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@embanner
Created August 14, 2017 23:01
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"Let's load our data and visualize it.\n",
"\n",
"### Load Data\n",
"\n",
"- Give labels to columns\n",
"- Convert purchase date strings to python `datetime` type\n",
"- Extract year of purchase from date\n",
"- Compute number of days since the purchase"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>purchase_amount</th>\n",
" <th>date_of_purchase</th>\n",
" <th>year_of_purchase</th>\n",
" <th>days_since</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>760</td>\n",
" <td>25.0</td>\n",
" <td>2009-11-06</td>\n",
" <td>2009</td>\n",
" <td>2247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>860</td>\n",
" <td>50.0</td>\n",
" <td>2012-09-28</td>\n",
" <td>2012</td>\n",
" <td>1190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1200</td>\n",
" <td>100.0</td>\n",
" <td>2005-10-25</td>\n",
" <td>2005</td>\n",
" <td>3720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1420</td>\n",
" <td>50.0</td>\n",
" <td>2009-07-09</td>\n",
" <td>2009</td>\n",
" <td>2367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1940</td>\n",
" <td>70.0</td>\n",
" <td>2013-01-25</td>\n",
" <td>2013</td>\n",
" <td>1071</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id purchase_amount date_of_purchase year_of_purchase days_since\n",
"0 760 25.0 2009-11-06 2009 2247\n",
"1 860 50.0 2012-09-28 2012 1190\n",
"2 1200 100.0 2005-10-25 2005 3720\n",
"3 1420 50.0 2009-07-09 2009 2367\n",
"4 1940 70.0 2013-01-25 2013 1071"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from datetime import datetime\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"transactions = pd.read_csv('purchases.txt', delimiter='\\t', header=None)\n",
"transactions.columns = ['customer_id', 'purchase_amount', 'date_of_purchase']\n",
"transactions.date_of_purchase = pd.to_datetime(transactions.date_of_purchase)\n",
"transactions['year_of_purchase'] = transactions.date_of_purchase.map(lambda date: date.year)\n",
"transactions['days_since'] = datetime(2016, 1, 1) - transactions.date_of_purchase\n",
"transactions.days_since = transactions.days_since.map(lambda days: days.days) # pull out integer number of days\n",
"\n",
"transactions.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>purchase_amount</th>\n",
" <th>year_of_purchase</th>\n",
" <th>days_since</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>51243.000000</td>\n",
" <td>51243.000000</td>\n",
" <td>51243.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>62.337195</td>\n",
" <td>2010.869699</td>\n",
" <td>1631.939309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>156.606801</td>\n",
" <td>2.883072</td>\n",
" <td>1061.076889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5.000000</td>\n",
" <td>2005.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>25.000000</td>\n",
" <td>2009.000000</td>\n",
" <td>733.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>30.000000</td>\n",
" <td>2011.000000</td>\n",
" <td>1500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>60.000000</td>\n",
" <td>2013.000000</td>\n",
" <td>2540.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4500.000000</td>\n",
" <td>2015.000000</td>\n",
" <td>4016.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" purchase_amount year_of_purchase days_since\n",
"count 51243.000000 51243.000000 51243.000000\n",
"mean 62.337195 2010.869699 1631.939309\n",
"std 156.606801 2.883072 1061.076889\n",
"min 5.000000 2005.000000 1.000000\n",
"25% 25.000000 2009.000000 733.000000\n",
"50% 30.000000 2011.000000 1500.000000\n",
"75% 60.000000 2013.000000 2540.000000\n",
"max 4500.000000 2015.000000 4016.000000"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transactions.ix[:, 'purchase_amount':].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Number of Purchases per Year"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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7ZAhsvXXc2YiISDZJpRj5FNi3mv37ASsalo7kq0mTwoq455wTdyYiIpJtUpmW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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1043ccc10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = transactions.year_of_purchase.value_counts().sort_index().plot()\n",
"axes.set_xlabel('Year')\n",
"axes.set_ylabel('Number of Purchases')\n",
"axes.set_ylim([0, 7000]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Average Purchase Amount per Year"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Rjz0WhkvWWw8OOyz0Nhx2WLxeDBERya6MT6w0s58CvwN6RkUfADe5+0cx2roO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\ny/8D1HBS3ODYuLYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1045b1b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = transactions.groupby('year_of_purchase').purchase_amount.mean().plot()\n",
"axes.set_xlabel('Year')\n",
"axes.set_ylabel('Avergage Purchase Amount in Dollars')\n",
"axes.set_ylim([0, 80]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Total Purchase Amounts per Year"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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aQ/MJq85D+oTy955J58Z6IiKVtnQpDB8O/frBF1/APvuE+TFHH63l1CI1XUUL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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112c229d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = transactions.groupby('year_of_purchase').sum().purchase_amount.plot()\n",
"axes.set_xlabel('Year')\n",
"axes.set_ylabel('Total Revenue in Dollars')\n",
"axes.set_ylim([0, 500000]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statistical Segmentation\n",
"\n",
"In this section, we'll perform k-means clustering to automatically discover customer *profiles*, which will allow us to determine different types of customers (e.g. infrequeny high-spenders, frequeny low-spenders).\n",
"\n",
"### From Transactions to Customers\n",
"\n",
"Let's go from viewing our data as individual transactions and instead view it in terms of individual customers. For each customer customer, we will compute the following:\n",
"\n",
"- `recency` (number of days since most recent purchase)\n",
"- `frequency` (number of purchases)\n",
"- `amount` (log of average purchase amount)\n",
"\n",
"We will accomplish this with an sql-like `groupby()` operation on `customer_id` and applying an agrregation function to summarize a customer by his/her list of transactions in terms of these three attributes.\n",
"\n",
"Since each customer has a unique ID, let's also index each customer with its ID.\n",
"\n",
"We take the log of `amount` because it's easier to visualize in plots."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>customer_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3.401197</td>\n",
" <td>1.0</td>\n",
" <td>3829.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>4.268698</td>\n",
" <td>7.0</td>\n",
" <td>343.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>4.751865</td>\n",
" <td>10.0</td>\n",
" <td>758.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>2.995732</td>\n",
" <td>1.0</td>\n",
" <td>1401.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>3.912023</td>\n",
" <td>2.0</td>\n",
" <td>2970.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount frequency recency\n",
"customer_id \n",
"10 3.401197 1.0 3829.0\n",
"80 4.268698 7.0 343.0\n",
"90 4.751865 10.0 758.0\n",
"120 2.995732 1.0 1401.0\n",
"130 3.912023 2.0 2970.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def summarize_customer(customer_transactions):\n",
" d = {'recency': customer_transactions.days_since.min(),\n",
" 'frequency': len(customer_transactions),\n",
" 'amount': customer_transactions.purchase_amount.mean()}\n",
" \n",
" return pd.Series(d)\n",
"\n",
"customers = transactions.groupby('customer_id').apply(summarize_customer).reset_index()\n",
"customers = customers.set_index('customer_id')\n",
"customers.amount = np.log(customers.amount)\n",
"\n",
"customers.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>18417.000000</td>\n",
" <td>18417.000000</td>\n",
" <td>18417.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.582192</td>\n",
" <td>2.782375</td>\n",
" <td>1253.037900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.767593</td>\n",
" <td>2.936888</td>\n",
" <td>1081.437868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.609438</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.075775</td>\n",
" <td>1.000000</td>\n",
" <td>244.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.401197</td>\n",
" <td>2.000000</td>\n",
" <td>1070.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.912023</td>\n",
" <td>3.000000</td>\n",
" <td>2130.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>8.411833</td>\n",
" <td>45.000000</td>\n",
" <td>4014.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount frequency recency\n",
"count 18417.000000 18417.000000 18417.000000\n",
"mean 3.582192 2.782375 1253.037900\n",
"std 0.767593 2.936888 1081.437868\n",
"min 1.609438 1.000000 1.000000\n",
"25% 3.075775 1.000000 244.000000\n",
"50% 3.401197 2.000000 1070.000000\n",
"75% 3.912023 3.000000 2130.000000\n",
"max 8.411833 45.000000 4014.000000"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customers.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Long Ago Are Customers Making Their Most Recent Purchase?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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q/HsPAhYBve7+bXffCCwETjKz4/Mxs4HTgbe4+53ufhtwPnC2mfV0PmEalixZUnUJhYmU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\nERGR2vv/0QKFxfaHQCsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112c01550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = customers.recency.hist()\n",
