Advanced Analytics

Build Custom Analytics and Reporting Systems

Analytics Architecture Design

Step 1: Analytics Framework Setup

`
SLOS → Advanced → Analytics → Framework Design
`

Analytics Architecture:
`json
{
“analytics_framework”: {
“data_collection”: {
“methods”: [“eventtracking”, “databasequeries”, “apicalls”, “userinteractions”],
“frequency”: “real_time”,
“retention”: “7_years”,
“anonymization”: “automatic”
},
“data_processing”: {
“engine”: “stream_processing”,
“aggregation”: “multi_level”,
“transformation”: “real_time”,
“validation”: “schema_based”
},
“data_storage”: {
“primary”: “timeseriesdatabase”,
“secondary”: “data_warehouse”,
“caching”: “redis_cluster”,
“backup”: “automated”
},
“analytics_engine”: {
“querylanguage”: “SQLenhanced”,
“visualization”: “custom_dashboards”,
“export”: “multiple_formats”,
“apiaccess”: “restgraphql”
}
},
“analytics_capabilities”: {
“realtimeanalytics”: true,
“predictive_analytics”: true,
“anomaly_detection”: true,
“segmentation”: “advanced”,
“attribution_modeling”: true,
“privacycompliance”: “gdprccpa_compliant”
}
}
`

Step 2: Event Tracking System

`php
// Advanced event tracking and analytics system
class AnalyticsEngine {
private $event_collectors = [];
private $data_processors = [];
private $storage_backends = [];
private $privacy_filters = [];

public function __construct() {
$this->initializeEventCollectors();
$this->setupDataProcessors();
$this->configureStorageBackends();
$this->setupPrivacyFilters();
}

private function initializeEventCollectors() {
// Register event collectors for different data sources
$this->event_collectors = [
‘consent_events’ => new ConsentEventCollector(),
‘user_interactions’ => new UserInteractionCollector(),
‘system_events’ => new SystemEventCollector(),
‘compliance_events’ => new ComplianceEventCollector(),
‘performance_events’ => new PerformanceEventCollector()
];

// Hook into WordPress actions
addaction(‘slosconsentgiven’, [$this->eventcollectors[‘consent_events’], ‘trackConsentGiven’]);
addaction(‘slosconsentwithdrawn’, [$this->eventcollectors[‘consent_events’], ‘trackConsentWithdrawn’]);
addaction(‘wplogin’, [$this->eventcollectors[‘userinteractions’], ‘trackUserLogin’]);
addaction(‘wplogout’, [$this->eventcollectors[‘userinteractions’], ‘trackUserLogout’]);
}

private function setupDataProcessors() {
$this->data_processors = [
‘realtimeprocessor’ => new RealTimeDataProcessor(),
‘batch_processor’ => new BatchDataProcessor(),
‘aggregation_processor’ => new AggregationDataProcessor(),
‘anomaly_detector’ => new AnomalyDetectionProcessor()
];
}

private function configureStorageBackends() {
$this->storage_backends = [
‘time_series’ => new TimeSeriesStorage(),
‘relational’ => new RelationalStorage(),
‘cache’ => new CacheStorage(),
‘archive’ => new ArchiveStorage()
];
}

private function setupPrivacyFilters() {
$this->privacy_filters = [
‘anonymization’ => new DataAnonymizationFilter(),
‘pseudonymization’ => new DataPseudonymizationFilter(),
‘aggregation_filter’ => new AggregationPrivacyFilter(),
‘retention_filter’ => new DataRetentionFilter()
];
}

public function trackEvent($eventtype, $eventdata, $context = []) {
// Apply privacy filters first
$filtereddata = $this->applyPrivacyFilters($eventdata, $context);

// Enrich event data
$enricheddata = $this->enrichEventData($filtereddata, $context);

// Store event
$this->storeEvent($eventtype, $enricheddata);

// Process event in real-time
$this->processEventRealTime($eventtype, $enricheddata);

// Trigger analytics hooks
doaction(‘slosanalyticseventtracked’, $eventtype, $enricheddata);
}

private function applyPrivacyFilters($event_data, $context) {
$filtereddata = $eventdata;

foreach ($this->privacy_filters as $filter) {
$filtereddata = $filter->apply($filtereddata, $context);
}

return $filtered_data;
}

private function enrichEventData($event_data, $context) {
// Add metadata
$eventdata[‘metadata’] = [
‘timestamp’ => time(),
‘session_id’ => $this->getSessionId(),
‘useragent’ => $SERVER[‘HTTPUSERAGENT’] ?? ”,
‘ipaddress’ => $this->anonymizeIpAddress($SERVER[‘REMOTE_ADDR’] ?? ”),
‘geo_location’ => $this->getGeoLocation(),
‘device_info’ => $this->getDeviceInfo()
];

// Add context information
$eventdata[‘context’] = $context;

// Add calculated fields
$eventdata[‘calculated’] = [
‘hourofday’ => date(‘H’, time()),
‘dayofweek’ => date(‘N’, time()),
‘weekofyear’ => date(‘W’, time()),
‘month’ => date(‘m’, time()),
‘year’ => date(‘Y’, time())
];

return $event_data;
}

private function storeEvent($eventtype, $eventdata) {
// Store in time-series database for real-time analytics
$this->storagebackends[‘timeseries’]->store($eventtype, $eventdata);

// Store in relational database for complex queries
$this->storagebackends[‘relational’]->store($eventtype, $event_data);

// Cache recent data
$this->storagebackends[‘cache’]->store($eventtype, $event_data, 3600); // 1 hour TTL
}

private function processEventRealTime($eventtype, $eventdata) {
// Update real-time metrics
$this->dataprocessors[‘realtimeprocessor’]->process($eventtype, $event_data);

// Check for anomalies
$anomalyscore = $this->dataprocessors[‘anomalydetector’]->analyze($eventtype, $event_data);
if ($anomaly_score > 0.8) {
$this->handleAnomaly($eventtype, $eventdata, $anomaly_score);
}
}

private function handleAnomaly($eventtype, $eventdata, $anomaly_score) {
// Log anomaly
errorlog(“Analytics anomaly detected: {$eventtype} with score {$anomaly_score}”);

// Trigger alert
doaction(‘slosanalyticsanomalydetected’, $eventtype, $eventdata, $anomaly_score);

// Store anomaly for analysis
$this->storagebackends[‘relational’]->storeAnomaly($eventtype, $eventdata, $anomalyscore);
}

public function getAnalyticsData($query, $time_range = null, $filters = []) {
// Parse query
$parsed_query = $this->parseAnalyticsQuery($query);

