Summary of data statistics and analysis skills for PHP public account development

Summary of data statistics and analysis skills for PHP public account development

With the rapid development of the Internet, more and more companies and individuals have begun to operate their own public accounts . As an important communication channel, public accounts can effectively promote products and services and attract users. However, just having a public account is not enough. We also need to perform data statistics and analysis on the public account to understand our user groups, understand their interests and needs, and make operational decisions based on these data. This article will introduce some techniques for developing public account data statistics and analysis in PHP, as well as specific code examples.

1. Statistical analysis to increase the number of public account subscriptions

1. Statistical fan growth trend

By counting the number of new fans of the public account every day, we Can understand the growth trend of users. The following is a simple sample code:

// 查询当日新增粉丝数
$sql = "SELECT COUNT(*) as total FROM fans WHERE DATE(create_time) = CURDATE()";
$result = mysqli_query($connection, $sql);
$row = mysqli_fetch_assoc($result);
$today_subscribers = $row['total'];

2. User follow and unfollow analysis

By counting the number of people who follow and unfollow each day, we can understand the user's interests and Churn situation. The following is a sample code:

// 查询当日关注人数
$sql = "SELECT COUNT(*) as total FROM subscribe WHERE DATE(update_time) = CURDATE()";
$result = mysqli_query($connection, $sql);
$row = mysqli_fetch_assoc($result);
$today_subscribers = $row['total'];

// 查询当日取消关注人数
$sql = "SELECT COUNT(*) as total FROM unsubscribe WHERE DATE(update_time) = CURDATE()";
$result = mysqli_query($connection, $sql);
$row = mysqli_fetch_assoc($result);
$today_unsubscribers = $row['total'];

2. Analyze user behavior and interests

1. Statistics on articles with the highest number of user clicks

By counting users’ views on each By measuring the number of clicks on an article, we can understand how users like different types of articles and adjust our content strategy. The following is a sample code:

// 查询点击量最高的文章
$sql = "SELECT a.article_id, a.title, COUNT(*) as total FROM article_view AS v
        LEFT JOIN article AS a ON v.article_id = a.article_id
        GROUP BY v.article_id
        ORDER BY total DESC
        LIMIT 10";
$result = mysqli_query($connection, $sql);
while ($row = mysqli_fetch_assoc($result)) {
    echo $row['title'] . '，点击量：' . $row['total'] . '<br>';
}

2. Count the articles with the highest number of shares shared by users

By counting the number of shares shared by users for each article, we can understand the users’ views on different types of articles. Article recommendation willingness, thereby adjusting our content strategy. The following is a sample code:

// 查询分享量最高的文章
$sql = "SELECT a.article_id, a.title, COUNT(*) as total FROM article_share AS s
        LEFT JOIN article AS a ON s.article_id = a.article_id
        GROUP BY s.article_id
        ORDER BY total DESC
        LIMIT 10";
$result = mysqli_query($connection, $sql);
while ($row = mysqli_fetch_assoc($result)) {
    echo $row['title'] . '，分享量：' . $row['total'] . '<br>';
}

3. Operational decision-making and effect analysis

1. Statistics of user conversion rates from different channels

By counting users from different Through the conversion rate of channels (such as friend recommendations, Weibo, WeChat groups, etc.), we can understand which channels are more likely to convert users into fans, so we can decide to invest more resources and energy to expand these channels. The following is a sample code:

// 统计不同渠道的用户转化率
$sql = "SELECT channel, COUNT(*) as total FROM subscribe
        GROUP BY channel";
$result = mysqli_query($connection, $sql);
$total_users = 0;
while ($row = mysqli_fetch_assoc($result)) {
    $total_users += $row['total'];
}

$result = mysqli_query($connection, $sql);
while ($row = mysqli_fetch_assoc($result)) {
    $conversion_rate = round(($row['total'] / $total_users) * 100, 2);
    echo $row['channel'] . '，转化率：' . $conversion_rate . '%<br>';
}

2. Statistics on the effects of different promotion activities

By counting the clicks, conversion rates and ROI (return on investment) of different promotion activities, We can learn which promotions perform best and adjust and optimize our promotion strategies. The following is a sample code:

// 统计不同推广活动的点击量和转化率
$sql = "SELECT campaign, SUM(clicks) as total_clicks, SUM(conversion) as total_conversion FROM campaign_stats
        GROUP BY campaign";
$result = mysqli_query($connection, $sql);
while ($row = mysqli_fetch_assoc($result)) {
    $conversion_rate = round(($row['total_conversion'] / $row['total_clicks']) * 100, 2);
    echo $row['campaign'] . '，点击量：' . $row['total_clicks'] . '，转化率：' . $conversion_rate . '%<br>';
}

// 统计不同推广活动的ROI
$sql = "SELECT campaign, SUM(revenue) as total_revenue, SUM(cost) as total_cost FROM campaign_stats
        GROUP BY campaign";
$result = mysqli_query($connection, $sql);
while ($row = mysqli_fetch_assoc($result)) {
    $ROI = round(($row['total_revenue'] - $row['total_cost']) / $row['total_cost'] * 100, 2);
    echo $row['campaign'] . '，ROI：' . $ROI . '%<br>';
}

Through the above code examples, we can develop a complete public account data statistics and analysis system based on PHP, so as to better understand and grasp our user groups and optimize operations. Strategies to enhance the influence and effectiveness of public accounts. Of course, depending on the actual application scenario, the database table names, field names, etc. in the code need to be adjusted according to the actual situation. Hope this article helps you!

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