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//ACTION REQUIRED: TAG CONFIGURATION | |
var killDate = new Date("MM/DD/YYYY"); //INPUT CAMPAIGN END DATE | |
var vendorName = ""; //INPUT NAME OF MARKETING VENDOR | |
var requestNum = ""; //INPUT TRACKING REQUEST NUMBER | |
var tagName = ""; //INPUT NAME OF TAG/PIXEL | |
//Get current date and remove hours | |
var todaysDate = new Date(); | |
todaysDate.setHours(0,0,0,0); |
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if(_satellite.getVar('Order Value')){ | |
var orderValue; | |
var img = new Image(); | |
orderValue = _satellite.getVar('Order Value'); | |
img.src = "https://marketingpixel.example.org/track/conv/?adid=2dngvjx&order_value=" + orderValue ; | |
document.body.appendChild(img); | |
} |
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# We will use data structures and data analysis tools provided in Pandas library | |
import pandas as pd | |
# Import retail sales data from an Excel Workbook into a data frame | |
path = '/Documents/analysis/python/examples/2015sales.xlsx' | |
xlsx = pd.ExcelFile(path) | |
df = pd.read_excel(xlsx, 'Sheet1') | |
# Let's add a new boolean column to our dataframe that will identify a duplicated order line item (False=Not a duplicate; True=Duplicate) | |
df['is_duplicated'] = df.duplicated(['order_id', 'order_item_id']) |
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#import the urlprase library to break url into components | |
from urlparse import urlparse | |
#import pandas for data processing | |
from pandas import DataFrame, Series | |
import pandas as pd | |
#import URL data | |
df = pd.read_csv("/Users/Documents/analysis/my_urls.csv") |
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try { | |
if (window.dataLayer.page.attributes.length){ | |
var refinements=""; | |
for (var i = 0; i < window.dataLayer.page.attributes.length; i++) { | |
if (dataLayer.page.attributes[i].attributeInfo){ | |
refinements += dataLayer.page.attributes[i].attributeInfo.refinementValue + ";"; | |
} | |
} | |
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for (var i = 0; i < dataLayer.cart.item.length; i++) { | |
s.products += ",;" + dataLayer.cart.item[i].productInfo.productID; | |
} | |
s.products = s.products.substr(1); |
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for (var i = 0; i < dataLayer.event.length; i++) { | |
if(dataLayer.event[i].eventInfo.eventName == "Checkout"){ | |
s.events += ",scCheckout"; | |
break; | |
} | |
} |
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for (var i = 0; i < dataLayer.transaction.item.length; i++) { | |
var payType = dataLayer.transaction.total.paymentMethods[i].paymentMethod; | |
switch(payType) { | |
case "CREDIT_CARD": | |
s.eVar45 = dataLayer.transaction.total.paymentMethods[i].paymentMethod; | |
s.events += ",event27"; | |
break; | |
case "PAYPAL": |
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#import the pandas library | |
import pandas as pd | |
#pull adobe report download into a dataframe; ignorning the comment header | |
df = pd.read_csv("/Users/jasonthompson/Documents/analysis/python/sp/adobe_id.csv", comment="#", header=1) | |
#export cleaned data to a new csv file | |
df.to_csv("/Users/jasonthompson/Documents/analysis/python/sp/adobe_id_no-comments.csv", encoding='utf-8') |
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/*YouTube Player updates required by Adobe Video Tracking module */ | |
var n=0; | |
jQuery('iframe').each(function() { | |
var src = jQuery(this).attr('src'); | |
if(src){ | |
if (src.indexOf('youtube.com') > -1) { | |
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