Can AI Indeed Assess Web Content Effectiveness?
AI instruments are taking over, but what does this mean for the future of UI, copywriting, and digital content creation?
With AI being applied to almost every business function there is, AI consultants are now exploring how this technology can bring value to processes that intersect with creativity and often produce unmeasurable outcomes. Adobe has recently revealed its AI-based tool called Catchy Content, which is designed to determine if images, videos, and text in blog posts, store listings, and landing pages are effective. In essence, this instrument is built to understand customers’ preferences and tune online content to increase engagement.
With the help of Sensei, Adobe’s state-of-the-art AI platform, Catchy Content analyzes data of the content that a particular target audience is naturally attracted to. Then it extracts the meaning of the content you’ve created by assessing color palettes, writing tone, word choice, and other attributes to check against customers’ preferences. Afterward, it ‘scores’ the content in terms of customer engagement and suggests certain adjustments that might help to make it more attractive for an intended audience.
Adobe presents these analytics on a dashboard, where the system marks content pieces that might need some tweaking. It can even suggest stock images that can potentially perform better. Furthermore, the system can be tuned to reach specific objectives like increasing conversion or reading time.
Admittedly, Catchy Content is a very cold-hearted, calculative approach to evaluating creative work. When AI provides its opinions on our creativity, we, as humans, become justifiably reluctant to trust it. When experts try to explain why a particular art piece is appealing, they often do it using abstract and inconclusive terms. In other words, the reasons for the success of a creative work can rarely be explained by data. However, it’s critical to understand that Catchy Content has little to do with making one’s work better in terms of creativity. Its main goals revolve around increasing website traffic, engaging blog post readers for longer, and enhancing other definitive business metrics.
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While AI-powered content analytics is not entirely a new thing, existing platforms like Parse.ly can rate your content but are unable to offer ways to make it more engaging. As Catchy Content has yet to go live and has been only shown to the general public as a demo product, it’s hard to say how exactly it scores the content, let alone provides suggestions for its enhancement. In other words, the reliability and accuracy of this system have yet to be confirmed. Catchy Content relies on big data, but, as we know it, the prediction accuracy of AI-based systems strongly depends on the quality of the injected data.
Adobe believes that this tool will find its most use among B2B content creators and e-commerce platforms. This shouldn’t be surprising as journalistic content often requires a far more creative approach, making it unattainable for AI to adequately rate it.
Regardless, one tricky question inevitably emerges: if the same AI system will tweak the content of thousands of different websites, won’t they all look the same? Hyman Chung, Senior Product Manager at Adobe, believes that as the number of users of Catchy Content will increase, the system’s suggestions will become more diversified.
This is a very recurring argument that many AI companies make, stating that their algorithms will become smarter as they get used more. While there is no doubt about that, it’s paramount for the system to be effective prior to that point. In Adobe’s case, it might be a hard proposition, as the Catchy Content algorithm has to know the preferences of Hawaiian female surfers, San-Francisco-based software developers, and any other narrow target audiences equally well.
Adobe revealed Catchy Content during its latest Sneak, which means that it’s an R&D project with an uncertain destiny. Regardless of the tool’s future, Adobe makes an important point about customer personalization. Brands are usually focused on collecting and processing data about customers, but not the content these customers consume. By understanding how exactly a certain type of customer reacts to specific content, brands can significantly enhance personalization at scale. While personalized content helps with conversion, its real catch is in building long-lasting relationships with customers.
The idea behind Catchy Content is rather ambitious. Nobody knows for sure, but it’s likely that the underlying model will require a considerable amount of training before it becomes as effective as Adobe imagines it to be. For now, it seems that it can find its most use by offloading tedious, monotonous and least creative parts of content creation from humans to the algorithm. Choosing the most engaging photo among hundreds of similar stock images is rarely what excites creatives, and even the demo version of Adobe’s product can already tackle such issues.
The idea behind Catchy Content is a natural byproduct of this AI fever the tech sector is experiencing. There is no doubt that, sooner or later, AI will play an increasingly important role in catering content to users’ specific needs. If not Adobe, then some other company will make AI a standard tool in content creators’ arsenal.
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