{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Price elasticity of demand notebook\n",
"\n",
"This notebook helps you understand the impact that changes in price will have on consumer demand for a given product. Business analysts that measure price elasticity and business users that require elasticity as an input to make pricing decisions will benefit from this notebook.\n",
"\n",
"Following this workflow will allow you to identify relationships between price and demand, maximize revenue by properly pricing products, monitor price elasticities for changes in price and demand, and reduce manual processes used to obtain and update price elasticities. See the full description of the use case in the business overview for more background information."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"from datetime import date, timedelta\n",
"\n",
"import datarobot as dr\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"dr_dark_blue = \"#08233F\"\n",
"dr_orange = \"#FF7F0E\"\n",
"dr_blue = \"#1F77B4\"\n",
"\n",
"pd.set_option(\"display.max_columns\", None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to DataRobot\n",
"\n",
"Read more about different options for [connecting to DataRobot from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call\n",
"# dr.Client(config_path='path-to-drconfig.yaml')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import data\n",
"\n",
"Access the training dataset [here](https://datarobot.app.box.com/s/lzver7v68s3y693zgy0zqm5vvxp3br17)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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PctOnSale
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hotDay
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sunnyDay
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Heck 97% Pork Sausages 400g
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" Date SKUName Sales PriceBaseline \\\n",
"0 7/1/12 Heck 97% Pork Sausages 400g 109673 3.25 \n",
"1 7/2/12 Heck 97% Pork Sausages 400g 131791 3.25 \n",
"2 7/3/12 Heck 97% Pork Sausages 400g 134711 3.25 \n",
"3 7/4/12 Heck 97% Pork Sausages 400g 97640 3.25 \n",
"4 7/5/12 Heck 97% Pork Sausages 400g 129538 3.25 \n",
"\n",
" PriceActual PctOnSale \\\n",
"0 2.93 9.96 \n",
"1 2.97 8.65 \n",
"2 2.96 8.96 \n",
"3 2.92 10.08 \n",
"4 2.93 9.80 \n",
"\n",
" Marketing hotDay sunnyDay \\\n",
"0 July In Store Credit Card Signup Discount; In ... No No \n",
"1 July In Store Credit Card Signup Discount; In ... No No \n",
"2 July In Store Credit Card Signup Discount; In ... No No \n",
"3 July In Store Credit Card Signup Discount; In ... Yes No \n",
"4 July In Store Credit Card Signup Discount; ID5... No No \n",
"\n",
" EconChangeGDP EconJobsChange AnnualizedCPI \n",
"0 0.5 NaN 0.02 \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv(\"1. DR_DEMO_priceOpt_training.csv\")\n",
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project\n",
"\n",
"The snippets below create and configure a DataRobot project."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Create a Datarobot project\n",
"proj = dr.Project.create(train, project_name=\"Price elasticity of demand\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Create a dataframe from the training data\n",
"informative = [\n",
" feat_list for feat_list in proj.get_featurelists() if feat_list.name == \"Informative Features\"\n",
"][0]\n",
"# Update the feature list by subtracting parents and adding new variables provided by DataRobot\n",
"new_fl = proj.create_featurelist(\"new_fl\", list((set(informative.features) - {\"Date (Year)\"})))\n",
"# Create a new feature list to force a monotonic relationship between price and demand\n",
"mono_dec = proj.create_featurelist(\"mono_dec\", list(({\"PriceActual\"})))\n",
"advanced_options = dr.AdvancedOptions(\n",
" monotonic_decreasing_featurelist_id=mono_dec.id, only_include_monotonic_blueprints=False\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initiate Autopilot"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Project(Price elasticity of demand)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"proj.analyze_and_model(\n",
" target=\"Sales\",\n",
" mode=dr.enums.AUTOPILOT_MODE.FULL_AUTO,\n",
" advanced_options=advanced_options,\n",
