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Hot off the presses: Make heat maps for device performance


Heat maps are cool.

Searchers’ behavior changes by hour and by day. Heat maps let you see the hot spots where you should fire up your bids — and the moments when you could save cold, hard cash by putting your ads on ice.

I’ve shared a heat map-generating script with you before, but now the tech team at Brainlabs (my employer) has improved it. In addition to looking at the overall behavior over time, you can now see performance segmented by device.

You can also get suggestions for hourly ad schedules and device bid modifiers. (Normally, you can only set six ad schedules a day and one device bid modifier for all time — but you could, say, use this handy script to change your ad schedules and mobile bid modifiers every hour.)

What does the script do? First, it averages the data, so you don’t have to worry about getting a whole number of weeks into the date range. And it smoothes the data, too, using a five-wide weighted smoothing window to get a moving average. That means you can see trends more easily, because the noise of random variation is reduced.

You can skip particular days or even set up multiple date ranges to avoid longer periods — the week around Christmas may well be anomalous, for example — but that means you may want a longer date range to make up the number of days.

But because you don’t want to be distracted by fluctuations, you need to start off with a decent amount of data. We recommend at least six weeks’ worth (as you need several instances of each day of the week) and (if you’re looking at conversion rates) long enough to get 1,500 conversions.

So, how do you turn up the heat and put your data on the map? We’ve made a template with the formatting all set up for you — the first step is to go to the File menu and make your own copy.

heat-map-make-a-copy

Then go to AdWords, set up a new Script, and copy in the code below. You’ll need to change a few settings:

  • spreadsheetUrl is the URL of your copy of the heat map template.
  • dateRanges lists time periods you want to get data from. Each date range uses the format "yyyy-mm-dd, yyyy-mm-dd".
    • You can speecify multiple date ranges — just have them comma separated, like ["20150601, 20150930", "20160601, 20160930"]
  • ignoreDates is used to exclude data from particular days. This is a comma-separated list of dates in the “yyyy-mm-dd” format.
    • For example ["20151225", "20160101"] would exclude last Christmas and New Year’s Day.
  • fields lists the different metrics to make into heat maps. Put them in quotes, separated by commas. These are all metrics that can simply be added up and averaged (rather than ones that need fancier calculations).
    • You can choose as many as you like from Impressions, Clicks, Cost, Conversions and ConversionValue.
  • calculatedFields is for metrics that can’t just be added up: they have to be calculated from the summable metrics after they’ve been averaged and smoothed. This is a comma-separated list, in quotes, of one field divided by another.
    • For example ["Clicks/Impressions", "Conversions/Clicks"] would give CTR and conversion rate.
  • devices lists the devices to produce heat maps for.
    • You can choose as many as you like from Mobile, Tablet and Desktop.
    • Leave this blank, [], to only have the total stats for all device traffic.
  • If suggestAdSchedules is true, the script will suggest hourly bid multipliers that you could use in ad schedules. This is based on conversion rate.
  • If suggestDeviceBidModifiers is true, the script will suggest hourly bid modifiers for each of the devices in the devices list. This is based on the device’s conversion rate.
  • baseDeviceModifiersOnBiddingMultiplier only matters if suggestDeviceBidModifiers is true. If it’s true, then the script will give device bid modifiers assuming that the suggested ad schedules are already in place.
    • For example, suppose that at a certain hour mobile bids should be increased by 30%, and the suggested ad schedule for that hour is 10%. If this is false, the the mobile modifier will be given as 30%. If this is true, then the mobile modifier will be given as 18%, because when this and the 10% ad schedule are applied, the bid will be increased by 30%.
  • campaignNameContains and campaignNameDoesNotContain filter which campaigns the script gets data from. For example, if campaignNameContains is ["Brand", "Generic"] then only campaigns with names containing “brand” or “generic” are included. If campaignNameDoesNotContain is ["Display", "Competitor"] then any campaigns with names containing “display” or “competitor” are ignored.
    • This is not case-sensitive.
    • Leave blank, [], to include all campaigns.
    • If you need to put a double quote into campaignNameContains or campaignNameDoesNotContain, put a backslash before it.
  • ignorePausedCampaigns should be set to “true” if you only want to look at currently active campaigns, or “false” if you want to include them.
    • Removed campaigns are always ignored — they’re deleted, so it’s not like you’re going to be putting any ad schedules in them!

