Bridging Accuracy and Profitability: AI Models for Marketing Campaign Targeting and Brand Sentiment Analysis

Abstract

Artificial Intelligence (AI) is transforming marketing by enabling firms to predict customer responses and monitor brand sentiment in real time. This study evaluates the effectiveness of machine learning and transformer-based models for two key tasks: campaign response prediction and sentiment analysis. Using the Marketing Campaign dataset and a Tweets sentiment dataset, traditional algorithms (Logistic Regression, Random Forest, XGBoost, LightGBM, and SVM) were compared with DistilBERT, a transformer-based model. To address class imbalance in campaign data, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Results demonstrate that the Random Forest model, after SMOTE, achieved 95% accuracy and an F1-score of 0.95, outperforming other classifiers and improving conversion targeting efficiency. For sentiment analysis, DistilBERT reached an accuracy of 77% with strong performance in detecting negative sentiment (F1 = 0.77), allowing early identification of reputational risks. To validate practical relevance, these methods were applied to BranditOfficial, a wedding photography and videography business in Islamabad. The Random Forest uplift model identified the top 15–20% of followers as high-probability converters, increasing projected monthly profit from PKR 400,000 to PKR 488,000—an incremental gain of PKR 88,000 (over PKR 1 million annually). DistilBERT enabled proactive engagement by flagging 72% of negative feedback trends early, while positive comments were repurposed as client testimonials. This study contributes to marketing scholarship by integrating ROI-oriented managerial metrics—Precision@k, Incremental Lift, and Expected Profit—into model evaluation and by demonstrating their application in an SME context. The findings underscore that AI-driven decision-making can simultaneously enhance profitability and safeguard brand reputation, bridging the gap between academic research and real-world marketing practice.