" featurelist_id=new_fl.id,\n",
" worker_count=-1,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import scoring data\n",
"\n",
"Import the [scoring dataset](https://datarobot.app.box.com/s/7qgnaw595unuot3w2le9609g5wzc58ek) and use it to simulate the effects of different price levels."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
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" Date SKUName PriceBaseline \\\n",
"0 2/27/20 Heck 97% Pork Sausages 400g 3.25 \n",
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"2 2/27/20 Linda McCartney Rosemary Vegetarian Sausages x... 2.00 \n",
"3 2/27/20 Goodfella's Stonebaked Thin Margherita Pizza 345g 1.50 \n",
"4 2/27/20 New Covent Garden Potato & Leek Soup 600g 1.50 \n",
"\n",
" PriceActual PctOnSale Marketing \\\n",
"0 2.93 9.96 July In Store Credit Card Signup Discount; In ... \n",
"1 1.17 10.37 July In Store Credit Card Signup Discount; In ... \n",
"2 1.78 11.21 July In Store Credit Card Signup Discount; In ... \n",
"3 1.33 11.09 July In Store Credit Card Signup Discount; In ... \n",
"4 1.33 11.16 July In Store Credit Card Signup Discount; In ... \n",
"\n",
" hotDay sunnyDay EconChangeGDP EconJobsChange AnnualizedCPI \n",
"0 No No 0.5 NaN 0.02 \n",
"1 No No 0.5 NaN 0.02 \n",
"2 No No 0.5 NaN 0.02 \n",
"3 No No 0.5 NaN 0.02 \n",
"4 No No 0.5 NaN 0.02 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score = pd.read_csv(\"2. DR_DEMO_priceOpt_forScoring.csv\")\n",
"score.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"tags": []
},
"outputs": [
{
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" SKUName discountLevel Date PriceBaseline \\\n",
"0 Heck 97% Pork Sausages 400g 0.74 2/27/20 3.25 \n",
"1 Heck 97% Pork Sausages 400g 0.75 2/27/20 3.25 \n",
"2 Heck 97% Pork Sausages 400g 0.76 2/27/20 3.25 \n",
"3 Heck 97% Pork Sausages 400g 0.77 2/27/20 3.25 \n",
"4 Heck 97% Pork Sausages 400g 0.78 2/27/20 3.25 \n",
"\n",
" PriceActual PctOnSale Marketing \\\n",
"0 2.4050 9.96 July In Store Credit Card Signup Discount; In ... \n",
"1 2.4375 9.96 July In Store Credit Card Signup Discount; In ... \n",
"2 2.4700 9.96 July In Store Credit Card Signup Discount; In ... \n",
"3 2.5025 9.96 July In Store Credit Card Signup Discount; In ... \n",
"4 2.5350 9.96 July In Store Credit Card Signup Discount; In ... \n",
"\n",
" hotDay sunnyDay EconChangeGDP EconJobsChange AnnualizedCPI \n",
"0 No No 0.5 NaN 0.02 \n",
"1 No No 0.5 NaN 0.02 \n",
"2 No No 0.5 NaN 0.02 \n",
"3 No No 0.5 NaN 0.02 \n",
"4 No No 0.5 NaN 0.02 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create different price points in the scoring dataset\n",
"skuList = score[\"SKUName\"].unique()\n",
"discountList = range(74, 126, 1)\n",
"discountList = [discountList / 100 for discountList in discountList]\n",
"\n",
"index = pd.MultiIndex.from_product([skuList, discountList], names=[\"SKUName\", \"discountLevel\"])\n",
"indexDF = pd.DataFrame(index=index).reset_index()\n",
"scorePerm = pd.merge(indexDF, score, on=[\"SKUName\"], how=\"left\")\n",
"scorePerm[\"PriceActual\"] = scorePerm[\"discountLevel\"] * scorePerm[\"PriceBaseline\"]\n",
"scorePerm.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# scorePerm.to_csv(\"2. DR_DEMO_priceOpt_forScoring_allPerm.csv\",index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test predictions \n",
"\n",
"Use the scoring dataset with the top-performing model to generate demand predictions for each stock keeping unit (SKU) at different price points."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"proj = dr.Project.get(project_id=\"your-project-id\")\n",
"model = dr.ModelRecommendation.get(\n",
" proj.id, dr.enums.RECOMMENDED_MODEL_TYPE.RECOMMENDED_FOR_DEPLOYMENT\n",
").get_model()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"dataset = proj.upload_dataset(scorePerm)\n",
"pred_job = model.request_predictions(dataset.id)\n",
"preds = pred_job.get_result_when_complete()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [
{
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"
281880.897949
\n",
"
\n",
"
\n",
"
2
\n",
"
Heck 97% Pork Sausages 400g
\n",
"
0.76
\n",
"
2/27/20
\n",
"
3.25
\n",
"
2.4700
\n",
"
9.96
\n",
"
July In Store Credit Card Signup Discount; In ...
