Part2-1_Purchase_Analytics_Descriptive_Analysis_by_Segment.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# Customer Analytics in FMGC Industry (Part 2)\n",
    "#### by Sooyeon Won\n",
    "\n",
    "### Keywords \n",
    "\n",
    "- Marketing Mix \n",
    "- STP framework\n",
    "- Purchase Analytics \n",
    "- Descriptive Analysis by Segments\n",
    "- Data Visualisations \n",
    "\n",
    "### Contents \n",
    "\n",
    "<ul>\n",
    "<li><a href=\"#Introduction\">1. Introduction</a></li> \n",
    "<li><a href=\"#Preparation\">2. Data Preparation</a></li>\n",
    "<li><a href=\"#Exploration\">3. Data Exploration</a></li>\n",
    "<li><a href=\"#Analysis\">4. Data Analysis</a></li>\n",
    "&emsp;4.1. Customer Analytics<br>\n",
    "&emsp;4.2. Purchase Analytics <br>\n",
    "&emsp;&emsp;&emsp;i. Descriptive Analyses by Segment <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-1. The Proportion of each Segment <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-2. Purchase Occasions and Purchase Incidences <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-3. Brand Chocie <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; i-4. Revenue Comparison between segments <br>\n",
    "&emsp;&emsp;&emsp;ii. Predictive Analyses <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; ii-1. Modeling Purchase Incidence<br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; ii-2. Modeling Brand Choice <br>\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp; ii-3. Modeling Purchase Quantity <br>\n",
    "<li><a href=\"#Conclusion\">5. Conclusion</a></li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Analysis\n",
    "### 4.2. Purchase Analytics\n",
    "- Data Preparation from Part 1\n",
    "- i. Descriptive Analysis by Segments <br>\n",
    "&emsp;&emsp;&emsp; i-1. The Proportion of each Segment <br>\n",
    "&emsp;&emsp;&emsp; i-2. Purchase Occasions and Purchase Incidences: How often each segment group go shopping? <br>\n",
    "&emsp;&emsp;&emsp; i-3. Brand Chocie: Which brand of products the customers purchase? <br>\n",
    "&emsp;&emsp;&emsp; i-4. Revenue Comparison between segments: How much money they spend? <br>\n",
    "\n",
    "### Data Preparation from Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the relevant libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "plt.rc(\"font\", size=14)\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.cluster import KMeans \n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "purchase_df = pd.read_csv('purchase data.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th>Marital status</th>\n",
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       "      <th>Income</th>\n",
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       "           Day  Incidence  Brand  Quantity  Last_Inc_Brand  Last_Inc_Quantity  \\\n",
       "ID                                                                              \n",
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       "200000001   16          0      0         0               0                  0   \n",
       "200000001   18          0      0         0               0                  0   \n",
       "\n",
       "           Price_1  Price_2  Price_3  Price_4  ...  Promotion_3  Promotion_4  \\\n",
       "ID                                             ...                             \n",
       "200000001     1.59     1.87     2.01     2.09  ...            0            0   \n",
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       "200000001     1.52     1.89     1.99     2.09  ...            0            0   \n",
       "\n",
       "           Promotion_5  Sex  Marital status  Age  Education  Income  \\\n",
       "ID                                                                    \n",
       "200000001            0    0               0   47          1  110866   \n",
       "200000001            0    0               0   47          1  110866   \n",
       "200000001            0    0               0   47          1  110866   \n",
       "200000001            0    0               0   47          1  110866   \n",
       "200000001            0    0               0   47          1  110866   \n",
       "\n",
       "           Occupation  Settlement size  \n",
       "ID                                      \n",
       "200000001           1                0  \n",
       "200000001           1                0  \n",
       "200000001           1                0  \n",
