Google 會運用 AI 技術將內容翻譯成你偏好的語言，但可能會出錯。

實驗報表

製作標準 A/B 實驗報表的主要方式有兩種：

直接實驗報表：查詢 experiment 資源，取得指標。這個選項會在單一回應中提供控制組和實驗組的指標，以及升幅和 p 值等統計比較資料。這是製作廣告活動內實驗報表的唯一方法。
廣告活動報表：查詢 campaign 資源取得指標，並使用 campaign.experiment_type 區分基礎和實驗廣告活動。這個選項僅適用於使用獨立控制組和實驗組廣告活動的實驗，例如系統管理實驗。

對於標準實驗，本指南主要著重於直接實驗報表，這類報表與支援報表的所有實驗類型相容。如果是提升評估研究，您也可以使用專屬的提升評估資源，製作品牌提升、轉換提升和搜尋提升報表。

直接實驗報表

您可以直接查詢 experiment 資源，擷取控制組和實驗組之間的成效指標和統計比較資料。

指標和統計顯著程度

對於點擊、曝光、費用、轉換和轉換價值等核心指標，experiment 資源會在同一列中提供實驗組指標 (例如 metrics.clicks) 和控制組指標 (例如 metrics.control_clicks)。

此外，這份報表也提供欄位，協助您評估實驗組之間差異的統計顯著程度：

metrics.*_p_value：如果實驗對指標沒有實際影響，則觀察到的結果發生的機率。p 值越低，統計顯著性越高。
metrics.*_point_estimate：實驗組與控制組相比，指定指標的預估升幅百分比 (正值或負值)。與 margin_of_error 搭配使用時，可描述信賴區間，並針對預估差異提供規定的信賴水準。預估數量為 (實驗組 / 控制組 - 1)。點估計值是信賴區間的中心。
metrics.*_margin_of_error：信賴區間的半徑，以 point_estimate 為中心。計算時會採用預設信賴水準，具體數值取決於實驗類型。

experiment 資源支援下列核心指標欄位，包括實驗組值、控制組值，以及先前列出的統計欄位：

clicks
impressions
cost_micros
conversions
cost_per_conversion
conversion_value
conversion_value_per_cost

具體來說，轉換的統計欄位可透過下列 absolute_change 欄位取得，而非相對值：

metrics.conversions_absolute_change_p_value：虛無假設的 p 值，表示實驗對轉換絕對變化沒有影響。範圍介於 0 到 1。
metrics.conversions_absolute_change_point_estimate：預估實驗對轉換絕對變化量的影響時，所採用的點估計值。
metrics.conversions_absolute_change_margin_of_error：預估實驗對轉換絕對變化影響時的誤差範圍。

如需協助建構 experiment 資源的有效查詢，請使用 Google Ads 查詢建立工具。

查詢示例

下列 GAQL 查詢會擷取實驗的主要指標：

SELECT
  experiment.experiment_id,
  experiment.name,
  experiment.type,
  metrics.clicks,
  metrics.control_clicks,
  metrics.clicks_point_estimate,
  metrics.clicks_margin_of_error,
  metrics.clicks_p_value,
  metrics.conversions,
  metrics.control_conversions,
  metrics.conversions_absolute_change_point_estimate,
  metrics.conversions_absolute_change_margin_of_error,
  metrics.conversions_absolute_change_p_value
FROM experiment
WHERE experiment.experiment_id = EXPERIMENT_ID

解讀結果

您可以使用 p 值、點估計值和誤差範圍欄位，判斷實驗是否產生具統計顯著性的結果。舉例來說，如果 conversions_absolute_change_p_value 低於所選門檻 (例如 95% 信賴水準的 0.05)，且 conversions_absolute_change_point_estimate - conversions_absolute_change_margin_of_error 大於零，則實驗組的轉換成效明顯優於控制組。

以下 Python 程式碼片段示範如何根據 p 值和升幅估計值評估結果：

Java

private void evaluateExperiment(
    GoogleAdsClient googleAdsClient, long customerId, GoogleAdsRow row) {
  Metrics metrics = row.getMetrics();
  String experimentResourceName = row.getExperiment().getResourceName();