\n",
"
No
\n",
"
No
\n",
"
0.5
\n",
"
NaN
\n",
"
0.02
\n",
"
2
\n",
"
115643.445312
\n",
"
285639.309922
\n",
"
\n",
"
\n",
"
3
\n",
"
Heck 97% Pork Sausages 400g
\n",
"
0.77
\n",
"
2/27/20
\n",
"
3.25
\n",
"
2.5025
\n",
"
9.96
\n",
"
July In Store Credit Card Signup Discount; In ...
\n",
"
No
\n",
"
No
\n",
"
0.5
\n",
"
NaN
\n",
"
0.02
\n",
"
3
\n",
"
115643.445312
\n",
"
289397.721895
\n",
"
\n",
"
\n",
"
4
\n",
"
Heck 97% Pork Sausages 400g
\n",
"
0.78
\n",
"
2/27/20
\n",
"
3.25
\n",
"
2.5350
\n",
"
9.96
\n",
"
July In Store Credit Card Signup Discount; In ...
\n",
"
No
\n",
"
No
\n",
"
0.5
\n",
"
NaN
\n",
"
0.02
\n",
"
4
\n",
"
115643.445312
\n",
"
293156.133867
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" SKUName discountLevel Date PriceBaseline \\\n",
"0 Heck 97% Pork Sausages 400g 0.74 2/27/20 3.25 \n",
"1 Heck 97% Pork Sausages 400g 0.75 2/27/20 3.25 \n",
"2 Heck 97% Pork Sausages 400g 0.76 2/27/20 3.25 \n",
"3 Heck 97% Pork Sausages 400g 0.77 2/27/20 3.25 \n",
"4 Heck 97% Pork Sausages 400g 0.78 2/27/20 3.25 \n",
"\n",
" PriceActual PctOnSale Marketing \\\n",
"0 2.4050 9.96 July In Store Credit Card Signup Discount; In ... \n",
"1 2.4375 9.96 July In Store Credit Card Signup Discount; In ... \n",
"2 2.4700 9.96 July In Store Credit Card Signup Discount; In ... \n",
"3 2.5025 9.96 July In Store Credit Card Signup Discount; In ... \n",
"4 2.5350 9.96 July In Store Credit Card Signup Discount; In ... \n",
"\n",
" hotDay sunnyDay EconChangeGDP EconJobsChange AnnualizedCPI row_id \\\n",
"0 No No 0.5 NaN 0.02 0 \n",
"1 No No 0.5 NaN 0.02 1 \n",
"2 No No 0.5 NaN 0.02 2 \n",
"3 No No 0.5 NaN 0.02 3 \n",
"4 No No 0.5 NaN 0.02 4 \n",
"\n",
" prediction revenue \n",
"0 115643.445312 278122.485977 \n",
"1 115643.445312 281880.897949 \n",
"2 115643.445312 285639.309922 \n",
"3 115643.445312 289397.721895 \n",
"4 115643.445312 293156.133867 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predsAll = pd.concat([scorePerm, preds], axis=1)\n",
"predsAll[\"revenue\"] = (\n",
" predsAll[\"prediction\"] * predsAll[\"PriceActual\"]\n",
") # - predsAll['prediction']*predsAll['PriceBaseline']*0.2\n",
"predsAll.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View distribution of demand\n",
"\n",
"Use the snippets below to view the distribution of demand for different price points for one SKU."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"oneSKU = predsAll[predsAll[\"SKUName\"] == \"New Covent Garden Potato & Leek Soup 600g\"]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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