       "200000001           1                0  \n",
       "200000001           1                0  \n",
       "\n",
       "[5 rows x 23 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_df.head() # Glance at the first dataset(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 58693 entries, 200000001 to 200000500\n",
      "Data columns (total 23 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Day                58693 non-null  int64  \n",
      " 1   Incidence          58693 non-null  int64  \n",
      " 2   Brand              58693 non-null  int64  \n",
      " 3   Quantity           58693 non-null  int64  \n",
      " 4   Last_Inc_Brand     58693 non-null  int64  \n",
      " 5   Last_Inc_Quantity  58693 non-null  int64  \n",
      " 6   Price_1            58693 non-null  float64\n",
      " 7   Price_2            58693 non-null  float64\n",
      " 8   Price_3            58693 non-null  float64\n",
      " 9   Price_4            58693 non-null  float64\n",
      " 10  Price_5            58693 non-null  float64\n",
      " 11  Promotion_1        58693 non-null  int64  \n",
      " 12  Promotion_2        58693 non-null  int64  \n",
      " 13  Promotion_3        58693 non-null  int64  \n",
      " 14  Promotion_4        58693 non-null  int64  \n",
      " 15  Promotion_5        58693 non-null  int64  \n",
      " 16  Sex                58693 non-null  int64  \n",
      " 17  Marital status     58693 non-null  int64  \n",
      " 18  Age                58693 non-null  int64  \n",
      " 19  Education          58693 non-null  int64  \n",
      " 20  Income             58693 non-null  int64  \n",
      " 21  Occupation         58693 non-null  int64  \n",
      " 22  Settlement size    58693 non-null  int64  \n",
      "dtypes: float64(5), int64(18)\n",
      "memory usage: 10.7 MB\n"
     ]
    }
   ],
   "source": [
    "purchase_df.info() # Glance at the first dataset(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day                  0\n",
       "Incidence            0\n",
       "Brand                0\n",
       "Quantity             0\n",
       "Last_Inc_Brand       0\n",
       "Last_Inc_Quantity    0\n",
       "Price_1              0\n",
       "Price_2              0\n",
       "Price_3              0\n",
       "Price_4              0\n",
       "Price_5              0\n",
       "Promotion_1          0\n",
       "Promotion_2          0\n",
       "Promotion_3          0\n",
       "Promotion_4          0\n",
       "Promotion_5          0\n",
       "Sex                  0\n",
       "Marital status       0\n",
       "Age                  0\n",
       "Education            0\n",
       "Income               0\n",
       "Occupation           0\n",
       "Settlement size      0\n",
       "dtype: int64"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "purchase_df.isnull().sum() # Check missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(purchase_df.duplicated()) # Check duplicated values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There are 58,693 purchase records with 23 features in this dataset. The dataset does not contain missing nor duplicated values. The explanation of each feature is as follows: \n",
    ">- ID (numerical): a unique identifier of a customer.\t\n",
    ">- Day\t(numerical) when the customer has visited the store.  \t\t\t\t\n",
    ">- Incidence\t(categorical) Purchase Incidence. Did the customer purchase a product?\n",
    "\t\t0\tThe customer has not purchased an item from the category of interest \t\n",
    "\t\t1\tThe customer has purchased an item from the category of interest \t\n",
    ">- Brand\t(categorical) Shows which brand the customer has purchased.\t\n",
    "                0\tNo brand was purchased\t\n",
    "\t\t1,2,3,4,5\tChocolate Product Brand ID\t\t\t\t\t\n",
    ">- Quantity\t(numerical)\tNumber of items bought by the customer from the product category of interest\t\n",
    ">- Last_Inc_Brand\t(categorical)\tShows which brand the customer has purchased on their previous store visit\t\n",
    "                0\tNo brand was purchased\t\n",
    "\t\t1,2,3,4,5\tChocolate Product Brand ID\t\n",
    ">- Last_Inc_Quantity (numerical)\tNumber of items bought by the customer from the product category of interest during their previous store visit.\t\t\t\t\t\n",
    ">- Price_1,2,3,4,5\t(numerical)\tPrice of an item from Brand 1,2,3,4,5 on a particular day\t\n",
    ">- Promotion_1,2,3,4,5 (categorical) Indicator whether Brand 1,2,3,4,5 was on promotion or not on a particular day\t\n",
