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Marketing Campaign Performance & Analysis

Presented By: Araoye A. A.

Date: 2/13/2025

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INTRODUCTION

Perform exploratory data analysis (EDA) on a marketing dataset to uncover key insights that can guide strategic decision-making.

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  • Objective: To analyze the performance of marketing strategies based on key metrics.
  • Scope:
      • Evaluating campaign effectiveness by Campaign Type, Channel Used, Location, and Target Audience.
      • Identifying patterns in key performance indicators (KPIs) such as ROI, CTR, CPC and Conversion Rate.
      • Providing actionable recommendations to optimize marketing stratehies.
  • Dataset Name: Marketing Campaign Dataset
  • Key Columns:
    • Company, Campaign_Type, Target_Audience, Duration, Channels_Used, Conversion_Rate, Acquisition_Cost, ROI, Location, Date, Clicks, Impressions, Engagement_Score, Customer_Segment,
  • Size: 200,005 rows (24.5MB)

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Findings & Insights �CTR vs Impressions

  • Higher impressions do not always guarantee a higher CTR.

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  • Google Ads & Website Channels have high impressions but varied CTR results

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  • Men aged 18-24 had the highest conversion rate.

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  • Women 25-34 had a balanced Conversion Date against CPC.

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By Marketing Channel

  • Facebook Ads and Websites offer strong ROI with relatively low CPA.

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  • Facebook Ads had lower CPA and better conversion efficiency.

By Location

  • Los Angeles & Miami generated the highest ROI.

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  • Houston had lower CPA making it a cost-effective region.

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Actionable Recommendations/ Conclusions & Next Steps

Optimize Ads Spend:

  • Shift budget towards high ROI channels like Facebook and Website and also enhance Ad creatives for other channels like Google Ads and Instagram.

Refine Targeting Strategy:

  • Prioritize audience segments with high CTR and Conversions like Men 18-24, Men 25-24 and Women 34-44 as they generate high ROI and high conversion rate at low CPC.
  • Adjust strategy for underperforming segments

Leverage Regional Performance Insights:

  • Increase efforts in Los Angeles and Miami for better Conversions.
  • Expand Houston campaigns due to its lower CPC.

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Conclusion

  • Focus on high performing strategies, reallocate budget wisely, and enhance engagement,

Next Steps

  • Implement A/B testing, refine targeting, and optimize high-ROI campaigns.

Final Thought

  • Aligning data-driven insights with marketing execution will maximize efficiency and results.

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Code & Methodology�

  • Programming Language: Python
  • Libraries Used: Pandas, Matplotlib, Seaborn, NumPy, Parser from DateUtil
  • Key Analysis Steps:
    • Data Cleaning & Preprocessing
    • Exploratory Data Analysis
    • Statistical Analysis
    • Visualization & Interpretation

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