  // 1. Evaluate conversion success as a primary success signal if available.
  // - Point Estimate: Represents the estimated average lift or difference in conversions.
  // - Margin of Error: Outlines the confidence interval bounds. Note that the margin_of_error
  //   provided by the API is calculated for a preset confidence level which is set based on the
  //   experiment type.
  // - Lower Bound: (Point Estimate - Margin of Error). If this value is above 0,
  //   we have statistical significance that performance has improved.
  double convPValue = metrics.getConversionsAbsoluteChangePValue();
  double convLift = metrics.getConversionsAbsoluteChangePointEstimate();
  double convError = metrics.getConversionsAbsoluteChangeMarginOfError();
  double convLowerBound = convLift - convError;

  if (convPValue <= P_VALUE_THRESHOLD) {
    if (convLowerBound > 0) {
      System.out.printf(
          "Significant Success: Conversions increased. Even at the lower bound, the lift is %.2f."
              + " Promoting changes.%n",
          convLowerBound);
      promoteExperiment(googleAdsClient, customerId, experimentResourceName);
      return;
    } else if ((convLift + convError) < 0) {
      System.out.printf(
          "Significant Decline: Even the upper bound (%.2f) is below zero. Ending experiment.%n",
          convLift + convError);
      endExperiment(googleAdsClient, customerId, experimentResourceName);
      return;
    }
  }

  // 2. Fall back to evaluating click metrics if conversions are inconclusive.
  double clickPValue = metrics.getClicksPValue();
  double clickLift = metrics.getClicksPointEstimate();
  double clickError = metrics.getClicksMarginOfError();
  double clickLowerBound = clickLift - clickError;

  if (clickPValue <= P_VALUE_THRESHOLD && clickLowerBound > 0) {
    System.out.printf("Click volume is significantly up (+%.1f%%).%n", clickLift * 100);

    // Graduation is only supported for separate campaign experiments, not
    // intra-campaign experiments where there is no separate treatment campaign.
    ExperimentType experimentType = row.getExperiment().getType();
    if (experimentType != ExperimentType.ADOPT_BROAD_MATCH_KEYWORDS
        && experimentType != ExperimentType.ADOPT_AI_MAX) {
      System.out.println("Graduating treatment campaign for further manual analysis.");
      graduateExperiment(googleAdsClient, customerId, experimentResourceName);
    } else {
      System.out.println(
          "Intra-campaign trial detected: graduation is not supported. Continuing to run the"
              + " experiment to gather more conversion data.");
    }
  } else {
    // 3. Print status if no action was taken.
    System.out.printf(
        "Inconclusive: No significant lift in Conversions (p=%.2f) or Clicks (p=%.2f). Current"
            + " estimated lift: %.2f +/- %.2f. Allowing the experiment to continue running.%n",
        convPValue, clickPValue, convLift, convError);
  }
}
EvaluateAndUpdateExperiment.java

C#

private static void EvaluateExperiment(GoogleAdsClient client, long customerId, GoogleAdsRow row)
{
    // This function evaluates performance metrics and immediately takes action
    // to update the experiment's status (promote, end, or graduate) if
    // statistical significance thresholds are met.
    var metrics = row.Metrics;
    string experimentResourceName = row.Experiment.ResourceName;

    bool hasConvMetrics = metrics.HasConversionsAbsoluteChangePValue
        && metrics.HasConversionsAbsoluteChangePointEstimate
        && metrics.HasConversionsAbsoluteChangeMarginOfError;

    bool hasClickMetrics = metrics.HasClicksPValue
        && metrics.HasClicksPointEstimate
        && metrics.HasClicksMarginOfError;