    "> Rest features (Sex, Marital status, Age, Education, Income, Occupation, Settlement size) are consistent with the features in demographic dataset. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Segmentation Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = pickle.load(open('scaler.pickle', 'rb'))\n",
    "pca = pickle.load(open('pca.pickle', 'rb'))\n",
    "kmeans_pca = pickle.load(open('kmeans_pca.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standardisation of  Demographic Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = purchase_df[['Sex', 'Marital status', 'Age', 'Education', 'Income', 'Occupation', 'Settlement size']]\n",
    "purchase_segm_std = scaler.transform(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA Transformation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "purchase_segm_pca = pca.transform(purchase_segm_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-means Clustering with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict clusters based on K-means Clustering with PCA\n",
    "purchase_segm_kmeans_pca= kmeans_pca.predict(purchase_segm_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checkpoint \n",
    "purchase_predictors = purchase_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>47</td>\n",
       "      <td>1</td>\n",
       "      <td>110866</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 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_4  \\\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_5  Sex  Marital status  Age  Education  Income  Occupation  \\\n",
       "0            0    0               0   47          1  110866           1   \n",
       "1            0    0               0   47          1  110866           1   \n",
       "2            0    0               0   47          1  110866           1   \n",
       "3            0    0               0   47          1  110866           1   \n",
       "4            0    0               0   47          1  110866           1   \n",
       "\n",
       "   Settlement size  Segment  \n",
       "0                0        0  \n",
       "1                0        0  \n",
       "2                0        0  \n",
       "3                0        0  \n",
       "4                0        0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_predictors['Segment'] = purchase_segm_kmeans_pca\n",
    "purchase_predictors= purchase_predictors.reset_index()\n",
    "purchase_predictors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i. Descriptive Analysis by Segments\n",
    "\n",
    "In this section, I focus on descriptive analysis of the purchase data by individuals and then by segments to gain insight into customer shopping habits. \n",
    "- i-1. The Proportion of each Segment\n",
    "- i-2. Purchase Occasions and Purchase Incidences: How often each segment group go shopping?\n",
    "- i-3. Brand Choice: Which brand of products the customers purchase?\n",
    "- i-4. Revenue Comparison between segments: How much money they spend?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i-1. Segment Proportion \n",
    "\n",
    "Based on the customer data from the company's loyalty program, I firstly compare the size of each segment. To do so, I aggregated the data by Customers then grouped by each segment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregate the Data by Customers  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>N_Visits</th>\n",
       "      <th>N_Purchases</th>\n",
       "      <th>Avg_N_Purchases</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200000001</th>\n",
       "      <td>101</td>\n",
       "      <td>9</td>\n",
       "      <td>0.089109</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000002</th>\n",
       "      <td>87</td>\n",
       "      <td>11</td>\n",
       "      <td>0.126437</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000003</th>\n",
       "      <td>97</td>\n",
       "      <td>10</td>\n",
       "      <td>0.103093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000004</th>\n",
       "      <td>85</td>\n",
       "      <td>11</td>\n",
       "      <td>0.129412</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000005</th>\n",
       "      <td>111</td>\n",
       "      <td>13</td>\n",
       "      <td>0.117117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_Visits  N_Purchases  Avg_N_Purchases  Segment\n",
       "ID                                                        \n",
