Script 711: Ad Group CPA Performance Outlier

Purpose:

The Python script identifies ad groups within campaigns that have abnormally high Cost Per Acquisition (CPA) performance, tagging them as outliers.

To Elaborate

The script is designed to analyze advertising data and identify ad groups within campaigns that exhibit unusually high CPA performance. It processes data over a 30-day period, excluding the most recent three days to account for conversion lag. The script aggregates key metrics such as publication cost, conversions, revenue, and clicks for each ad group within a campaign. It then calculates performance features like cost per conversion, return on ad spend (ROAS), conversion rate, and average cost per click (CPC). Using statistical methods, specifically the Interquartile Range (IRQ), the script identifies outliers in CPA performance. Ad groups with CPA significantly higher than the campaign average are tagged as anomalies, allowing marketers to focus on optimizing these underperforming segments.

Walking Through the Code

  1. Data Preparation
    • The script begins by defining a 30-day lookback period, excluding the last three days to mitigate conversion lag.
    • It filters the input data to include only relevant columns and aggregates metrics like publication cost, conversions, revenue, and clicks for each ad group within a campaign.
    • Rows without cost or conversions are removed to ensure meaningful analysis.
  2. Feature Calculation
    • The script calculates several performance metrics: cost per conversion, ROAS, conversion rate, and average CPC.
    • These metrics are essential for identifying anomalies in ad group performance.
  3. Anomaly Detection
    • The script defines functions to detect anomalies using statistical methods like IRQ.
    • It checks each feature for outliers, calculating upper and lower bounds based on IRQ thresholds.
    • Ad groups with CPA above the calculated threshold are flagged as outliers.
  4. Outlier Identification
    • For each campaign, the script identifies ad groups with CPA significantly higher than the campaign average.
    • It tags these outliers with a descriptive message indicating their CPA performance relative to the campaign average.
  5. Output Preparation
    • The script compiles the identified anomalies into a DataFrame, ready for output.
    • It prints the results in a table format for easy review and further action.

Vitals

  • Script ID : 711
  • Client ID / Customer ID: 636182884 / 68766
  • Action Type: Bulk Upload (Preview)
  • Item Changed: AdGroup
  • Output Columns: Account, Campaign, Group, AUTOMATION - Outlier
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
  • Created by dwaidhas@marinsoftware.com on 2024-02-22 17:39
  • Last Updated by dwaidhas@marinsoftware.com on 2024-03-20 21:26
> See it in Action

Python Code

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##
## name: Ad Group CPA Performance Outlier
## description: Tag AdGroup if CPA performance is abnormally high within Campaign
##  
## 
## author: Dana Waidhas 
## created: 2024-02-22
## 

RPT_COL_DATE = 'Date'
RPT_COL_GROUP = 'Group'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP_ID = 'Group ID'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_COST_PER_CONV = 'Cost/Conv. $'
RPT_COL_ROAS = 'ROAS'
RPT_COL_CONV_RATE = 'Conv. Rate %'
RPT_COL_AVG_CPC = 'Avg. CPC $'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_CONV = 'Conv.'
RPT_COL_REVENUE = 'Revenue $'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_AUTOMATION_OUTLIER = 'AUTOMATION - Outlier'

outputDf[BULK_COL_AUTOMATION_OUTLIER] = numpy.nan

## Data Prep

print(inputDf[RPT_COL_DATE].min(), inputDf[RPT_COL_DATE].max())

# 30-day lookback without most recent 3 days due to conversion lag
start_date = pd.to_datetime(datetime.date.today() - datetime.timedelta(days=33))
end_date = pd.to_datetime(datetime.date.today() - datetime.timedelta(days=3))

df_reduced = inputDf[ (inputDf[RPT_COL_DATE] >= start_date) & (inputDf[RPT_COL_DATE] <= end_date) ]

if (df_reduced.shape[0] > 0):
    print("reduced dates\\n", min(df_reduced[RPT_COL_DATE]), max(df_reduced[RPT_COL_DATE]))
else:
    print("no more input to process")