"axes.set_xlabel('Days')\n",
"axes.set_ylabel('Frequency');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Many Purchases are Customers Making?\n",
"\n",
"Most customers make less than 5 purchases, but there is a long tail with a small number of customers making 40+ purchases."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OWcB5UpZTlip1WtA8BBwzQvsx5TYzMzOzXabTS04XAhdJOgIYmvh7NPAW4N+q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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112c59410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = customers.frequency.hist(bins=40)\n",
"axes.set_xlabel('Number of Transactions')\n",
"axes.set_ylabel('Frequency');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Much Are Customers Spending?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GODNyFPAVsn4jd5Lda2RmRHwLICJWkhUOV5CdxXgJ8M6IeLZsHYuBW4AbgLuBR8juOVLu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\n/aKsUQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1184c7790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = np.log(customers.amount).hist(bins=40)\n",
"axes.set_xlabel('Average Log Dollars')\n",
"axes.set_ylabel('Frequency');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Discovering Customer *Profiles*\n",
"\n",
"Let's perform k-means clustering to see if we can discover certain customer profiles. First we'll produce a scatter matrix of our normalized customer data to see relationships between different features."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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MbNx4Gx/72Ed7ZC49d66M48ePcv68g/r6WmbOTGHTpg0EAgHef38PmqaRkjKT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\n7EeIA0yZEk98fBX9/dX4fFnYbMl4vbuAneTkfIzCwq8yd24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9Bcs6QCAwjpdeCtDamkwg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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a530b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas.tools.plotting import scatter_matrix\n",
"import matplotlib.pyplot as plt\n",
"\n",
"nb_customer = len(customers)\n",
"\n",
"sample_idxs = np.random.choice(customers.index, size=nb_customer/10, replace=False)\n",
"customer_sample = customers.ix[sample_idxs]\n",
"customer_sample = (customer_sample - customer_sample.mean()) / customer_sample.std()\n",
"\n",
"scatter_matrix(customer_sample, diagonal='kde');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run `k`-means for Different Values of `k` and Visualize the Clusters"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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SyvjgtcD7hjFj5zuLsjz+h3FVq2ZbVnQ/AbyF3qPdd1GuWZ5S9zXnApsBR/Tz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\nIRtvSZIkSZJKyMZbkiRJkqQSsvGWJEmSJKmEbLwlSZIkSSohG29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vZ1VUtQgh9AohHBdC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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118ef20d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import copy\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.cluster import KMeans\n",
"\n",
"nb_cluster = 9\n",
"cluster_sizes = np.arange(1, nb_cluster+1)\n",
"\n",
"X = np.array(customer_sample)\n",
"K = [KMeans(n_clusters=nb_cluster).fit(X) for nb_cluster in cluster_sizes]\n",
"\n",
"fig, axes_matrix = plt.subplots(nrows=3, ncols=3, figsize=[10, 20])\n",
"columns = ['amount', 'frequency', 'recency']\n",
"\n",
"for k, nb_cluster, axes in zip(K, cluster_sizes, axes_matrix.flatten()):\n",
" C = k.cluster_centers_\n",
" \n",
" # Normalize to accentuate color differences\n",
" C_ = copy.deepcopy(C)\n",
" C_ -= C_.min(axis=0)\n",
" C_ /= C_.sum(axis=0)\n",
" C_ /= C_.sum(axis=1, keepdims=True)\n",
" \n",
" # Plot the clusters\n",
" df = pd.DataFrame(C, columns=columns)\n",
" axes.imshow(df, interpolation='nearest')\n",
" axes.set_xticks(np.arange(3))\n",
" axes.set_xticklabels(columns, rotation=90)\n",
" axes.set_yticks(np.arange(nb_cluster))\n",
" axes.set_ylabel('Cluster ID')\n",
" axes.set_title('k={}'.format(nb_cluster))\n",
" \n",
" # Transform clusters back into original scale for interpretable clusters and annotate\n",
" mu, std = customers.ix[sample_idxs].mean(), customers.ix[sample_idxs].std()\n",
" M, S = np.array(mu), np.array(std)\n",
" C = C*S + M\n",
" C[:, 0] = np.exp(C[:, 0])\n",
" for x in xrange(nb_cluster):\n",
" for y in xrange(3):\n",
" axes.annotate(str(int(C[x][y])) if C[x][y] else '', xy=(y, x), \n",
" horizontalalignment='center',\n",
" verticalalignment='center')\n",
" \n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster Distributions\n",
"\n",
"Let's visualize how customers were distributed into each of the cluster sets learned above."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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AFzD/rg830+z68HGa3SPOHuyQJY2bRXGpeVXdQrN1ze5vJefzVFV9Zb6OA9ljMckq4HRg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\nl+SkYY1XkhZTCxkpSRPBfNTSsQHYOOpBaJG0ecZ7C/1LhH6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RkjQ/81HSIVkyhXeSJwGb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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118f0e410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes_matrix = plt.subplots(nrows=3, ncols=3, figsize=[10, 10])\n",
"\n",
"for k, axes in zip(K, axes_matrix.flatten()):\n",
" y = k.predict(X)\n",
" pd.Series(y).value_counts().sort_index().plot(ax=axes, kind='bar')\n",
" axes.set_xlabel('Cluster ID')\n",
" axes.set_ylabel('Frequency')\n",
" \n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Managerial Segmentation\n",
"\n",
"Inspired by the customer profiles determined automatically by k-means clustering, in this section we'll manually segment customers into different profiles.\n",
"\n",
"### Incorporating a Customer's First Purchase\n",
"\n",
"In addition to summarizing a customer by `recency`, `frequency`, and `amount`, we will also include the number of days since his/her `first_purchase`."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>customer_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>30.000000</td>\n",
" <td>3829.0</td>\n",
" <td>1.0</td>\n",
" <td>3829.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>71.428571</td>\n",
" <td>3751.0</td>\n",
" <td>7.0</td>\n",
" <td>343.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>115.800000</td>\n",
" <td>3783.0</td>\n",
" <td>10.0</td>\n",
" <td>758.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>20.000000</td>\n",
" <td>1401.0</td>\n",
" <td>1.0</td>\n",
" <td>1401.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>50.000000</td>\n",
" <td>3710.0</td>\n",
" <td>2.0</td>\n",
" <td>2970.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount first_purchase frequency recency\n",
"customer_id \n",
"10 30.000000 3829.0 1.0 3829.0\n",
"80 71.428571 3751.0 7.0 343.0\n",
"90 115.800000 3783.0 10.0 758.0\n",
"120 20.000000 1401.0 1.0 1401.0\n",
"130 50.000000 3710.0 2.0 2970.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def summarize_customer(customer_transactions):\n",
" d = {'recency': customer_transactions.days_since.min(),\n",
" 'first_purchase': customer_transactions.days_since.max(),\n",
" 'frequency': len(customer_transactions),\n",
" 'amount': customer_transactions.purchase_amount.mean()}\n",
" \n",
" return pd.Series(d)\n",
"\n",
"customers = transactions.groupby('customer_id').apply(summarize_customer).reset_index()\n",
"customers = customers.set_index('customer_id')\n",
"\n",
"customers.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>18417.000000</td>\n",
" <td>18417.000000</td>\n",
" <td>18417.000000</td>\n",
" <td>18417.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>57.792985</td>\n",
" <td>1984.009882</td>\n",
" <td>2.782375</td>\n",
" <td>1253.037900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>154.360109</td>\n",
" <td>1133.405441</td>\n",
" <td>2.936888</td>\n",
" <td>1081.437868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>21.666667</td>\n",
" <td>988.000000</td>\n",
" <td>1.000000</td>\n",
" <td>244.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>30.000000</td>\n",
" <td>2087.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1070.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>50.000000</td>\n",
" <td>2992.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2130.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4500.000000</td>\n",
" <td>4016.000000</td>\n",
" <td>45.000000</td>\n",
" <td>4014.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount first_purchase frequency recency\n",
"count 18417.000000 18417.000000 18417.000000 18417.000000\n",
"mean 57.792985 1984.009882 2.782375 1253.037900\n",
"std 154.360109 1133.405441 2.936888 1081.437868\n",
"min 5.000000 1.000000 1.000000 1.000000\n",
"25% 21.666667 988.000000 1.000000 244.000000\n",
"50% 30.000000 2087.000000 2.000000 1070.000000\n",
"75% 50.000000 2992.000000 3.000000 2130.000000\n",
"max 4500.000000 4016.000000 45.000000 4014.000000"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customers.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segment Customers Into Different Bins *Manually*\n",
"\n",
"As a first step, let's segment customers as *active* and *non-active* depending on how long its been since their most recent transaction. For any customer $\\texttt{c}$, we'll use the following segmentation rules:\n",
"\n",
"$$\n",
" \\text{segment}(\\texttt{c}) = \n",
" \\begin{cases} \n",
" \\texttt{inactive} & \\texttt{c.recency} > \\text{3 years} \\\\\n",
