  Segment         ID\n",
       "6         0        1        0        0        0        1  200000001\n",
       "11        0        0        0        0        1        1  200000001\n",
       "19        1        0        0        0        0        1  200000001\n",
       "24        0        0        0        1        0        1  200000001\n",
       "29        0        1        0        0        0        1  200000001"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copy segment and ID from the purchases DataFrame\n",
    "df_brand_features['Segment'] = df_purchase_incidents['Segment']\n",
    "df_brand_features['ID'] = df_purchase_incidents['ID']\n",
    "df_brand_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Brand_1</th>\n",
       "      <th>Brand_2</th>\n",
       "      <th>Brand_3</th>\n",
       "      <th>Brand_4</th>\n",
       "      <th>Brand_5</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200000001</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000002</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000003</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000004</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000005</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Brand_1   Brand_2  Brand_3   Brand_4   Brand_5  Segment\n",
       "ID                                                                 \n",
       "200000001  0.111111  0.444444      0.0  0.222222  0.222222        1\n",
       "200000002  0.000000  0.090909      0.0  0.909091  0.000000        0\n",
       "200000003  0.000000  0.800000      0.0  0.000000  0.200000        1\n",
       "200000004  0.000000  0.090909      0.0  0.000000  0.909091        1\n",
       "200000005  0.000000  0.000000      0.0  0.000000  1.000000        3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the average brand choice per customer\n",
    "df_avg_brand_choice_per_customer = df_brand_features.groupby(['ID']).mean()\n",
    "df_avg_brand_choice_per_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Brand_1</th>\n",
       "      <th>Brand_2</th>\n",
       "      <th>Brand_3</th>\n",
       "      <th>Brand_4</th>\n",
       "      <th>Brand_5</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>well-off</th>\n",
       "      <td>0.041928</td>\n",
       "      <td>0.117422</td>\n",
       "      <td>0.030610</td>\n",
       "      <td>0.613623</td>\n",
       "      <td>0.196417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fewer-opportunities</th>\n",
       "      <td>0.138299</td>\n",
       "      <td>0.679494</td>\n",
       "      <td>0.021034</td>\n",
       "      <td>0.061875</td>\n",
       "      <td>0.099298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>career-focused</th>\n",
       "      <td>0.248771</td>\n",
       "      <td>0.402305</td>\n",
       "      <td>0.154976</td>\n",
       "      <td>0.058902</td>\n",
       "      <td>0.135045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard</th>\n",
       "      <td>0.072871</td>\n",
       "      <td>0.146808</td>\n",
       "      <td>0.039976</td>\n",
       "      <td>0.112395</td>\n",
       "      <td>0.627951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Brand_1   Brand_2   Brand_3   Brand_4   Brand_5\n",
       "Segment                                                              \n",
       "well-off             0.041928  0.117422  0.030610  0.613623  0.196417\n",
       "fewer-opportunities  0.138299  0.679494  0.021034  0.061875  0.099298\n",
       "career-focused       0.248771  0.402305  0.154976  0.058902  0.135045\n",
       "standard             0.072871  0.146808  0.039976  0.112395  0.627951"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the average brand choice per segment\n",
    "df_avg_brand_choice_per_segment = df_avg_brand_choice_per_customer.groupby(['Segment']).mean()\n",
    "df_avg_brand_choice_per_segment.rename(index=segment_labels, inplace=True)\n",
    "df_avg_brand_choice_per_segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.heatmap(df_avg_brand_choice_per_segment, vmin=0, vmax=1, cmap='PuBu', annot=True)\n",
    "ax.set_title('Average Brand Choice by Segment');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose that the 5 brands are arranged in ascending order of price where Brand 1 is the cheapest one and Brand 2 is the most expensive. We can take this into account when conducting the brand analysis.\n",
    "\n",
    "- Well-Off consumers tend to prefer the Brand 4 but not the most expensive Brand 5.\n",
    "- Consumers in Fewer-Opportunities segment have a strong preference for brand 2. Almost 70% of the consumers use this brand of chocolate. Since the Brand 2 is not the cheapest one, the price is not what matters the most for this segment.\n",
    "- Career-Focused is the most heterogeneous segment. Have a preference for Brand 2 and a weaker preference for Brands 1 and 3. They do not like to buy Brand 4. Their preference is scattered all around. One idea is try to influence them to try out different brands.\n",
