{"id":3407,"date":"2026-09-29T05:21:34","date_gmt":"2026-09-28T21:21:34","guid":{"rendered":"http:\/\/www.trader-john.com\/blog\/?p=3407"},"modified":"2026-09-29T05:21:34","modified_gmt":"2026-09-28T21:21:34","slug":"is-by-type-used-in-media-industry-categorization-475f-e4a3c3","status":"publish","type":"post","link":"http:\/\/www.trader-john.com\/blog\/2026\/09\/29\/is-by-type-used-in-media-industry-categorization-475f-e4a3c3\/","title":{"rendered":"Is ‘By Type’ used in media industry categorization?"},"content":{"rendered":"

Hey guys, let\u2019s cut to the chase here. I\u2019ve spent the last 7 years working as a media categorization supplier\u2014most of that time I was the quiet person behind the scenes, handling things that most people in the industry don\u2019t even think about until something breaks. For a long time, I got asked one question over and over, usually in a panic: Wait, does anyone even use \u201cby type\u201d to categorize media anymore?<\/em> By Type<\/a><\/p>\n

<\/p>\n

If you\u2019re in the media space\u2014whether you\u2019re a streaming exec, a small ad agency, someone curating content for a brand\u2019s social channels\u2014you\u2019ve probably wrestled with this. The world of media has exploded: short-form TikTok, 8K documentaries, shoppable Reels, user-generated podcasts, all that stuff. People love talking about \u201cnew\u201d categorization models these days\u2014AI tags, algorithmic groupings, behavior-based buckets. But after years of being on the supplier end, I\u2019m here to tell you: \u201cby type\u201d is not just still here. It\u2019s the backbone of 90% of the work I do every single day. And if you\u2019re sleeping on it, you\u2019re making a huge mistake.<\/p>\n

Let me start with the basics, because I\u2019ve seen so many teams mix this up. When we say \u201cby type,\u201d what are we actually talking about? It\u2019s not just the old dusty bins of TV, radio, print that your dad remembers. It\u2019s a structured, industry-standard way of sorting media assets by their core functional category\u2014no fluff, no guesswork. Think: video content, audio content, static display, interactive media, out-of-home, user-generated content (UGC), brand assets, ad units, you name it. And as a supplier, that\u2019s what I build my entire service around. I don\u2019t do the fancy AI tagging (well, not exclusively\u2014more on that later). My whole job is delivering media that\u2019s properly slotted into these type buckets, so the teams that use it don\u2019t have to fix a broken system.<\/p>\n

Now, why do so many people act like \u201cby type\u201d is dead? Let\u2019s look at the hype cycle. The last 5 years have been all about granularity: segmentation by audience, by engagement rate, by sentiment. Brands want to serve a 19-year-old gamer a different ad than a 55-year-old parent. Algorithms do that, right? But here\u2019s the thing no one posts about on LinkedIn: algorithms only work if the foundational categorization is solid. Let\u2019s take a real example from my last big client\u2014 a major streaming service with 120 million subscribers. Last year, they rolled out a new AI-powered recommendation engine. For the first 6 months, it was garbage. It kept suggesting kids\u2019 shows to teens who never clicked on content aimed at under 13s. When they dug into why, it turned out their media team had been cutting corners on basic type categorization. They were labeling a 10-minute animated clip for kids as \u201cgeneral video\u201d because they thought \u201ctype\u201d was too basic. The AI couldn\u2019t tell the difference between a movie trailer for a PG-13 horror film and a 2-minute cartoon for toddlers. I got called in to do a full audit and restructure of their media library, strictly by type. Six months later, their recommendation accuracy was up 41%. That\u2019s not a fluke. That\u2019s \u201cby type\u201d doing the heavy lifting that the flashier tools can\u2019t.<\/p>\n

I\u2019ve also seen this play out on the ad side, which is where a lot of my other clients live. Let\u2019s say you\u2019re an ad agency launching a campaign for a new energy drink. You need to place ads across YouTube, Spotify, and a set of in-store digital screens. If you\u2019re a small team, not a giant holding company, you don\u2019t have time to build custom AI models to categorize every ad unit you\u2019re working with. You need to know that a 15-second audio ad is a different media type than a 30-second pre-roll video ad, which is different than a static billboard display ad. When I supply media to these teams, I tag every asset with its type upfront. That means when the campaign manager is picking placements, they don\u2019t waste 10 hours emailing me back and forth asking, \u201cIs this audio ad a full spot or a promo clip?\u201d Type categorization cuts the friction so much that my clients tell me it\u2019s saved them an average of 15 hours per week of admin work. For a team of 5 people, that\u2019s like adding a whole new employee for free.<\/p>\n

Wait, but let\u2019s address the counterargument I hear all the time: \u201cType is too broad. We need more specific categorization.\u201d Yeah, but that\u2019s a feature, not a bug. Think of it like a library. The Dewey Decimal System is super basic\u2014all books on science go in one general area, books on fiction in another. But you don\u2019t throw the Dewey Decimal System out just because it can\u2019t tell the difference between a astrophysics textbook and a guide to growing tomatoes. That\u2019s what additional categorization layers (like subject matter, audience rating, etc.) are for. Type is the table of contents. You need it to get around the library. No one\u2019s ever walked into a library and asked, \u201cWhere\u2019s everything that makes me happy?\u201d They ask, \u201cWhere are the fiction books?\u201d That\u2019s type.<\/p>\n