// Apply time range filter
if ($time_range) {
$parsedquery[‘filters’][‘timestamp’] = $timerange;
}

// Apply additional filters
$parsedquery[‘filters’] = arraymerge($parsed_query[‘filters’], $filters);

// Execute query
return $this->executeAnalyticsQuery($parsed_query);
}

private function parseAnalyticsQuery($query) {
// Simple query parser – in production, use a proper query language
$parts = explode(‘ ‘, trim($query));

return [
‘metric’ => $parts[0] ?? ‘count’,
‘event_type’ => $parts[1] ?? ‘*’,
‘group_by’ => $parts[2] ?? null,
‘filters’ => []
];
}

private function executeAnalyticsQuery($parsed_query) {
// Try cache first
$cachekey = ‘analytics‘ . md5(jsonencode($parsedquery));
$cachedresult = $this->storagebackends[‘cache’]->get($cache_key);

if ($cached_result) {
return $cached_result;
}

// Execute query against appropriate storage backend
if ($this->isTimeSeriesQuery($parsed_query)) {
$result = $this->storagebackends[‘timeseries’]->query($parsed_query);
} else {
$result = $this->storagebackends[‘relational’]->query($parsedquery);
}

// Cache result
$this->storagebackends[‘cache’]->store($cachekey, $result, 300); // 5 minute TTL

return $result;
}

private function isTimeSeriesQuery($query) {
// Determine if query should use time-series storage
return isset($query[‘filters’][‘timestamp’]) ||
inarray($query[‘metric’], [‘countover_time’, ‘rate’, ‘increase’]);
}

public function getRealTimeMetrics() {
return $this->dataprocessors[‘realtime_processor’]->getCurrentMetrics();
}

public function generateReport($report_type, $parameters = []) {
$reportgenerator = new ReportGenerator($reporttype, $parameters);
return $report_generator->generate();
}

public function exportAnalyticsData($format, $query, $time_range = null) {
$data = $this->getAnalyticsData($query, $time_range);

switch ($format) {
case ‘csv’:
return $this->exportToCSV($data);
case ‘json’:
return json_encode($data);
case ‘xml’:
return $this->exportToXML($data);
default:
throw new Exception(“Unsupported export format: {$format}”);
}
}

private function exportToCSV($data) {
if (empty($data)) return ”;

$output = fopen(‘php://temp’, ‘r+’);

// Write headers
if (is_array($data) && isset($data[0])) {
fputcsv($output, array_keys($data[0]));
}

// Write data
foreach ($data as $row) {
fputcsv($output, $row);
}

rewind($output);
$csv = streamgetcontents($output);
fclose($output);

return $csv;
}

private function exportToXML($data) {
$xml = new SimpleXMLElement(‘‘);

foreach ($data as $row) {
$item = $xml->addChild(‘item’);
foreach ($row as $key => $value) {
$item->addChild($key, htmlspecialchars($value));
}
}

return $xml->asXML();
}

// Utility methods
private function getSessionId() {
if (!session_id()) {
session_start();
}
return session_id();
}

private function anonymizeIpAddress($ip) {
// Anonymize IP address for privacy compliance
if (filtervar($ip, FILTERVALIDATEIP, FILTERFLAG_IPV4)) {
$parts = explode(‘.’, $ip);
return $parts[0] . ‘.’ . $parts[1] . ‘.0.0’;
} elseif (filtervar($ip, FILTERVALIDATEIP, FILTERFLAG_IPV6)) {
$parts = explode(‘:’, $ip);
return $parts[0] . ‘:’ . $parts[1] . ‘::’;
}
return ‘unknown’;
}

private function getGeoLocation() {
// Get geo location from IP (simplified)
return [
‘country’ => ‘Unknown’,
‘region’ => ‘Unknown’,
‘city’ => ‘Unknown’
];
}

private function getDeviceInfo() {
$useragent = $SERVER[‘HTTPUSERAGENT’] ?? ”;
return [
‘browser’ => $this->detectBrowser($user_agent),
‘os’ => $this->detectOS($user_agent),
‘devicetype’ => $this->detectDeviceType($useragent)
];
}

private function detectBrowser($user_agent) {
if (strpos($user_agent, ‘Chrome’) !== false) return ‘Chrome’;
if (strpos($user_agent, ‘Firefox’) !== false) return ‘Firefox’;
if (strpos($user_agent, ‘Safari’) !== false) return ‘Safari’;
if (strpos($user_agent, ‘Edge’) !== false) return ‘Edge’;
return ‘Unknown’;
}

private function detectOS($user_agent) {
if (strpos($user_agent, ‘Windows’) !== false) return ‘Windows’;
if (strpos($user_agent, ‘Mac’) !== false) return ‘macOS’;
if (strpos($user_agent, ‘Linux’) !== false) return ‘Linux’;
if (strpos($user_agent, ‘Android’) !== false) return ‘Android’;
if (strpos($user_agent, ‘iOS’) !== false) return ‘iOS’;
return ‘Unknown’;
}

private function detectDeviceType($user_agent) {
if (strpos($user_agent, ‘Mobile’) !== false) return ‘Mobile’;
if (strpos($user_agent, ‘Tablet’) !== false) return ‘Tablet’;
return ‘Desktop’;
}
}
`

Advanced Segmentation and Targeting

Step 1: User Segmentation Engine

`
SLOS → Advanced → Analytics → User Segmentation
`

Segmentation Architecture:
`json
{
“segmentation_engine”: {
“segmentation_types”: [
{
“type”: “behavioral_segmentation”,
“description”: “Segment based on user behavior patterns”,
“attributes”: [“pageviews”, “timespent”, “conversionactions”, “engagementscore”]
},
{
“type”: “demographic_segmentation”,
“description”: “Segment based on user demographics”,
“attributes”: [“age”, “location”, “language”, “device_type”]
},
{
“type”: “consentbasedsegmentation”,
“description”: “Segment based on consent preferences”,
“attributes”: [“consentcategories”, “consentlevel”, “withdrawal_history”]
},
{
“type”: “lifecycle_segmentation”,
“description”: “Segment based on user lifecycle stage”,
“attributes”: [“registrationdate”, “lastactivity”, “loyaltyscore”, “churnrisk”]
}
],
“segmentation_rules”: {
“rule_engine”: “declarative”,
“operators”: [“equals”, “contains”, “greaterthan”, “lessthan”, “between”, “in”],
“logic”: [“AND”, “OR”, “NOT”],
“nesting”: “unlimited”
},
“segment_calculation”: {
“frequency”: “real_time”,
“method”: “streaming_aggregation”,
“caching”: “intelligent”,
“persistence”: “database”
}
},
“segmentation_capabilities”: {
“dynamic_segments”: true,
“nested_segments”: true,
“segmentoverlapanalysis”: true,
“segmentperformancetracking”: true,
“predictive_segmentation”: true
}
}
`

