Why ChatGPT Struggles with Video Content Development: How Videoquant Solves

The Rising Role of ChatGPT in Video Content Optimization

The dawn of 2023 saw a burgeoning interest in language models, particularly with the prominence of ChatGPT and its contemporaries. A common curiosity arose among digital enthusiasts, including our team: how can GPT’s recent advances serve to enhance video content spanning from YouTube uploads to high-profile TV commercials?

Through our experiments, we discerned that GPT, despite its linguistic capabilities, struggles to forecast high-performing video content ideas and associated metadata like scripts, titles and descriptions. This discovery led us to file two patent applications, delving into this challenge and how we approached solving it.

Why ChatGPT Struggles with Video Content Development

Fundamentally, ChatGPT’s design centers around predicting word sequences derived from its extensive exposure to pre-existing text. Upon receiving a prompt, it generates a response based on the most recurrently observed context. This intrinsic feature reveals its Achilles’ heel in video content development. ChatGPT remains oblivious to the success or failure of a YouTube video or a TV commercial. To compound the issue, given that most video content performs poorly (>90% fail based on our research), ChatGPT inadvertently proposes content prone to perform poorly, basing its suggestions on prevalent trends that have a propensity to struggle.

Why ChatGPT Proposes Video Content Prone to Perform Poorly

Here’s a closer look at the core reasons behind its subpar performance in generating compelling video content ideas:

  1. No Knowledge of Video Performance: GPT has no inherent knowledge of what a successful vs. unsuccessful video looks like. This is an entirely different domain. While it’s adept at generating text based on patterns from its vast training data, this data doesn’t encompass video performance metrics, such as revenue, views, subscribers, or brand awareness driven by the video.
  2. Biased Towards Underperforming Concepts Due To How It’s Built: Most video content fails, but GPT is trained to mirror what’s most common. Therefore, it’s output is most likely to skew towards content with high failure probability. In fact, we’re in the process of quantifying this. At its core, GPT shines in recognizing and reproducing common word sequences. Counterintuitively, this is problematic in the realm of video content. Based on our data, where 90% of video content fails to meet performance expectations, GPT’s inclination towards these dominant patterns means it is skewed towards suggesting ideas rooted in historically unsuccessful content. This predisposition towards familiarity, ironically, might steer creators towards the very pitfalls they seek to avoid.
  3. No Grasp of Current Events & Emerging Trends: Core to its current design, GPT lacks real-time insight into current events and emerging trends. Consequently, when generating ideas for videos & TV commercials, GPT will not align with the latest buzz or hot topics, causing content creators to miss out on timely opportunities and audience engagement.
  4. No Visual & Auditory Context: Focusing primarily on text, GPT remains oblivious to the vital visual and auditory aspects of videos. These components are often the differentiators between a video’s triumph and its downfall.
  5. Propagates Saturated Content: There’s an inherent irony in GPT’s methodology. By emphasizing commonly observed sequences, GPT inadvertently propagate content ideas that are often already oversaturated in the market. This is further compounded by the observation that most of the content doesn’t even perform.
  6. GPT Wasn’t Trained For Video Data: While output from GPT may *appear* as valid for video, looks are deceiving. ChatGPT wasn’t trained on video, so we additionally incur out-of-sample error.

Videoquant: Bridging ChatGPT’s Gaps

This year, we embarked on a pioneering journey by filing two patent applications, pinpointing how ChatGPT can be reoriented to suggest viable video concepts with higher success potential. The linchpin to our approach is “educating” ChatGPT on successful video content. We achieve this by integrating ChatGPT as a subtle, nuanced layer atop our vast database, a repository of insights from over 20 million TV commercials and social media videos. This marriage of ChatGPT’s textual genius with our performance data allows us to craft TV commercial & social video content that prioritize video success rather than simply mirroring most common patterns.

The Rationale Behind Incorporating ChatGPT in Video Strategy

With the aforementioned modifications, deploying ChatGPT as a thin supplementary layer offers tangible benefits. Yet, it’s crucial to perceive ChatGPT not as the primary driving force, but rather the embellishing touch. The real challenge lies in deciphering video content and concepts that resonate with targeted goals, be it revenue, views, subscriber growth, or brand amplification. Once these insights are harvested, ChatGPT acts as the perfect tool to refine the raw idea, transforming it into a polished, ready-to-launch video concept, thanks to the insights from Videoquant.

Most TV & video ads have anemic results; ~80% is caused by non-ideal creative strategy. VQ is a patent-pending system to avoid ineffective video concepts using outcomes data from 10M+ TV ads and online videos.


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