    // 1. Evaluate conversion success as a primary success signal if available.
    // - Point Estimate: Represents the estimated average lift or difference in conversions.
    // - Margin of Error: Outlines the confidence interval bounds. Note that the margin_of_error
    //   provided by the API is calculated for a preset confidence level which is set based on
    //   the experiment type.
    // - Lower Bound: (Point Estimate - Margin of Error). If this value is above 0,
    //   we have statistical significance that performance has improved.
    if (hasConvMetrics)
    {
        double convPValue = metrics.ConversionsAbsoluteChangePValue;
        double convLift = metrics.ConversionsAbsoluteChangePointEstimate;
        double convError = metrics.ConversionsAbsoluteChangeMarginOfError;
        double convLowerBound = convLift - convError;

        if (convPValue <= P_VALUE_THRESHOLD)
        {
            if (convLowerBound > 0)
            {
                Console.WriteLine(
                    $"Significant Success: Conversions increased. Even at the lower" +
                    $" bound, the lift is {convLowerBound:F2}. Promoting changes.");
                PromoteExperiment(client, customerId, experimentResourceName);
                return;
            }
            else if ((convLift + convError) < 0)
            {
                Console.WriteLine(
                    $"Significant Decline: Even the upper bound ({convLift + convError:F2}) " +
                    $"is below zero. Ending experiment.");
                EndExperiment(client, customerId, experimentResourceName);
                return;
            }
        }
    }

    // 2. Evaluate click volume as a secondary signal.
    // This is helpful as an early indicator or for lower-volume accounts.
    if (hasClickMetrics)
    {
        double clickPValue = metrics.ClicksPValue;
        double clickLift = metrics.ClicksPointEstimate;
        double clickError = metrics.ClicksMarginOfError;
        double clickLowerBound = clickLift - clickError;

        if (clickPValue <= P_VALUE_THRESHOLD && clickLowerBound > 0)
        {
            // We have a directional winner: high confidence in more traffic,
            // but not enough data to confirm conversion impact yet.
            Console.WriteLine(
                $"Click volume is significantly up (+{clickLift * 100:F1}%).");

            // Graduation is only supported for separate campaign experiments, not
            // intra-campaign experiments where there is no separate treatment campaign.
            if (row.Experiment.Type != ExperimentType.AdoptBroadMatchKeywords
                && row.Experiment.Type != ExperimentType.AdoptAiMax)
            {
                Console.WriteLine("Graduating treatment campaign for further manual analysis.");
                GraduateExperiment(client, customerId, experimentResourceName);
            }
            else
            {
                Console.WriteLine(
                    "Intra-campaign trial detected: graduation is not supported. " +
                    "Continuing to run the experiment to gather more conversion data.");
            }
            return;
        }
    }

    // 3. Print status if no action was taken.
    if (hasConvMetrics || hasClickMetrics)
    {
        string convStatus = hasConvMetrics
            ? $"Conversions (p={metrics.ConversionsAbsoluteChangePValue:F2}, " +
              $"lift={metrics.ConversionsAbsoluteChangePointEstimate:F2} +/- " +
              $"{metrics.ConversionsAbsoluteChangeMarginOfError:F2})"
            : "Conversions (not populated)";

        string clickStatus = hasClickMetrics
            ? $"Clicks (p={metrics.ClicksPValue:F2}, " +
              $"lift={metrics.ClicksPointEstimate:F2} +/- " +
              $"{metrics.ClicksMarginOfError:F2})"
            : "Clicks (not populated)";

        Console.WriteLine(
            $"Inconclusive: No significant action taken. {convStatus}, {clickStatus}. " +
            "Allowing the experiment to continue running.");
    }
    else
    {
        Console.WriteLine(
            "Conversion and click performance metrics are not yet populated. " +
            "Allowing the experiment to continue running.");
    }
}EvaluateAndUpdateExperiment.cs

PHP

This example is not yet available in PHP; you can take a look at the other languages.

Python

def evaluate_experiment(
    client: GoogleAdsClient, customer_id: str, row: GoogleAdsRow
) -> None:
    """Evaluates the performance of the experiment and updates it accordingly
    (for example, promotes, ends, or graduates).

    Checks conversion and click metrics against statistical significance thresholds
    to determine the appropriate action to take on the experiment.