Step 2: Segmentation Engine Implementation

`php
// Advanced user segmentation and targeting engine
class SegmentationEngine {
private $segment_definitions = [];
private $user_segments = [];
private $segment_cache = [];
private $rule_engine = null;

public function __construct() {
$this->rule_engine = new SegmentationRuleEngine();
$this->loadSegmentDefinitions();
$this->initializeSegmentCache();
}

private function loadSegmentDefinitions() {
global $wpdb;

$segments = $wpdb->get_results(“
SELECT * FROM wpslossegments
WHERE active = 1
“);

foreach ($segments as $segment) {
$this->segment_definitions[$segment->id] = [
‘id’ => $segment->id,
‘name’ => $segment->name,
‘description’ => $segment->description,
‘rules’ => json_decode($segment->rules, true),
‘createdat’ => $segment->createdat,
‘updatedat’ => $segment->updatedat
];
}
}

private function initializeSegmentCache() {
// Initialize cache for frequently accessed segments
$this->segment_cache = new SegmentCache();
}

public function createSegment($name, $description, $rules) {
global $wpdb;

// Validate rules
if (!$this->validateSegmentRules($rules)) {
throw new Exception(‘Invalid segment rules’);
}

$segmentid = wpgenerate_password(12, false);

$wpdb->insert(‘wpslossegments’, [
‘id’ => $segment_id,
‘name’ => $name,
‘description’ => $description,
‘rules’ => json_encode($rules),
‘active’ => 1,
‘createdat’ => currenttime(‘mysql’),
‘updatedat’ => currenttime(‘mysql’)
]);

$this->segmentdefinitions[$segmentid] = [
‘id’ => $segment_id,
‘name’ => $name,
‘description’ => $description,
‘rules’ => $rules,
‘createdat’ => currenttime(‘mysql’),
‘updatedat’ => currenttime(‘mysql’)
];

return $segment_id;
}

public function updateSegment($segment_id, $name, $description, $rules) {
global $wpdb;

if (!isset($this->segmentdefinitions[$segmentid])) {
throw new Exception(‘Segment not found’);
}

// Validate rules
if (!$this->validateSegmentRules($rules)) {
throw new Exception(‘Invalid segment rules’);
}

$wpdb->update(‘wpslossegments’, [
‘name’ => $name,
‘description’ => $description,
‘rules’ => json_encode($rules),
‘updatedat’ => currenttime(‘mysql’)
], [‘id’ => $segment_id]);

$this->segmentdefinitions[$segmentid][‘name’] = $name;
$this->segmentdefinitions[$segmentid][‘description’] = $description;
$this->segmentdefinitions[$segmentid][‘rules’] = $rules;
$this->segmentdefinitions[$segmentid][‘updatedat’] = currenttime(‘mysql’);

// Clear cache for this segment
$this->segmentcache->clearSegment($segmentid);

return true;
}

public function deleteSegment($segment_id) {
global $wpdb;

if (!isset($this->segmentdefinitions[$segmentid])) {
throw new Exception(‘Segment not found’);
}

$wpdb->update(‘wpslossegments’, [
‘active’ => 0,
‘updatedat’ => currenttime(‘mysql’)
], [‘id’ => $segment_id]);

unset($this->segmentdefinitions[$segmentid]);
$this->segmentcache->clearSegment($segmentid);

return true;
}

public function evaluateUserSegment($userid, $segmentid) {
// Check cache first
$cachedresult = $this->segmentcache->getUserSegment($userid, $segmentid);
if ($cached_result !== null) {
return $cached_result;
}

if (!isset($this->segmentdefinitions[$segmentid])) {
return false;
}

$segment = $this->segmentdefinitions[$segmentid];
$userdata = $this->getUserData($userid);

$result = $this->ruleengine->evaluateRules($segment[‘rules’], $userdata);

// Cache result
$this->segmentcache->setUserSegment($userid, $segment_id, $result);

return $result;
}

public function getUserSegments($user_id) {
$user_segments = [];

foreach ($this->segmentdefinitions as $segmentid => $segment) {
if ($this->evaluateUserSegment($userid, $segmentid)) {
$usersegments[] = $segmentid;
}
}

return $user_segments;
}

public function getSegmentUsers($segment_id, $limit = null, $offset = 0) {
if (!isset($this->segmentdefinitions[$segmentid])) {
return [];
}

// For large user bases, this needs optimization
// In production, pre-calculate segment memberships
$users = $this->getAllUsers();

$segment_users = [];
foreach ($users as $user_id) {
if ($this->evaluateUserSegment($userid, $segmentid)) {
$segmentusers[] = $userid;
if ($limit && count($segment_users) >= $limit + $offset) {
break;
}
}
}

return arrayslice($segmentusers, $offset, $limit);
}

public function getSegmentSize($segment_id) {
// Use cached count if available
$cachedcount = $this->segmentcache->getSegmentSize($segment_id);
if ($cached_count !== null) {
return $cached_count;
}

$users = $this->getSegmentUsers($segment_id);
$count = count($users);

// Cache the count
$this->segmentcache->setSegmentSize($segmentid, $count);

return $count;
}

public function analyzeSegmentOverlap($segment_ids) {
$overlap_analysis = [];

// Calculate pairwise overlaps
for ($i = 0; $i < count($segment_ids); $i++) { for ($j = $i + 1; $j < count($segment_ids); $j++) { $segmenta = $segmentids[$i];
$segmentb = $segmentids[$j];

$overlap = $this->calculateSegmentOverlap($segmenta, $segmentb);
$overlapanalysis[“{$segmenta}{$segmentb}”] = $overlap;
}
}

return $overlap_analysis;
}

private function calculateSegmentOverlap($segmenta, $segmentb) {
$usersa = $this->getSegmentUsers($segmenta);
$usersb = $this->getSegmentUsers($segmentb);

$intersection = arrayintersect($usersa, $users_b);
$union = arrayunique(arraymerge($usersa, $usersb));

return [
‘overlap_count’ => count($intersection),
‘overlap_percentage’ => count($union) > 0 ? (count($intersection) / count($union)) * 100 : 0,
‘jaccard_similarity’ => count($union) > 0 ? count($intersection) / count($union) : 0
];
}

public function getSegmentPerformance($segmentid, $timerange = 30) {
// Analyze how well this segment performs for various metrics
$segmentusers = $this->getSegmentUsers($segmentid);

if (empty($segment_users)) {
return null;
}

$analytics = new AnalyticsEngine();

