Más pruebas A / B incondicionales: V2 del script de AdWords gratuito

/**
* Brainlabs A/B Testing Tool with Statistical Relevance Calculator
*
* This script will pause and activate campaigns and shopping campaigns every hour.
* The script will calculate the statistical relevance of the results and email
* if a sufficient confidence is achieved.
*
* Version: 2.0
* AdWords script maintained on brainlabsdigital.com
**/

function main() {

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//The A/B testing

// Labels used for the Search/Display campaigns being tested
// Leave as blank, "", to skip
var campaignLabelA = "Control";
var campaignLabelB = "Experiment";

// Labels used for Shopping campaigns being tested
// Leave as blank, "", to skip
var shoppingLabelA = "Shopping Control";
var shoppingLabelB = "Shopping Experiment";

// The confidence levels at which to reject the null hypothesis for the trials
// Set to a number between 0 and 1
// We recommend 0.95
var confidenceThreshold = 0.95;

// Date range over which to take data for statistical relevance calculation
// Choose from TODAY, YESTERDAY, LAST_7_DAYS, THIS_WEEK_SUN_TODAY, LAST_WEEK, LAST_14_DAYS,
// LAST_30_DAYS, LAST_BUSINESS_WEEK, LAST_WEEK_SUN_SAT, THIS_MONTH, LAST_MONTH, ALL_TIME
var reportDate = "LAST_30_DAYS";

// People who will be alerted when statistical significance is achieved
// Separate multiple recipients with a comma
// Leave blank, "", top skip sending emails
var emailRecipients = "eve@example.com"; // e.g "alice@example.com, bob@example.com"

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var campaignCTR = {
campaignType: "campaigns",
metricA: "Impressions",
metricB: "Clicks",
rateName: "CTR",
testName: "campaign CTR",
labelA: campaignLabelA,
labelB: campaignLabelB,
confidenceThreshold: confidenceThreshold,
reportDate: reportDate,
};

var campaignConversionRate = {
campaignType: "campaigns",
metricA: "Clicks",
metricB: "ConvertedClicks",
rateName: "conversion rate",
testName: "campaign conversion rate",
labelA: campaignLabelA,
labelB: campaignLabelB,
confidenceThreshold: confidenceThreshold,
reportDate: reportDate,
};

var shoppingCTR = {
campaignType: "shoppingCampaigns",
metricA: "Impressions",
metricB: "Clicks",
rateName: "CTR",
testName: "shopping campaign CTR",
labelA: shoppingLabelA,
labelB: shoppingLabelB,
confidenceThreshold: confidenceThreshold,
reportDate: reportDate,
};

var shoppingConversionRate = {
campaignType: "shoppingCampaigns",
metricA: "Clicks",
metricB: "ConvertedClicks",
rateName: "conversion rate",
testName: "shopping campaign conversion rate",
labelA: shoppingLabelA,
labelB: shoppingLabelB,
confidenceThreshold: confidenceThreshold,
reportDate: reportDate,
};

var objects = [campaignCTR, campaignConversionRate, shoppingCTR, shoppingConversionRate];

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//date info
var days = [31,28,31,30,31,30,31,31,30,31,30,31];

var date = new Date();
var timeZone = AdWordsApp.currentAccount().getTimeZone();
var month = parseInt(Utilities.formatDate(date, timeZone, "MM"), 10) - 1;
var dayOfMonth = parseInt(Utilities.formatDate(date, timeZone, "dd"), 10);
var hour = parseInt(Utilities.formatDate(date, timeZone, "HH"), 10);
var year = parseInt(Utilities.formatDate(date, timeZone, "YYYY"), 10);

if(leapYear(year)) days[1] = 29;

var totalDays = 0;

for(var i = 0; i < month; i++){
totalDays += days[i];
}

totalDays += dayOfMonth;

Logger.log("Day of year: " + totalDays);

Logger.log("hour: " + hour);

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var campaignTypeArray = [];

for(var i = 0; i < objects.length; i++){
if(objects[i]['labelA'] !== '' && objects[i]['labelB'] !== ''){
if(campaignTypeArray.indexOf(objects[i]['campaignType']) === -1){
enable_pause(objects[i], totalDays, hour);
campaignTypeArray.push(objects[i]['campaignType']);
}
}
}

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for(var i = 0; i < objects.length; i++){ if(objects[i]['confidenceThreshold'] >= 0 && objects[i]['confidenceThreshold'] <= 1){

objects[i]['results'] = allStats(objects[i]);
objects[i]['confidenceLevelData'] = calculation(objects[i]['results']);
objects[i]['confidenceLevel'] = objects[i]['confidenceLevelData']['confidence'];
Logger.log("Experiment: " + objects[i]['testName'] + " Result: " + objects[i]['confidenceLevel']);
}
}