" \\texttt{active} & \\text{otherwise}\n",
" \\end{cases}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>segment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>inactive</th>\n",
" <td>48.112771</td>\n",
" <td>2546.168377</td>\n",
" <td>1.814479</td>\n",
" <td>2178.110832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active</th>\n",
" <td>67.367605</td>\n",
" <td>1427.983584</td>\n",
" <td>3.739713</td>\n",
" <td>338.055946</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount first_purchase frequency recency\n",
"segment \n",
"inactive 48.112771 2546.168377 1.814479 2178.110832\n",
"active 67.367605 1427.983584 3.739713 338.055946"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customers['segment'] = customers.recency.map(lambda recency: 'inactive' if recency > 3*365 else 'active')\n",
"\n",
"customers.groupby('segment').mean().ix[['inactive', 'active']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can get more fine-grained by adding a `cold` label, which is somewhere in between `active` and `inactive`:\n",
"\n",
"$$\n",
" \\text{segment}(\\texttt{c}) = \n",
" \\begin{cases} \n",
" \\texttt{inactive} & \\text{if } \\texttt{c.recency} < \\text{3 years} \\\\\n",
" \\texttt{cold} & \\text{if } \\text{2 years} < \\texttt{c.recency} \\leq \\text{3 years} \\\\\n",
" \\texttt{active} & \\text{otherwise}\n",
" \\end{cases}\n",
"$$\n",
"\n",
"Since it's getting a little complicated, we'll break out our classification logic into a standalone function."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>segment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>inactive</th>\n",
" <td>48.112771</td>\n",
" <td>2546.168377</td>\n",
" <td>1.814479</td>\n",
" <td>2178.110832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cold</th>\n",
" <td>51.739893</td>\n",
" <td>1432.117183</td>\n",
" <td>2.303205</td>\n",
" <td>857.781398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active</th>\n",
" <td>71.410500</td>\n",
" <td>1426.914220</td>\n",
" <td>4.111338</td>\n",
" <td>203.602773</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount first_purchase frequency recency\n",
"segment \n",
"inactive 48.112771 2546.168377 1.814479 2178.110832\n",
"cold 51.739893 1432.117183 2.303205 857.781398\n",
"active 71.410500 1426.914220 4.111338 203.602773"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def classify_customer(recency):\n",
" return 'inactive' if recency > 3*365 else 'cold' if recency > 2*365 else 'active'\n",
"\n",
"customers.segment = customers.recency.apply(classify_customer)\n",
"\n",
"customers.groupby('segment').mean().ix[['inactive', 'cold', 'active']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can get even more complex with a `warm` label:\n",
"\n",
"$$\n",
" \\text{segment}(\\texttt{c}) = \n",
" \\begin{cases} \n",
" \\texttt{inactive} & \\text{if } \\texttt{c.recency} < \\text{3 years} \\\\\n",
" \\texttt{cold} & \\text{if } \\text{2 years} < \\texttt{c.recency} \\leq \\text{3 years} \\\\\n",
" \\texttt{warm} & \\text{if } \\text{1 years} < \\texttt{c.recency} \\leq \\text{2 years} \\\\\n",
" \\texttt{active} & \\text{otherwise}\n",
" \\end{cases}\n",
"$$\n",
"\n",
"For this, we'll utilize boolean masks to make the solution read a bit cleaner."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>segment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>inactive</th>\n",
" <td>48.112771</td>\n",
" <td>2546.168377</td>\n",
" <td>1.814479</td>\n",
" <td>2178.110832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cold</th>\n",
" <td>51.739893</td>\n",
" <td>1432.117183</td>\n",
" <td>2.303205</td>\n",
" <td>857.781398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warm</th>\n",
" <td>69.562155</td>\n",
" <td>1319.590398</td>\n",
" <td>2.872319</td>\n",
" <td>489.939734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active</th>\n",
" <td>72.080944</td>\n",
" <td>1465.843461</td>\n",
" <td>4.560763</td>\n",
" <td>99.740645</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount first_purchase frequency recency\n",
"segment \n",
"inactive 48.112771 2546.168377 1.814479 2178.110832\n",
"cold 51.739893 1432.117183 2.303205 857.781398\n",
"warm 69.562155 1319.590398 2.872319 489.939734\n",
"active 72.080944 1465.843461 4.560763 99.740645"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"customers['segment'] = 'inactive'\n",
"customers.segment[(customers.recency <= 365*3) & (customers.recency > 365*2)] = 'cold'\n",
"customers.segment[(customers.recency <= 365*2) & (customers.recency > 365*1)] = 'warm'\n",
"customers.segment[customers.recency <= 365*1] = 'active'\n",
"\n",
"customers.groupby('segment').mean().ix[['inactive', 'cold', 'warm', 'active']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now for the most complex and fine-grained solution. Note that we perform the above segmentation as a first step and then define a this more fine-grained segmentation in terms of it:\n",
"\n",
"$$\n",
" \\text{segment}(\\texttt{c}) = \n",
" \\begin{cases} \n",
" \\texttt{inactive} & \\text{if } \\texttt{c.recency} < \\text{3 years} \\\\\n",
" \\texttt{new warm} & \\text{if } \\texttt{c.warm} \\land \\texttt{c.first-purchase} \\leq \\text{2 years} \\\\\n",
" \\texttt{warm low value} & \\text{if } \\texttt{c.warm} \\land \\texttt{c.amount} < $100 \\\\\n",
" \\texttt{warm high value} & \\text{if } \\texttt{c.warm} \\land \\texttt{c.amount} \\geq $100 \\\\\n",
" \\texttt{new active} & \\text{if } \\texttt{c.active} \\land \\texttt{c.first-purchase} \\leq \\text{1 year} \\\\\n",
" \\texttt{active low value} & \\text{if } \\texttt{c.active} \\land \\texttt{c.amount} < $100 \\\\\n",
" \\texttt{active high value} & \\text{if } \\texttt{c.active} \\land \\texttt{c.amount} \\geq $100 \\\\\n",
" \\end{cases}\n",
"$$\n",
"\n",
"Here's a visualization of the low and high value rules."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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cc4scbiTQmUewjQqco39aagSQiIiItKDmDAnuh2uCsyZ8BSi9FREREa80JylJ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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a8f0390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure()\n",
"plt.xlim(0, 2)\n",
"plt.ylim(0, 200)\n",
"\n",
"plt.axvspan(0, 1, facecolor='r', alpha=0.5); plt.axhspan(0, 100, facecolor='y', alpha=0.5)\n",
"plt.axvspan(1, 2, facecolor='g', alpha=0.5); #plt.axvspan(0, 200, facecolor='g', alpha=0.5)\n",
"\n",
"plt.text(1.25, 50, 'Warm (Low Value)')\n",
"plt.text(1.25, 150, 'Warm (High value)')\n",
"plt.text(.25, 50, 'Active (Low value)')\n",
"plt.text(.25, 150, 'Active (High value)')\n",
"\n",
"plt.xlabel('Recency in Years')\n",
"plt.ylabel('Average Amount Spent in Dollars')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define this beast as a function because we'll be re-using it in the future. "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"segments = ['inactive',\n",
" 'cold',\n",
" 'warm high value',\n",
" 'warm low value',\n",
" 'new warm',\n",
" 'active high value',\n",
" 'active low value',\n",
" 'new active'\n",
"]\n",
"\n",
"def do_segment(customers):\n",
" customers['segment'] = 'inactive'\n",
"\n",
" customers.segment[(customers.recency <= 365*3) & (customers.recency > 365*2)] = 'cold'\n",
" customers.segment[(customers.recency <= 365*2) & (customers.recency > 365*1)] = 'warm'\n",
" customers.segment[customers.recency <= 365*1] = 'active'\n",
"\n",
" customers.segment[(customers.segment == 'warm') & (customers.first_purchase <= 365*2)] = 'new warm'\n",
" customers.segment[(customers.segment == 'warm') & (customers.amount < 100)] = 'warm low value'\n",
" customers.segment[(customers.segment == 'warm') & (customers.amount >= 100)] = 'warm high value'\n",
"\n",
" customers.segment[(customers.segment == 'active') & (customers.first_purchase <= 365)] = 'new active'\n",
" customers.segment[(customers.segment == 'active') & (customers.amount < 100)] = 'active low value'\n",
" customers.segment[(customers.segment == 'active') & (customers.amount >= 100)] = 'active high value'\n",
" \n",
" return customers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's segment up the customers!"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>segment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>inactive</th>\n",
" <td>48.112771</td>\n",
" <td>2546.168377</td>\n",
" <td>1.814479</td>\n",
" <td>2178.110832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cold</th>\n",
" <td>51.739893</td>\n",
" <td>1432.117183</td>\n",
" <td>2.303205</td>\n",
" <td>857.781398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warm high value</th>\n",
" <td>327.407457</td>\n",
" <td>2015.352941</td>\n",
" <td>4.714286</td>\n",
" <td>455.126050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warm low value</th>\n",
" <td>38.591926</td>\n",
" <td>2063.639290</td>\n",
" <td>4.531632</td>\n",
" <td>474.377358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new warm</th>\n",
" <td>66.599026</td>\n",
" <td>516.622601</td>\n",
" <td>1.044776</td>\n",
" <td>509.304904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active high value</th>\n",
" <td>240.045740</td>\n",
" <td>1985.909250</td>\n",
" <td>5.888307</td>\n",
" <td>88.820244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active low value</th>\n",
" <td>40.724525</td>\n",
" <td>2003.801992</td>\n",
" <td>5.935406</td>\n",
" <td>108.361002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new active</th>\n",
" <td>77.133847</td>\n",
" <td>90.013889</td>\n",
" <td>1.045635</td>\n",
" <td>84.990741</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" amount first_purchase frequency recency\n",
"segment \n",