       "200000001       101            9         0.089109        0\n",
       "200000002        87           11         0.126437        3\n",
       "200000003        97           10         0.103093        0\n",
       "200000004        85           11         0.129412        0\n",
       "200000005       111           13         0.117117        1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_freq = purchase_predictors[['ID','Incidence']].groupby(['ID']).count()\n",
    "purchase_freq = purchase_predictors[['ID','Incidence']].groupby(['ID']).sum()\n",
    "segm_info = purchase_predictors[['ID', 'Segment']].groupby(['ID']).mean()\n",
    "\n",
    "traffic_freq = traffic_freq.rename(columns = {'Incidence': 'N_Visits'})\n",
    "purchase_freq = purchase_freq.rename(columns = {'Incidence': 'N_Purchases'})\n",
    "\n",
    "# Average number of purchases by customer ID\n",
    "df_customer = traffic_freq.join(purchase_freq)\n",
    "df_customer['Avg_N_Purchases'] = df_customer.N_Purchases/ df_customer.N_Visits\n",
    "\n",
    "purchase_descr = df_customer.join(segm_info)\n",
    "purchase_descr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group-by each segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>Segment</th>\n",
       "      <th>Segment Proportions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fewer-Opportunities</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Career-Focused</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Standard</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Well-Off</td>\n",
       "      <td>0.194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Segment  Segment Proportions\n",
       "0  Fewer-Opportunities                0.378\n",
       "1       Career-Focused                0.222\n",
       "2             Standard                0.206\n",
       "3             Well-Off                0.194"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the proportions of each segment and set the appropriate column name. \n",
    "segm_prop = purchase_descr[['N_Visits', 'Segment']].groupby(['Segment']).count() / purchase_descr.shape[0]\n",
    "segm_prop = segm_prop.rename(columns = {'N_Visits': 'Segment Proportions'}).reset_index()\n",
    "segm_prop.Segment.replace({0: 'Fewer-Opportunities', 1: 'Career-Focused',   2: \"Standard\", 3:\"Well-Off\"}, inplace=True)\n",
    "segm_prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Proportion of Purchases by Segment')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[7,5])\n",
    "sns.barplot(data = segm_prop, x = 'Segment', y = 'Segment Proportions')\n",
    "plt.xlabel('Segment', fontsize = 12)\n",
    "plt.ylabel('Averag Purchase Incidences ', fontsize = 12)\n",
    "plt.title('Average Proportion of Purchases by Segment', fontsize = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The largest segment is \"Fewer-Opportunities\". Almost 40 percent of customers belong to this segment. The second largest segment is   \"career-focused\". 22.2 % of customers are in this segment. The proportion of the other groups \"Standard\" and \"Well-Off\" are similar to ca. 20%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i-2. Purchase Occasions and Purchase Incidences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>Segment</th>\n",
       "      <th>N_Visits</th>\n",
       "      <th>N_Purchases</th>\n",
       "      <th>Avg_N_Purchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fewer-Opportunities</td>\n",
       "      <td>113.894180</td>\n",
       "      <td>22.793651</td>\n",
       "      <td>0.201679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Career-Focused</td>\n",
       "      <td>123.216216</td>\n",
       "      <td>39.603604</td>\n",
       "      <td>0.281944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Standard</td>\n",
       "      <td>117.699029</td>\n",
       "      <td>24.902913</td>\n",
       "      <td>0.212773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Well-Off</td>\n",
       "      <td>117.185567</td>\n",
       "      <td>34.731959</td>\n",
       "      <td>0.285639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Segment    N_Visits  N_Purchases  Avg_N_Purchases\n",
       "0  Fewer-Opportunities  113.894180    22.793651         0.201679\n",
       "1       Career-Focused  123.216216    39.603604         0.281944\n",
       "2             Standard  117.699029    24.902913         0.212773\n",