# reduce to needed columns
df_reduced = df_reduced[[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, RPT_COL_DATE, RPT_COL_PUB_COST, RPT_COL_CONV, RPT_COL_REVENUE, RPT_COL_CLICKS]].copy()

# sum metrics across dates for numerical columns only
agg_columns = {RPT_COL_PUB_COST: 'sum', RPT_COL_CONV: 'sum', RPT_COL_REVENUE: 'sum', RPT_COL_CLICKS: 'sum'}
df_group_perf = df_reduced.groupby([RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP]).agg(agg_columns)

# remove rows without cost or conversions
df_group_perf = df_group_perf[(df_group_perf[RPT_COL_CONV] > 0) & (df_group_perf[RPT_COL_PUB_COST] > 0)]

# index by campaign
df_group_perf = df_group_perf.reset_index().set_index([RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN]).sort_index()

# calculate features
df_group_perf[RPT_COL_COST_PER_CONV] = (df_group_perf[RPT_COL_PUB_COST] / df_group_perf[RPT_COL_CONV])
df_group_perf[RPT_COL_ROAS] = df_group_perf[RPT_COL_REVENUE] / df_group_perf[RPT_COL_PUB_COST]
df_group_perf[RPT_COL_CONV_RATE] = df_group_perf[RPT_COL_CONV] / df_group_perf[RPT_COL_CLICKS]
df_group_perf[RPT_COL_AVG_CPC] = (df_group_perf[RPT_COL_PUB_COST] / df_group_perf[RPT_COL_CLICKS])

## Define Anomaly Fuctions

# Finds anomalies using a certain function (e.g. sigma rule, IRQ etc.)
# data: DataFrame
#     Dataset with features
# func: func
#     Function to use to find anomalies
# features: list
#     Feature list
# thresh: int
#     Threshold value (e.g. 2/3 * sigma, 2/3 * IRQ)
# Returns: tuple
def get_feature_anomalies(data, func, features=None, thresh=3):

    if features:
        features_to_check = features
    else:
        features_to_check = data.columns 
        
    outliers_over = pd.Series(data=[False] * data.shape[0], index=data[features_to_check].index, name='is_outlier')
    outliers_under = pd.Series(data=[False] * data.shape[0], index=data[features_to_check].index, name='is_outlier')

    anomalies_summary = {}
    for feature in features_to_check:
        anomalies_mask_over, anomalies_mask_under, upper_bound, lower_bound = func(data, feature, thresh=thresh)
        anomalies_mask_combined = pd.concat([anomalies_mask_over, anomalies_mask_under], axis=1).any(axis=1)
        anomalies_summary[feature] = [upper_bound, lower_bound, sum(anomalies_mask_combined), 100*sum(anomalies_mask_combined)/len(anomalies_mask_combined)]
        outliers_over[anomalies_mask_over[anomalies_mask_over].index] = True
        outliers_under[anomalies_mask_under[anomalies_mask_under].index] = True
        
#         print("anomalies_mask_combined: ", anomalies_mask_combined)
#         print("Outliers: ", outliers)
        
    anomalies_summary = pd.DataFrame(anomalies_summary).T
    anomalies_summary.columns=['upper_bound', 'lower_bound', 'anomalies_count', 'anomalies_percentage']
    
    anomalies_ration = round(anomalies_summary['anomalies_percentage'].sum(), 2)
#     print(f'Total Outliers Ration: {anomalies_ration} %')
    
    return anomalies_summary, outliers_over, outliers_under

# Finds outliers/anomalies using IRQ 
# data: DataFrame
# col: str
# thresh: int
#     Number of IRQ to apply 
# Returns: Series 
#     Boolean Series Mask of outliers 
def is_anomaly_irq(data, col, thresh):