"inactive 48.112771 2546.168377 1.814479 2178.110832\n",
"cold 51.739893 1432.117183 2.303205 857.781398\n",
"warm high value 327.407457 2015.352941 4.714286 455.126050\n",
"warm low value 38.591926 2063.639290 4.531632 474.377358\n",
"new warm 66.599026 516.622601 1.044776 509.304904\n",
"active high value 240.045740 1985.909250 5.888307 88.820244\n",
"active low value 40.724525 2003.801992 5.935406 108.361002\n",
"new active 77.133847 90.013889 1.045635 84.990741"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customers = do_segment(customers)\n",
"\n",
"customers.groupby('segment').mean().ix[segments]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Many Customers Fall Into Each Segment?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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KFuB7E/p3DtNLq+IuSUQWaQawURbGfQ3Z9dx9WtwViTSHxthI0ZhZJ8g8DD06wQiFGpGy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\nAxVp35RcmaOFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112d3bc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=[5, 5])\n",
"\n",
"axes = customers.segment.value_counts().plot(kind='pie');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Much 2015 Revenue is Generated by an Average Customer in Each Segment?\n",
"\n",
"Because the only customers that are generating revenue for us currently are `active`, all other segments are zeroed out."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JQWN/YA9gN2AscBLw7oh4Nv/yzMzMrMqGEzQ2B94VEbdIuoYUNL4XEb8upjQzMzOruuGM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\nEWcwEoIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ff81350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"revenue_by_segments = customers.groupby('segment').amount.mean().ix[segments]\n",
"revenue_by_segments.ix[['inactive', 'cold', 'warm high value', 'warm low value', 'new warm']] = 0\n",
"axes = revenue_by_segments.plot(kind='bar')\n",
"axes.set_xlabel('Segment')\n",
"axes.set_ylabel('Average Revenue');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scoring\n",
"\n",
"In this section, we'll predict customer spending and activity given historical data about that customer.\n",
"\n",
"### Adding Maximum Amount Spent\n",
"\n",
"We'll also add the maximum amount a customer has purchased to our representation of a customer for its predicitive capabilities."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def summarize_customer(group):\n",
" d = {'recency': group.days_since.min(),\n",
" 'first_purchase': group.days_since.max(),\n",
" 'frequency': len(group),\n",
" 'avg_amount': group.purchase_amount.mean(),\n",
" 'max_amount': group.purchase_amount.max()}\n",
" \n",
" return pd.Series(d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Collect All Transactions and Customer Data Through 2014\n",
"\n",
"We'll leave out transactions in 2015 for prediction."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>avg_amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>max_amount</th>\n",
" <th>recency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>30.0</td>\n",
" <td>3464.0</td>\n",
" <td>1.0</td>\n",
" <td>30.0</td>\n",
" <td>3464.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>80</td>\n",
" <td>70.0</td>\n",
" <td>3386.0</td>\n",
" <td>6.0</td>\n",
" <td>80.0</td>\n",
" <td>302.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>90</td>\n",
" <td>115.8</td>\n",
" <td>3418.0</td>\n",
" <td>10.0</td>\n",
" <td>153.0</td>\n",
" <td>393.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>120</td>\n",
" <td>20.0</td>\n",
" <td>1036.0</td>\n",
" <td>1.0</td>\n",
" <td>20.0</td>\n",
" <td>1036.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>130</td>\n",
" <td>50.0</td>\n",
" <td>3345.0</td>\n",
" <td>2.0</td>\n",
" <td>60.0</td>\n",
" <td>2605.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id avg_amount first_purchase frequency max_amount recency\n",
"0 10 30.0 3464.0 1.0 30.0 3464.0\n",
"1 80 70.0 3386.0 6.0 80.0 302.0\n",
"2 90 115.8 3418.0 10.0 153.0 393.0\n",
"3 120 20.0 1036.0 1.0 20.0 1036.0\n",
"4 130 50.0 3345.0 2.0 60.0 2605.0"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transactions_2014 = transactions[transactions.days_since > 365].reset_index(drop=True)\n",
"customers_2014 = transactions_2014.groupby('customer_id').apply(summarize_customer).reset_index()\n",
"\n",
"customers_2014.recency = customers_2014.recency - 365\n",
"customers_2014.first_purchase = customers_2014.first_purchase - 365\n",
"\n",
"customers_2014.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compute Revenue By Customer in 2015\n",
"\n",
"This is what we're interested in predicting."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>revenue_2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>80</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>480</td>\n",
" <td>45.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>830</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>850</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>860</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id revenue_2015\n",
"0 80 80.0\n",
"1 480 45.0\n",
"2 830 50.0\n",
"3 850 60.0\n",
"4 860 60.0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transactions_2015 = transactions[transactions.year_of_purchase == 2015].reset_index(drop=True)\n",
"\n",
"revenue_2015 = transactions_2015.groupby('customer_id').purchase_amount.sum()\n",
"revenue_2015 = pd.DataFrame(revenue_2015).reset_index()\n",
"revenue_2015.columns = ['customer_id', 'revenue_2015']\n",
"\n",
"revenue_2015.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Join Customer Revenue in 2015 With Pre-2015 Data\n",
"\n",
"For customers who didn't make a purchase in 2015, let's set their 2015 revenue to `0`. We'll mark if they were active in 2015 with a binary indicator variable."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avg_amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>max_amount</th>\n",
" <th>recency</th>\n",
" <th>revenue_2015</th>\n",
" <th>active_2015</th>\n",
" </tr>\n",
" <tr>\n",
" <th>customer_id</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>10</th>\n",
" <td>30.0</td>\n",
" <td>3464.0</td>\n",
" <td>1.0</td>\n",
" <td>30.0</td>\n",
" <td>3464.0</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>70.0</td>\n",
" <td>3386.0</td>\n",
" <td>6.0</td>\n",
" <td>80.0</td>\n",
" <td>302.0</td>\n",
" <td>80.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>115.8</td>\n",
" <td>3418.0</td>\n",
" <td>10.0</td>\n",
" <td>153.0</td>\n",
" <td>393.0</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>20.0</td>\n",
" <td>1036.0</td>\n",
" <td>1.0</td>\n",
" <td>20.0</td>\n",
" <td>1036.0</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>50.0</td>\n",
" <td>3345.0</td>\n",
" <td>2.0</td>\n",
" <td>60.0</td>\n",
" <td>2605.0</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_amount first_purchase frequency max_amount recency \\\n",
"customer_id \n",
"10 30.0 3464.0 1.0 30.0 3464.0 \n",
"80 70.0 3386.0 6.0 80.0 302.0 \n",
"90 115.8 3418.0 10.0 153.0 393.0 \n",
"120 20.0 1036.0 1.0 20.0 1036.0 \n",
"130 50.0 3345.0 2.0 60.0 2605.0 \n",
"\n",
" revenue_2015 active_2015 \n",
"customer_id \n",
"10 0.0 False \n",
"80 80.0 True \n",
"90 0.0 False \n",
"120 0.0 False \n",
"130 0.0 False "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customer_data = customers_2014.merge(revenue_2015, how='left', on='customer_id')\n",
"customer_data = customer_data.fillna(value=0)\n",
"customer_data = customer_data.set_index('customer_id')\n",
"\n",
"customer_data['active_2015'] = customer_data.revenue_2015 > 0\n",
"\n",
"customer_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict Customer Activity in 2015 With Logistic Regression\n",
"\n",
"Use the following features:\n",
"\n",
"- Average amount spent\n",
"- First purchase date\n",
"- Frequency\n",
"- Maximum amount spent\n",
"- Recency"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
" fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
" multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
" solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"X, y = np.array(customer_data.ix[:, :'recency']), customer_data.active_2015\n",
"\n",
"clf = LogisticRegression(C=1e5)\n",
"clf.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's view the intercept and coefficients."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intercept -0.517422911443\n",
"avg_amount 0.000396789561747\n",
"first_purchase -9.66452726225e-06\n",
"frequency 0.216683306594\n",
"max_amount -0.000147928361809\n",
"recency -0.00197598593708\n"
]
}
],
"source": [
"print 'intercept', clf.intercept_[0]\n",
"for name, coef in zip(customer_data.columns[:5], clf.coef_.flatten()):\n",
" print name, coef"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict Customer 2015 Revenue with Linear Regression\n",
"\n",
"Now let's predict revenue for each customer in 2015. First we'll filter away customers who had no revenue in 2015 to train our model so that each data point has a non-zero response variable."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avg_amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>max_amount</th>\n",
" <th>recency</th>\n",
" <th>revenue_2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3886.000000</td>\n",
" <td>3886.000000</td>\n",
" <td>3886.000000</td>\n",
" <td>3886.000000</td>\n",
" <td>3886.000000</td>\n",
" <td>3886.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>67.781735</td>\n",
" <td>1636.163664</td>\n",
" <td>4.740607</td>\n",
" <td>88.328806</td>\n",
" <td>306.346372</td>\n",
" <td>92.304406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>160.057636</td>\n",
