Polite language that invokes culture or authority helps products sell, according to research on online products in Japan.
The same research method could reveal top-selling words in English, Chinese, and other languages.
Computer science graduate student Reid Pryzant and linguist Dan Jurafsky, both of Stanford University, applied a machine learning technique to analyze more than 90,000 food and health-related product descriptions and their sales data on the Japanese e-commerce marketplace Rakuten.
The more of those keywords a description contained, the better the product sold, according to the research results, which appear in an article presented at the SIGIR Workshop on eCommerce in Tokyo, Japan.
“Using words that appeal to tradition—we also saw that on American menus and even on the back of potato chip bags.”
“Product descriptions are fundamentally a kind of social discourse, one whose linguistic contents have real control over consumer purchasing behavior,” the researchers write. “Business owners employ narratives to portray their products, and consumers react accordingly.”
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 says. “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 says. “But the model was good at predicting sales on the first try, which was a gratifying result.”
That the model worked surprised the researchers. The technique has been widely used in software for image analysis but rarely on language.
“Adversarial learning is a really hot topic right now,” Jurafsky says. “But it’s been challenging to get it to work for language. So this is really exciting technically and suggests other potential applications.”
Researchers found that product descriptions associated with higher sales were politer, using Japanese words and suffixes that indicated respect to the customer. These descriptions were also more informative, with lists of features or properties.
The descriptions of products that sold better also invoked framings of tradition or authority, with words like “long-standing shop” or “staff,” or mentioned the cultural function of the item, using words such as “Christmas”, “year-end gift,” and “souvenir.”
“There is definitely an ethical question here.”
Those results echo some of Jurafsky’s previous findings on the language of restaurant menus and food advertising, described in his 2014 book The Language of Food: A Linguist Reads the Menu.
“Using words that appeal to tradition—we also saw that on American menus and even on the back of potato chip bags,” Jurafsky says. “Talking about authenticity and tradition is just a really useful framing device.”
Because this research took place with Japanese descriptions, Jurafsky and Pryzant are eager to expand the study to English and other languages.
“It’ll be really interesting to see how the language differs when we start looking at English and Chinese,” Jurafsky says. “After all, different cultures appeal to tradition and display their attitudes toward customers in different ways.”
While this kind of research on language and sales might help businesses sell more products, Jurafsky says it’s important to think about whether these results could also make it easier for some businesses to manipulate their customers.
“There is definitely an ethical question here,” Jurafsky says. “Framing is a tool for persuading people—we see it every day in politics. Linguists worry about this a lot.
“From my perspective as a linguist, I think the more we know about how people are using language to influence us, the better. If we as consumers know that people are using certain kinds of framings, that has to help us spot when we’re being manipulated.”
Source: Stanford University