    "- Standard consumers prefer the most expensive brand. It seems that this cluster enjoys this brand. With no additional information, we can speculate that this segment is looking for a luxury status. This alone may be an opportunity to rise of Brand 5.\n",
    "\n",
    "The mostly bought brands are Brand 4 followed by Brand 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does these brand preferences affect our bottom line?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we calculate revenue for each brand. Then we calculate the total revenue for each of the four segments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function which accept calculates the revenue for each brand\n",
    "def calc_revenue(row):\n",
    "    brand = row['Brand']\n",
    "    if brand == 0:\n",
    "        # When brand=0, then no chocolate candy was bought\n",
    "        return 0\n",
    "    return row[f'Price_{brand}'] * row['Quantity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the function on the DataFrame for each row\n",
    "df_purchases['Revenue'] = df_purchases.apply(calc_revenue, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_brand_revenue_func(brand):\n",
    "    def calc_revenue(row):\n",
    "        if row['Brand'] == brand:\n",
    "            return row[f'Price_{brand}'] * row['Quantity']\n",
    "        return 0\n",
    "    return calc_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purchases['Revenue_Brand_1'] = df_purchases.apply(create_brand_revenue_func(1), axis=1)\n",
    "df_purchases['Revenue_Brand_2'] = df_purchases.apply(create_brand_revenue_func(2), axis=1)\n",
    "df_purchases['Revenue_Brand_3'] = df_purchases.apply(create_brand_revenue_func(3), axis=1)\n",
    "df_purchases['Revenue_Brand_4'] = df_purchases.apply(create_brand_revenue_func(4), axis=1)\n",
    "df_purchases['Revenue_Brand_5'] = df_purchases.apply(create_brand_revenue_func(5), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue_columns = ['SegmentLabel', 'Revenue_Brand_1', 'Revenue_Brand_2', 'Revenue_Brand_3',\n",
    "                   'Revenue_Brand_4', 'Revenue_Brand_5', 'Revenue']\n",
    "df_revenue =  df_purchases[revenue_columns].groupby(['SegmentLabel']).sum()\n",
    "\n",
    "# Rename column\n",
    "df_revenue.rename(columns={'Revenue': 'Total_Revenue'}, inplace=True)\n",
    "\n",
    "# Add segment's size in relation to other segments\n",
    "df_revenue['Segment_Proportion'] = df_segment_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Revenue_Brand_1</th>\n",
       "      <th>Revenue_Brand_2</th>\n",
       "      <th>Revenue_Brand_3</th>\n",
       "      <th>Revenue_Brand_4</th>\n",
       "      <th>Revenue_Brand_5</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Segment_Proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SegmentLabel</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>career-focused</th>\n",
       "      <td>2611.19</td>\n",
       "      <td>4855.36</td>\n",
       "      <td>3909.17</td>\n",
       "      <td>861.38</td>\n",
       "      <td>2439.75</td>\n",
       "      <td>14676.85</td>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fewer-opportunities</th>\n",
       "      <td>2258.90</td>\n",
       "      <td>13868.30</td>\n",
       "      <td>716.25</td>\n",
       "      <td>1629.31</td>\n",
       "      <td>2230.50</td>\n",
       "      <td>20703.26</td>\n",
       "      <td>0.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard</th>\n",
       "      <td>736.09</td>\n",
       "      <td>1746.42</td>\n",
       "      <td>664.75</td>\n",
       "      <td>2363.84</td>\n",
       "      <td>19441.06</td>\n",
       "      <td>24952.16</td>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>well-off</th>\n",
       "      <td>699.47</td>\n",
       "      <td>1298.23</td>\n",
       "      <td>731.35</td>\n",
       "      <td>14185.57</td>\n",
       "      <td>5509.69</td>\n",
       "      <td>22424.31</td>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Revenue_Brand_1  Revenue_Brand_2  Revenue_Brand_3  \\\n",
       "SegmentLabel                                                             \n",
       "career-focused               2611.19          4855.36          3909.17   \n",
       "fewer-opportunities          2258.90         13868.30           716.25   \n",
       "standard                      736.09          1746.42           664.75   \n",
       "well-off                      699.47          1298.23           731.35   \n",
       "\n",
       "                     Revenue_Brand_4  Revenue_Brand_5  Total_Revenue  \\\n",
       "SegmentLabel                                                           \n",
       "career-focused                861.38          2439.75       14676.85   \n",
       "fewer-opportunities          1629.31          2230.50       20703.26   \n",
       "standard                     2363.84         19441.06       24952.16   \n",
       "well-off                    14185.57          5509.69       22424.31   \n",
