Predicting sales of online products from advertising language | Stanford News

Challenges of language analysis

Online vendors have long struggled to figure out why the exact same product offered on different websites has varying sales figures.

Previous research focused on online consumers’ reactions to product reviews and word-of-mouth recommendations. But product descriptions haven’t received as much attention because studying the effects of language on consumer habits is a difficult task, according to researchers.

The problem is that many words are associated with high sales simply because they signal the product’s brand or pricing strategy, the researchers said. For example, if a product’s description includes brand names like “Nike” or phrases like “free shipping,” its sales will be higher than a description that doesn’t. But these are words that advertisers can’t change.

“We’re more interested in framing,” Jurafsky said. “How do advertisers frame the text to appeal to people independent of those other obvious sales factors?”

To address that challenge, Pryzant suggested applying adversarial machine learning, a new statistical method in which predictive models are pitted against each other. In this case, the results identified words associated with high sales, but not influenced by price or brand.

“The idea came quickly, but fitting the technique to our needs was hard and took time,” Pryzant said. “But the model was good at predicting sales on the first try, which was a gratifying result.”

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Predicting sales of online products from advertising language | Stanford News.