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Agent-Generated Analysis Code
Executed
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('campaign_data.csv')
# Calculate Cost Per Lead and filter last quarter
df['CPL'] = df['spend'] / df['leads']
df['date'] = pd.to_datetime(df['date'])
last_q = df[df['date'] >= '2024-10-01']
# Rank by CPL (lower = better performance)
ranked = last_q.groupby('campaign').agg({
'spend': 'sum', 'leads': 'sum', 'CPL': 'mean'
}).sort_values('CPL')
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Campaign Performance — Cost Per Lead (₹)
200150100500
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Agent Insights
Email Q3 is the top performer at ₹92 CPL — 52% below the channel average of ₹191. Low spend (₹8,200) but consistently high conversion rate suggests an engaged, warm audience.
Meta improved 15% month-on-month — CPL dropped from ₹134 in October to ₹114 in November. Creative refresh and audience narrowing in the Nov campaign likely drove this improvement.
Google and Display underperforming at ₹194 and ₹208 CPL respectively. Recommend reducing budget allocation until creative or targeting is optimised.
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Auto-Generated Report
Ready to ExportCampaign Performance Summary — Q4 2024
Analysis of 247 campaign records across 6 active channels reveals significant performance variation. Email marketing delivered the strongest unit economics at ₹92 per lead, while Meta campaigns showed a positive trend with a 15% cost reduction between October and November.
Recommendation
Reallocate 20–30% of the Google and Display budget to Email and Meta channels in Q1 2025. Based on current conversion rates, this reallocation is projected to generate an additional 180–220 qualified leads at the same total spend of ₹1,39,700.