This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: As marketers increasingly turn to artificial intelligence (AI) tools to create content and improve the efficiency of their workflows, a new study by a Penn State researcher and collaborators proposes an approach to deploying AI-generated marketing content at scale while reducing the need for costly and time-consuming traditional testing. Wreetabrata "Wreeto" Kar, assistant professor of marketing at Penn State's Smeal College of Business, and his co-authors developed a framework that trains AI models to screen new AI-generated content using performance data from a business's previous marketing campaigns.
The models then provide marketers with content recommendations and ratings that can streamline their decision-making process. Building on the example of a large-scale email marketing campaign, the researchers demonstrated how the new approach could help predict the success of a campaign through a combination of data analysis and marketer evaluation. The study was recently published in the Journal of Marketing Research.
In the following Q&A, Kar spoke about the importance of incorporating a business's unique context into its AI applications and how human capital is vital to the successful use of these new technologies. Marketers now use AI across almost every part of their work, though content creation is the most visible use. Teams use it to draft emails, social media posts, ads and product descriptions.
Many also use it to create images and short videos. But AI is moving well beyond writing copy. Marketers use it to brainstorm campaign ideas and research their markets, analyzing data to divide customers into groups for personalized messaging.
Some firms also use it to predict what customers will do next and to automate parts of the customer journey. The biggest benefit is speed. AI helps marketers create and explore far more ideas than before.
It makes personalization much easier, since you can write a different message for each type of customer. It also helps teams respond faster to what customers are doing. But more content also means more choices.
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