       "3             Well-Off  117.185567    34.731959         0.285639"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean by the four segments to determine the average customer behaivour in each segment.\n",
    "segments_mean = purchase_descr.groupby(['Segment']).mean().reset_index()\n",
    "segments_mean.Segment.replace({0: 'Fewer-Opportunities', 1: 'Career-Focused',   2: \"Standard\", 3:\"Well-Off\"}, inplace=True)\n",
    "segments_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the standard deviation by segments. It determines how homogoneus each of the segments is.\n",
    "segments_std = purchase_descr.groupby(['Segment']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Proportion of Purchases by Segment')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average proportion of purchases.\n",
    "avg_N_Purchase_in_percentage = segments_mean.Avg_N_Purchases*100\n",
    "plt.figure(figsize=[7,5])\n",
    "sns.barplot(data = segments_mean, x = 'Segment', y = 'Avg_N_Purchases',\n",
    "              yerr = segments_std['Avg_N_Purchases'])\n",
    "plt.xlabel('Segment', fontsize = 12)\n",
    "plt.ylabel('Averag Purchase Incidences ', fontsize = 12)\n",
    "plt.title('Average Proportion of Purchases by Segment', fontsize = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i-3. Brand Choice \n",
    "\n",
    "Now we come to the question: Which brand is the customer going to choose?  I focused on the observations only where customers have bought at least one chocolate candy bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14638, 25)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select only rows where incidence is 1. This means that I filtered the times a purchase was made.\n",
    "purchase_incidence = purchase_predictors.query('Incidence ==1')\n",
    "purchase_incidence.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>0</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>0</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>0</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>0</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>0</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        0  200000001\n",
       "11        0        0        0        0        1        0  200000001\n",
       "19        1        0        0        0        0        0  200000001\n",
       "24        0        0        0        1        0        0  200000001\n",
       "29        0        1        0        0        0        0  200000001"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we make dummies for each of the five brands.\n",
    "brand_dummies = pd.get_dummies(purchase_incidence['Brand'], prefix = 'Brand', prefix_sep = '_')\n",
    "brand_dummies['Segment'], brand_dummies['ID'] = purchase_incidence['Segment'], purchase_incidence['ID']\n",
    "brand_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_ID = brand_dummies.groupby(['ID'], as_index = True).mean() # group by each customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_brand_segment = brand_ID.groupby(['Segment'], as_index = True).mean() # group by each segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.heatmap(mean_brand_segment, vmin = 0, vmax = 1, cmap = 'PuBu', annot = True)\n",
    "plt.yticks([0, 1, 2, 3], ['Fewer-Opportunities', 'Career-Focused', 'Standard', 'Well-Off'], rotation = 10, fontsize = 12)\n",
    "plt.title('Average Brand Choice by Segment', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note that each chocolate brands are listed in ascending order of price. This means that brand 1 is the cheapest brand, while brand 5 is the most expensive one. \n",
    ">- **Fewer-Opportunities** segment shows an extremely strong preference for brand_2. Almost 70 percent of the segment chooses this brand of chocolate. Brand_2 Chocolate certainly is not the cheapest one. Thus, we can guess that the price of chocolate bars is not really matters to our customers to purchase. \n",
    ">- **Career-Focused** segment: ca. 63 percent of the career focus segment buys brand_5 which is the most expensive brand. It seems that this cluster of young ambitious career focused individuals enjoys this fancy candy bar with no additional information. It can be speculated that the career focus segment is looking for luxury status and this alone may be an opportunity to raise the price of brand 5 even further.\n",
    ">- The **Well-Off** segment enjoys one of the most luxurious brands but not the most expensive one. Brand_4 is the most popularly bought brand followed by brand_5. \n",