// Calculate key performance indicators
$consent_rate = $analytics->getAnalyticsData(
“consentrate segment:{$segmentid}”,
“-{$time_range}d”
);

$engagement_score = $analytics->getAnalyticsData(
“avgengagement segment:{$segmentid}”,
“-{$time_range}d”
);

$conversion_rate = $analytics->getAnalyticsData(
“conversionrate segment:{$segmentid}”,
“-{$time_range}d”
);

return [
‘segmentid’ => $segmentid,
‘segmentsize’ => count($segmentusers),
‘timerangedays’ => $time_range,
‘metrics’ => [
‘consentrate’ => $consentrate,
‘engagementscore’ => $engagementscore,
‘conversionrate’ => $conversionrate
]
];
}

public function createDynamicSegment($name, $description, $base_segment, $modifiers) {
// Create a dynamic segment based on another segment with modifications
if (!isset($this->segmentdefinitions[$basesegment])) {
throw new Exception(‘Base segment not found’);
}

$baserules = $this->segmentdefinitions[$base_segment][‘rules’];
$modifiedrules = $this->applyModifiers($baserules, $modifiers);

return $this->createSegment($name, $description, $modified_rules);
}

private function applyModifiers($rules, $modifiers) {
// Apply modifications to segment rules
$modified_rules = $rules;

foreach ($modifiers as $modifier) {
switch ($modifier[‘type’]) {
case ‘add_condition’:
$modifiedrules = $this->addConditionToRules($modifiedrules, $modifier[‘condition’]);
break;
case ‘remove_condition’:
$modifiedrules = $this->removeConditionFromRules($modifiedrules, $modifier[‘condition_id’]);
break;
case ‘modify_condition’:
$modifiedrules = $this->modifyConditionInRules($modifiedrules, $modifier[‘conditionid’], $modifier[‘newcondition’]);
break;
}
}

return $modified_rules;
}

private function validateSegmentRules($rules) {
// Basic validation of segment rules structure
return isarray($rules) && $this->ruleengine->validateRules($rules);
}

private function getUserData($user_id) {
// Get comprehensive user data for segmentation
$user = getuserdata($userid);

if (!$user) {
return null;
}

// Get additional user data from SLOS
$consentdata = $this->getUserConsentData($userid);
$behaviordata = $this->getUserBehaviorData($userid);
$engagementdata = $this->getUserEngagementData($userid);

return [
‘userid’ => $userid,
’email’ => $user->user_email,
‘registrationdate’ => $user->userregistered,
‘lastlogin’ => getusermeta($userid, ‘last_login’, true),
‘role’ => $user->roles[0] ?? ‘subscriber’,
‘consentdata’ => $consentdata,
‘behaviordata’ => $behaviordata,
‘engagementdata’ => $engagementdata
];
}

private function getUserConsentData($user_id) {
global $wpdb;

return $wpdb->get_row($wpdb->prepare(“
SELECT * FROM wpslosuser_consent
WHERE user_id = %d
ORDER BY created_at DESC
LIMIT 1
“, $userid), ARRAYA);
}

private function getUserBehaviorData($user_id) {
$analytics = new AnalyticsEngine();

return [
‘pageviews’ => $analytics->getAnalyticsData(“pageviews user:{$user_id}”, “-30d”),
‘timespent’ => $analytics->getAnalyticsData(“timespent user:{$user_id}”, “-30d”),
‘actionstaken’ => $analytics->getAnalyticsData(“actions user:{$userid}”, “-30d”)
];
}

private function getUserEngagementData($user_id) {
// Calculate engagement score based on various factors
$behaviordata = $this->getUserBehaviorData($userid);

$engagement_score = 0;

if ($behaviordata[‘pageviews’] > 10) $engagement_score += 20;
if ($behaviordata[‘timespent’] > 300) $engagement_score += 30; // 5+ minutes
if ($behaviordata[‘actionstaken’] > 5) $engagement_score += 25;

// Cap at 100
$engagementscore = min($engagementscore, 100);

return [
‘engagementscore’ => $engagementscore,
‘engagementlevel’ => $this->getEngagementLevel($engagementscore)
];
}

private function getEngagementLevel($score) {
if ($score >= 80) return ‘high’;
if ($score >= 50) return ‘medium’;
return ‘low’;
}

private function getAllUsers() {
// Get all user IDs (simplified – in production, use pagination)
$users = get_users([‘fields’ => ‘ID’]);
return $users;
}

// Helper methods for rule manipulation
private function addConditionToRules($rules, $condition) {
// Add condition to rules (simplified implementation)
if (!isset($rules[‘conditions’])) {
$rules[‘conditions’] = [];
}
$rules[‘conditions’][] = $condition;
return $rules;
}

private function removeConditionFromRules($rules, $condition_id) {
// Remove condition from rules (simplified implementation)
if (isset($rules[‘conditions’])) {
unset($rules[‘conditions’][$condition_id]);
}
return $rules;
}

private function modifyConditionInRules($rules, $conditionid, $newcondition) {
// Modify condition in rules (simplified implementation)
if (isset($rules[‘conditions’][$condition_id])) {
$rules[‘conditions’][$conditionid] = $newcondition;
}
return $rules;
}
}
`

Predictive Analytics and Machine Learning

Step 1: Predictive Modeling Framework

`
SLOS → Advanced → Analytics → Predictive Analytics
`

Predictive Analytics Architecture:
`json
{
“predictive_engine”: {
“models”: [
{
“model”: “consentwithdrawalprediction”,
“description”: “Predict users likely to withdraw consent”,
“algorithm”: “gradient_boosting”,
“features”: [“engagementscore”, “consentage”, “interactionfrequency”, “complainthistory”],
“accuracy_target”: “85%”
},
{
“model”: “complianceriskprediction”,
“description”: “Predict compliance violation risk”,
“algorithm”: “random_forest”,
“features”: [“userbehavior”, “consentpatterns”, “geographiclocation”, “devicefingerprint”],
“accuracy_target”: “90%”
},
{
“model”: “churn_prediction”,
“description”: “Predict user churn probability”,
“algorithm”: “neural_network”,
“features”: [“activitytrend”, “consentchanges”, “supporttickets”, “featureusage”],
“accuracy_target”: “82%”
},
{
“model”: “lifecycle_prediction”,
“description”: “Predict user lifecycle stage transitions”,
“algorithm”: “markov_chain”,
“features”: [“currentstage”, “timeinstage”, “engagementmetrics”, “external_events”],
“accuracy_target”: “88%”
}
],
“training_pipeline”: {
“data_preparation”: “automated”,
“featureengineering”: “domainspecific”,
“model_selection”: “automated”,
“validation”: “cross_validation”,
“deployment”: “continuous”
},
“realtimescoring”: {
“latency_target”: “<100ms", "throughputtarget”: “1000predictionspersecond”,
“caching_strategy”: “intelligent”,
“fallbackmechanism”: “rulebased”
}
},
“predictive_capabilities”: {
“realtimepredictions”: true,
“batch_predictions”: true,
“model_explainability”: true,
“bias_detection”: true,
“performance_monitoring”: true
}
}
`