Then authorize and preview the script. If it doesn’t work, check the Logs in case there was an error with any of the settings. And take care — if you run (or even preview) the script again, it will overwrite what’s already in the sheet.

Note that you’re quite likely to get fractional results, even with metrics like clicks that have to be whole numbers. This is because the heat map shows the average per hour, not the total.

If you want to tinker further, there are a couple of Advance Settings:

  • You can change how the data gets smoothed, by changing the smoothingWindow and smoothingWeight arrays.
  • By default, suggested ad schedules and bid modifiers will be kept between +35% and -35%. If you’d like them to go lower or higher, then you can change minBidMultiplierSuggestion and maxBidMultiplierSuggestion in the Advanced Settings section.
/**
*
* Heat Map Creation Tool – with Devices
*
* This script calculates the smoothed average performance of each hour of each day
* of the week, and outputs this into a heat map and graph in a Google Sheet. This
* can be done for all data and for device data. It also suggests ad schedules and
* device bid adjustments based on conversion rates.
*
* Version: 2.0
* Google AdWords Script maintained on brainlabsdigital.com
*
**/
//////////////////////////////////////////////////////////////////////////////
// Options
var spreadsheetUrl = https://docs.google.com/YOUR-SPREADSHEET-URL-HERE;
// The URL of the Google Doc the results will be put into.
// Copy the template at https://docs.google.com/spreadsheets/d/19OsCHG5JE_TqHHCZK1HNXyHizrJZ0_iT6dpqUOzvRB4/edit#gid=1022438191
// so you have the correct formatting and charts set up.
var dateRanges = [2016-09-01,2016-10-31];
// The start and end date of the date range for your data
// You can have multiple ranges, eg [“2016-06-01,2016-07-31″,”2016-09-01,2016-10-31”]
// would get data from June, July, September and October 2015.
// Format for each range is “yyyy-mm-dd,yyyy-mm-dd” (where the first date is the
// start of the range and the second is the end).
var ignoreDates = [];
// List any single days that are within your date range but whose data you do not
// want to use in calculations, for instance if they had atypical performance or
// there were technical issues with your site.
// eg [“2016-02-14″,”2016-03-27”] would mean data from Valentine’s Day and Easter
// 2016 would be ignored.
// Format for each day is “yyyy-mm-dd”
// Leave as [] if unwanted.
var fields = [Impressions, Clicks, Conversions];
// Make heat maps of these fields.
// Allowed values: “Impressions”, “Clicks”, “Cost”, “Conversions”,
// “ConversionValue”
var calculatedFields = [Clicks/Impressions,Conversions/Clicks];
// Make heat maps of a stat calculated by dividing one field by another.
// For example “Clicks/Impressions” will give the average clicks divided by the
// average impressions (ie the CTR).
// Allowed fields: “Impressions”, “Clicks”, “Cost”, “Conversions”,
// “ConversionValue”
var devices = [Mobile];
// Make heat maps and bid modifier suggestions for these devices
// Allowed fields: “Mobile”, “Tablet”, “Desktop”
var suggestAdSchedules = true;
// If true, the script will suggest hourly ad schedules, based on conversion rate.
var suggestDeviceBidModifiers = true;
// If true, the script will suggest bid modifiers for the devices specified above,
// based on the devices’ conversion rates.