" <td>1101.254072</td>\n",
" <td>3.786875</td>\n",
" <td>222.147175</td>\n",
" <td>519.457413</td>\n",
" <td>217.448347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>30.000000</td>\n",
" <td>649.750000</td>\n",
" <td>2.000000</td>\n",
" <td>30.000000</td>\n",
" <td>23.000000</td>\n",
" <td>30.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>40.000000</td>\n",
" <td>1604.000000</td>\n",
" <td>4.000000</td>\n",
" <td>50.000000</td>\n",
" <td>97.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>60.000000</td>\n",
" <td>2666.000000</td>\n",
" <td>7.000000</td>\n",
" <td>80.000000</td>\n",
" <td>328.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4500.000000</td>\n",
" <td>3647.000000</td>\n",
" <td>40.000000</td>\n",
" <td>4500.000000</td>\n",
" <td>3544.000000</td>\n",
" <td>4500.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avg_amount first_purchase frequency max_amount recency \\\n",
"count 3886.000000 3886.000000 3886.000000 3886.000000 3886.000000 \n",
"mean 67.781735 1636.163664 4.740607 88.328806 306.346372 \n",
"std 160.057636 1101.254072 3.786875 222.147175 519.457413 \n",
"min 5.000000 1.000000 1.000000 5.000000 1.000000 \n",
"25% 30.000000 649.750000 2.000000 30.000000 23.000000 \n",
"50% 40.000000 1604.000000 4.000000 50.000000 97.000000 \n",
"75% 60.000000 2666.000000 7.000000 80.000000 328.000000 \n",
"max 4500.000000 3647.000000 40.000000 4500.000000 3544.000000 \n",
"\n",
" revenue_2015 \n",
"count 3886.000000 \n",
"mean 92.304406 \n",
"std 217.448347 \n",
"min 5.000000 \n",
"25% 30.000000 \n",
"50% 50.000000 \n",
"75% 100.000000 \n",
"max 4500.000000 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"active_customers = customer_data[customer_data.active_2015].reset_index(drop=True)\n",
"\n",
"active_customers.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will now attempt to predict revenue given the following features:\n",
"\n",
"- Averge Amount spent\n",
"- Max Amount spent"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"X, y = np.array(active_customers[['avg_amount', 'max_amount']]), active_customers.revenue_2015\n",
"\n",
"lr = LinearRegression()\n",
"lr.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's view the intercept and coefficients."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intercept 20.7470894137\n",
"avg_amount 0.674855123886\n",
"max_amount 0.292254204972\n"
]
}
],
"source": [
"print 'intercept', lr.intercept_\n",
"for name, coef in zip(['avg_amount', 'max_amount'], lr.coef_):\n",
" print name, coef"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize Linear Regression Predictions\n",
"\n",
"If our model was perfect, we would see a straight diagonoal line up and to the left."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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T0wJuv/0HZcqRiFSCsdYA9ZvZa9z9D8DzZF8MNb1Iak2hMicCpV2eQkthTDya\n10lEshlrADQf6Itev6NIeRHJqpTLU2gpjIlLSzqISNyYAiB3X53ttUiplHJlaq2CLSIy8Y0pADKz\nN4/1gu7+2/yzI5JdKZsx1GQiIjLxjbUJ7EFC/550P5+RqA+QFE0pmzHUZCIiMnGNdR6go4E/j76+\nB3gcOB84IdrOB3qjcyIVQTM5i4jIcMbaB+h/06/N7N+Bi9y9I5bkt2b2JHA58KPCZlEkN5rJWURE\nRpPPWmDHEmqAMj0O/MX4siMyfprJWURERpNPAPQI8Dkz27MifPT6c9E5kbJJz+Q8MHAtYR6fIwkz\nOV9DZ2eHmsMKQE2LIjIR5BMAnQc0AU+ZWZeZdQFPRcfOK2TmRHKlmZyLp6+vj4ULFzNjxgyam5tp\naGhg4cLF9Pf3lztrY6LATUTi8lkMdR2hQ/Tngd9G2/8D/jw6J1I2Q2dyjtNMzuNVrU2L1R64iUhx\n5LsY6ovAtwucF5Fx00zOxVHNi8QODdzmAmvo6rqIlpalrFp1Z5lzJyLlkk8TGGbWama/NLOnzez1\n0bHlZvauwmZPJHft7W00Ns4hrP59FNBKY+MczeQ8DtXatKg+YSIynJwDIDP7GHAlsBKoZXDiw37g\nE4XL2rD3P83M/svMfm9mu83snVnSXBYFZy+Z2d1mVpdxfj8zu87MnjOz7WZ2u5kdkZGm1sy+Z2bb\nzKzfzG4ys4OK/XwyfumZnFOpFB0dHaRSKVatulND4MehWpsWqzVwE5Hiy6cG6ELgw+7+z8Cu2PFf\nE4bIF9tBhJmpzyfLrNRmdjFwAfAR4CTgRaAzPmoNuBpYTJi4cS7wWuA/Mi51GzATWBClnQvcUMgH\nkeKqr69n0aJFFds0U03STYs1NRcRmpKeBNqoqVlGU9Ng02KldTSu1sBNRIovnwDoaGBjluOvEIKT\nonL3Ve7+T+7+Y8LSHJmWAZe7+0/d/SHgLEKA824AM5sCfAhY7u6r3X0jcA5wqpmdFKWZSRjV9nfu\n/mt3v48Q+H3AzKYX+xlFRrNixQpaW1u55ZZbSnbPkZoWK7uj8STCr+9g4AYXkWcPABGZIPL5C/A4\ncHyW4wsp8zxAZnY0MB24J33M3V8AHgBOiQ6dSOj8HU+ziTCsJZ1mDtAfBUdpXYQap5OLlX+R0axf\nv57Jkw/k3HPPpa2tjXPOOYfJkw/kwQcfLPq9R2parNQRYqEJbDdhxZ7BwC3s71YTmEiC5TMK7Erg\nOjPbn1Bvl4jqAAAgAElEQVQDc5KZtRAmQjy3kJnLw3RCkLIl4/iW6BzANGBHFBgNl2Y68If4SXcf\nMLO+WBqRkjvllNPYuXMycCPpEU07d36ck056Kzt2vFSSPGQuElvJI8QGm8A+BHwL6AHqCP8T3Tsh\nmsBSqRS9vb3U1dWpubdC6HtSHXIOgNz9JjN7GfgicCChr8zTwDJ3/36B81eVli9fztSpU4cca2lp\noaWlpUw5knLL/IOYzx/IFStWsHPny4TgZzDQAGfnztAcdvbZZxfnAUYwlo7G5foQGDotwjVMpGkR\ntOZd5dH3ZOza29tpb28fcmzbtm2lzYS7j3kj1PgcBewf7R8IHJHLNQq5Eeq23xnbPzo69uaMdD8H\nropevwMYAKZkpHmCEMRB6BO0NeN8DbATeNcI+ZkF+Pr1613E3X3r1q3e1NTshJpJB/yww6YN2W9q\nava+vr5Rr7V06dLoPZsdPLZtdsCXLl1agifa26ZNm6J8tWXk67sOeCqVKku+0vr6+vb6Hoy1zCtZ\nU1Oz19QcGpX7Zoc2r6k51JuamsudtcTS92R81q9fn/4dneWliCFyShz6DO0A6kuRuTHkZ0gAFB17\nmtDBOb0/BXgZ+NvY/ivAX8fSzIiudVK0/8YoSDohluYMwqi36SPkRwGQDJHtDyJMdTg+5z+QN910\n04iBxs0331z8BxrG4HN+N3qu71bcH/5UKuUdHR1lD8gKodKDziTS92T8Sh0A5dQE5u67zawbOAwo\nyzjXaC6eOgZHgP25mR0H9Ln7k4Qh7p83sx5Crc7lhLXKfgyhU7SZrQCuNLN+YDtwLfArj5bycPdH\nzawTuDGa92gy8HWg3d2fKdGjSpUbrm9M+P1uBf5ELn1lTjvtNEJF5Meja4TmnDDrQw2nnnpq0Z5l\nNO3tbbS0LKWzs3XPscbG5oqafDKz71I1q+Rmx6TS96T65DMK7LPAFWb2pkJnZoxOJAzDX0/4FPhX\nYANwKYC7f5UQrNxA6Ol4ALDI3XfErrEc+ClwO6F57GnCnEBxS4BHCaO/fkqYSOSjxXggKY9iz1kz\n2h/E0CF3cH+0EUnhegOEeDw+omkyMFDWEU2afLK0NL9R5dH3pPrkMwrsO4S+P78xsx2E5qU93P3Q\nQmRsOO6+mlECN3e/BLhkhPOvECYGuXCENM8Dlb3Ko+SlVB0Vh/5BPDN2ZnX0tW7I/mh/IAevdxVw\nBHA/YeaGLUBrRfyBnUi1LJVMa95VHn1PqlCubWbA2cAHh9tK0W5XqRvqA1QVStlRMVvfmKF9gHLr\nK1MNfW0q1aZNmyZMHyD3idu5u5rpezI+Fd0JWpsCoGqXS0fFQnxgZvuDmO8osOGuV4g/sBMtOIjL\nNhJvIn0oTaTO3ROFvif5qdgAiNDs9BngV8B/A/8CHFCKTFbLpgCo8nV0dES/YNmHknd0dBTlAzPz\nD+J4/0AW6g/sRA8O3DU0WaRaVHIA9I+EYeCrgB8R+v78WykyWS2bAqDKN5YaoCR9YE70Z9XQZJHq\nUeoAKJdRYGcB57v7Qnd/N/BXwJlmphUFpWqMtqq5u9PZ2cHAwLWEjstHEoaqX0NnZ0fFrHJeCOlh\n+hP5WccyNFlEkimX4OUoYGV6x93Ti4O+ttCZEimmkVY1T9IHZhKeVUOTRWQ4uQyD34cwc1vcTmDf\nwmVHpPjSc9Z0d3fT09MzZD2u0YauV8oHZiEWW6yWZx0PDU0WkeHkEgAZcIuZvRI7tj/wLTN7MX3A\n3f+mUJkTKabh56yZRJgiKj7b8kXkN29oYRVyDqOkBAfVMEu1iJReLn/RbwX+AGyLbW2EWZTjx0QK\nptizNWcKzUK7gRMYOtvyCcDusjcLLVnSSlfXWsKv3magja6utbS05DdnZ7bmwFNOOXZCBQeapVpE\nsipFT+ukbGgUWMGUa3j20FFDKYeO6Gv5Rw0Va0TT1q1b/bTT5k3oofAiUvkqeRSYSMkUuqZjrIaO\nEvsR8Czw4z2jxMrZLFSsTstLlrRy332/o9RlLSJSTvmsBSZSVMOtoj7WVdPH6/rrv85JJ72VrVs/\ns+fYIYdM45vf/EbR7pkpWyfnSZPS/69k77S8zz65/zqXu6xFRMpFNUBScQpV05Fv/6Hzz7+Q55/f\nSbxG5Pnnd/Kxj12Q03Xy0dfXx8KFi5kxYwbNzc00NDSwcOFi+vv72b17N+FXdugcRrAMmMSuXbty\nvl8ShsKLiGSjAEgqzmhzt/z+978fMagZKYgYTbknBxyp6S+Uy24GO2bHv+7Oa9i65skRkaRSACQV\nZ7jZms0uBCbx4Q9/eMSgZjz9h4pVIzKW2qjRgi8zi8plM3AFYWDmFdTUbM67f9JoM2Or+UtEJqxS\n9LROyoZGgRVMtlXPYT+HG0Zcs2q8I6VWrVo14vvvuuuunJ4jl9FsY1modf369b7vvgcMud6++x7g\nGzduzClfccVaYV5EJBcaBSaJlq4pee655/bM3fLtb387OrsC+AgjNUvlUoOTrVZmaD+b06J7zWW4\nfjaj1ezkUhs1luaoM85oZufOycDXCDVAX2Pnzsk0Ni7Mev9sMvOseXJEJJFKEWUlZUM1QHkbqaZk\nLDUjaWOpARrpXoPvr8mofaoZUoM0lpqd0fJy44037lUjNbg6+3ej5/vunpqu8dZOlWtuJRGRsSh1\nDVDZg4aJtCkAyt/gB3/bXk1cuTZrjRREuLu/4x2NUXPa0Oa1OXPeGgVbNQ5Th+Ql7NeMKb9powVu\n2YKQkZqjLr300hGvd+mll+ZdxjK6TZs2eUdHR1knwxSZyBQAVfGmACg/YwlwRgtq4kYKIsK9JjnU\nxgKBb2UJiLLn5T3vec+YA7LR0sHqYYOQVCq114ft0BqgTZ45S/VINUDFmkU6230mWpCgmjOR0lAA\nVMWbAqD8jKWJK5+OutmCiG9/+9tZAoHmWED0xhHzcsQRR8Ty+/NYEDI0v2nZArdwr+a8gpDa2sOz\nBGv7eW3t4eMu4/GYyEGCas5ESkOdoCVxxjLvT7xT9Fg76noISvdIpVL87ne/i/bSnaRTQAfwdcLQ\n83Rn4ux5Of744znssMMIHaXfDjQDDcDi6DpD587JtthoWFg1vtjoPGBsQ+yPPfZNwP7EO1XD/tHx\n4RV7vp9yLV1SbOWeF0pEiqgUUVZSNlQDlLe9a0q+uVdNR1NTs69bt27EJpZVq1b5xRdf7CeeeNKQ\n9x522LSMWpPjHfqiGpx4zcgmH+wDFK+1CX2APvGJT3hTU7ObHeJD+wjVOuy3p1YgsykolUoNU/s0\n9hqg8TZj5dKMmItSNa+VQ7FrzkRkkJrAqnhTAJS/vZu4Ju0VZIT9SVmbWHp6emJBzqQoYLnC4VaH\nv/DsnZpnOnwl48M7/YGXfRTYnDlzRvyw7+rqGrEpaP7806PnGAxCzA7x+fNPH7WMxvphPFw/nGLN\n9zORg4SJHNyJVBoFQFW8KQAav7HUlGTrPByCn3TQk67hGb1T89CA6bse+vWk03/S4c3R15D+hBNO\nGPHD/s1vPt6z9dFJBzjz558+4vmRjPZhvG7dujEFONn6Ro3HRA8SilVzJiJDKQCq4k0BUGGMPny8\nY8gH7IoVK2IfwB0OliXIGO5at3qYXTqePh4QDW0CO+6440YJqMyHjjBLN49N8s7Ozth7U545imss\ngcJpp709a97mzn1HWTvrTuQgQTNli5RGqQOgfYbtHCRSBp2dnaxcuTLaWwO8GngAOAXYEh1Pd9id\nB8A111wT7c8F7ope70eYOfp1hM7KawidWNNWR19PAeqBAwkdlCEsOPpibB9gH2CAww8/nFmz3sJv\nfnMRAwMe5WE1NTXLmDHjWB5++HcMdqgm+upAK3feeWcsn0dG94XQqTl0gh5t7a2HHvof4E8ZeduP\nBx98kBde6Cd0Qh6898CA09nZSnd3d1HX9Wpvb6OlZSmdnYP5amxspr29bYR3VYf0TNnd3d309PRQ\nV1enNdJEJgAFQFIR7r77bt773vdHH+JpZwPxpSf2IQQz6Q+fEMT89re/jfbfDDwfvX4BuI0QEDQD\nFxICkXnR+5ZFx9PXmhd9/TRhmYmajHvXAAN0dXVF+5OIByGNjc0sXHg6y5cvZ7hlOA4//PBoP3sw\nNtpIrM7OTvr7n42e6SSghxAMPsALL6TzMvwSIMX80E5CkFBfXz/hnkkkyTQMXsqqr6+PhQsXc8YZ\nC3nhhd0MDqOeChwEQ4Z7HwRsJL1iOVwAzCSsifUXhAAnnn4tsDR6fQJDh6IfxdCh6OkaocboOisY\nHCKfAm6Kjl8M/JYQbA116qmnRq8yh5p/H4CTTz55XCuvP/DAA9GruYTAbRFwGHBzLFVxhrmPVX19\nPYsWLVKgICIVTzVAUlZLlrRy111rCM1O1xFqRjqBbWQ256SbkkLwAnAw8AjwwWh/uPTfAf4v486P\nAD8k1AKtJgRTRwAD0fnszVTwMvBZBoOsucAaurouAi5h/vzT+dnPLoj6hB0HnAU8CMAZZ5zB/Pmn\nM2/ebO69N/emopNPPjl6Fa9BaiUEhW3Av5FZ01VTs4zGxtGDKxGRpFEAJGWTnmQu9PP5I4PNN/Ga\njrh0M9WhQD+hWaqNELR8cIT0y4EpxAMW+DhwPiHwAjieEKjURPvD9RnaRqgVyt7X5r//+7/5/Oe/\nEPWFmQS8ash9V6++iMbGOaRSqZybipqamth33wPYufPjhCDnyIy8NBNqvMrXDyeVStHb2zshm8BE\nZGJRE5iUTW9vL+FH8KXoyNeBbiBe05ECVkbH00HIdkIAcB3wa+BLsfRx6fTptIMz+cI3GAx+bgT+\nK3r9DCEY+jjxZqrQZ2gSoWkOhgu2nn32WVatupPOzk6G1moNnUEYyLmpKJVKsXPny8DRhCDn7Rl5\nqQXu3PPcN95446izZRdKuilzxowZNDc309DQwMKFi+nv7x/9zSIiZaAASMpm0qRJhCDh9dGRKwjL\nSnyVUHPyd8AMBpebOBcwYGeU/oPA1cAmwo9yZtByAYM/4sPVDqVfp4OlDxJqgoy9+wztZrApbOS+\nNgMD8aa0ve87lmUvMoWAEUKwlgK+PUxeNoc7zZtHqUzUpTBEZOJSACRls3v3bsKP4BaGdl5eT6gV\n2p8QFN1KGJm1P2F4+18Thq0fHHvfJQwOXU8HLUcDP47uNlzt0BxCk9sFwKmEJqW3EPr6xD3MQQdN\nIQRnewdbmR2Zi7H2VggY09esBz7M4Ai33DtVF4rWyxKRaqQ+QFI2gzVA6Q9Ooq9PA58hBDCfjr3j\nEMIcOHdE+22EkVCtpBcija5MCIj+MdrPNgz+AkJ/n7XRNh+4ndCM9CVCsLWCeJ+hF1/cDsBxxx3P\nAQfsz9q1w/e1aWhooKmpma6uvecLyrdT8mDAeFHsWd4J3EM5+/0M1kyVZwi+iEg+VAMkZRM+0GHv\nD85phB/NdI1Q+qsT+ufcGntfKyGAiad7FfDz2PWyDYN/mcE+QAB90dcU8EuG7zP0NR566Am6u3sZ\nTbaV4Bsb5+QdnIRapd0MbZY7jzAVQOjzk0qlStbvZ2i+oNxD8CVZUqkUK1euVA2j5E0BkJTN008/\nHb3K/OD8CeGD/h+AuwmBzr2EIORBID0Z4Q8INT9Dm15CunsJHach1Oqck3GP9LxBqwkB0uOEGosb\no/PD9Rn6EwMD17B16xZCLVVonsvW3yU9OWAqlaKjo2PcwUm6VqmmZnN0778HPkNNzWaampo599xz\ny1LTMpiv/OY3EsmFOtxLwZRivY2kbGgtsJxceOGF0bpb6TWkfuFwSGzNpewrsg9u+4yyzten9lrL\nC74Yrb+VTnPpkPW4Brfh1vq6KPbe+Hb8mNfzGo/e3t7YqvdhO+ywaf7YY4/tSTPcavDFpPWypFTK\nueadFFep1wJTDZCUzc6dOxnapDOPsITF5CiFZbxjH0K/nXRT18gjskLH6fS1/0iYL2iAMHvyO6M0\nXyCMMAuzKS9YsIBQMTq0Y3HodzOJ0AR3bez68Wa6SXmN7srF+edfyPPP7yTe5Pf88zv52McuKOt/\nxoWu7SoGNZlUP3W4l4IqRZSVlA3VAOUk1P4Q+2qxGoT0iux7r6oOX47VymRbub02qpE5OHbN9D0+\nG52b6nCFh9Xgv7bn2meddVaUNnM1+f2yHGt26BtSQ3TXXXcVrbw2bdo0Yu3U2942T/8ZZ7F161bV\nTk0QHR0dI9b6dnR0lDuLMg6qAaowZvZxM3vczF42s7Vm9pZy52kiWLFiBYOdkHcTans8lmLvSQRD\nzctu4EdRmnnR/mEM7eC8jdBXqA74DYMdoycB/xKdS48w+yDwKcJcRLt56qmnomtOycjxLsIQ/Gxr\njaXzArt27aJYRhtt9ctfrtZ/xllojqKJQx3upZAUAI3AzN4P/CuhneQEwqdpp5m9uqwZmwBuuumm\n6FX6RzAd/MwnND/B8B2RZ0Zf001d3yKM3vpUtB9Ga4U1so5l6Ciud5N9hFlowjr22GOja1wF3AVc\nSlhjawC4nqEB2TWETtiDs1QX8w/w0HmA4lbHXhdu4sWJQE0mE4s63EshKQAa2XLgBnf/jrs/Shhz\n/BLwofJmq/qtXbuW0J8nvVZWOhB5kBBnwvAf9OcwONPzNOB0wsSAF8XSvi/jvengaTZD5x6KBzO7\nWbx4MfvuewBhosMt0b0ejt47XED27ZL8AR46D1C2ZTpA/xkPNZY5iqS6FHp6CUkuTYQ4DDPbl/Bp\nmV5oCnd3M+sCTilbxiaUAQabuWDoCu5zCEGIM3TywkkMBh5TgPtj14vXhAy3mOkT0dfsH4i7du1i\n3br7OOmkt7JzZ2tGmuGu+bWSTD649zxAaccDfZx22jzuu69wEy9OBEObTPb+3iU1MKxm6Q733d3d\nOS8oLDJEKToaVeMGvIbwaXNyxvGvAPcP8x51gh4j9nRIHW4I+wWxjsvpbb6HYesHRh2c08PnM4e6\nHxp1ah56burUw2LXyt6ROD50/Oabb/alS5f6zTffHBt6O3jNmppDfdast5R0uPlgPtIduK/Y09FZ\nQ9GzG+57l/TO4SKVptSdoMseaFTqpgCouEYLRGBKNOoqMwjC4ViHX3p67p3BLT5P0ND3pQOBVCrl\ns2a9JecPxEoJLsaSj1QqVfJ5gCpZpXzvRGRkpQ6AzD0+8kbSoiawl4D3uPt/xY7fAkx197/O8p5Z\nwPq5c+cyderUIedaWlpoaWkpbqaryIoVKzj33I8SFjT9BkObubZHqdK/C3GZo8Xi+zXA2wlNRIcz\nadINHH98Pd///m1Dqsj7+/tpaVlKZ+fg+mFNTaEJa7R5ayql2r1S8lFNVGYilaO9vZ329vYhx7Zt\n28aaNWsAZrv7hmLnQQHQCMxsLfCAuy+L9o3QW/dad78iS/pZwPr169cza9as0ma2CoXiTC+Impa5\nP+QdHH30G9i+fTu7d++mpmZfnn12y56zhx02LVqiIhgtqNEHoohI5diwYQOzZ8+GEgVA6gQ9siuB\nW8xsPbCOMCrsQOCWcmZqoti4cWPU2fjl2NGhwc+UKVOYNGkSCxYs4Pbbb9/rGplBTC5BTX19vQIf\nEZGEUgA0Anf/YTTnz2WE8dYPAk3u/mx5czYxHH/88ezY8RK33HIL99xzDwsWLODss8/O6RqZQYyC\nGhERGQsFQKNw9+sJM+BJkZx99tk5Bz4iIiLjoYkQRUREJHEUAImIiEjiKAASERGRxFEAJCIiIomj\nAEhEREQSRwGQiIiIJI4CIBEREUkcBUAiIiKSOAqAREREJHEUAImIiEjiKAASERGRxFEAJCIiIomj\nAEhEREQSRwGQiIiIJI4CIBEREUkcBUAiIiKSOAqAREREJHEUAImIiEjiKAASERGRxFEAJCIiIomj\nAEhEREQSRwGQiIiIJI4CIBEREUkcBUAiIiKSOAqAREREJHEUAImIiEjiKAASERGRxFEAJCIiIomj\nAEhEREQSRwGQiIiIJI4CIBEREUkcBUAiIiKSOAqAREREJHEUAImIiEjiKAASERGRxFEAJCIiIomj\nAEhEREQSRwGQiIiIJI4CIBEREUkcBUAiIiKSOAqAREREJHGqKgAys38ws1+Z2Ytm1jdMmiPN7M4o\nzTNm9lUzm5SR5s1mtsbMXjaz/zWzT2e5ztvNbL2Z/cnMUmb2wWI9V9K1t7eXOwtVSeWWO5VZflRu\nuVOZVb6qCoCAfYEfAt/MdjIKdDqAfYA5wAeBs4HLYmleBXQCjwOzgE8Dl5jZubE0bwB+CtwDHAdc\nA9xkZqcX+HkE/aHIl8otdyqz/Kjccqcyq3z7lDsDuXD3SwFGqI1pAt4IvMPdnwN+Z2b/CPyLmV3i\n7ruApYRA6u+i/UfM7ATg74Gbout8DHjM3T8T7W8ys7cBy4G7i/FsIiIiUjrVVgM0mjnA76LgJ60T\nmAr8ZSzNmij4iaeZYWZTY2m6Mq7dCZxS+CyLiIhIqU20AGg6sCXj2JbYufGmmWJm+xUgnyIiIlJG\nZW8CM7MvAxePkMSBme6eKnZWCnCN/QEeeeSRAlwqObZt28aGDRvKnY2qo3LLncosPyq33KnMchf7\n7Ny/FPcrewAEfA24eZQ0j43xWs8Ab8k4Ni12Lv11WpY0PoY0L7j7KyPc/w0AS5cuHWN2JW327Nnl\nzkJVUrnlTmWWH5Vb7lRmeXsDcF+xb1L2AMjdtwJbC3S5+4F/MLNXx/oBnQFsAx6OpfmimdW4+0As\nzSZ33xZLsyjj2mdEx0fSCZwJPAH8Ke+nEBERSZ79CcFPZyluZu5eivsUhJkdCRwKvAv4JDA3OtXj\n7i9Gw+A3Ak8TmtVeA3wH+La7/2N0jSnAo4TRXF8BjgVWAMvcfUWU5g3A74DrgX8DFgBXA83untk5\nWkRERKpMtQVANwNnZTn1DndfE6U5kjBP0NuBF4FbgM+5++7Ydd4EXEdoLnsOuNbdv5Zxr7nAVcBf\nAE8Bl7n7dwv8SCIiIlIGVRUAiYiIiBTCRBsGLyIiIjIqBUAiIiKSOAqAxkCLsBaPmX3czB6PymSt\nmWVOYzAhmdlpZvZfZvZ7M9ttZu/MkuYyM3vazF4ys7vNrC7j/H5mdp2ZPWdm283sdjM7IiNNrZl9\nz8y2mVm/md1kZgcV+/mKwcw+Z2brzOwFM9tiZneYWUOWdCq3GDM7z8x+Ez3LNjO7z8wWZqRRmY3A\nzD4b/Z5emXFc5RZjZl+Iyim+PZyRpnLKzN21jbIBXwCWEeYs6styfhJh1FgnYVRZE/AH4IuxNK8C\n/g+4FZgJvI/QSfvcWJo3AH8EvgrMAD4O7AROL3cZFKlc30+YLuAswhpuNwB9wKvLnbcSPPtCwiK9\n7wIGgHdmnL84Kov/D3gT8COgF5gcS/NNwpQL84ATCPNm/CLjOiuBDcCJwFuBFNBW7ufPs8w6gNbo\n9+dYwoLFTwAHqNxGLLfF0c/bMUAd8EXgFcIEsyqz0cvvLYS56DYCV+pnbcSy+gLwW+Bw4IhoO7RS\ny6zsBVZNG2F1+WwB0CJCoPLq2LGPAv3APtH+xwgjzvaJpfky8HBs/yvAbzOu3Q50lPvZi1Sea4Fr\nYvtGGHH3mXLnrcTlsJu9A6CngeWx/SnAy8D7YvuvAH8dSzMjutZJ0f7MaP+EWJomYBcwvdzPXYBy\ne3X0fG9TueVcdluBc1Rmo5bTwcAmYD7wM4YGQCq3vcvrC8CGEc5XVJmpCawwtAhrjsxsX2A2cE/6\nmIef5C4m4PPmwsyOJqxHFy+bF4AHGCybEwkTmcbTbAI2x9LMAfrdfWPs8l2EWc9PLlb+S+gQwrP0\ngcptLMxskpl9ADgQuE9lNqrrgJ+4+73xgyq3EdVHTfu9ZtZmYWqaiiwzBUCFoUVYc/dqoIbszzt9\n7+SJMp3wyzxS2UwDdkR/QIZLM53QFLuHh9nP+6jyMjYzI0xO+kt3T/cxULkNw8zeZGbbCf9dX0/4\nD3sTKrNhRYHi8cDnspxWuWW3FjibUCNzHnA0sCbqn1NxZVb2pTDKxSbWIqwiSXM9YZLSU8udkSrx\nKHAcoVb6vcB3LEz2KlmY2esIAXaju+8sd36qhbvHl7B4yMzWAf9L6PP6aHlyNbwk1wB9jdDxdrht\nJrktwppt8dT0uZHSFGIR1mr0HKHzb7bnfWbv5InyDCEwHqlsngEmW1jaZaQ0maMnagjLyVRtGZvZ\nN4Bm4O3u/n+xUyq3Ybj7Lnd/zN03uvv/A35DGNihMstuNqEj7wYz22lmOwmdcpeZ2Q5CjYTKbRQe\n1tdMETrfV9zPWmIDIHff6u6pUbZdo18JCIukHmtmr44dy7YI69zoGxVPk7kI64KMa49lEdaqE/1X\ntZ7Y80bNGgsowSrAlczdHyf8IsfLZgqhfTtdNusJnf7iaWYARzH483I/cIiZnRC7/ALCH6EHipX/\nYoqCn3cRlr/ZHD+ncsvJJGA/ldmwuggjDY8n1JwdB/waaAOOc/fHULmNyswOJgQ/T1fkz1q5e41X\nwwYcSfgF+CdCUJP+hTgoOj+J8B/VSuDNhPbPLcDlsWtMIfSAv5VQdf9+wpD3v4uleQOwnTAabAZw\nPrCDUA1b9nIoQrm+D3iJocPgtwKHlztvJXj2g6KfoeMJIxo+Ee0fGZ3/TFQWf0X4Q/wjoJuhw0Wv\nBx4nrHs3G/gVew8X7SD84X4LobloE/Ddcj9/nmV2PWFk5WmE/wjT2/6xNCq3vcvtS1GZvZ4w9PjL\nhA+Z+SqznMoxcxSYym3vMrqCsEj56wnD0+8mfBYeVollVvYCq4YNuJnQXJO5zY2lOZIwL8kfo2/4\nV4BJGdd5E7Ca8KG/GfhUlnvNJUTBL0c/GK3lfv4il+35hDkfXiZE9ieWO08leu55hMAn82fq32Jp\nLiEEzS8RRgPWZVxjP+DrhObE7cC/A0dkpDmE8F/rNkLwcCNwYLmfP88yy1ZeA8BZGelUbkOf5SZC\nc/7LhP/A7yIKflRmOZXjvcQCIJVb1jJqJ0xl8jLhM+424OhKLTMthioiIiKJk9g+QCIiIpJcCoBE\nRBEwl7cAAAS9SURBVEQkcRQAiYiISOIoABIREZHEUQAkIiIiiaMASERERBJHAZCIiIgkjgIgERER\nSRwFQCIiIpI4CoBEREQkcRQAiciYmdluMxuIvmZuA2b2T2XI09pYHl42s0fM7JOlzoeIVJd9yp0B\nEakq02OvPwBcCjQAFh37Y7Y3mVmNuw8UKU9OWDzxn4H9gTOAb5rZc+5+a5HuKSJVTjVAIjJm7v6H\n9EZYidnd/dnY8ZfMrCmqjTndzDaa2SvAbDNrN7Pb4tczs2+aWUdsf5KZ/ZOZPW5mL5rZejN75xiy\n9mJ0/83ufhPwKHB6xr2OM7NOM/ujmT1tZivM7JDo3IVm9ljmRaP034jtv9fMHoxqmlJm9jkzmxSd\n2y967rPM7CdR/h81s4Wx93/UzP4v4x7vN7OXM45lu48hIgWjAEhEiuVLwCeAmcCmMb7nUuA9wIeA\nvwSuB35gZieN9aZmNh+oA3bEjh0G3Av8EjgeWAwcDXwvSvID4HVmdkrsPUcA84G2aL8RuAH4SvRM\nFwAfBTKb2y4BbgaOBX4G3GZmB8fOe5Zs7zk2wn0+NbYSEJGxUAAkIsXgwOfcfbW7P+bu20Z7g5kd\nRAgmznL3n7n7E+6+Argd+Mgob/+kmW2Papu6gF3AN2LnPwGscffL3b3H3TdG11xkZq+LarTuAZbE\n3vMBYLO7r432vwBc5u7tUd7uAi4HzsvIy7fd/T/d/THgH4BDgFmjPX/MWO8jIuOgPkAiUizrc0w/\ng9CH5xcZzT37AveP8t4VwBXAq4EvAp3uviF2/jigycy2Z7zPgWOApwi1Qf9qZsvcfTchGIo32b0Z\nmGVmX4wdqwH2STeDRX635+Lu/Wa2AzhilPzHjXifKG8iMk4KgESkWF7M2N/N3rXO+8ZeH0wISBYA\nWzPS/WmUez3v7o8Dj5vZ3wI9ZvaAu/8qdu1/B/6RwQ7baU9HX+8AvgWcYWYp4CTgbOD/b+fuXaMK\nojCMP6cQhDTiH6CdtrETtLQSRARBBBHFUiwFC5sVbATxgxCxVwxYCBEDIthpo/gJYinYyEYQBCsh\nx2LGeLlsknXVLZznV+3OvTNndmHhZWbuUgPZDGUlaanXn8xc6WS27/3L/PrcKyPqr34H49Tpt0ma\njAFI0rQsU87fdM0Cw/r6LWXraltmPpu0SGZ+jYh54DKwuza/APbVkLRWv28RsQgco5xZepmZ7+u1\njIhXwM7MnFtrjDEsA1sjYlNm/gxKuzpz+Ft1JG3AACRpWh4DpyPiCCWQnKQcVh7C6nbRdWAuIjZT\ntr22AHuBYWYu/EateeBcROzPzCXgGnAiIm4BV4AvlC23w5l5qtPvNrAAfAJu9MYcAHfrU1z3atss\nsCMzB2PO6ykl5F2MiJvAHuDoP6gjaQMegpY0FZm5CFwCrlLCTQB3evecrfecB94BDyj/6/NhvaFH\n1BpSzu8M6vuPlLAxAzwC3lDODH3udX1I2W7bPmJu94FDwAHgOfAEOAN0V5XWfcKrzut4Hec1cBC4\nMEEdSX8oMkf9XiVJkv5frgBJkqTmGIAkSVJzDECSJKk5BiBJktQcA5AkSWqOAUiSJDXHACRJkppj\nAJIkSc0xAEmSpOYYgCRJUnMMQJIkqTk/AGRyz3e8HnJtAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c8e4810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.scatter(y, lr.predict(X))\n",
"plt.xlabel('True Revenue')\n",
"plt.ylabel('Predicted Revenue')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Much Expected Revenue Will We Have in 2016?\n",
"\n",
"Apply the model to today's data."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AghHtYwV33XUsq1evNuRI0n2cIUdTYAFw8FR3QpLUcU48liRJnWTIkSRJnWTI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\nigAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a3beb10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"customers_2015 = transactions.groupby('customer_id').apply(summarize_customer).reset_index()\n",
"\n",
"X = np.array(customers_2015.ix[:, 'avg_amount':])\n",
"customers_2015['active_2016'] = clf.predict_proba(X)[:, 1]\n",
"\n",
"X = np.array(customers_2015[['avg_amount', 'max_amount']])\n",
"customers_2015['revenue_2016'] = lr.predict(X)\n",
"\n",
"customers_2015['expected_revenue'] = customers_2015.active_2016 * customers_2015.revenue_2016\n",
"axes = customers_2015.expected_revenue.plot(kind='hist', bins=300)\n",
"axes.set_xlim([0, 200])\n",
"axes.set_xlabel('Expected 2016 Revenue');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Many Customers Have a 2016 Expected Revenue of $> \\$50$? "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1672"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(customers_2015.expected_revenue > 50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Customer Lifetime Value "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this section, we'll make long-term predictions given our historical data for how many customers will be in each segment using a markov-simulation. We'll then use these predictions along with our previous calculations of average customer revenue per segment to predict long-term revenue, along with a discount factor."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dropping Maximum Amount Spent as a Feature\n",
"\n",
"We won't include maximum amount spent in our customer representation as we are returning to customer segmentations."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def summarize_customer(group):\n",
" d = {'recency': group.days_since.min(),\n",