Step 2: Predictive Analytics Engine

`php
// Advanced predictive analytics and machine learning engine
class PredictiveAnalyticsEngine {
private $models = [];
private $feature_engine = null;
private $model_trainer = null;
private $prediction_cache = null;

public function __construct() {
$this->feature_engine = new FeatureEngineeringEngine();
$this->model_trainer = new ModelTrainingEngine();
$this->prediction_cache = new PredictionCache();
$this->loadModels();
}

private function loadModels() {
// Load trained models from storage
$this->models = [
‘consentwithdrawal’ => $this->loadModel(‘consentwithdrawal_prediction’),
‘compliancerisk’ => $this->loadModel(‘compliancerisk_prediction’),
‘churn’ => $this->loadModel(‘churn_prediction’),
‘lifecycle’ => $this->loadModel(‘lifecycle_prediction’)
];
}

private function loadModel($model_name) {
// Load model from file or database
$modelpath = WPCONTENTDIR . “/slos-models/{$modelname}.model”;

if (fileexists($modelpath)) {
return unserialize(filegetcontents($model_path));
}

return null;
}

public function predictConsentWithdrawal($user_id, $context = []) {
if (!$this->models[‘consent_withdrawal’]) {
return $this->fallbackConsentWithdrawalPrediction($user_id);
}

// Check cache first
$cachekey = “consentwithdrawal{$userid}”;
$cachedprediction = $this->predictioncache->get($cache_key);

if ($cached_prediction) {
return $cached_prediction;
}

// Extract features
$features = $this->featureengine->extractConsentWithdrawalFeatures($userid, $context);

// Make prediction
$prediction = $this->models[‘consent_withdrawal’]->predict([$features]);

$result = [
‘userid’ => $userid,
‘prediction’ => $prediction[0],
‘probability’ => $prediction[1] ?? null,
‘confidence’ => $this->calculateConfidence($prediction),
‘timestamp’ => time(),
‘featuresused’ => arraykeys($features)
];

// Cache prediction (short TTL for real-time accuracy)
$this->predictioncache->set($cachekey, $result, 1800); // 30 minutes

return $result;
}

public function predictComplianceRisk($user_id, $context = []) {
if (!$this->models[‘compliance_risk’]) {
return $this->fallbackComplianceRiskPrediction($user_id);
}

$cachekey = “compliancerisk{$userid}”;
$cachedprediction = $this->predictioncache->get($cache_key);

if ($cached_prediction) {
return $cached_prediction;
}

$features = $this->featureengine->extractComplianceRiskFeatures($userid, $context);
$prediction = $this->models[‘compliance_risk’]->predict([$features]);

$result = [
‘userid’ => $userid,
‘risk_level’ => $this->mapRiskScoreToLevel($prediction[0]),
‘risk_score’ => $prediction[0],
‘probability’ => $prediction[1] ?? null,
‘confidence’ => $this->calculateConfidence($prediction),
‘timestamp’ => time(),
‘featuresused’ => arraykeys($features),
‘recommendations’ => $this->generateRiskRecommendations($prediction[0])
];

$this->predictioncache->set($cachekey, $result, 3600); // 1 hour

return $result;
}

public function predictChurn($user_id, $context = []) {
if (!$this->models[‘churn’]) {
return $this->fallbackChurnPrediction($user_id);
}

$cachekey = “churn{$user_id}”;
$cachedprediction = $this->predictioncache->get($cache_key);

if ($cached_prediction) {
return $cached_prediction;
}

$features = $this->featureengine->extractChurnFeatures($userid, $context);
$prediction = $this->models[‘churn’]->predict([$features]);

$result = [
‘userid’ => $userid,
‘churn_probability’ => $prediction[0],
‘churnrisklevel’ => $this->mapChurnProbabilityToLevel($prediction[0]),
‘confidence’ => $this->calculateConfidence($prediction),
‘timestamp’ => time(),
‘featuresused’ => arraykeys($features),
‘retention_recommendations’ => $this->generateRetentionRecommendations($prediction[0])
];

$this->predictioncache->set($cachekey, $result, 7200); // 2 hours

return $result;
}

public function predictLifecycleStage($user_id, $context = []) {
if (!$this->models[‘lifecycle’]) {
return $this->fallbackLifecyclePrediction($user_id);
}

$cachekey = “lifecycle{$user_id}”;
$cachedprediction = $this->predictioncache->get($cache_key);

if ($cached_prediction) {
return $cached_prediction;
}

$features = $this->featureengine->extractLifecycleFeatures($userid, $context);
$prediction = $this->models[‘lifecycle’]->predict([$features]);

$result = [
‘userid’ => $userid,
‘currentstage’ => $this->getCurrentLifecycleStage($userid),
‘predictednextstage’ => $prediction[0],
‘transition_probability’ => $prediction[1] ?? null,
‘timetotransition’ => $prediction[2] ?? null,
‘confidence’ => $this->calculateConfidence($prediction),
‘timestamp’ => time(),
‘featuresused’ => arraykeys($features)
];

$this->predictioncache->set($cachekey, $result, 86400); // 24 hours

return $result;
}

public function trainModel($modelname, $trainingdata = null) {
if (!$training_data) {
$trainingdata = $this->prepareTrainingData($modelname);
}

$model = $this->modeltrainer->train($modelname, $training_data);

// Save trained model
$this->saveModel($model_name, $model);

// Update loaded models
$this->models[$model_name] = $model;

// Clear related caches
$this->predictioncache->clearPattern(“{$modelname}_*”);

return [
‘modelname’ => $modelname,
‘training_completed’ => true,
‘accuracy_score’ => $model->getAccuracy(),
‘training_time’ => time()
];
}

private function prepareTrainingData($model_name) {
switch ($model_name) {
case ‘consent_withdrawal’:
return $this->prepareConsentWithdrawalTrainingData();
case ‘compliance_risk’:
return $this->prepareComplianceRiskTrainingData();
case ‘churn’:
return $this->prepareChurnTrainingData();
case ‘lifecycle’:
return $this->prepareLifecycleTrainingData();
default:
throw new Exception(“Unknown model: {$model_name}”);
}
}

private function prepareConsentWithdrawalTrainingData() {
global $wpdb;