var baseDeviceModifiersOnBiddingMultiplier = true;
// If true, then the device bid modifiers given will be adjusted to take into
// account the suggested ad schedules.
// For example suppose that at a certain hour device bids should be increased by
// 30%, and the suggested ad schedule for that hour is 10%.
// If this is false, the the device modifier will be given as 30%.
// If this is true, then the device modifier will be given as 18%, because when
// this and the 10% ad schedules are applied this increases the bid by 30%.
var campaignNameDoesNotContain = [];
// Use this if you want to exclude some campaigns.
// For example [“Display”] would ignore any campaigns with ‘Display’ in the name,
// while [“Display”,”Competitors”] would ignore any campaigns with ‘display’ or
// ‘competitors’ in the name. Case insensitive.
// Leave as [] to not exclude any campaigns.
var campaignNameContains = [];
// Use this if you only want to look at some campaigns.
// For example [“Brand”] would only look at campaigns with ‘Brand’ in the name,
// while [“Brand”,”Generic”] would only look at campaigns with ‘brand’ or ‘generic’
// in the name. Case insensitive.
// Leave as [] to include all campaigns.
var ignorePausedCampaigns = true;
// Set this to true to only look at currently active campaigns.
// Set to false to include campaigns that had impressions but are currently paused.
//////////////////////////////////////////////////////////////////////////////
// Advanced settings.
var smoothingWindow = [2, 1, 0, 1, 2 ];
var smoothingWeight = [0.25, 0.75, 1, 0.75, 0.25];
// The weights used for smoothing.
// The smoothingWindow gives the relative hour (eg 0 means the current hour,
// -2 means 2 hours before the current hour) and the smoothingWeight gives the
// weighting for that hour.
var minBidMultiplierSuggestion = 0.35;
var maxBidMultiplierSuggestion = 0.35;
// The minimum and maximum for the suggested bidding multipliers.
//////////////////////////////////////////////////////////////////////////////
function main() {
// Check the spreadsheet works.
var spreadsheet = checkSpreadsheet(spreadsheetUrl, the spreadsheet);
// Check the field names are correct, and get a list with the correct capitalisation
var allowedFields = [Conversions, ConversionValue, Impressions, Clicks, Cost];
var fieldsToCheck = [];
for (var i=0; i<calculatedFields.length; i++) {
if (calculatedFields[i].indexOf(/) === 1) {
throw Calculated Field + calculatedFields[i] + does not contain ‘/’;
}
var components = calculatedFields[i].split(/);
fieldsToCheck = fieldsToCheck.concat(components);
calculatedFields[i] = checkFieldNames(allowedFields, components, calculatedFields, false);
}
var fieldsToCheck = fieldsToCheck.concat(fields);
if (suggestAdSchedules || suggestDeviceBidModifiers) {
var fieldsToCheck = fieldsToCheck.concat([Clicks, Conversions]);
}
var allFields = checkFieldNames(allowedFields, fieldsToCheck, fields, true);
// Check there are date ranges and fields
// – otherwise there’d be no data to put into heat maps
if (dateRanges.length == 0) {
throw No date ranges given.;
}
if (allFields.length == 0) {
throw No fields were specified.;
}
// Check the device names are correct, and make WHERE statements for them
var allowedDevices = [Mobile, Tablet, Desktop];
devices = checkFieldNames(allowedDevices, devices, devices, true);
var whereStatements = []; // The blank one is for all devices
for (var i=0; i<devices.length; i++) {
if (devices[i] == Mobile) {
whereStatements.push(AND Device = HIGH_END_MOBILE );
} else {
whereStatements.push(AND Device = + devices[i].toUpperCase() + );
}
}
var dayNames = [Monday,Tuesday,Wednesday,Thursday,Friday,Saturday,Sunday];
var dailyData = {}
var numberDays = {};
var smoothedData = {};
var fieldsIncDevice = allFields.slice();
for (var i=0; i<devices.length; i++) {
fieldsIncDevice = fieldsIncDevice.concat(allFields.map(function (a) {return devices[i] + a;}));
}
// Initialise data
for (var d=0; d<dayNames.length; d++) {
smoothedData[dayNames[d]] = {};
numberDays[dayNames[d]] = 0;
smoothedData[dayNames[d]] = {};
for (var h=0; h<24; h++) {
smoothedData[dayNames[d]][h+] = {};
for (var f=0; f<fieldsIncDevice.length; f++) {
smoothedData[dayNames[d]][h+][fieldsIncDevice[f]] = 0;
}
}
}
// Get all the campaign IDs (based on campaignNameDoesNotContain, campaignNameContains
// and ignorePausedCampaigns options).
var campaignIds = getCampaignIds();
// Construct the reports
for (var d=0; d<dateRanges.length; d++) {
for (var i=0; i<whereStatements.length; i++) {
if (i == 0) {
var fieldNames = allFields;
} else {
var fieldNames = allFields.map(function (a) {return devices[i1] + a;});
}
var report = AdWordsApp.report(SELECT DayOfWeek, Date, HourOfDay, + allFields.join(, ) + +
FROM CAMPAIGN_PERFORMANCE_REPORT +
WHERE CampaignId IN [ + campaignIds.join(,) + ] +
whereStatements[i] +
DURING + dateRanges[d].replace(//g,)
);
var rows = report.rows();
while (rows.hasNext()) {
var row = rows.next();
if (ignoreDates.indexOf(row[Date]) > 1) {
continue;
}
if (dailyData[row[Date]] == undefined) {
dailyData[row[Date]] = {};
dailyData[row[Date]][Day] = row[DayOfWeek];
for (var h=0; h<24; h++) {
dailyData[row[Date]][h+] = {};
for (var f=0; f<fieldsIncDevice.length; f++) {
dailyData[row[Date]][h+][fieldsIncDevice[f]] = 0;
}
}
}
for (var f=0; f<allFields.length; f++) {
dailyData[row[Date]][row[HourOfDay]][fieldNames[f]] += parseInt(row[allFields[f]].replace(/,/g,),10);
}
} // end while
}// end for whereStatements
}// end for dateRanges
// Daily data is smoothed and totalled for each day of week
for (var date in dailyData) {
var day = dailyData[date][Day];
numberDays[day]++;
var dateBits = date.split();
var yesterday = new Date(dateBits[0],parseInt(dateBits[1],10)1,parseInt(dateBits[2],10)1);
var tomorrow = new Date(dateBits[0],parseInt(dateBits[1],10)1,parseInt(dateBits[2],10)+1);
yesterday = Utilities.formatDate(yesterday, UTC, yyyy-MM-dd);
tomorrow = Utilities.formatDate(tomorrow, UTC, yyyy-MM-dd);
for (var h=0; h<24; h++) {
for (var f=0; f<fieldsIncDevice.length; f++) {
var totalWeight = 0;
var smoothedTotal = 0;
for (var w=0; w<smoothingWindow.length; w++) {
if (h + smoothingWindow[w] < 0) {
if (dailyData[yesterday] != undefined) {
totalWeight += smoothingWeight[w];
smoothedTotal += smoothingWeight[w] * dailyData[yesterday][(h + smoothingWindow[w] + 24)][fieldsIncDevice[f]];
}
} else if (h + smoothingWindow[w] > 23) {
if (dailyData[tomorrow] != undefined) {
totalWeight += smoothingWeight[w];
smoothedTotal += smoothingWeight[w] * dailyData[tomorrow][(h + smoothingWindow[w] 24)][fieldsIncDevice[f]];
}
} else {
totalWeight += smoothingWeight[w];
smoothedTotal += smoothingWeight[w] * dailyData[date][(h + smoothingWindow[w])][fieldsIncDevice[f]];
}
}
if (totalWeight != 0) {
smoothedData[day][h][fieldsIncDevice[f]] += smoothedTotal / totalWeight;
