PHP provides an input basis for AI models by collecting user data (such as browsing history, geographical location) and pre-processing; 2. Use curl or gRPC to connect to AI models to obtain click-through rate and conversion rate prediction results; 3. Dynamically adjust advertising display frequency, target population and other strategies based on predictions; 4. Test different advertising variants through A/B and record data, and combine statistical analysis to optimize the effect; 5. Use PHP to monitor traffic sources and user behaviors and integrate with third-party APIs such as Google Ads to achieve automated delivery and continuous feedback optimization, ultimately improving CTR and CVR and reducing CPC, and fully implementing the closed loop of AI-driven advertising system.

The core of PHP's development of AI-driven advertising delivery is to use PHP to process data, connect to AI models, and integrate them into the advertising delivery system to ultimately optimize advertising effects. Simply put, it is to use PHP language to connect the "brain" of AI with the "hands and feet" of advertising.

Solution

PHP plays a bridge role in AI-powered advertising delivery. First of all, PHP needs to collect user data, such as browsing history, search keywords, geographical location, etc. These data are the basis for analysis of AI models. Secondly, PHP needs to interact with the AI model, pass the data to the model, and receive the model's prediction results. These predictions may include user click-through rates, conversion rates, and more to a specific ad. Finally, PHP needs to dynamically adjust the advertising delivery strategy based on the model's prediction results, such as adjusting the ad placement position, delivery time, target users, etc.

Specifically, the following steps can be taken:

1. Data collection and preprocessing: Use PHP scripts to collect user behavior data from various sources (such as website logs, databases, APIs). Preprocessing operations such as cleaning, deduplication, and formatting of these data to make them meet the requirements of the AI model. For example, convert the user's IP address into geographic location information and encode the product category that the user browses.

   

2. AI model docking: select the right AI model, such as click-through rate prediction model, conversion rate prediction model, etc. You can use PHP's curl library or gRPC and other technologies to interact with the AI model. The AI model can be deployed on a local server or using an AI platform provided by a cloud service provider.

3. Advertisement delivery strategy adjustment: Use PHP scripts to dynamically adjust the ad serving strategy based on the prediction results of the AI model. For example, if an AI model predicts that a user has a high click-through rate on an ad, it can increase the frequency of the ad's display. If the AI model predicts that a user's conversion rate to an ad is very low, it can reduce the frequency of the ad's display.

4. Performance evaluation and feedback: Use PHP scripts to collect actual performance data of ad delivery, such as click-through rate, conversion rate, cost, etc. Feedback this data to the AI model for continuous optimization of the model. For example, machine learning algorithms can be used to automatically adjust the parameters of the AI model based on the actual effect data of advertising.

PHP advertising performance optimization solution

PHP advertising performance optimization is not only a simple code optimization, but also requires combining data analysis and user behavior insights. The core is to improve the click-through rate (CTR), conversion rate (CVR) of your ads and reduce cost per click (CPC).

How to optimize PHP ad code using A/B test?

A/B testing is an effective way to optimize advertising performance. Multiple ad variants can be created, such as different titles, different copywriting, different pictures, and more. Then, use PHP scripts to randomly assign users to different ad variants. Collect click-through rate, conversion rate and other data for each ad variant, and then use statistical analysis methods to find the best-performing ad variant.

For example, you can create a PHP script with two ad variants:

 <?php

$variants = [
    &#39;variant_a&#39; => [
        &#39;title&#39; => &#39;Buy now! &#39;,
        &#39;description&#39; => &#39;Limited time offer, not to be missed! &#39;
    ],
    &#39;variant_b&#39; => [
        &#39;title&#39; => &#39;Great value for special offer! &#39;,
        &#39;description&#39; => &#39;Snap it now, limited quantity! &#39;
    ]
];

$variant = (rand(0, 1) == 0) ? &#39;variant_a&#39; : &#39;variant_b&#39;;

echo &#39;<a href="your_landing_page.php">&#39;;
echo &#39;<h2>&#39; . $variants[$variant][&#39;title&#39;] . &#39;</h2>&#39;;
echo &#39;<p>&#39; . $variants[$variant][&#39;description&#39;] . &#39;</p>&#39;;
echo &#39;</a>&#39;;

// Record impressions and clicks for each variant in a database or log file // ...
?>

This script will randomly select an ad variant and display it to the user. At the same time, the impressions and clicks of each variant need to be recorded in the database or log file for subsequent statistical analysis.

How to use PHP for advertising traffic monitoring and analysis?

Traffic monitoring and analysis are important links in optimizing advertising performance. You can use PHP scripts to collect advertising traffic data, such as user sources, geographic location, access time, browsing pages, etc. Then, use data analysis tools, such as Google Analytics, Baidu Statistics, etc. to analyze these data to find out the rules of traffic sources, user behavior, etc.

For example, you can use PHP scripts to record the source of a user:

 <?php

$referrer = $_SERVER[&#39;HTTP_REFERER&#39;];

// Record referrer to database or log file// ...

?>

This script takes the user's source URL and logs it to a database or log file. By analyzing this data, you can understand the source channels of users and optimize your advertising delivery strategy.

How does PHP integrate with third-party advertising platform API?

Integration with third-party advertising platform APIs is the key to achieving automated advertising delivery. You can use PHP's curl library or HTTP clients such as Guzzle to interact with the third-party advertising platform API. Through the API, you can realize the functions of creating, modifying, deleting, serving, performance monitoring and other advertisements.

For example, you can use PHP's curl library to send a request to the Google Ads API:

 <?php

$ch = curl_init(&#39;https://ads.google.com/api/v1/campaigns&#39;);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, [
    &#39;Authorization: Bearer YOUR_ACCESS_TOKEN&#39;,
    &#39;Content-Type: application/json&#39;
]);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode([
    &#39;name&#39; => &#39;My Campaign&#39;,
    &#39;status&#39; => &#39;PAUSED&#39;
]));

$response = curl_exec($ch);
curl_close($ch);

// Process API response// ...

?>

This script sends a request to the Google Ads API to create an ad campaign. You need to replace YOUR_ACCESS_TOKEN for your Google Ads API access token.

In short, to use PHP to develop AI-driven advertising delivery, you need to deeply understand the characteristics of PHP, be familiar with the principles of AI models, and master the advertising delivery strategies. This is a challenging but also full of opportunities.

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