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.
Minhas et al. (Wed,) studied this question.