}
}
}
} // end for dailyData
Logger.log(Collected daily data.);
// Calculate the averages from the smoothed data
var hourlyAvg = {};
var totalConversions = 0;
var totalClicks = 0;
var deviceClicks = {};
var deviceConversions = {};
for (var i=0; i<devices.length; i++) {
deviceClicks[devices[i]] = 0;
deviceConversions[devices[i]] = 0;
}
for (var d=0; d<dayNames.length; d++) {
hourlyAvg[dayNames[d]] = {};
for (var h=0; h<24; h++) {
hourlyAvg[dayNames[d]][h+] = {}
if (numberDays[dayNames[d]] == 0) {
for (var f=0; f<fieldsIncDevice.length; f++) {
hourlyAvg[dayNames[d]][h+][fieldsIncDevice[f]] = ;
}
continue;
}
for (var f=0; f<fieldsIncDevice.length; f++) {
hourlyAvg[dayNames[d]][h+][fieldsIncDevice[f]] = smoothedData[dayNames[d]][h+][fieldsIncDevice[f]]/numberDays[dayNames[d]];
}
for (var c=0; c<calculatedFields.length; c++) {
var multiplier = smoothedData[dayNames[d]][h+][calculatedFields[c][0]];
var divisor = smoothedData[dayNames[d]][h+][calculatedFields[c][1]];
if (divisor == 0 || divisor == || multiplier == ) {
hourlyAvg[dayNames[d]][h+][calculatedFields[c].join(/)] = ;
} else {
hourlyAvg[dayNames[d]][h+][calculatedFields[c].join(/)] = multiplier / divisor;
}
for (var i=0; i<devices.length; i++) {
var multiplier = smoothedData[dayNames[d]][h+][devices[i]+calculatedFields[c][0]];
var divisor = smoothedData[dayNames[d]][h+][devices[i]+calculatedFields[c][1]];
if (divisor == 0 || divisor == || multiplier == ) {
hourlyAvg[dayNames[d]][h+][devices[i]+calculatedFields[c].join(/)] = ;
} else {
hourlyAvg[dayNames[d]][h+][devices[i]+calculatedFields[c].join(/)] = multiplier / divisor;
}
}
}
// Add up the clicks and conversions, for generating the suggested ad schedules
if (suggestAdSchedules || suggestDeviceBidModifiers) {
totalConversions += smoothedData[dayNames[d]][h+][Conversions];
totalClicks += smoothedData[dayNames[d]][h+][Clicks];
if (suggestDeviceBidModifiers) {
for (var i=0; i<devices.length; i++) {
deviceClicks[devices[i]] += smoothedData[dayNames[d]][h+][devices[i]+Clicks];
deviceConversions[devices[i]] += smoothedData[dayNames[d]][h+][devices[i]+Conversions];
}
}
}
}
}
// Calculate suggested ad schedules based on the average conversion rate
if (suggestAdSchedules || suggestDeviceBidModifiers) {
if (totalClicks == 0) {
var meanConvRate = 0;
} else {
var meanConvRate = totalConversions / totalClicks;
}
for (var d=0; d<dayNames.length; d++) {
for (var h=0; h<24; h++) {
if (meanConvRate == 0 || smoothedData[dayNames[d]][h+][Clicks] == 0) {
hourlyAvg[dayNames[d]][h+][AdSchedules] = ;
} else {
var convRate = smoothedData[dayNames[d]][h+][Conversions] / smoothedData[dayNames[d]][h+][Clicks];
// The suggested multiplier is generated from the mean.
// It is dampened by taking the square root.
var multiplier = Math.sqrt(convRate/meanConvRate)1;
if (multiplier > maxBidMultiplierSuggestion) {
multiplier = maxBidMultiplierSuggestion;
} else if (multiplier < minBidMultiplierSuggestion) {
multiplier = minBidMultiplierSuggestion;
}
hourlyAvg[dayNames[d]][h+][AdSchedules] = multiplier;
}
}
}
// Device level bid modifiers
if (suggestDeviceBidModifiers) {
var deviceConvRate = {};
for (var i=0; i<devices.length; i++) {
if (deviceClicks[devices[i]] == 0) {
deviceConvRate[devices[i]] = 0;
} else {
deviceConvRate[devices[i]] = deviceConversions[devices[i]] / deviceClicks[devices[i]];
}
}
for (var d=0; d<dayNames.length; d++) {
