This project focuses on optimizing marketing campaigns using mult-touch attribution modeling. We constructed analytical relationship clusters and identified patterns and trends across this wide swath of marketing campaign sentiment feedback to answer the following business questions.

Primary Question: What features most accurately predict campaign success, measured by CTR (Click-Through Rate) and ROI?

Secondary Questions:

Which audience segments respond most to CTAs?

What channels drive the highest ROI?

How can we cluster audiences for personalized targeting?

We built machine learning models to understand what makes a marketing campaign successful. Specifically, what factors most accurately predict Click-Through Rate (CTR) and Return on Investment (ROI)? We tested whether different audience segments, campaign traits (like cost and engagement), and marketing channels (like Email or YouTube) could help us predict and improve performance.

We used two powerful machine learning models: Random Forest and XGBoost, to analyze over 330,000 rows of campaign data. These models looked at patterns between features like Clicks, Engagement Score, Acquisition Cost, and Target Audience to predict outcomes like CTR. We also ran cluster analysis to group audience segments with similar behaviors and performance profiles, helping us personalize future strategies.

Marketing teams often struggle to know what works and why. By predicting performance and uncovering which features drive results, we give business teams data-backed recommendations on:

What types of campaigns to run

How to allocate marketing budget efficiently

This helps turn raw data into strategic insights that improve future

Our data is a marketing performance dataset that represents real-world scenarios where businesses want to learn about, analyze, and optimize marketing spend based on conversions and effectiveness of marketing campaigns measured across 14 unqiue qualatative and quantitative variables.

Random Forest (via ranger)

The Random Forest model is an ensemble of tree-based models that are well-suited for structured tabular data with both numeric and categorical variables. We selected the Ranger implementation due to:

Speed: It is highly optimized for large datasets and allows for parallel processing.

Robustness: It handles noise, missing values, and irrelevant features better than simpler linear models.

Interpretability: Variable importance metrics provide clear insights into feature influence, which is crucial for marketing attribution discussions.

Use Case Fit: Random Forest works well when the signal is nonlinear and subtle. This is ideal for modeling complex user behavior and campaign performance such as our analytics data where there may be interaction effects between audiences, channels, and content types.

XGBoost

XGBoost is a gradient boosting framework that builds an ensemble of weak learners (trees) in a stage-wise manner, optimizing for predictive performance. We included XGBoost for its:

Superior accuracy on structured prediction tasks.

Built-in feature importance scoring, which helps with interpreting business drivers.

Tolerance for unbalanced data and robustness to multicollinearity.

Use Case Fit: XGBoost is excellent for predictive tasks in marketing where micro-patterns in user engagement (e.g., subtle differences in channel preference or demographic responsiveness) need to be captured, in order to build predictive recommendations to optimize future campaigns.

Engagement_Score was also highly influential, showing a strong correlation between how engaged users were and their likelihood to click on a CTA.

CTR as a computed target gave better insight than raw Clicks or Impressions.

ROI is average (5.01) despite the low engagement, suggesting that cost efficiency or click volume still balances out performance.

Strategy: Focus on re-engagement tactics — perhaps the content isn't resonating, or delivery timing is off. A/B test messaging or try new creatives.

Strategy: This is your star cluster — the campaigns here are efficient and drive strong traffic. Consider increasing budget or replicating the campaign style to similar audiences.

Strategy: These users are engaged but not converting into ROI effectively. You likely need to refine CTAs, optimize landing pages, or shorten the conversion funnel to capture value.

Clicks and Engagement Drive Success Campaigns with higher click volumes and stronger engagement (likes, shares, comments) consistently led to higher CTR. Impressions alone had no predictive power—visibility isn’t enough; user interaction matters.

Top Audience Segments Are Clear Men aged 18–24 and Tech Enthusiasts stood out with the highest predicted CTRs and engagement scores. These segments are highly responsive to compelling CTAs and digital content.

Email and Website Deliver Results While most channels performed similarly on CTR, Email and Website consistently yielded better ROI. Instagram showed weaker ROI despite similar engagement rates, suggesting it may be less efficient for conversion.

Cluster 2 Is the MVP One audience cluster stood out: high clicks, high ROI, strong performance across the board. This group is your top priority for scaling. On the other hand, Cluster 3 had low performance on all fronts—these campaigns likely need rework or divestment.

Low R², But High Value Although the models explained very little variance in CTR (low R²), they still identified clear patterns and directional signals. This suggests more granular data (like session behavior or creative type) is needed to improve future models.

2. ​Deploy in a Dashboard Visualize model predictions, audience segments, and ROI patterns in a Tableau or Shiny dashboard for internal use.

4. Experiment with Uplift Models Test incrementality to see which users respond because of the campaign, not just correlation.

5. Track Real Campaign Outcomes Use model predictions as benchmarks and compare them to actual campaign CTRs to validate impact over time.