       "\n",
       "                     Segment_Proportion  \n",
       "SegmentLabel                             \n",
       "career-focused                    0.208  \n",
       "fewer-opportunities               0.376  \n",
       "standard                          0.220  \n",
       "well-off                          0.196  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make some graph to help us communicate the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new DataFrame\n",
    "df_revenue_plot_data = df_revenue[[f'Revenue_Brand_{i}' for i in range(1, 6)]].copy()\n",
    "df_revenue_plot_data.rename(columns={ f'Revenue_Brand_{i}': f'Brand {i}' for i in range(1, 6) }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns_palette = sns.cubehelix_palette(8, start=.5, rot=-.75, dark=0.3)\n",
    "sns_palette_as_cmap = ListedColormap(sns_palette)\n",
    "# sns.palplot(sns_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(14, 6))\n",
    "df_revenue_plot_data.plot(kind='bar', stacked=True, cmap=sns_palette_as_cmap, ax=ax[0])\n",
    "ax[0].set_title('Revenue by Segment')\n",
    "ax[0].set_xlabel('Segment')\n",
    "ax[0].set_ylabel('Revenue')\n",
    "ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45, horizontalalignment='right');\n",
    "\n",
    "ax[1].pie(\n",
    "    x=df_segment_proportions['Proportion'], \n",
    "    labels=df_segment_proportions.index.values,\n",
    "    autopct='%1.1f%%',\n",
    "    colors=['lightskyblue', 'red', 'gold', 'green']\n",
    ")\n",
    "ax[1].set_title('Proportions of Store Visits by Segment');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>SegmentLabel</th>\n",
       "      <th>career-focused</th>\n",
       "      <th>fewer-opportunities</th>\n",
       "      <th>standard</th>\n",
       "      <th>well-off</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_Revenue</th>\n",
       "      <td>14676.85</td>\n",
       "      <td>20703.26</td>\n",
       "      <td>24952.16</td>\n",
       "      <td>22424.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SegmentLabel   career-focused  fewer-opportunities  standard  well-off\n",
       "Total_Revenue        14676.85             20703.26  24952.16  22424.31"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_revenue[['Total_Revenue']].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standard segment brings the most revenue followed by the well-off and fewer-opportunities. \n",
    "\n",
    "Although the standard group is the second largest segment (22 %), it brings the highest revenue (24,952). This is interesting finding. We can also see how this segment favours the Brand 5. It seems that they are the most prominent segment for the store with regard to the chocolate candy bars. \n",
    "\n",
    "Career-focused group is almost as large as the standard segment (21%) but it brings a bit more than half of the revenue (14,676). The career-focused account for the smallest revenue.\n",
    "\n",
    "The well-off and fewer-opportunities spend the same amount of money on chocolate candy bars. However, fewer-opportunities is almost twice as large as the well-off segment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the revenue by brand:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "df_revenue_plot_data.T.plot(kind='bar', stacked=True, color=segment_colours, ax=ax)\n",
    "ax.set_title('Revenue by Brand')\n",
    "ax.set_ylabel('Revenue')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=0, horizontalalignment='center');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand 1     6305.65\n",
       "Brand 2    21768.31\n",
       "Brand 3     6021.52\n",
       "Brand 4    19040.10\n",
       "Brand 5    29621.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_revenue_plot_data.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Brand 1: Generates the second-lowest revenue. Preferred by Career-focused and Fewer-Opportunities segments.\n",
    "\n",
    "- Brand 2: Second-largest revenue. Mostly generated by Fewer-Opportunities segment followed by Career-Focused segment.\n",
    "\n",
    "- Brand 3: The lowest revenue figures. Its highest contributor is Career-focused segment. This segment can be influenced more by the Brand 3. Perhaps, if Brand 3 reduces the price, then the Career-Focused segment could pivot towards Brand 3 instead of choosing the cheaper Brand 1 and Brand 2. Although it is not certain, it might be worth testing.\n",
    "\n",
    "- Brand 4: Revenue is almost exclusively generated by the Well-Off segment. The part of this segment who did not choose Brand 4, bought an even more expensive Brand 5. The Well-Off segment seem to be loyal and not effected by price. Therefore, it might be worthwhile to cautiously increase its price. The hypothesis is that Brand 4 are likely to retain most of its customers while increasing the revenue per sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_purchases.to_csv('data/fmcg-store-purchases-augmented.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}