无需ML插件：基于BigQuery SQL预测Google Ads预算调整效果

核心假设与计算逻辑 #

由于不使用ML插件，我们基于历史数据的线性关联和关键指标稳定性做合理假设：

• 预算调整比例与展示量增长/下降呈线性相关（忽略极端预算调整的边际效应）
• 平均CPC、CPM在预算小幅调整（如±10%）时保持稳定（基于历史投放的竞争环境变化不大）
• CPA、转化量依赖历史转化数据：转化量 = 总点击数（总花费/平均CPC），CPA = 总花费/转化数

基于历史数据的SQL实现 #

1. 定义历史数据表结构（模拟业务数据） #

假设你的Google Ads数据存储在your_project.your_dataset.google_ads_historical_data，先创建模拟表（可替换为实际业务表）：

CREATE OR REPLACE TABLE your_project.your_dataset.google_ads_historical_data AS
SELECT
  DATE '2022-08-10' AS date,
  786.48 AS spend,
  65495 AS impressions,
  0.56 AS avg_cpc,
  12.01 AS avg_cpm,
  1404 AS conversions -- 由总点击数推导：786.48/0.56=1404，有实际转化数据可直接替换
UNION ALL
-- 可添加更多历史日/月/年度数据
SELECT DATE '2022-08-11', 820.50, 68375, 0.57, 12.00, 1439;

2. 日维度：预算±10%的效果预测 #

WITH historical_daily_baseline AS (
  SELECT
    date,
    spend AS baseline_spend,
    impressions AS baseline_impressions,
    avg_cpc AS baseline_cpc,
    avg_cpm AS baseline_cpm,
    conversions AS baseline_conversions,
    spend / conversions AS baseline_cpa -- 计算基准CPA
  FROM your_project.your_dataset.google_ads_historical_data
)
SELECT
  date,
  baseline_spend,
  -- 预算调整后的花费
  baseline_spend * 1.1 AS spend_increase_10pct,
  baseline_spend * 0.9 AS spend_decrease_10pct,
  -- 展示量预测（线性关联）
  baseline_impressions * 1.1 AS impressions_increase_10pct,
  baseline_impressions * 0.9 AS impressions_decrease_10pct,
  -- CPC/CPM保持稳定
  baseline_cpc AS cpc_increase_10pct,
  baseline_cpc AS cpc_decrease_10pct,
  baseline_cpm AS cpm_increase_10pct,
  baseline_cpm AS cpm_decrease_10pct,
  -- 转化量与CPA预测
  baseline_conversions * 1.1 AS conversions_increase_10pct,
  baseline_conversions * 0.9 AS conversions_decrease_10pct,
  baseline_cpa AS cpa_increase_10pct, -- 假设转化效率不变，CPA稳定
  baseline_cpa AS cpa_decrease_10pct
FROM historical_daily_baseline;

3. 月/年度维度：聚合后预测 #

只需将维度替换为月/年度聚合即可：

WITH historical_monthly_baseline AS (
  SELECT
    DATE_TRUNC(date, MONTH) AS month,
    SUM(spend) AS baseline_spend,
    SUM(impressions) AS baseline_impressions,
    AVG(avg_cpc) AS baseline_cpc,
    AVG(avg_cpm) AS baseline_cpm,
    SUM(conversions) AS baseline_conversions,
    SUM(spend)/SUM(conversions) AS baseline_cpa
  FROM your_project.your_dataset.google_ads_historical_data
  GROUP BY DATE_TRUNC(date, MONTH)
)
SELECT
  month,
  baseline_spend,
  baseline_spend * 1.1 AS spend_increase_10pct,
  baseline_spend * 0.9 AS spend_decrease_10pct,
  baseline_impressions * 1.1 AS impressions_increase_10pct,
  baseline_impressions * 0.9 AS impressions_decrease_10pct,
  baseline_cpc AS cpc_increase_10pct,
  baseline_cpc AS cpc_decrease_10pct,
  baseline_cpm AS cpm_increase_10pct,
  baseline_cpm AS cpm_decrease_10pct,
  baseline_conversions * 1.1 AS conversions_increase_10pct,
  baseline_conversions * 0.9 AS conversions_decrease_10pct,
  baseline_cpa AS cpa_increase_10pct,
  baseline_cpa AS cpa_decrease_10pct
FROM historical_monthly_baseline;

4. 通用X%预算调整的动态计算 #

通过BigQuery会话变量实现任意比例调整：

-- 设置预算调整比例（正数为增加，负数为减少，如0.2=20%增加，-0.1=10%减少）
DECLARE @budget_adjustment_pct FLOAT64 DEFAULT 0.1;

WITH baseline_data AS (
  SELECT
    SUM(spend) AS total_baseline_spend,
    SUM(impressions) AS total_baseline_impressions,
    AVG(avg_cpc) AS avg_baseline_cpc,
    AVG(avg_cpm) AS avg_baseline_cpm,
    SUM(conversions) AS total_baseline_conversions,
    SUM(spend)/SUM(conversions) AS avg_baseline_cpa
  FROM your_project.your_dataset.google_ads_historical_data
  -- 可添加时间过滤条件，如WHERE date BETWEEN '2022-01-01' AND '2022-12-31'
)
SELECT
  total_baseline_spend,
  total_baseline_spend * (1 + @budget_adjustment_pct) AS adjusted_spend,
  total_baseline_impressions * (1 + @budget_adjustment_pct) AS adjusted_impressions,
  avg_baseline_cpc AS adjusted_cpc,
  avg_baseline_cpm AS adjusted_cpm,
  total_baseline_conversions * (1 + @budget_adjustment_pct) AS adjusted_conversions,
  avg_baseline_cpa AS adjusted_cpa
FROM baseline_data;

样本数据验证（2022-08-10） #

用给定的单条数据计算预算±10%的结果：

• 原花费：786.48英镑，展示量：65495，CPC：0.56，CPM：12.01，转化数：1404，CPA：0.56
• 预算增加10%：花费865.13英镑，展示量72044，CPC0.56，CPM12.01，转化数1544，CPA0.56
• 预算减少10%：花费707.83英镑，展示量58945，CPC0.56，CPM12.01，转化数1264，CPA0.56

内容的提问来源于stack exchange，提问作者Ian