for (var i=0; i<devices.length; i++) {
for (var h=0; h<24; h++) {
if (hourlyAvg[dayNames[d]][h+][AdSchedules] == || deviceConvRate[i] == 0 || smoothedData[dayNames[d]][h+][devices[i] + Clicks] == 0) {
hourlyAvg[dayNames[d]][h+][devices[i] + BidModifiers] = ;
} else {
var convRate = smoothedData[dayNames[d]][h+][devices[i] + Conversions] / smoothedData[dayNames[d]][h+][devices[i] + Clicks];
// We calculate the multiplier we want to end up with
var endMultiplier = Math.sqrt(convRate/deviceConvRate[devices[i]])1;
if (baseDeviceModifiersOnBiddingMultiplier) {
// The bid modifier is calculated so that if the bidding multiplier is set up as an
// ad schedule, this is the correct device bid modifier to get the desired multiplier
var modifier = ((1+endMultiplier)/(1+hourlyAvg[dayNames[d]][h+][AdSchedules])) 1;
} else {
var modifier = endMultiplier;
}
if (modifier > maxBidMultiplierSuggestion) {
modifier = maxBidMultiplierSuggestion;
} else if (modifier < minBidMultiplierSuggestion) {
modifier = minBidMultiplierSuggestion;
}
hourlyAvg[dayNames[d]][h+][devices[i] + BidModifiers] = modifier;
}
}
}
}
}
} // end if suggestAdSchedules or suggestDeviceBidModifiers
Logger.log(Averaged and smoothed data.);
// Make the heat maps on the spreadsheet
var sheet0 = spreadsheet.getSheets()[0];
var calculatedFieldNames = calculatedFields.map(function (arr){return arr.join(/)});
var baseFields = checkFieldNames(allowedFields, fields, , true).concat(calculatedFieldNames);
var allFieldNames = baseFields.slice();
for (var i=0; i<devices.length; i++) {
allFieldNames = allFieldNames.concat(baseFields.map(function (a) {return devices[i] + a;}));
}
if (suggestAdSchedules) {
allFieldNames.push(AdSchedules);
}
if (suggestDeviceBidModifiers) {
for (var i=0; i<devices.length; i++) {
allFieldNames.push(devices[i] + BidModifiers);
}
}
if (sheet0.getName() == Template) {
sheet0.setName(allFieldNames[0].replace(/[A-Z\/]/g, function (x){return + x;}).trim());
}
for (var f=0; f<allFieldNames.length; f++) {
var fieldName = allFieldNames[f].replace(/[A-Z\/]/g, function (x){return + x;}).trim();
var sheet = spreadsheet.getSheetByName(fieldName);
if (sheet == null) {
sheet = sheet0.copyTo(spreadsheet);
sheet.setName(fieldName);
}
sheet.getRange(1, 1).setValue(fieldName);
//Post the heat map data
var sheetData = [];
sheetData.push([].concat(dayNames)); // The header
var totalValue = 0;
for (var h=0; h<24; h++) {
var rowData = [h];
for (var d=0; d<dayNames.length; d++) {
if (hourlyAvg[dayNames[d]][h+][allFieldNames[f]] == undefined) {
rowData.push();
} else {
rowData.push(hourlyAvg[dayNames[d]][h+][allFieldNames[f]]);
}
totalValue += hourlyAvg[dayNames[d]][h+][allFieldNames[f]];
}
sheetData.push(rowData);
}
sheet.getRange(3, 1, sheetData.length, sheetData[0].length).setValues(sheetData);
// Work out which format to use and format the numbers in the heat map
var averageValue = totalValue / (24*7);
if (averageValue < 50) {
var format = #,##0.0;
} else {
var format = #,###,##0;
}
if (allFieldNames[f].indexOf(/) > 1) {
var components = allFieldNames[f].split(/);
var multiplierIsMoney = (components[0] == Cost || components[0] == ConversionValue);
var divisorIsMoney = (components[1] == Cost || components[1] == ConversionValue);
if ((!multiplierIsMoney && !divisorIsMoney) || (multiplierIsMoney && divisorIsMoney)) {
// If neither component is monetary, or both components are, then the result is a percentage