    ">- **Standard** segment is the most heterogeneous segment. It seems that people from the standard segment prefer brand_2 and a weaker preference for brands 1 and 3. It's obvious that this segment don't like buying brand 4. Nevertheless, their preference is scattered all around. One idea could be to try to influence them to try to different brands.   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i-4. Dissecting the Revenue by Segment\n",
    "\n",
    "In this section, I explore the revenue by segment. The revenue is calculated by multiplying product price with purchased quantity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1596: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1745: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "# Filter the datapoints by each brand.\n",
    "brand_1 = purchase_predictors.query('Brand ==1')\n",
    "brand_2 = purchase_predictors.query('Brand ==2')\n",
    "brand_3 = purchase_predictors.query('Brand ==3')\n",
    "brand_4 = purchase_predictors.query('Brand ==4')\n",
    "brand_5 = purchase_predictors.query('Brand ==5')\n",
    "\n",
    "# Compute the revenue of each brand\n",
    "brand_1.loc[:, 'Revenue_Brand_1'] = brand_1['Price_1'] * brand_1['Quantity']\n",
    "brand_2.loc[:, 'Revenue_Brand_2'] = brand_2['Price_2'] * brand_2['Quantity']\n",
    "brand_3.loc[:, 'Revenue_Brand_3'] = brand_3['Price_3'] * brand_3['Quantity']\n",
    "brand_4.loc[:, 'Revenue_Brand_4'] = brand_4['Price_4'] * brand_4['Quantity']\n",
    "brand_5.loc[:, 'Revenue_Brand_5'] = brand_5['Price_5'] * brand_5['Quantity']\n",
    "\n",
    "# Aggregate the revenues \n",
    "brand_1_segm = brand_1.groupby('Segment')['Revenue_Brand_1'].sum()\n",
    "brand_2_segm = brand_2.groupby('Segment')['Revenue_Brand_2'].sum()\n",
    "brand_3_segm = brand_3.groupby('Segment')['Revenue_Brand_3'].sum()\n",
    "brand_4_segm = brand_4.groupby('Segment')['Revenue_Brand_4'].sum()\n",
    "brand_5_segm = brand_5.groupby('Segment')['Revenue_Brand_5'].sum()\n",
    "\n",
    "segments_brand_revenue = pd.DataFrame({'Segment' :  ['Fewer-Opportunities', 'Career-Focused', 'Standard', 'Well-Off'] ,\n",
    "                                      'Revenue_Brand_1': brand_1_segm.values, \n",
    "                                      'Revenue_Brand_2': brand_2_segm.values, \n",
    "                                      'Revenue_Brand_3': brand_3_segm.values, \n",
    "                                      'Revenue_Brand_4': brand_4_segm.values, \n",
    "                                      'Revenue_Brand_5': brand_5_segm.values})\n",
    "\n",
    "segments_brand_revenue.loc[:, 'Total_Rev'] = segments_brand_revenue.sum(axis =1)\n",
    "segments_brand_revenue.loc[:,'Segment Proportions'] = segm_prop['Segment Proportions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "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>Segment</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_Rev</th>\n",
       "      <th>Segment Proportions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fewer-Opportunities</td>\n",
       "      <td>2258.90</td>\n",
       "      <td>13909.78</td>\n",
       "      <td>722.06</td>\n",
       "      <td>1805.59</td>\n",
       "      <td>2214.82</td>\n",
       "      <td>20911.15</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Career-Focused</td>\n",
       "      <td>736.09</td>\n",
       "      <td>1791.78</td>\n",
       "      <td>664.75</td>\n",
       "      <td>2363.84</td>\n",
       "      <td>19456.74</td>\n",
       "      <td>25013.20</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Standard</td>\n",
       "      <td>2611.19</td>\n",
       "      <td>4768.52</td>\n",
       "      <td>3909.17</td>\n",
       "      <td>861.38</td>\n",
       "      <td>2439.75</td>\n",
       "      <td>14590.01</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Well-Off</td>\n",
       "      <td>699.47</td>\n",
       "      <td>1298.23</td>\n",
       "      <td>725.54</td>\n",
       "      <td>14009.29</td>\n",
       "      <td>5509.69</td>\n",
       "      <td>22242.22</td>\n",
       "      <td>0.194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Segment  Revenue_Brand_1  Revenue_Brand_2  Revenue_Brand_3  \\\n",
       "0  Fewer-Opportunities          2258.90         13909.78           722.06   \n",
       "1       Career-Focused           736.09          1791.78           664.75   \n",
       "2             Standard          2611.19          4768.52          3909.17   \n",