    Args:
        client: an initialized GoogleAdsClient instance.
        customer_id: a client customer ID.
        row: a GoogleAdsRow containing the experiment and metrics.
    """
    # This function evaluates performance metrics and immediately takes action
    # to update the experiment's status (promote, end, or graduate) if
    # statistical significance thresholds are met.
    metrics = row.metrics
    experiment_resource_name = row.experiment.resource_name

    has_conv_metrics = (
        "conversions_absolute_change_p_value" in metrics
        and "conversions_absolute_change_point_estimate" in metrics
        and "conversions_absolute_change_margin_of_error" in metrics
    )
    has_click_metrics = (
        "clicks_p_value" in metrics
        and "clicks_point_estimate" in metrics
        and "clicks_margin_of_error" in metrics
    )

    # 1. Evaluate conversion success as a primary success signal if available.
    # - Point Estimate: Represents the estimated average lift or difference in conversions.
    # - Margin of Error: Outlines the confidence interval bounds. Note that the margin_of_error provided by the API is calculated for a preset confidence level which is set based on the experiment type.
    # - Lower Bound: (Point Estimate - Margin of Error). If this value is above 0,
    #   we have statistical significance that performance has improved.
    if has_conv_metrics:
        conv_p_value = metrics.conversions_absolute_change_p_value
        conv_lift = metrics.conversions_absolute_change_point_estimate
        conv_error = metrics.conversions_absolute_change_margin_of_error
        conv_lower_bound = conv_lift - conv_error

        if conv_p_value <= P_VALUE_THRESHOLD:
            if conv_lower_bound > 0:
                print(
                    "Significant Success: Conversions increased. Even at the lower"
                    f" bound, the lift is {conv_lower_bound:.2f}. Promoting"
                    " changes."
                )
                promote_experiment(
                    client, customer_id, experiment_resource_name
                )
                return
            elif (conv_lift + conv_error) < 0:
                print(
                    "Significant Decline: Even the upper bound"
                    f" ({conv_lift + conv_error:.2f}) is below zero. Ending"
                    " experiment."
                )
                end_experiment(client, customer_id, experiment_resource_name)
                return

        # 2. Evaluate click volume as a secondary signal.
        # This is helpful as an early indicator or for lower-volume accounts.
        click_p_value = metrics.clicks_p_value
        click_lift = metrics.clicks_point_estimate
        click_error = metrics.clicks_margin_of_error
        click_lower_bound = click_lift - click_error

        if click_p_value <= P_VALUE_THRESHOLD and click_lower_bound > 0:
            # We have a directional winner: high confidence in more traffic,
            # but not enough data to confirm conversion impact yet.
            print(f"Click volume is significantly up (+{click_lift*100:.1f}%).")

            # Graduation is only supported for separate campaign experiments, not
            # intra-campaign experiments where there is no separate treatment campaign.
            experiment_type_name = row.experiment.type_.name
            if (
                experiment_type_name != "ADOPT_BROAD_MATCH_KEYWORDS"
                and experiment_type_name != "ADOPT_AI_MAX"
            ):
                print(
                    "Graduating treatment campaign for further manual analysis."
                )
                graduate_experiment(
                    client, customer_id, experiment_resource_name
                )
            else:
                print(
                    "Intra-campaign trial detected: graduation is not supported. "
                    "Continuing to run the experiment to gather more conversion data."
                )
            return

    # 3. Print status if no action was taken.
    if has_conv_metrics or has_click_metrics:
        conv_status = (
            f"Conversions (p={metrics.conversions_absolute_change_p_value:.2f}, "
            f"lift={metrics.conversions_absolute_change_point_estimate:.2f} +/- "
            f"{metrics.conversions_absolute_change_margin_of_error:.2f})"
            if has_conv_metrics
            else "Conversions (not populated)"
        )
        click_status = (
            f"Clicks (p={metrics.clicks_p_value:.2f}, "
            f"lift={metrics.clicks_point_estimate:.2f} +/- "
            f"{metrics.clicks_margin_of_error:.2f})"
            if has_click_metrics
            else "Clicks (not populated)"
        )
        print(
            f"Inconclusive: No significant action taken. {conv_status}, {click_status}."
            " Allowing the experiment to continue running."
        )
    else:
        print(
            "Conversion and click performance metrics are not yet populated. "
            "Allowing the experiment to continue running."
        )evaluate_and_update_experiment.py

小茹

def evaluate_experiment(client, customer_id, row)
  # This function evaluates performance metrics and immediately takes action
  # to update the experiment's status (promote, end, or graduate) if
  # statistical significance thresholds are met.
  metrics = row.metrics
  experiment_resource_name = row.experiment.resource_name

  has_conv_metrics = !metrics.nil? &&
                     metrics.has_conversions_absolute_change_p_value? &&
                     metrics.has_conversions_absolute_change_point_estimate? &&
                     metrics.has_conversions_absolute_change_margin_of_error?
  has_click_metrics = !metrics.nil? &&
                      metrics.has_clicks_p_value? &&
                      metrics.has_clicks_point_estimate? &&
                      metrics.has_clicks_margin_of_error?