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var accountName = AdWordsApp.currentAccount().getName();
var emailSubject = "AdWords - " + accountName + " - A/B test results";
var emailBody = "The A/B tests in the AdWords account " + accountName + " have statistically significant results:nnn";

var trigger = 0;

for(var i = 0; i < objects.length; i++){ if(objects[i].hasOwnProperty('confidenceLevel')){ if(objects[i]['confidenceLevel'] >= objects[i]['confidenceThreshold']){

trigger = 1;

// Create properties for the campaign group with the better rate
winnerStats(objects[i]);

emailBody += "The test for " + objects[i]['testName'] + " shows statistically significant results. ";
emailBody += "The null hypothesis - that the control and experiment have the same rate - can be rejected ";
emailBody += "with " + percent(objects[i]['confidenceLevel'], 2) + " certainty. ";

emailBody += "The winner is campaigns labelled with "" + objects[i]['winner']['label'] + "" which have ";
emailBody += "a " + objects[i]['rateName'] + " of " + objects[i]['winner']['rate'] + ". ";
emailBody += "The loser is campaigns labelled with "" + objects[i]['loser']['label'] + "" which have ";
emailBody += "a " + objects[i]['rateName'] + " of " + objects[i]['loser']['rate'] + ".nn";

}
}
}

if(trigger === 1 && emailRecipients !== ''){
MailApp.sendEmail(emailRecipients, emailSubject, emailBody);
}

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}

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// Reporting functions

/**
* Returns stats for campaign experiment type
*
* @param object campaignExperiment the object housing the details
* @return object the results
*/
function allStats(object){

var results = {};

results['control'] = getStats(object, object['labelA']);
results['experiment'] = getStats(object, object['labelB']);

return results;

}

/**
* Returns stats for campaign experiment type
*
* @param object campaignExperiment the object housing the details
* @param object the results
* @return object the results
*/
function getStats(object, label){

var campaignType = object['campaignType'];
var date = object['reportDate'];
var metricA = object['metricA'];
var metricB = object['metricB'];

var results = {
metricA: 0,
metricB: 0
};

var iterator = eval(objectIterator(campaignType, label));
while(iterator.hasNext()){
var object = iterator.next();
var stats = object.getStatsFor(date);
results['metricA'] += eval("stats.get"+metricA+"();");
results['metricB'] += eval("stats.get"+metricB+"();");
}

return results;

}

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// Management functions

/**
* Determine which campaign group has a better rate once statistical significance has been established
*
* @param object campaignExperiment the object housing the details
*/
function winnerStats(campaignExperiment){

var controlRate = campaignExperiment['results']['control']['metricB']/campaignExperiment['results']['control']['metricA'];
var experimentRate = campaignExperiment['results']['experiment']['metricB']/campaignExperiment['results']['experiment']['metricA'];

var controlRatePercent = percent(controlRate, 2);
var experimentRatePercent = percent(experimentRate, 2);

if(controlRate >= experimentRate){
campaignExperiment['winner'] = {label: campaignExperiment['labelA'], rate: controlRatePercent};
campaignExperiment['loser'] = {label: campaignExperiment['labelB'], rate: experimentRatePercent};
}
else{
campaignExperiment['loser'] = {label: campaignExperiment['labelA'], rate: controlRatePercent};
campaignExperiment['winner'] = {label: campaignExperiment['labelB'], rate: experimentRatePercent};
}

}

/**
* Returns true if leap year, false otherwise
*
* @param int year the object housing the details
* @param boole is current year a leap year
*/
function leapYear(year){
return ((year % 4 == 0) && (year % 100 != 0)) || (year % 400 == 0);
}

/**
* Will pause or enable campaigns based on labels
*
* @param object campaignExperiment the object housing the details
* @param int totalDays the number of days since Jan 1st
* @param int hour the hour of the day
*/

function enable_pause(campaignExperiment, totalDays, hour){

var campaignType = campaignExperiment['campaignType'];
var labelA = campaignExperiment['labelA'];
var labelB = campaignExperiment['labelB'];

if(totalDays % 2 === 0){
if(hour % 2 === 0){
EnableCampaigns(campaignType, labelA)
PauseCampaigns(campaignType, labelB)
}
else{
EnableCampaigns(campaignType, labelB)
PauseCampaigns(campaignType, labelA)
}
}
else{
if(hour % 2 === 0){
EnableCampaigns(campaignType, labelB)
PauseCampaigns(campaignType, labelA)
}
else{
EnableCampaigns(campaignType, labelA)
PauseCampaigns(campaignType, labelB)
}
}
}

/**
* Produces string which can be passed to eval() to create an iterator object.
* Allows dynamic creation of iterators for different types of object.
*
* @param String campaignType the type of iterator to produce e.g "campaigns" or "shoppingCampaigns"
* @param String label for filtering
* @return String Correctly parsed AdWords iterator object
*/
function objectIterator(campaignType, label){

var iterator = "AdWordsApp." + campaignType + "()";
iterator += ".withCondition('LabelNames CONTAINS_ANY " + '["' + label + '"]' + "')";
iterator += ".get();";

return iterator;