// Get historical consent withdrawal data
$data = $wpdb->get_results(“
SELECT
u.ID as user_id,
u.user_registered,
COALESCE(uc.consentgiven, 0) as consentgiven,
COALESCE(uc.consentwithdrawn, 0) as consentwithdrawn,
COALESCE(a.pageviews, 0) as pageviews,
COALESCE(a.timespent, 0) as timespent
FROM wp_users u
LEFT JOIN wpslosuserconsent uc ON u.ID = uc.userid
LEFT JOIN wpslosuseranalytics a ON u.ID = a.userid
WHERE u.userregistered < DATESUB(NOW(), INTERVAL 30 DAY)
“);

$training_data = [];
foreach ($data as $row) {
$features = $this->featureengine->extractConsentWithdrawalFeatures($row->userid);
$label = $row->consent_withdrawn > 0 ? 1 : 0;

$trainingdata[] = arraymerge($features, [‘label’ => $label]);
}

return $training_data;
}

private function prepareComplianceRiskTrainingData() {
// Similar implementation for compliance risk training data
return [];
}

private function prepareChurnTrainingData() {
// Similar implementation for churn training data
return [];
}

private function prepareLifecycleTrainingData() {
// Similar implementation for lifecycle training data
return [];
}

private function saveModel($model_name, $model) {
$modelpath = WPCONTENTDIR . “/slos-models/{$modelname}.model”;
$modeldir = dirname($modelpath);

if (!isdir($modeldir)) {
mkdir($model_dir, 0755, true);
}

fileputcontents($model_path, serialize($model));
}

// Fallback prediction methods
private function fallbackConsentWithdrawalPrediction($user_id) {
// Rule-based fallback
$userdata = $this->getUserData($userid);
$risk_score = 0;

if ($userdata[‘dayssincelastactivity’] > 90) $risk_score += 0.3;
if ($userdata[‘complaintcount’] > 0) $risk_score += 0.2;
if ($userdata[‘consentchanges’] > 2) $risk_score += 0.1;

return [
‘userid’ => $userid,
‘prediction’ => $risk_score > 0.3 ? 1 : 0,
‘probability’ => $risk_score,
‘confidence’ => ‘low’,
‘method’ => ‘rulebasedfallback’
];
}

private function fallbackComplianceRiskPrediction($user_id) {
return [
‘userid’ => $userid,
‘risk_level’ => ‘medium’,
‘risk_score’ => 0.5,
‘confidence’ => ‘low’,
‘method’ => ‘rulebasedfallback’
];
}

private function fallbackChurnPrediction($user_id) {
return [
‘userid’ => $userid,
‘churn_probability’ => 0.2,
‘churnrisklevel’ => ‘low’,
‘confidence’ => ‘low’,
‘method’ => ‘rulebasedfallback’
];
}

private function fallbackLifecyclePrediction($user_id) {
return [
‘userid’ => $userid,
‘currentstage’ => $this->getCurrentLifecycleStage($userid),
‘predictednextstage’ => ‘engaged’,
‘confidence’ => ‘low’,
‘method’ => ‘rulebasedfallback’
];
}

// Helper methods
private function calculateConfidence($prediction) {
// Simplified confidence calculation
if (is_array($prediction) && isset($prediction[1])) {
$probability = $prediction[1];
if ($probability > 0.8) return ‘high’;
if ($probability > 0.6) return ‘medium’;
return ‘low’;
}
return ‘unknown’;
}

private function mapRiskScoreToLevel($score) {
if ($score > 0.7) return ‘high’;
if ($score > 0.4) return ‘medium’;
return ‘low’;
}

private function mapChurnProbabilityToLevel($probability) {
if ($probability > 0.7) return ‘high’;
if ($probability > 0.4) return ‘medium’;
return ‘low’;
}

private function generateRiskRecommendations($risk_score) {
$recommendations = [];

if ($risk_score > 0.7) {
$recommendations[] = ‘Immediate compliance review required’;
$recommendations[] = ‘Enhanced monitoring activated’;
} elseif ($risk_score > 0.4) {
$recommendations[] = ‘Regular compliance monitoring recommended’;
$recommendations[] = ‘Additional consent verification suggested’;
}

return $recommendations;
}

private function generateRetentionRecommendations($churn_probability) {
$recommendations = [];

if ($churn_probability > 0.7) {
$recommendations[] = ‘Urgent retention campaign needed’;
$recommendations[] = ‘Personalized re-engagement email’;
} elseif ($churn_probability > 0.4) {
$recommendations[] = ‘Targeted retention offers’;
$recommendations[] = ‘Survey to understand concerns’;
}

return $recommendations;
}

private function getCurrentLifecycleStage($user_id) {
// Determine current lifecycle stage based on user data
$userdata = $this->getUserData($userid);

if ($userdata[‘dayssince_registration’] < 7) return 'new'; if ($userdata[‘engagementscore’] > 80) return ‘champion’;
if ($userdata[‘engagementscore’] > 50) return ‘engaged’;
if ($userdata[‘dayssincelastactivity’] > 60) return ‘at_risk’;

return ‘regular’;
}

private function getUserData($user_id) {
// Get comprehensive user data for predictions
global $wpdb;

$user = getuserdata($userid);
if (!$user) return null;

$analytics = $wpdb->get_row($wpdb->prepare(“
SELECT * FROM wpslosuser_analytics
WHERE user_id = %d
“, $userid), ARRAYA);

return [
‘userid’ => $userid,
‘dayssinceregistration’ => floor((time() – strtotime($user->user_registered)) / 86400),
‘dayssincelastactivity’ => isset($analytics[‘lastactivity’]) ?
floor((time() – strtotime($analytics[‘last_activity’])) / 86400) : 999,
‘engagementscore’ => $analytics[‘engagementscore’] ?? 0,
‘complaintcount’ => $analytics[‘complaintcount’] ?? 0,
‘consentchanges’ => $analytics[‘consentchanges’] ?? 0
];
}
}
`