  # 1. Evaluate conversion success as a primary success signal if available.
  # - Point Estimate: Represents the estimated average lift or difference in conversions.
  # - Margin of Error: Outlines the confidence interval bounds. Note that the margin_of_error
  #   provided by the API is calculated for a preset confidence level which is set based on the experiment type.
  # - Lower Bound: (Point Estimate - Margin of Error). If this value is above 0,
  #   we have statistical significance that performance has improved.
  if has_conv_metrics
    conv_p_value = metrics.conversions_absolute_change_p_value
    conv_lift = metrics.conversions_absolute_change_point_estimate
    conv_error = metrics.conversions_absolute_change_margin_of_error
    conv_lower_bound = conv_lift - conv_error

    if conv_p_value <= P_VALUE_THRESHOLD
      if conv_lower_bound > 0
        puts sprintf(
          "Significant Success: Conversions increased. Even at the lower " \
          "bound, the lift is %.2f. Promoting changes.",
          conv_lower_bound,
        )
        promote_experiment(client, customer_id, experiment_resource_name)
        return
      elsif (conv_lift + conv_error) < 0
        puts sprintf(
          "Significant Decline: Even the upper bound (%.2f) is below zero. " \
          "Ending experiment.",
          conv_lift + conv_error,
        )
        end_experiment(client, customer_id, experiment_resource_name)
        return
      end
    end
  end

  # 2. Evaluate click volume as a secondary signal.
  # This is helpful as an early indicator or for lower-volume accounts.
  if has_click_metrics
    click_p_value = metrics.clicks_p_value
    click_lift = metrics.clicks_point_estimate
    click_error = metrics.clicks_margin_of_error
    click_lower_bound = click_lift - click_error

    if click_p_value <= P_VALUE_THRESHOLD && click_lower_bound > 0
      # We have a directional winner: high confidence in more traffic,
      # but not enough data to confirm conversion impact yet.
      puts sprintf("Click volume is significantly up (+%.1f%%).", click_lift * 100)

      # Graduation is only supported for separate campaign experiments, not
      # intra-campaign experiments where there is no separate treatment campaign.
      experiment_type = row.experiment.type
      if experiment_type != :ADOPT_BROAD_MATCH_KEYWORDS && experiment_type != :ADOPT_AI_MAX
        puts "Graduating treatment campaign for further manual analysis."
        graduate_experiment(client, customer_id, experiment_resource_name)
      else
        puts "Intra-campaign trial detected: graduation is not supported. " \
             "Continuing to run the experiment to gather more conversion data."
      end
      return
    end
  end

  # 3. Print status if no action was taken.
  if has_conv_metrics || has_click_metrics
    conv_status = if has_conv_metrics
      sprintf(
        "Conversions (p=%.2f, lift=%.2f +/- %.2f)",
        metrics.conversions_absolute_change_p_value,
        metrics.conversions_absolute_change_point_estimate,
        metrics.conversions_absolute_change_margin_of_error,
      )
    else
      "Conversions (not populated)"
    end

    click_status = if has_click_metrics
      sprintf(
        "Clicks (p=%.2f, lift=%.2f +/- %.2f)",
        metrics.clicks_p_value,
        metrics.clicks_point_estimate,
        metrics.clicks_margin_of_error,
      )
    else
      "Clicks (not populated)"
    end

    puts "Inconclusive: No significant action taken. #{conv_status}, #{click_status}. " \
         "Allowing the experiment to continue running."
  else
    puts "Conversion and click performance metrics are not yet populated. " \
         "Allowing the experiment to continue running."
  end
endevaluate_and_update_experiment.rb

Perl

This example is not yet available in Perl; you can take a look at the other languages.