" 'first_purchase': group.days_since.max(),\n",
" 'frequency': len(group),\n",
" 'amount': group.purchase_amount.mean()}\n",
" \n",
" return pd.Series(d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segment Customers Using All Historical Data From the Beginning of Time"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" <th>segment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>30.000000</td>\n",
" <td>3829.0</td>\n",
" <td>1.0</td>\n",
" <td>3829.0</td>\n",
" <td>inactive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>80</td>\n",
" <td>71.428571</td>\n",
" <td>3751.0</td>\n",
" <td>7.0</td>\n",
" <td>343.0</td>\n",
" <td>active low value</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>90</td>\n",
" <td>115.800000</td>\n",
" <td>3783.0</td>\n",
" <td>10.0</td>\n",
" <td>758.0</td>\n",
" <td>cold</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>120</td>\n",
" <td>20.000000</td>\n",
" <td>1401.0</td>\n",
" <td>1.0</td>\n",
" <td>1401.0</td>\n",
" <td>inactive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>130</td>\n",
" <td>50.000000</td>\n",
" <td>3710.0</td>\n",
" <td>2.0</td>\n",
" <td>2970.0</td>\n",
" <td>inactive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id amount first_purchase frequency recency \\\n",
"0 10 30.000000 3829.0 1.0 3829.0 \n",
"1 80 71.428571 3751.0 7.0 343.0 \n",
"2 90 115.800000 3783.0 10.0 758.0 \n",
"3 120 20.000000 1401.0 1.0 1401.0 \n",
"4 130 50.000000 3710.0 2.0 2970.0 \n",
"\n",
" segment \n",
"0 inactive \n",
"1 active low value \n",
"2 cold \n",
"3 inactive \n",
"4 inactive "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customers_2015 = transactions.groupby('customer_id').apply(summarize_customer).reset_index()\n",
"customers_2015 = do_segment(customers_2015)\n",
"\n",
"customers_2015.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segment Customers Using Historical Data Through 2014\n",
"\n",
"Let's adjust `recency` and `first_purchase` so it's relative to 2015."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>amount</th>\n",
" <th>first_purchase</th>\n",
" <th>frequency</th>\n",
" <th>recency</th>\n",
" <th>segment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>30.0</td>\n",
" <td>3464.0</td>\n",
" <td>1.0</td>\n",
" <td>3464.0</td>\n",
" <td>inactive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>80</td>\n",
" <td>70.0</td>\n",
" <td>3386.0</td>\n",
" <td>6.0</td>\n",
" <td>302.0</td>\n",
" <td>active low value</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>90</td>\n",
" <td>115.8</td>\n",
" <td>3418.0</td>\n",
" <td>10.0</td>\n",
" <td>393.0</td>\n",
" <td>warm high value</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>120</td>\n",
" <td>20.0</td>\n",
" <td>1036.0</td>\n",
" <td>1.0</td>\n",
" <td>1036.0</td>\n",
" <td>cold</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>130</td>\n",
" <td>50.0</td>\n",
" <td>3345.0</td>\n",
" <td>2.0</td>\n",
" <td>2605.0</td>\n",
" <td>inactive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id amount first_purchase frequency recency segment\n",
"0 10 30.0 3464.0 1.0 3464.0 inactive\n",
"1 80 70.0 3386.0 6.0 302.0 active low value\n",
"2 90 115.8 3418.0 10.0 393.0 warm high value\n",
"3 120 20.0 1036.0 1.0 1036.0 cold\n",
"4 130 50.0 3345.0 2.0 2605.0 inactive"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transactions_2014 = transactions[transactions.days_since > 365].reset_index(drop=True)\n",
"customers_2014 = transactions_2014.groupby('customer_id').apply(summarize_customer).reset_index() # customers THROUGH 2014\n",
"\n",
"customers_2014.recency = customers_2014.recency - 365\n",
"customers_2014.first_purchase = customers_2014.first_purchase - 365\n",
"\n",
"customers_2014 = do_segment(customers_2014)\n",
"\n",
"customers_2014.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compute Transition Matrix $\\mathbf{T}$ and Visualize It\n",
"\n",
"Let's define $\\mathbf{T}$ such that $\\mathbf{T}_{ij}$ is the probability that a customer in segment $i$ in 2014 will transition to segment $j$ in 2015.\n",
"\n",
"Formally, if we define random variables $s_{2014}$ and $s_{2015}$ indicating the segment of a customer in 2014 and 2015 respectively, then we have\n",
"\n",
"$$\n",
"\\mathbf{T}_{ij} = P(s_{2015} = j | s_{2014} = i).\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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Lxs+dO5fjx4/TokULpk6dyl133VWBGSuloqOj+eGHH0hNTSUgIICRI0fSrFmzQuOPHTvG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FLy9RTwuWxRdQEPnDWztlW9OkbSqcAEYFvgAfq6e6aXPfZu0S2VMZJWBg4ExgEvAbOAyyLi1ayB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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112d3b690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=[5, 5])\n",
"\n",
"merged_df = customers_2014.merge(customers_2015, how='outer', on='customer_id')\n",
"\n",
"nb_segment = len(segments)\n",
"X = np.zeros([nb_segment, nb_segment])\n",
"T = pd.DataFrame(X, columns=segments, index=segments, dtype='int')\n",
"for idx, row in merged_df[['segment_x', 'segment_y']].iterrows():\n",
" if row.isnull().sum() > 0:\n",
" continue # skip NaN segments (this is a bug)\n",
" T.ix[row.segment_x, row.segment_y] += 1\n",
"\n",
"T = T.div(T.sum(axis=1), axis=0) # normalize rows\n",
"\n",
"# Plot T\n",
"plt.imshow(T, 'gray', interpolation='nearest', vmin=-1)\n",
"plt.xticks(np.arange(nb_segment), segments, rotation=90)\n",
"plt.yticks(np.arange(nb_segment), segments)\n",
"for x in xrange(nb_segment):\n",
" for y in xrange(nb_segment):\n",
" plt.annotate(str(T.round(2).iloc[x, y]) if T.iloc[x, y] else '', xy=(y, x), \n",
" horizontalalignment='center',\n",
" verticalalignment='center')\n",
" \n",
"plt.ylabel('Start Segment')\n",
"plt.xlabel('End Segment');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define the initial state of the markov simulation to be the segment distribution in 2015."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2015</th>\n",
" <th>2016</th>\n",
" <th>2017</th>\n",
" <th>2018</th>\n",
" <th>2019</th>\n",
" <th>2020</th>\n",
" <th>2021</th>\n",
" <th>2022</th>\n",
" <th>2023</th>\n",
" <th>2024</th>\n",
" <th>2025</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>inactive</th>\n",
" <td>9158.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cold</th>\n",
" <td>1903.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warm high value</th>\n",
" <td>119.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warm low value</th>\n",
" <td>901.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new warm</th>\n",
" <td>938.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active high value</th>\n",
" <td>573.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active low value</th>\n",
" <td>3313.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new active</th>\n",
" <td>1512.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2015 2016 2017 2018 2019 2020 2021 2022 2023 \\\n",
"inactive 9158.0 0 0 0 0 0 0 0 0 \n",
"cold 1903.0 0 0 0 0 0 0 0 0 \n",
"warm high value 119.0 0 0 0 0 0 0 0 0 \n",
"warm low value 901.0 0 0 0 0 0 0 0 0 \n",
"new warm 938.0 0 0 0 0 0 0 0 0 \n",
"active high value 573.0 0 0 0 0 0 0 0 0 \n",
"active low value 3313.0 0 0 0 0 0 0 0 0 \n",
"new active 1512.0 0 0 0 0 0 0 0 0 \n",
"\n",
" 2024 2025 \n",
"inactive 0 0 \n",
"cold 0 0 \n",
"warm high value 0 0 \n",
"warm low value 0 0 \n",
"new warm 0 0 \n",
"active high value 0 0 \n",
"active low value 0 0 \n",
"new active 0 0 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"years = transactions.year_of_purchase.unique()\n",
"years = np.sort(years) + 10\n",
"\n",
"year_df = pd.DataFrame(columns=years, index=segments).fillna(value=0)\n",
"year_df[2015] = customers_2015.segment.value_counts().astype('float64')\n",
"\n",
"year_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compute Forecasted Segments via Markov Simulation\n",
"\n",
"The idea behind a markov simulation is that if we have an inital state and rules governing transition probabilites between states, then we can compute the most likely next state. In our case study, a state consists of the customer segment count for one year and the transition probabilities are encoded in $\\mathbf{T}$.\n",
"\n",
"To demonstrate how we can apply a markov chain to our case study, let's consider a simpler example in which we only have two segments; `inactive` and `active`. Let $i_{2015}$ and $i_{2016}$ be the number of customer in the `inactive` customers in 2015 and 2016 respectively, and define $a_{2015}$ and $a_{2016}$ similarly.\n",
"\n",
"\n",
"Say we are given that $\\vec{s_{2015}} = \\begin{bmatrix} i_{2015} & a_{2015} \\end{bmatrix} = \\begin{bmatrix} 10 & 20 \\end{bmatrix}$ and that $\\mathbf{T} = \\begin{bmatrix} .7 & .3 \\\\ .4 & .6 \\end{bmatrix}$. We are interested in computing $\\vec{s_{2016}} = \\begin{bmatrix} i_{2016} & a_{2016} \\end{bmatrix}$, which is the most likely next state.\n",
"\n",
"Let's consider computing just $i_{2016}$ for now. What do we know about $i_{2016}$? Well, not much, but we do know that all 2016 `inactive` customers were either `inactive` or `active` in 2015. By $T$, we know that 0.7 \\* 10 = 7 `inactive` customers in 2015 will remain `inactive` in 2016 and that 0.4 \\* 20 = 8 `active` customers will become `inactive` in 2016 (this is the most *likely* outcome). Hence the total number of 2016 `inactive` customers is 7 + 8 = 15. We can repeat the same reasoning to compute $a_{2016} = 15$, which completes the computation for $\\vec{s_{2016}}$.\n",
"\n",
"Since all we are doing are multiplications and sums to compute $\\vec{s_{2016}}$, you should be wondering whether we can express this operation as a matrix multiplication. Well, it turns out we can as\n",
