Applied econometric modeling techniques in RStudio to evaluate how campaign engagement, advertising exposure, acquisition cost, campaign duration, campaign type, and channel mix influence digital marketing conversion rates.
This project analyzed a large-scale digital marketing campaign dataset to evaluate whether campaign engagement, advertising exposure, acquisition cost, duration, campaign type, and channel used meaningfully explain variation in conversion rates.
The objective was to apply econometric modeling and diagnostic testing to a marketing performance question, while interpreting the results through both statistical and practical business relevance.
How do campaign engagement, exposure, and cost efficiency influence conversion rates across digital marketing campaigns?
The analysis used Ordinary Least Squares regression across five progressively expanded specifications, moving from a bivariate model to a full baseline model with categorical controls.
The full model estimated the relationship between conversion performance and campaign-level variables while controlling for campaign format and channel differences.
The full model produced an adjusted R² of approximately -0.000028, indicating that the independent variables explained essentially none of the variation in Conversion Rate. This reflected the synthetic structure of the dataset rather than a modeling error.
| Variable | Actual Sign | Significant? | Practical Meaning |
|---|---|---|---|
| Clicks | Positive | No | Near-zero effect |
| Impressions | Negative | No | Near-zero effect |
| Engagement Score | Negative | No | Near-zero effect |
| Acquisition Cost | Positive | No | Near-zero effect |
| Campaign: Influencer | Positive | 10% only | Not practically meaningful |
Results remained stable across all robustness checks. Coefficients stayed near zero and statistically insignificant, confirming that the findings were driven by the synthetic data-generating process rather than model specification errors.
The key takeaway was that a statistically correct model cannot produce meaningful business insight when the underlying dataset lacks real relationships between variables. The project reinforced the importance of data quality, diagnostic testing, model validation, robustness checks, and practical interpretation beyond p-values.
View the full econometrics analysis, including model specification, regression outputs, diagnostic testing, robust standard errors, and final interpretation.
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