curl

相較於廣告活動報表的優點

與分別查詢廣告活動報表相比，直接實驗報表有幾項優點：

集中指標：在單一資料列中擷取控制組和實驗組的指標。
統計信賴度資料：提供計算出的 p 值、點估計值和誤差範圍。
效率：不必手動彙整或比較多份報表的結果。
廣告活動內支援：這是比較廣告活動內實驗控制組與實驗組的唯一方法，流量會在單一廣告活動內分配。

廣告活動報表

對於建立個別實驗組廣告活動的實驗 (例如 SEARCH_CUSTOM)，您可以查詢 campaign 資源，並使用 campaign.experiment_type 識別 BASE (控制組) 和 EXPERIMENT (實驗組) 廣告活動。如果您需要更精細地劃分指標 (例如依廣告群組或關鍵字)，或查看 experiment 資源未提供的廣告活動中繼資料，這個方法就很實用。不過，您必須手動進行成效比較和統計計算。

廣告活動內實驗的流量拆分是在單一廣告活動內部進行，因此您無法使用廣告活動層級報表比較實驗組。查詢廣告活動內實驗的 campaign，只會傳回匯總總計。

品牌提升報表

您可以使用專屬的品牌提升評估資源，在 Google Ads API 中製作品牌提升研究報表。

品牌提升資源和細目檢視

查詢 LiftMeasurementFlight 資源，擷取檔期層級的品牌提升研究結果，包括客戶時區的檔期日期範圍 (start_date 和 end_date)。

如要分析不同區隔維度的品牌提升指標，請查詢對應的細目資源：

LiftMeasurementCampaign：按廣告活動劃分。
LiftMeasurementAgeRange：按年齡層劃分。
LiftMeasurementDevice：資料按裝置劃分。
LiftMeasurementGender：依性別細分。
LiftMeasurementVideo：依影片素材資源細分。

BrandLiftMeasurementType 列舉會指定研究的評估類型，例如品牌知名度或廣告印象。

品牌提升查詢範例

由於廣告活動中繼資料和升幅統計資料位於不同的資源，因此查詢廣告活動詳細資料和成效統計資料時，需要使用不同的 GAQL 查詢。

航班詳細資料查詢

如要擷取品牌提升檔期的中繼資料 (例如狀態、評估類型和排定日期)，請查詢 LiftMeasurementFlight 資源：

SELECT
  lift_measurement_flight.lift_measurement_config_id,
  lift_measurement_flight.lift_measurement_flight_id,
  lift_measurement_flight.name,
  lift_measurement_flight.status,
  lift_measurement_flight.lift_type,
  lift_measurement_flight.start_date,
  lift_measurement_flight.end_date
FROM lift_measurement_flight
WHERE lift_measurement_flight.lift_measurement_config_id = CONFIG_ID

統計資料查詢

如要擷取品牌提升指標，請查詢適當的細目資源 (或 LiftMeasurementConfig 的整體研究設定)。舉例來說，下列查詢會從 LiftMeasurementCampaign 擷取按廣告活動細分的品牌提升指標：

SELECT
  lift_measurement_campaign.lift_measurement_config_id,
  lift_measurement_campaign.campaign,
  metrics.absolute_brand_lift,
  metrics.relative_brand_lift
FROM lift_measurement_campaign
WHERE lift_measurement_campaign.lift_measurement_config_id = CONFIG_ID

轉換提升報表

您可以使用 Google Ads API 中的轉換提升報表，評估廣告對轉換的增量影響。

轉換提升設定和指標

查詢 LiftMeasurementConfig 資源，擷取轉換提升研究設定和成效資料。

這項資源提供轉換提升指標，可用於評估成效增幅：

metrics.conversion_lift_baseline_conversions：研究的基準組轉換次數。
metrics.conversion_lift_exposed_conversions：在實驗組中觀察到的轉換。
metrics.incremental_conversions：歸因於廣告的轉換增量。
metrics.cost_per_incremental_conversion：單次轉換增量費用。
metrics.relative_conversion_lift：轉換相對提升。
metrics.conversion_lift_baseline_conversion_value：基準組轉換價值。
metrics.conversion_lift_exposed_conversion_value：實驗組的轉換價值。
metrics.incremental_conversion_value：廣告帶來的轉換價值增幅。
metrics.relative_conversion_value_lift：轉換價值的相對提升。