       "3             Well-Off           699.47          1298.23           725.54   \n",
       "\n",
       "   Revenue_Brand_4  Revenue_Brand_5  Total_Rev  Segment Proportions  \n",
       "0          1805.59          2214.82   20911.15                0.378  \n",
       "1          2363.84         19456.74   25013.20                0.222  \n",
       "2           861.38          2439.75   14590.01                0.206  \n",
       "3         14009.29          5509.69   22242.22                0.194  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segments_brand_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "brand_rev = segments_brand_revenue.iloc[:,1:6].sum(axis=0)\n",
    "ax = brand_rev.sort_values(ascending=False).plot(kind='barh', figsize=(8,4), color=\"b\", fontsize=13);\n",
    "ax.set_alpha(0.8)\n",
    "ax.set_title(\"Total Revenue per Brand\", fontsize=18)\n",
    "ax.set_xlabel(\"Total Revenue\", fontsize=18);\n",
    "\n",
    "# create a list to collect the plt.patches data\n",
    "totals = []\n",
    "# find the values and append to list\n",
    "for i in ax.patches:\n",
    "    totals.append(i.get_width())\n",
    "# set individual bar lables using above list\n",
    "total = sum(totals)\n",
    "# set individual bar lables using above list\n",
    "for i in ax.patches:\n",
    "    # get_width pulls left or right; get_y pushes up or down\n",
    "    ax.text(i.get_width()+.3, i.get_y()+.38, \\\n",
    "            str(round((i.get_width()), 2))+'$', fontsize=12, color='dimgrey')\n",
    "# invert for largest on top \n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">- Brand 3 has the lowest revenue compared to the other products. It is the middle brand in terms of price, and its highest contributor is the \"Standard\" segment. Standard segment would like the first three brands, so they can be influenced to buy more of the 3rd brand. Maybe if brand 3 reduces its price it is likely that the standard segment would pivot towards it. <br><br>\n",
    ">- The customers of Brand 4 seem mainly from the \"Well-Off\" segment. The customers in this segment who did not choose this brand bought an even more expensive alternative: Brand 5. Therefore, they seem to be loyal and not really affected by price. Therefore brand 4 could try cautiously increasing its price. The hypothesis here is they would likely retain most of its customers and increase the revenue per sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Revenue of Chocolate Products by Segment')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
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   ],
   "source": [
    "ax1 = sns.set_style()\n",
    "fig, ax1 = plt.subplots(figsize=[7,5])\n",
    "sns.lineplot(data = segm_prop, marker='o', sort = False, ax=ax1, color = 'black')\n",
    "ax1.set_ylabel('Segment Proportion', fontsize = 12)\n",
    "ax1.set_ylim([0,0.5])\n",
    "ax2 = ax1.twinx()\n",
    "sns.barplot(data = segments_brand_revenue, x = 'Segment', y = 'Total_Rev',  alpha=0.6)\n",
    "ax2.set_ylabel('Total Revenue ', fontsize = 12)\n",
    "\n",
    "plt.xlabel('Segment', fontsize = 12)\n",
    "\n",
    "plt.title('Total Revenue of Chocolate Products by Segment', fontsize = 13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last plot, I compare the size of each segment with the revenue from each segment. The size of each segment is presented using a line. \n",
    "- \"Career-Focused\" brings the most revenue followed by \"Well-Off\" and \"Fewer-Opportunities\", while the \"Standard\" segment brings the least.  Considering that \"Career-Focused\" is not the largest segment, it is a surprising finding.  In Part 1 of the analysis, we found out that \"Career-focused\" segment was buying the most expensive brand. Indeed, it seems that they are by far the most prominent segment for the store regarding chocolates. \n",
    "- \"Standard\" segment is almost as big as \"Career-focused\". However, it brings far less revenue to the company. In fact, \"Standard\" segment contributes the least of all segments in comparison. \n",
    "- \"Well-Off\" and \"Fewer-Opportunities\" segments spend similar amount of money on chocolates. However, note that \"Fewer-Opportunities\" is twice the size of \"Well-Off\" segment. <br>\n",
    "\n",
    "So far, we examined different measures together. In the next part of analysis, I focused on the point of view and explore the revenue table from a brand's perspective.\n"
   ]
  }
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