}

/**
* Pause all campaigns of specific type which have a specific label
*
* @param String campaignType the type of campaign to change
* @param String label for filtering
*/
function PauseCampaigns(campaignType, label){

var iterator = eval(objectIterator(campaignType, label));
while(iterator.hasNext()){
var object = iterator.next();
object.pause();
}
}

/**
* Enable all campaigns of specific type which have a specific label
*
* @param String campaignType the type of campaign to change
* @param String label for filtering
*/
function EnableCampaigns(campaignType, label){

var iterator = eval(objectIterator(campaignType, label));
while(iterator.hasNext()){
var object = iterator.next();
object.enable();
}
}

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// Statistical analysis functions

/**
* Return a confidence level for rejecting the null hypothesis that the two sets of
* results are not statistically distinguishable. Takes an object of the form:
*
* var results = {
* control: {metricA: xxx, metricB: xxx},
* experiment: {metricA: xxx, metricB: xxx}
* }
*
* @param Object results the data to analyse
* @return Object outcome the confidence for rejecting null hypothesis
*/
function calculation(results){

var e1a = results['control']['metricA'];
var e1b = results['control']['metricB'];

var e2a = results['experiment']['metricA'];
var e2b = results['experiment']['metricB'];

var e1r = e1b/e1a;
var e2r = e2b/e2a;

var p1_p2 = Math.abs(e1r-e2r);
var p = (e1b+e2b)/(e1a+e2a);

var se_p = Math.sqrt(p*(1-p)*((1/e1a)+(1/e2a)));

var z = p1_p2/se_p;

// The confidence for rejecting the null hypothesis
var rejectNullConfidence = normDist(z);
// The range of values at the null hypothesis rejection confience level
var top = topInverse(rejectNullConfidence);
var bottom = bottomInverse(rejectNullConfidence);

var outcome = {confidence: rejectNullConfidence, top: top, bottom: bottom};

return outcome;

/**
* Find the top and bottom limit of the range. Within parent function
* scope to take advantage of closure. Referencing variables: p1_p2, se_p
*
* @param float cdf the number to parse as a percentage
* @return string the range bound
*/
function topInverse(cdf){
return percent(p1_p2 + baseInverse(cdf) * se_p, 2);
}
function bottomInverse(cdf){
return percent(p1_p2 - baseInverse(cdf) * se_p, 2);
}

}

/**
* Parse number as percentage with dec digits after the decimal point
*
* @param float x the number to parse as a percentage
* @param int dec the number of digits after the decimal place
* @return string the parameter number parsed as a percentage string
*/
function percent(x, dec){
return Math.round(x*100*Math.pow(10,dec))/Math.pow(10,dec) + "%";
}

/**
* The inverse of the CDF
*
* @param float cdf the CDF for the normal distribution
* @return float the CDF inverse
*/
// Inverse confidence level
function baseInverse(cdf){
return normal_cdf_inverse(1-((1-cdf)/2));
}

// Source: http://picomath.org/javascript/normal_cdf_inverse.js.html
function rational_approximation
// Abramowitz and Stegun formula 26.2.23.
// The absolute value of the error should be less than 4.5 e-4.
var c = [2.515517, 0.802853, 0.010328];
var d = [1.432788, 0.189269, 0.001308];
var numerator = (c[2]*t + c[1])*t + c[0];
var denominator = ((d[2]*t + d[1])*t + d[0])*t + 1.0;
return t - numerator / denominator;
}

// Source: http://picomath.org/javascript/normal_cdf_inverse.js.html
function normal_cdf_inverse(p) {
// See article above for explanation of this section.
if (p < 0.5) {
// F^-1(p) = - G^-1(p)
return -rational_approximation( Math.sqrt(-2.0*Math.log(p)) );
} else {
// F^-1(p) = G^-1(1-p)
return rational_approximation( Math.sqrt(-2.0*Math.log(1.0-p)) );
}
}

// Source: http://picomath.org/javascript/erf.js.html
function erf(x) {
// constants
var a1 = 0.254829592;
var a2 = -0.284496736;
var a3 = 1.421413741;
var a4 = -1.453152027;
var a5 = 1.061405429;
var p = 0.3275911;

// Save the sign of x
var sign = 1;
if (x < 0) {
sign = -1;
}
x = Math.abs(x);

// A&S formula 7.1.26
var t = 1.0/(1.0 + p*x);
var y = 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*Math.exp(-x*x);

return sign*y;
}

/**
* Find the CDF from the normal distribution
*
* @param float z the z-score of the distribution
* @return float the CDF
*/
function normDistCDF(z) {
var cdf = (0.5 * (1.0 + erf(Math.abs(z)/Math.sqrt(2))));
return cdf;
}

/**
* Parse CDF as a confidence level
*
* @param float cdf the CDF for the normal distribution
* @return float the confidence level
*/
function normDist(z){
return 1-2*(1-normDistCDF(z));
}