analise_cartoes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit card analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "credit_card = pd.read_csv('credit_card.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nowadays, most of people use credit cards to all kind of payment, and its getting more and more frequently everyday.\n",
    "In that sense, incontables fintechs and banks are trying to keep their custumers as happy as possible with the services offereded. From a dataset about credit card usage and customer's preference, we can draw a kind of specific and apropriate product to each kind of a company customer. We currently have 3 kinds of rewards offered by our company, this document's goal is to show us the most effective and if we are able to create a new one to replace or agregate our grid of benefits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two hypotheses underlying this study:\n",
    "- The first one is that all customers use the credit cards with the same proposits: acummulate rewards,   whatever the rewards is;\n",
    "- The second one is that most of customers preferer rewards known as air miles and we would like to develop something special to this kind of public;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first goal is to understand how the custumer develop hers habits using credit card and what she expect from it as reward or not; And, once the credit card is a key in the customer's routine, how its impacted by lifestyle and preferences. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The program of rewards in a credit card has a simple objective: Retain the client and make she more active in our plataform. Our main performance indicator of success key is the rate of customers sticking to our reward's program and how it can be more atractive to them and profitable. For this study, we choose to look upon a sample of transactions reflecting a year of credit card uses from different customers profiles.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decisions to be made upon results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the results, we're going to decided wheter or not to create a new category of reward for different kinds of customers, and if we decide to create, wich is the best option to approach theses customers under our plataform, keeping an eye on our main aspect and giving the best experience to them.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Structure and Groups\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The kind of reward can be chosen by our customers, and we have three main choices: `Air miles`, `CashBack` and `Points`; And our customers are clasified by their income level that it's associated with their lifestyle and habits.\n",
    "Our distribution of clients by rewards's category is the one as detailed below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explode = (0.1, 0, 0)\n",
    "\n",
    "rewards_df = credit_card['Reward']\n",
    "\n",
    "rewards_counts = rewards_df.value_counts().tolist()\n",
    "rewards = rewards_df.drop_duplicates().tolist()\n",
    "\n",
    "_, ax1 = plt.subplots()\n",
    "ax1.pie(rewards_counts, explode=explode, labels=rewards, autopct='%1.2f%%', shadow=False, startangle=360, colors=['indigo', 'purple', 'orchid'])\n",
    "ax1.axis('equal') \n",
    "plt.title('1. Most Popular Rewards')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main customers are medium level income, and our products are desinged to them, but with this study we'd like to creat a new approach to other kind of clients and increase our revenues and the power of our brand. \n",
    "\n",
    "The percentage or our customers are below, as so their preferences: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Reward</th>\n",
       "      <th>Air Miles</th>\n",
       "      <th>Cash Back</th>\n",
       "      <th>Points</th>\n",
       "      <th>Total</th>\n",
       "      <th>% Customers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income Level</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",
       "      <td>High</td>\n",
       "      <td>1494</td>\n",
       "      <td>1538</td>\n",
       "      <td>1494</td>\n",
       "      <td>4526</td>\n",
       "      <td>25.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Low</td>\n",
       "      <td>1538</td>\n",
       "      <td>1467</td>\n",
       "      <td>1456</td>\n",
       "      <td>4461</td>\n",
       "      <td>24.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Medium</td>\n",
       "      <td>3029</td>\n",
       "      <td>2994</td>\n",
       "      <td>2990</td>\n",
       "      <td>9013</td>\n",
       "      <td>50.1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Reward        Air Miles  Cash Back  Points  Total % Customers\n",
       "Income Level                                                 \n",
       "High               1494       1538    1494   4526       25.1%\n",
       "Low                1538       1467    1456   4461       24.8%\n",
       "Medium             3029       2994    2990   9013       50.1%"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cross_data = pd.crosstab(credit_card['Income Level'], credit_card['Reward'])\n",
    "df_cross_data['Total'] = df_cross_data.sum(axis=1)\n",
    "df_cross_data['% Customers'] = credit_card['Income Level'].value_counts(normalize=True).mul(100).round(1).astype(str) + '%'\n",
    "df_cross_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As demonstrated in our table above, our client is very faithful to our methods and rewards, and we can take the following conclusion based in our second chart (below): \n",
    "- Most of our customers' spending is rewarded by `Air Miles`, so the financial volume and number of subscribers is higher in this category, but the audience that prefers Cashback has grown a lot over new business and technology advances, and thus we need to adapt our business model to satisfy this beech of the public and have greater market share."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = credit_card[['Average Balance','Reward']]\n",
    "df = df.groupby('Reward').sum()\n",
    "\n",
    "ax = df.plot(kind='bar', figsize=(8,4), color = ['indigo', 'purple', 'orchid'], fontsize=13, legend = None);\n",
    "ax.set_yticks([ 2000000, 4000000, 6000000, 8000000])\n",
    "plt.title('2. Balance by Reward')\n",
    "\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_x() - 0.10, i.get_height() + 200000, \\\n",
    "            ('${:,.2f}'.format(i.get_height())), fontsize=14, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main goal is to be present in people's lives and reward them through their spending. When we perform these types of analyzes, we seek to understand the profile of our customers and how they behave, thereby generating a worthwhile differential for our customer. Thus we find two niche markets that may be paramount to our development: Our most loyal clients and new clients looking for new solutions to their financial lives. Creating a specific product for each type of customer is a solution that will make old customers like our company even more and even spread it to friends and families, increasing our impact as a brand; And the creation of a product specific to new customers, with greater competitive advantages, such as points that never expire, or combinable points, that he can have access to any type of product or discounts on his invoices or cashbacks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Given that we have to kind of publics in our target, we have to draw different kind of approachs and products;\n",
    "2. We should focus on our second type of audience, and start sending notifications and pushs with double score boosts, and high return of cash back at certain times of the day, such as at lunchtime or on weekends, to encourage card usage and increase revenue through it.\n",
    "3. The product that should be developed for loyal customers in the area should be partnered with air travel companies to offer better rates and conditions to customers, and the higher the level of spending and use of cards, the greater the benefits with airline tickets. last minute promotion, even discount coupons sent via random notifications to certain customer groups."
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}