Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed automatically classify text. This paper compares performance ten such approaches (five lexicon-based, five machine learning algorithms) across 41 datasets covering major platforms, various sample sizes,...

Sentiment is fundamental to human communication. Countless marketing applications mine opinions from social media communication, news articles, customer feedback, or corporate Various sentiment analysis methods are available and new ones have recently been proposed. Lexicons can relate individual words expressions scores. In contrast, machine learning more complex interpret, but promise higher accuracy, i.e., fewer false classifications. We propose an empirical framework quantify these...

Smartphones have made it nearly effortless to share images of branded experiences. This research classifies social media brand imagery and studies user response. Aside from packshots (standalone product images), two types brand-related selfie appear online: consumer selfies (featuring brands consumers’ faces) an emerging phenomenon the authors term “brand selfies” (invisible consumers holding a product). The use convolutional neural networks identify these archetypes train language models...

Cause-related marketing (CM), which links corporate donations to consumer purchases, has ongoing momentum in marketing. As the magnitude and direction of consumers’ response CM are inconclusive, this meta-analysis synthesizes evidence on main moderator effects from 237 studies. On average, authors find a moderate effect for attitudinal (d = .458) weak behavioral .283; both ps < .001), with high underlying heterogeneity. A multivariate meta-regression moderators grounded along four...

This study presents the first field investigation of sales impact cause-related marketing promotions (CMPs) in retail settings. Whereas prior work primarily studies CMPs simplified experimental settings, actual fast-moving consumer goods markets are considerably more complex; ergo, consumers unlikely to consider and evaluate all brands detail. In this analysis based on 63 across 20 categories, authors therefore investigate short-term as a function brand category context which they executed....

Smartphones have made sharing images of branded experiences nearly effortless. This research classifies social media brand imagery and studies user response. Aside from packshots (standalone product images), two types brand-related selfie appear online: consumer selfies (featuring brands consumers' faces) an emerging phenomenon we term (invisible consumers holding a product). We use convolutional neural networks to identify these archetypes train language models infer response more than...

Sentiment is fundamental to human communication. Countless marketing applications mine opinions from social media communication, news articles, customer feedback, or corporate Various sentiment analysis methods are available and new ones have recently been proposed. Lexicons can relate individual words expressions scores. In contrast, machine learning more complex interpret, but promise higher accuracy, i.e., fewer false classifications. We propose an empirical framework quantify these...

In service marketing, customers typically pay more when they use more. Based on this principle, various non-linear pricing plans or flat-rate tariffs attempt to lure into higher and higher-revenue contracts. An emerging marketing practice we term precommitment-based turns these principles around asks extra the too little. For example, a local fitness club offers discount reach minimum training frequency, those who fall short premium. This form of aligns directly with customer objectives...