format = #,##0.00%;
}
}
if (allFieldNames[f] == AdSchedules || allFieldNames[f].substr(12) == BidModifiers) {
format = #,##0.00%;
}
sheet.getRange(4, 2, sheetData.length, sheetData[0].length).setNumberFormat(format);
// Update the chart title
var charts = sheet.getCharts();
if (sheet.getCharts().length === 0) {
Logger.log(Warning: chart missing from the + fieldName + sheet.);
} else {
var chart = charts[0];
chart = chart.modify().setOption(title, fieldName).build();
sheet.updateChart(chart);
}
}
Logger.log(Posted data to spreadsheet.);
Logger.log(Finished.);
}
// Check the spreadsheet URL has been entered, and that it works
function checkSpreadsheet(spreadsheetUrl, spreadsheetName) {
if (spreadsheetUrl.replace(/[AEIOU]/g,X) == https://docs.google.com/YXXR-SPRXXDSHXXT-XRL-HXRX) {
throw(Problem with + spreadsheetName + URL: make sure you’ve replaced the default with a valid spreadsheet URL.);
}
try {
var spreadsheet = SpreadsheetApp.openByUrl(spreadsheetUrl);
// Checks if you can edit the spreadsheet
var sheet = spreadsheet.getSheets()[0];
var sheetName = sheet.getName();
sheet.setName(sheetName);
return spreadsheet;
} catch (e) {
throw(Problem with + spreadsheetName + URL: ‘ + e + );
}
}
// Get the IDs of campaigns which match the given options
function getCampaignIds() {
var whereStatement = WHERE ;
var whereStatementsArray = [];
var campaignIds = [];
if (ignorePausedCampaigns) {
whereStatement += CampaignStatus = ENABLED ;
} else {
whereStatement += CampaignStatus IN [‘ENABLED’,’PAUSED’] ;
}
for (var i=0; i<campaignNameDoesNotContain.length; i++) {
whereStatement += AND CampaignName DOES_NOT_CONTAIN_IGNORE_CASE ‘ + campaignNameDoesNotContain[i].replace(//g,\\\”) + ;
}
if (campaignNameContains.length == 0) {
whereStatementsArray = [whereStatement];
} else {
for (var i=0; i<campaignNameContains.length; i++) {
whereStatementsArray.push(whereStatement + AND CampaignName CONTAINS_IGNORE_CASE “ + campaignNameContains[i].replace(//g,\\\”) + );
}
}
for (var i=0; i<whereStatementsArray.length; i++) {
var report = AdWordsApp.report(
SELECT CampaignId +
FROM CAMPAIGN_PERFORMANCE_REPORT +
whereStatementsArray[i] +
DURING LAST_30_DAYS);
var rows = report.rows();
while (rows.hasNext()) {
var row = rows.next();
campaignIds.push(row[CampaignId]);
}
}
if (campaignIds.length == 0) {
throw(No campaigns found with the given settings.);
}
return campaignIds;
}
// Verify that all field names are valid, and return a list of them with the
// correct capitalisation. If deduplicate is true, the list is deduplicated
function checkFieldNames(allowedFields, givenFields, souceName, deduplicate) {
var allowedFieldsLowerCase = allowedFields.map(function (str){return str.toLowerCase()});
var wantedFields = [];
var unrecognisedFields = [];
for (var i=0; i<givenFields.length; i++) {
var fieldIndex = allowedFieldsLowerCase.indexOf(givenFields[i].toLowerCase().replace( ,).trim());
if(fieldIndex === 1){
unrecognisedFields.push(givenFields[i]);
} else if(!deduplicate || wantedFields.indexOf(allowedFields[fieldIndex]) < 0) {
wantedFields.push(allowedFields[fieldIndex]);
}
}
if (unrecognisedFields.length > 0) {
throw unrecognisedFields.length + field(s) not recognised in ‘ + souceName + ‘: ‘ + unrecognisedFields.join(‘, ‘) +
‘. Please choose from ‘ + allowedFields.join(‘, ‘) + ‘.;
}
return wantedFields;

}

 

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