轉換提升報表還可依轉換動作區隔，並擷取連結的廣告活動詳細資料、實驗詳細資料和轉換目標。

自 v26起，您可以使用 Campaign.lift_measurement_config，直接從 campaign 資源中，找出與廣告活動連結的升幅評估設定。這樣就不必另外交叉比對 LiftMeasurementConfig 資源：

SELECT
  campaign.id,
  campaign.lift_measurement_config
FROM campaign
WHERE campaign.id = CAMPAIGN_ID

傳回的 lift_measurement_config 欄位包含資源名稱，格式為 customers/{customer_id}/liftMeasurementConfigs/{lift_measurement_config_id}。

注意：如果廣告活動已執行多項提升實驗，lift_measurement_config 會包含最近連結設定 (有效或無效) 的資源名稱。

轉換提升查詢範例

下列 GAQL 查詢會擷取轉換提升設定和主要成效指標。請注意，segments.conversion_lift_start_date 和 segments.conversion_lift_end_date 是 INT64 欄位，格式為不含引號的 8 位數整數，採用 YYYYMMDD 格式 (例如 20260501)：

SELECT
  lift_measurement_config.lift_measurement_config_id,
  segments.conversion_lift_start_date,
  segments.conversion_lift_end_date,
  segments.conversion_lift_included_conversion_action_types,
  metrics.conversion_lift_baseline_conversions,
  metrics.conversion_lift_exposed_conversions,
  metrics.incremental_conversions,
  metrics.cost_per_incremental_conversion,
  metrics.relative_conversion_lift,
  metrics.incremental_conversion_value,
  metrics.relative_conversion_value_lift
FROM lift_measurement_config
WHERE segments.conversion_lift_start_date = YYYYMMDD
  AND segments.conversion_lift_end_date = YYYYMMDD
  AND lift_measurement_config.lift_measurement_config_id = CONFIG_ID

搜尋提升報表

您可以使用 Google Ads API 的搜尋提升報表，評估廣告活動帶來的自然搜尋查詢量增幅。搜尋提升會量化看過廣告的使用者和未看過廣告的控制組，兩者對品牌或產品的搜尋行為差異。

搜尋提升資源和區隔

查詢 SearchLiftKeywordBundle 資源，擷取搜尋提升研究設定的關鍵字組合詳細資料。SearchLiftKeywordBundle 在 API 中為唯讀 (所有欄位均為 OUTPUT_ONLY)，關鍵字組合則是在 Google Ads 使用者介面中設定，或由 Google 代表 / 全球產品解決方案 (GPS) 設定。GAQL 中可查詢的欄位包括 resource_name、search_lift_keyword_bundle_id、source (SearchLiftMatchingInfoSource) 和 status (SearchLiftKeywordBundleStatus) (GAQL 查詢中無法選取重複的 keywords 欄位)。

查詢 LiftMeasurementConfig 資源，擷取搜尋提升指標和整體研究設定。

您可以使用下列維度區隔搜尋提升成效：

segments.search_lift_keyword_bundle：依關鍵字組合資源名稱區隔。與segments.search_property、segments.date和客層區隔 (segments.device、segments.age_range和segments.gender) 相容。
segments.search_property：依搜尋發生的搜尋資源 (由 SearchLiftSearchProperty 定義，例如 Google 搜尋或 YouTube) 進行區隔。請注意，segments.search_property 僅與 segments.search_lift_keyword_bundle 和 segments.date 相容；不支援將 segments.search_property 與客層區隔 (segments.device、segments.age_range 或 segments.gender) 結合，且無法通過 API 查詢驗證。
客層和日期區隔：segments.device、segments.age_range、segments.gender 和 segments.date。

搜尋提升指標

LiftMeasurementConfig 資源提供搜尋提升指標，可用於評估搜尋行為變化：

metrics.relative_search_lift：實驗組和控制組的搜尋查詢量差異，以控制組搜尋查詢量的分數表示。
metrics.relative_search_lift_p90_lower_bound 和 metrics.relative_search_lift_p90_upper_bound：相對搜尋提升 90% 信賴區間的下限和上限。
metrics.incremental_searches_p_value：搜尋次數增幅的 p 值，代表觀察到的搜尋查詢增幅是偶然發生的統計機率。
metrics.search_lift_cost_micros：這個搜尋提升評估區隔放送廣告曝光的總費用 (以百萬分之一為單位)。
metrics.search_lift_impressions：這個搜尋提升評估區隔放送的廣告曝光總數。