Custom Reporting and Visualization

Step 1: Report Generation Engine

`
SLOS → Advanced → Analytics → Custom Reporting
`

Reporting Architecture:
`json
{
“reporting_engine”: {
“report_types”: [
{
“type”: “compliance_reports”,
“description”: “GDPR/CCPA compliance status reports”,
“frequency”: “monthly”,
“audience”: [“compliance_officers”, “executives”],
“format”: [“PDF”, “Excel”, “HTML”]
},
{
“type”: “performance_reports”,
“description”: “System performance and analytics reports”,
“frequency”: “weekly”,
“audience”: [“developers”, “system_administrators”],
“format”: [“PDF”, “JSON”, “CSV”]
},
{
“type”: “userengagementreports”,
“description”: “User behavior and engagement analytics”,
“frequency”: “daily”,
“audience”: [“marketing”, “product_managers”],
“format”: [“PDF”, “Excel”, “Dashboard”]
},
{
“type”: “riskassessmentreports”,
“description”: “Compliance risk and predictive analytics”,
“frequency”: “quarterly”,
“audience”: [“risk_managers”, “auditors”],
“format”: [“PDF”, “Excel”, “Interactive”]
}
],
“report_generation”: {
“engine”: “template_based”,
“datasources”: [“database”, “analyticsengine”, “external_apis”],
“scheduling”: “automated”,
“distribution”: “multi_channel”
},
“visualization_engine”: {
“library”: “chartjsd3″,
“interactivity”: “full”,
“responsiveness”: “mobile_first”,
“accessibility”: “wcag_compliant”
}
},
“reporting_capabilities”: {
“scheduled_reports”: true,
“adhocreports”: true,
“custom_dashboards”: true,
“data_export”: true,
“report_sharing”: true
}
}
`

Step 2: Custom Reporting Engine

`php
// Advanced custom reporting and visualization engine
class CustomReportingEngine {
private $report_templates = [];
private $data_sources = [];
private $visualization_engine = null;
private $scheduler = null;

public function __construct() {
$this->visualization_engine = new VisualizationEngine();
$this->scheduler = new ReportScheduler();
$this->loadReportTemplates();
$this->initializeDataSources();
}

private function loadReportTemplates() {
$this->report_templates = [
‘compliance_status’ => [
‘name’ => ‘Compliance Status Report’,
‘template’ => ‘compliance_status.twig’,
‘data_queries’ => [
‘consentstats’ => ‘compliance.consentstatistics’,
‘violationlog’ => ‘compliance.violationlog’,
‘audittrail’ => ‘compliance.audittrail’
],
‘charts’ => [‘consenttrends’, ‘violationchart’, ‘compliancescoregauge’],
‘schedule’ => ‘monthly’
],
‘user_engagement’ => [
‘name’ => ‘User Engagement Report’,
‘template’ => ‘user_engagement.twig’,
‘data_queries’ => [
‘engagementmetrics’ => ‘analytics.userengagement’,
‘behaviorpatterns’ => ‘analytics.behaviorpatterns’,
‘conversionfunnel’ => ‘analytics.conversionfunnel’
],
‘charts’ => [‘engagementtrends’, ‘behaviorheatmap’, ‘funnel_chart’],
‘schedule’ => ‘weekly’
],
‘performance_dashboard’ => [
‘name’ => ‘Performance Dashboard’,
‘template’ => ‘performance_dashboard.twig’,
‘data_queries’ => [
‘systemmetrics’ => ‘performance.systemmetrics’,
‘responsetimes’ => ‘performance.responsetimes’,
‘errorrates’ => ‘performance.errorrates’
],
‘charts’ => [‘performancetrends’, ‘responsetimechart’, ‘errorrate_gauge’],
‘schedule’ => ‘daily’
],
‘risk_assessment’ => [
‘name’ => ‘Risk Assessment Report’,
‘template’ => ‘risk_assessment.twig’,
‘data_queries’ => [
‘riskscores’ => ‘predictive.riskscores’,
‘compliancepredictions’ => ‘predictive.compliancepredictions’,
‘churnanalysis’ => ‘predictive.churnanalysis’
],
‘charts’ => [‘riskheatmap’, ‘predictiontrends’, ‘churnprobabilitychart’],
‘schedule’ => ‘quarterly’
]
];
}

private function initializeDataSources() {
$this->data_sources = [
‘database’ => new DatabaseDataSource(),
‘analytics’ => new AnalyticsDataSource(),
‘predictive’ => new PredictiveDataSource(),
‘performance’ => new PerformanceDataSource(),
‘compliance’ => new ComplianceDataSource()
];
}

public function generateReport($report_type, $parameters = [], $format = ‘PDF’) {
if (!isset($this->reporttemplates[$reporttype])) {
throw new Exception(“Report type ‘{$report_type}’ not found”);
}

$template = $this->reporttemplates[$reporttype];

// Gather data from multiple sources
$reportdata = $this->gatherReportData($template[‘dataqueries’], $parameters);

// Generate visualizations
$charts = $this->generateReportCharts($template[‘charts’], $report_data);

// Apply template
$rendered_report = $this->renderReportTemplate($template[‘template’], [
‘data’ => $report_data,
‘charts’ => $charts,
‘parameters’ => $parameters,
‘generated_at’ => date(‘Y-m-d H:i:s’)
]);

// Convert to requested format
return $this->convertReportFormat($rendered_report, $format);
}

private function gatherReportData($data_queries, $parameters) {
$report_data = [];

foreach ($dataqueries as $key => $querypath) {
list($source, $query) = explode(‘.’, $query_path, 2);

if (isset($this->data_sources[$source])) {
$reportdata[$key] = $this->datasources[$source]->executeQuery($query, $parameters);
}
}

return $report_data;
}

private function generateReportCharts($chartdefinitions, $reportdata) {
$charts = [];

foreach ($chartdefinitions as $chartkey) {
$chartconfig = $this->getChartConfiguration($chartkey);
$chartdata = $this->extractChartData($chartconfig, $report_data);

$charts[$chartkey] = $this->visualizationengine->generateChart(
$chart_config[‘type’],
$chart_data,
$chart_config[‘options’]
);
}

return $charts;
}

private function getChartConfiguration($chart_key) {
$chart_configs = [
‘consent_trends’ => [
‘type’ => ‘line’,
‘datasource’ => ‘consentstats’,
‘options’ => [
‘title’ => ‘Consent Trends Over Time’,
‘x_axis’ => ‘date’,
‘y_axis’ => ‘count’,
‘colors’ => [‘#4CAF50’, ‘#F44336’]
]
],
‘violation_chart’ => [
‘type’ => ‘bar’,
‘datasource’ => ‘violationlog’,
‘options’ => [
‘title’ => ‘Compliance Violations by Type’,
‘xaxis’ => ‘violationtype’,
‘y_axis’ => ‘count’,
‘colors’ => [‘#FF9800’]
]
],
‘engagement_trends’ => [
‘type’ => ‘area’,
‘datasource’ => ‘engagementmetrics’,
‘options’ => [
‘title’ => ‘User Engagement Trends’,
‘x_axis’ => ‘date’,
‘yaxis’ => ‘engagementscore’,
‘colors’ => [‘#2196F3’]
]
],
‘behavior_heatmap’ => [
‘type’ => ‘heatmap’,
‘datasource’ => ‘behaviorpatterns’,
‘options’ => [
‘title’ => ‘User Behavior Heatmap’,
‘xaxis’ => ‘hourof_day’,
‘yaxis’ => ‘dayof_week’,
‘colors’ => [‘#E3F2FD’, ‘#2196F3’]
]
]
];

return $chartconfigs[$chartkey] ?? null;
}

private function extractChartData($chartconfig, $reportdata) {
$datasource = $chartconfig[‘data_source’];

if (!isset($reportdata[$datasource])) {
return [];
}

$rawdata = $reportdata[$data_source];