"\n",
"$$\n",
"\\vec{s'} = \\vec{s} * \\mathbf{T}.\n",
"$$\n",
"\n",
"Here is it expanded out a bit:\n",
"\n",
"$$\n",
"\\vec{s} * \\mathbf{T} =\n",
"\\begin{bmatrix}\n",
" \\texttt{---- } s \\texttt{ ----}\n",
"\\end{bmatrix}\n",
"\\begin{bmatrix}\n",
"\\mid & \\mid & & \\mid \\\\\n",
"T_1 & T_2 & \\cdots & T_S \\\\\n",
"\\mid & \\mid & & \\mid \\\\\n",
"\\end{bmatrix} =\n",
"\\begin{bmatrix}\n",
" \\vec{s} T_1 & \\vec{s} T_2 & \\cdots & \\vec{s} T_S\n",
"\\end{bmatrix}.\n",
"$$\n",
"\n",
"Note that $T_i$ denotes all the probabilities ending up in segment $i$. Hence the $j$th element in the sum $\\vec{s} T_i$ is $s_j * P(s' = i | s = j)$, which is the expected number of customers in segment $j$ to transition to segment $i$."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2015</th>\n",
" <th>2016</th>\n",
" <th>2017</th>\n",
" <th>2018</th>\n",
" <th>2019</th>\n",
" <th>2020</th>\n",
" <th>2021</th>\n",
" <th>2022</th>\n",
" <th>2023</th>\n",
" <th>2024</th>\n",
" <th>2025</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>inactive</th>\n",
" <td>9158.0</td>\n",
" <td>10517.0</td>\n",
" <td>11539.0</td>\n",
" <td>12636.0</td>\n",
" <td>12940.0</td>\n",
" <td>13186.0</td>\n",
" <td>13386.0</td>\n",
" <td>13542.0</td>\n",
" <td>13664.0</td>\n",
" <td>13759.0</td>\n",
" <td>13834.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cold</th>\n",
" <td>1903.0</td>\n",
" <td>1584.0</td>\n",
" <td>1711.0</td>\n",
" <td>874.0</td>\n",
" <td>821.0</td>\n",
" <td>782.0</td>\n",
" <td>740.0</td>\n",
" <td>709.0</td>\n",
" <td>684.0</td>\n",
" <td>665.0</td>\n",
" <td>650.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warm high value</th>\n",
" <td>119.0</td>\n",
" <td>144.0</td>\n",
" <td>165.0</td>\n",
" <td>160.0</td>\n",
" <td>156.0</td>\n",
" <td>152.0</td>\n",
" <td>149.0</td>\n",
" <td>146.0</td>\n",
" <td>143.0</td>\n",
" <td>141.0</td>\n",
" <td>139.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>warm low value</th>\n",
" <td>901.0</td>\n",
" <td>991.0</td>\n",
" <td>1058.0</td>\n",
" <td>989.0</td>\n",
" <td>938.0</td>\n",
" <td>884.0</td>\n",
" <td>844.0</td>\n",
" <td>813.0</td>\n",
" <td>789.0</td>\n",
" <td>771.0</td>\n",
" <td>756.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new warm</th>\n",
" <td>938.0</td>\n",
" <td>987.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active high value</th>\n",
" <td>573.0</td>\n",
" <td>657.0</td>\n",
" <td>639.0</td>\n",
" <td>624.0</td>\n",
" <td>607.0</td>\n",
" <td>593.0</td>\n",
" <td>581.0</td>\n",
" <td>571.0</td>\n",
" <td>562.0</td>\n",
" <td>554.0</td>\n",
" <td>547.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active low value</th>\n",
" <td>3313.0</td>\n",
" <td>3537.0</td>\n",
" <td>3305.0</td>\n",
" <td>3134.0</td>\n",
" <td>2954.0</td>\n",
" <td>2820.0</td>\n",
" <td>2717.0</td>\n",
" <td>2637.0</td>\n",
" <td>2575.0</td>\n",
" <td>2527.0</td>\n",
" <td>2490.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>new active</th>\n",
" <td>1512.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2015 2016 2017 2018 2019 2020 \\\n",
"inactive 9158.0 10517.0 11539.0 12636.0 12940.0 13186.0 \n",
"cold 1903.0 1584.0 1711.0 874.0 821.0 782.0 \n",
"warm high value 119.0 144.0 165.0 160.0 156.0 152.0 \n",
"warm low value 901.0 991.0 1058.0 989.0 938.0 884.0 \n",
"new warm 938.0 987.0 0.0 0.0 0.0 0.0 \n",
"active high value 573.0 657.0 639.0 624.0 607.0 593.0 \n",
"active low value 3313.0 3537.0 3305.0 3134.0 2954.0 2820.0 \n",
"new active 1512.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" 2021 2022 2023 2024 2025 \n",
"inactive 13386.0 13542.0 13664.0 13759.0 13834.0 \n",
"cold 740.0 709.0 684.0 665.0 650.0 \n",
"warm high value 149.0 146.0 143.0 141.0 139.0 \n",
"warm low value 844.0 813.0 789.0 771.0 756.0 \n",
"new warm 0.0 0.0 0.0 0.0 0.0 \n",
"active high value 581.0 571.0 562.0 554.0 547.0 \n",
"active low value 2717.0 2637.0 2575.0 2527.0 2490.0 \n",
"new active 0.0 0.0 0.0 0.0 0.0 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for year in year_df.columns[1:]:\n",
" year_df[year] = np.dot(year_df[year-1], T)\n",
" \n",
"year_df.round(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize Forecasted Segments by Differences\n",
"\n",
"Green means an increase in the number of customers from the preceding year and red means a decrease. Color intensities are relative to the trajectory of a single segment."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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GsHHraEqWcqRqzdKMf+c55s34H2OndUKny+utW/DR71Ty86Bxyyr3TC4vX4xg/64L/Lx3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3oLX3gbB2CMGQMMA860LGtJZZl/AXnAGZXr/0B1GDwS+A5fcBtTY9kP9b1xZTj9o876T1AZTo0x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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11fd663d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=[10, 10])\n",
"\n",
"diff_df = year_df.diff(axis=1).ix[:, 2016:]\n",
"\n",
"D = diff_df.copy() # scaled version of `diff_df` from -1 to +1\n",
"\n",
"pos_mask = D >= 0\n",
"pos_D = D[pos_mask]\n",
"scaled_pos_D = pos_D.div(pos_D.max(axis=1), axis=0)\n",
"D[pos_mask] = scaled_pos_D\n",
"\n",
"neg_mask = D < 0\n",
"neg_D = D[neg_mask]\n",
"scaled_neg_D = neg_D.div(neg_D.min(axis=1), axis=0)\n",
"D[neg_mask] = scaled_neg_D\n",
"D[neg_mask] *= -1\n",
"\n",
"plt.imshow(D.fillna(0), 'RdYlGn', interpolation='nearest', vmin=-1.2, vmax=1.2)\n",
"plt.xticks(np.arange(10), np.arange(2006, 2015+1), rotation=90)\n",
"plt.yticks(np.arange(nb_segment), segments)\n",
"\n",
"for x in xrange(nb_segment):\n",
" for y in xrange(10):\n",
" plt.annotate('+'+str(diff_df.round(0).iloc[x, y]) if diff_df.iloc[x, y] > 0 else str(diff_df.round(0).iloc[x, y]), xy=(y, x), \n",
" horizontalalignment='center',\n",
" verticalalignment='center')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Many Customers Will Be `inactive` in the Future?"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OBf4IPEaZXHsv8DPgF8CZ7emaJElS6+usPAccHRFnAC+nBJYZmXlfOzsnSZLU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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116741590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = year_df.ix['inactive'].plot();\n",
"axes.set_xlabel('Year')\n",
"axes.set_ylabel('Frequency');\n",
"axes.set_ylim([0, 14000]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Many Customers Will Be `cold` in the Future?"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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aaw7fSG1m+HDYaad81Eopzz+pDim9P195ZQ4xS5fmtr09Mv31qmy6qb0qkla2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\nGJEkSUX9f4f/8SqaVJmaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1207da590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axes = year_df.ix['cold'].plot();\n",
"axes.set_xlabel('Year')\n",
"axes.set_ylabel('Frequency')\n",
"axes.set_ylim([0, 2000]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Much Forecasted Revenue Will There Be?"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hY3cD55dt81TSw8HuJT1k7A5yoSEiNks6kXR3ywJgLelZIJflatZkIyZXk55N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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11cf1e7d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"revenue_df = year_df.mul(revenue_by_segments, axis=0)\n",
"axes = revenue_df.sum().plot()\n",
"axes.set_ylim([0, 400000]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Much Cumulative Revenue Will There Be?"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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N80zMrKo5oJjZRhkzJo6aTJsGvXrFeSY/+lG2W2Vm+c6neMxsg5TPM+nSBbbd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\nZjnHAcXMzMxyjgOKmZmZ5Zz/D81Xkp0iaVe1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a525610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"revenue_series = revenue_df.sum()\n",
"cumulative_revenue = revenue_series.cumsum()\n",
"cumulative_revenue.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Discount Rates\n",
"\n",
"Since a dollar today is worth more than a dollar tomorrow, let's create a discount factor to better judge the value of our revenue."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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UEScDa6aU1im7lq4iIm4AXk0p7d3i2tXAByml3cqrrHOKiM+BrVNK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\nkiQV5v8Bz/O44ByLil0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112e84590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"discount_rate = .1\n",
"days = np.arange(11)\n",
"discount = 1 / (1 + discount_rate)**days\n",
"\n",
"axes = pd.Series(discount).plot()\n",
"axes.set_xlabel('Years')\n",
"axes.set_ylabel('Discount')\n",
"axes.set_ylim([0, 1]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Much Discounted Yearly Revenue Wll There Be?"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Yw6ZLF7jwQmjVCkaMgB9+SDo7ERHJRCYFzS+ADzZwr622LB2RytOhQ2gUnjMn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\nAiaaH5dzgR2GXYNRrTn18OmUbYZdg1Gt+RTb3a1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"text/plain": [
"<matplotlib.figure.Figure at 0x116553e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"discounted_revenue = revenue_series * discount\n",
"\n",
"axes = discounted_revenue.plot()\n",
"axes.set_xlabel('Year')\n",
"axes.set_ylabel('Discounted Revenue')\n",
"axes.set_ylim([0, 400000]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How Much Discounted Cumulated Revenue Will There Be?"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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