A Natural Language Processing Approach to Predicting the Persuasiveness of Marketing Communications

Siham El Kihal, A. Selin Atalay, and Florian Ellsaesser, 2020, 20-104

Marketers today have vast amounts of textual data and unprecedented opportunities to reach their customers. Yet their decision problem is the same as ever: how to formulate such messages so that they are effective in persuading consumers. Here, Siham El Kihal, A. Selin Atalay, and Florian Ellsaesser investigate the question: What is the role of language complexity in predicting how persuasive a message will be?


The authors use a natural language processing approach to measure language complexity (in terms of content and syntax) and predict message persuasiveness. The systematic classification of syntactic complexity -- the complexity of grammatical structure -- has only recently become possible through advances in natural language processing. Specifically, theirs is one of the first studies to use convolutional neural networks to extract information about the syntactic structure of messages.

Their dataset includes 134 debates (with 129,480 sentences on different topics), attitude polls from the audience before and after debates, and a follow-up experiment to investigate how content complexity and syntactic complexity predict the persuasiveness of a message.

They find that syntactic complexity has a significant and strong impact on persuasion: When the syntax is more complex, persuasiveness of the message is diminished. Syntactic complexity is more predictive than content complexity of the success of a persuasive message. A message that is not complex in content can still be complex in syntax, impacting readability, reaction time, and recall.

Their model shows that using both content complexity and syntactic complexity as predictors  improves the accuracy of predicting message persuasiveness by about 32%, compared to a baseline model where use of language is not factored in.

Put into Practice

These findings provide guidance to decision makers or those who design marketing messages. First, the study highlights the importance of considering syntactic complexity when designing marketing messages. Second, the authors provide a tool that can be used to formulate and/or assess the persuasiveness of messages. For example, online retailers might use this approach to analyse product descriptions and how language complexity ties to quality perceptions, sales, and satisfaction.




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