如果升幅在統計上為正值，系統會回報下列標準化增量指標，以及對應的 90% 信賴區間下限和上限 (*_p90_lower_bound 和 *_p90_upper_bound)：

metrics.normalized_incremental_searches (包含 metrics.normalized_incremental_searches_p90_lower_bound 和 metrics.normalized_incremental_searches_p90_upper_bound)：廣告曝光帶來的預估搜尋查詢增幅，已對實驗組和控制組進行正規化。
metrics.normalized_incremental_searches_per_cost (包含 metrics.normalized_incremental_searches_per_cost_p90_lower_bound 和 metrics.normalized_incremental_searches_per_cost_p90_upper_bound)：每單位廣告支出預估產生的搜尋查詢增量。
metrics.normalized_incremental_searches_per_impression (包含 metrics.normalized_incremental_searches_per_impression_p90_lower_bound 和 metrics.normalized_incremental_searches_per_impression_p90_upper_bound)：每次廣告曝光產生的預估搜尋查詢增量。

搜尋提升查詢範例

下列 GAQL 查詢示範如何擷取搜尋提升關鍵字組合和成效指標。

關鍵字套裝組合查詢

如要擷取為搜尋提升評估設定的關鍵字組合，請按照下列步驟操作：

SELECT
  search_lift_keyword_bundle.resource_name,
  search_lift_keyword_bundle.search_lift_keyword_bundle_id,
  search_lift_keyword_bundle.source,
  search_lift_keyword_bundle.status
FROM search_lift_keyword_bundle

指標查詢

在關鍵字套件查詢中擷取的 search_lift_keyword_bundle.resource_name 值，會對應到下列查詢中 segments.search_lift_keyword_bundle 傳回的值。如要找出研究的 CONFIG_ID，請查詢 lift_measurement_config (例如 SELECT lift_measurement_config.lift_measurement_config_id, lift_measurement_config.name FROM lift_measurement_config)。

如要擷取按關鍵字組合和搜尋資源劃分的搜尋提升指標，請按照下列步驟操作：

SELECT
  lift_measurement_config.lift_measurement_config_id,
  segments.search_lift_keyword_bundle,
  segments.search_property,
  metrics.relative_search_lift,
  metrics.relative_search_lift_p90_lower_bound,
  metrics.relative_search_lift_p90_upper_bound,
  metrics.incremental_searches_p_value,
  metrics.search_lift_cost_micros,
  metrics.search_lift_impressions,
  metrics.normalized_incremental_searches,
  metrics.normalized_incremental_searches_p90_lower_bound,
  metrics.normalized_incremental_searches_p90_upper_bound
FROM lift_measurement_config
WHERE lift_measurement_config.lift_measurement_config_id = CONFIG_ID

最佳做法

下列最佳做法適用於標準 A/B 實驗 (例如廣告活動和廣告活動內實驗)。提升評估研究 (品牌提升、轉換提升和搜尋提升) 採用研究專屬設定，並回報 90% 信賴區間 (*_p90_lower_bound 和 *_p90_upper_bound)，而非 95% 信賴門檻。

選取適當的可信度：設定較低的 p 值門檻，可更快取得方向指引，尤其是在預算或轉換量較低的情況下。95% 信賴水準 (p 值 <= 0.05) 是學術標準，可能更適合在較長的時間範圍內取得更準確的結果。
實驗時間應足夠：實驗應執行至少 4 週，才能考量到每週成效週期、轉換延遲和學習期。
給予適應期：如果廣告活動採用自動出價或測試新功能，請忽略前 1 到 2 週的資料，讓出價模式和流量等級有時間根據分配比例重新調整。
採用 50/50 的分配比例：採用 50/50 的流量分配比例，通常最快能取得具統計顯著性的結果。
提前安排實驗：將實驗開始日期設在 3 到 7 天後，預留時間進行廣告審查和核准程序。

廣告活動組合實驗

提取地理資料