Script 711: Ad Group CPA Performance Outlier

Purpose

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

To Elaborate

The script is designed to analyze advertising data to identify ad groups within a campaign that exhibit unusually high Cost Per Acquisition (CPA) performance. It processes historical data, excluding the most recent days to account for conversion delays, and aggregates key metrics such as cost, conversions, revenue, and clicks. The script then calculates performance indicators like CPA, Return on Ad Spend (ROAS), conversion rate, and average cost per click. Using statistical methods, it identifies anomalies in CPA performance by comparing each ad group’s performance against the campaign’s median. Ad groups with CPA significantly higher than the campaign average are flagged as outliers, allowing marketers to take corrective actions to optimize their advertising spend.

Walking Through the Code

  1. Data Preparation
    • The script begins by defining a 30-day lookback period, excluding the most recent 3 days to account for conversion lag.
    • It filters the input data to include only the necessary columns and aggregates metrics like cost, conversions, revenue, and clicks by ad group within each campaign.
    • Rows without cost or conversions are removed to ensure meaningful analysis.
  2. Feature Calculation
    • The script calculates key performance metrics for each ad group, including CPA, ROAS, conversion rate, and average CPC.
    • These metrics are used to assess the performance of each ad group within its campaign.
  3. Anomaly Detection
    • The script defines functions to detect anomalies using statistical methods like the Interquartile Range (IRQ).
    • It identifies ad groups with CPA significantly higher than the campaign average, considering only those with spend above the campaign median.
  4. Outlier Identification
    • For each campaign, the script calculates the median CPA and identifies ad groups with CPA anomalies.
    • Outliers are tagged with a descriptive message indicating their CPA is much higher than the campaign average.
  5. Output Preparation
    • The script compiles the identified outliers into a DataFrame and prepares it for output, providing insights into ad group performance anomalies.

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 2024-11-27 06:58:46 GMT

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