// Transform data based on chart requirements
return $this->transformDataForChart($rawdata, $chartconfig);
}

private function transformDataForChart($rawdata, $chartconfig) {
// Transform raw data into chart-ready format
$transformed_data = [];

switch ($chart_config[‘type’]) {
case ‘line’:
case ‘area’:
$transformeddata = $this->transformTimeSeriesData($rawdata);
break;
case ‘bar’:
$transformeddata = $this->transformCategoricalData($rawdata);
break;
case ‘heatmap’:
$transformeddata = $this->transformHeatmapData($rawdata);
break;
case ‘pie’:
$transformeddata = $this->transformPieData($rawdata);
break;
}

return $transformed_data;
}

private function renderReportTemplate($template_name, $data) {
// Use Twig or similar templating engine
$templatepath = plugindirpath(FILE) . “templates/reports/{$templatename}”;

if (!fileexists($templatepath)) {
throw new Exception(“Report template ‘{$template_name}’ not found”);
}

// Simplified template rendering (in production, use Twig)
$templatecontent = filegetcontents($templatepath);

// Basic variable replacement
foreach ($data as $key => $value) {
if (isarray($value) || isobject($value)) {
$value = json_encode($value);
}
$templatecontent = strreplace(“{{{$key}}}”, $value, $template_content);
}

return $template_content;
}

private function convertReportFormat($content, $format) {
switch ($format) {
case ‘PDF’:
return $this->convertToPDF($content);
case ‘Excel’:
return $this->convertToExcel($content);
case ‘HTML’:
return $content;
case ‘JSON’:
return json_encode([‘content’ => $content]);
default:
throw new Exception(“Unsupported format: {$format}”);
}
}

public function scheduleReport($report_type, $schedule, $recipients, $parameters = []) {
return $this->scheduler->scheduleReport([
‘reporttype’ => $reporttype,
‘schedule’ => $schedule,
‘recipients’ => $recipients,
‘parameters’ => $parameters,
‘format’ => ‘PDF’
]);
}

public function createCustomReport($name, $description, $data_queries, $charts, $template) {
$reportid = ‘custom‘ . time();

$this->reporttemplates[$reportid] = [
‘name’ => $name,
‘description’ => $description,
‘template’ => $template,
‘dataqueries’ => $dataqueries,
‘charts’ => $charts,
‘custom’ => true
];

return $report_id;
}

public function exportReportData($report_type, $parameters = [], $format = ‘CSV’) {
$template = $this->reporttemplates[$reporttype];
$reportdata = $this->gatherReportData($template[‘dataqueries’], $parameters);

return $this->exportData($report_data, $format);
}

private function exportData($data, $format) {
switch ($format) {
case ‘CSV’:
return $this->exportToCSV($data);
case ‘JSON’:
return json_encode($data);
case ‘XML’:
return $this->exportToXML($data);
default:
throw new Exception(“Unsupported export format: {$format}”);
}
}

// Helper methods for data transformation
private function transformTimeSeriesData($data) {
$transformed = [‘labels’ => [], ‘datasets’ => []];

foreach ($data as $point) {
$transformed[‘labels’][] = $point[‘date’];
// Add dataset values…
}

return $transformed;
}

private function transformCategoricalData($data) {
$transformed = [‘labels’ => [], ‘data’ => []];

foreach ($data as $point) {
$transformed[‘labels’][] = $point[‘category’];
$transformed[‘data’][] = $point[‘value’];
}

return $transformed;
}

private function transformHeatmapData($data) {
// Transform for heatmap visualization
return $data;
}

private function transformPieData($data) {
$transformed = [‘labels’ => [], ‘data’ => []];

foreach ($data as $point) {
$transformed[‘labels’][] = $point[‘label’];
$transformed[‘data’][] = $point[‘value’];
}

return $transformed;
}

// Format conversion methods
private function convertToPDF($content) {
// Use TCPDF or similar library
requireonce plugindir_path(FILE) . ‘vendor/tcpdf/tcpdf.php’;

$pdf = new TCPDF();
$pdf->AddPage();
$pdf->writeHTML($content);
return $pdf->Output(”, ‘S’); // Return as string
}

private function convertToExcel($content) {
// Use PhpSpreadsheet or similar library
requireonce plugindir_path(FILE) . ‘vendor/phpoffice/phpspreadsheet/src/Bootstrap.php’;

$spreadsheet = new \PhpOffice\PhpSpreadsheet\Spreadsheet();
$sheet = $spreadsheet->getActiveSheet();
$sheet->setCellValue(‘A1’, ‘Report Content’);
$sheet->setCellValue(‘A2’, strip_tags($content));

$writer = new \PhpOffice\PhpSpreadsheet\Writer\Xlsx($spreadsheet);
ob_start();
$writer->save(‘php://output’);
return obgetclean();
}

private function exportToCSV($data) {
$output = fopen(‘php://temp’, ‘r+’);

// Write headers if data is array of arrays
if (isarray($data) && isset($data[0]) && isarray($data[0])) {
fputcsv($output, array_keys($data[0]));
}

// Write data
foreach ($data as $row) {
if (is_array($row)) {
fputcsv($output, $row);
}
}

rewind($output);
$csv = streamgetcontents($output);
fclose($output);

return $csv;
}

private function exportToXML($data) {
$xml = new SimpleXMLElement(‘‘);

foreach ($data as $key => $value) {
if (is_array($value)) {
$section = $xml->addChild($key);
foreach ($value as $item) {
if (is_array($item)) {
$itemElement = $section->addChild(‘item’);
foreach ($item as $itemKey => $itemValue) {
$itemElement->addChild($itemKey, htmlspecialchars($itemValue));
}
}
}
} else {
$xml->addChild($key, htmlspecialchars($value));
}
}

return $xml->asXML();
}
}
`

Support Resources

Documentation

1. Analytics API Reference
2. Data Privacy Guidelines
3. Visualization Best Practices

Help


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5. Machine learning engineers
6. Business intelligence consultants
7. Privacy compliance experts