    IRQ = data[col].quantile(0.66) - data[col].quantile(0.33)
    upper_bound = data[col].quantile(0.66) + (thresh * IRQ)
    lower_bound = data[col].quantile(0.33) - (thresh * IRQ)
#     print("IRQ calc: ", col, IRQ, upper_bound, lower_bound)
#     anomalies_mask = pd.concat([data[col] > upper_bound, data[col] < lower_bound], axis=1).any(1)
    anomalies_mask_over = data[col] > upper_bound
    anomalies_mask_under = data[col] < lower_bound
#     print("Anomalies mask: ", (anomalies_mask_over, anomalies_mask_under))
    
    return anomalies_mask_over, anomalies_mask_under, upper_bound, lower_bound

def find_peer_anomaly(df_slice, features, irq_threshold=1.8, outliers_desired=(True, True)):
    
    (want_outliers_over, want_outliers_under) = outliers_desired
   
    if (df_slice.shape[0] < 3):
        return
    
    idx = df_slice.index.unique()
    
    df_slice.reset_index(inplace=True)
    
    anomalies_summary_irq, outlier_over_irq, outlier_under_irq = get_feature_anomalies( \
                df_slice, \
                func=is_anomaly_irq, \
                features=features, \
                thresh=irq_threshold)
    
    median_cost = df_slice[RPT_COL_PUB_COST].median()
    
#     print(f"over: {outlier_over_irq}")
#     print("under: {outlier_under_irq}")
    
    # include over/under outliers as desired
    is_outlier_irq = np.logical_or(
                        np.logical_and(want_outliers_over, outlier_over_irq),
                        np.logical_and(want_outliers_under, outlier_under_irq)
    )
    
#     print("is_outlier\\n", is_outlier_irq)
    
    # ignore anomaly from low spend adgroups (greater than campaign median)
    is_outlier_irq = np.logical_and(is_outlier_irq, df_slice[RPT_COL_PUB_COST] > median_cost)
    
    if sum(is_outlier_irq) > 0:
        print(">>> ANOMALY", idx)
        print(anomalies_summary_irq)
        cols = [RPT_COL_GROUP, RPT_COL_PUB_COST, RPT_COL_CONV, RPT_COL_REVENUE] + features
        print(df_slice.loc[is_outlier_irq, cols])
        
    return is_outlier_irq

## Find CPA Anomalies

print("input shape:", df_group_perf.shape)
df_anomalies = pd.DataFrame(columns=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, BULK_COL_AUTOMATION_OUTLIER])

# annotate via Marin Dimensions
def rowFunc(row):
    return 'CPA ${:,.2f} is much higher than campaign avg ${:,.2f}'.format(
        row[RPT_COL_COST_PER_CONV], \
        row[RPT_COL_COST_PER_CONV + '_median']
    )

for campaign_idx in df_group_perf.index.unique():
    df_campaign = df_group_perf.loc[[campaign_idx]].copy()
    df_campaign[RPT_COL_COST_PER_CONV + '_median'] = df_campaign[RPT_COL_COST_PER_CONV].mean()
    df_campaign[BULK_COL_AUTOMATION_OUTLIER] = np.nan
    outliers = find_peer_anomaly(df_campaign, [RPT_COL_COST_PER_CONV], irq_threshold=2, outliers_desired=(True,False))

    if outliers is not None and sum(outliers) > 0:
        df_outliers = df_campaign.loc[outliers].copy()
        df_outliers[BULK_COL_AUTOMATION_OUTLIER] = df_outliers.apply(rowFunc, axis=1)
        print(df_outliers)
        df_anomalies = pd.concat([df_anomalies, df_outliers], axis=0)

## Prepare Output

print(tableize(df_anomalies))

outputDf = df_anomalies[[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, BULK_COL_AUTOMATION_OUTLIER]]

Post generated on 2025-03-11 01:25:51 GMT

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