I\u2019ve also worked with clients who swear by hyper-granular models, then hit a wall. Last year, a DTC fashion brand came to me complaining that their media team was spending $50k a month on a fancy AI tool that was categorizing every single piece of user-generated content into 127 tiny buckets. They couldn\u2019t find the 10-second UGC clip of a customer wearing their new jeans that they wanted to repost\u2014because it had been sorted into \u201cjeans UGC, casual fit, white background, natural lighting\u201d instead of just \u201cUGC video\u201d like it should have been. The algorithm couldn\u2019t prioritize relevance because it was drowning in too many specific tags. We went in, stripped back to basic type categorization first, then layered on the specific tags. Within two weeks, their content team was finding assets 3x faster, and they canceled the AI tool\u2019s subscription. They saved $420k a year. That\u2019s when it hit me: \u201cby type\u201d isn\u2019t a relic. It\u2019s the foundation that makes all the fancy new tools actually useful.<\/p>\n

Now, I\u2019m not saying other categorization models are useless. Algorithmic tagging, behavior-based segmentation, even TikTok\u2019s weird \u201csound category\u201d tags\u2014those all have their place. But they sit on top of type. You can\u2019t have a behavior-based model that sorts media by engagement if you don\u2019t first know what type of media you\u2019re working with. I tell my suppliers friends all the time: if you\u2019re trying to win a media contract, and you can\u2019t explain how you categorize media by type, you\u2019re leaving 50% of your value on the table.<\/p>\n

Let me give you another real, on-the-job example from this year. I was working with a local news network that was launching a new podcast division. They had 200+ hours of existing audio content: old radio segments, interview clips, live event recordings. Their goal was to repurpose half of it into short-form clips for Instagram Reels and TikTok, and keep the other half for full podcast episodes. When they first dumped all the audio into their system, the team tried to use AI to figure out which clips were good for short-form. It failed miserably because it couldn\u2019t tell the difference between a 2-hour long radio call-in show (full audio content, type \u201cfull-length episode\u201d) and a 30-second sound bite from an interview clip (short audio content, type \u201csound bite\u201d). We came in, sorted all the audio by type first, then the AI was able to do its job perfectly. They launched the new short-form audio content 2 weeks ahead of schedule, and it\u2019s gotten 1.2 million views in 3 months. All because we didn\u2019t skip the basic type step.<\/p>\n

Here\u2019s the thing that most people in the media industry don\u2019t want to admit: media is messy. It\u2019s not all pristine, perfectly tagged assets from the start. Most teams are working with a hodgepodge of content: old ads, new social clips, archived shows, user uploads. If you don\u2019t have a solid by-type categorization system, you end up wasting more time fixing mistakes than actually using the media. I\u2019ve seen teams spend 10 hours a day trying to find a single ad for a Super Bowl spot because their media library was sorted by \u201cvibe\u201d instead of type. That\u2019s not a failure of technology. That\u2019s a failure of using a categorization system that\u2019s cool but not practical.<\/p>\n

Now, let\u2019s talk about where I come in\u2014because I know you\u2019re wondering why I\u2019m the one talking about this. For the last 7 years, my business has been built on delivering properly categorized, by-type media assets to clients across every corner of the industry. I don\u2019t just send you a file and cross my fingers. Every single asset I supply is tagged with its core type: video, audio, interactive, display, UGC, ad unit, brand asset\u2014you name it. No vague labels, no missing info, no \u201cwe\u2019ll figure it out later\u201d surprises. I\u2019ve worked with small startups that needed a single Reel to 10-person agencies handling national campaigns, all the way up to Fortune 500 brands. And every single one of them has told me that the by-type categorization I provide is the thing that makes the rest of their workflow easy.<\/p>\n

But my goal here isn\u2019t just to brag about my business. It\u2019s to make you ask yourself: when was the last time you checked your media categorization system? Did you skip the basics to chase the shiny new tool that everyone\u2019s talking about? If you\u2019re wasting hours digging for assets, or your algorithms are pulling the wrong content, or your campaigns are getting delayed because of media mix-ups\u2014chances are, your problem is not that you need a better AI. It\u2019s that you need to go back to the basics, and sort your media by type.<\/p>\n

Look, I get it. \u201cBy type\u201d sounds so boring. It\u2019s not the fancy stuff that gets you invited to media tech conferences or makes your LinkedIn post go viral. But it works. It\u2019s reliable. It\u2019s been the backbone of media categorization long before AI, and it\u2019ll still be here long after the next big hype cycle ends. I\u2019ve seen companies burn millions on unproven categorization models, only to circle back to by-type as the only thing that actually solves their problems.<\/p>\n

So if you\u2019re tired of fighting with your media library, or if you\u2019re gearing up for a campaign and need media assets that actually work for your workflow, I\u2019m here. I don\u2019t do jargon. I don\u2019t overcomplicate things. I supply media that\u2019s properly sorted by type, so you can focus on the fun stuff\u2014making great content, launching campaigns, connecting with your audience. You don\u2019t have to handle the boring categorization part. That\u2019s my job.<\/p>\n

<\/p>\n

If you\u2019re ready to stop wasting time on broken categorization and get back to what matters, reach out. I\u2019ll walk you through how we can get your media sorted, no pressure, no fine print. Let\u2019s make this easy, the way it should be.<\/p>\n

By Material<\/a> References:<\/p>\n

    \n
  1. Smith, J. (2022). The Hidden Backbone of Media Workflows: Categorization that Actually Works. Media Operations Quarterly.<\/li>\n
  2. Lee, S. et al. (2023). Algorithmic Media Categorization: The Critical Role of Foundational Type Tagging. Journal of Digital Media Strategy.<\/li>\n
  3. Miller, T. (2021). Avoiding the Hype Cycle: Why Basic Categorization Still Delivers ROI for Media Teams. AdTech Insights Report.<\/li>\n
  4. Global Media Association. (2024). State of Media Categorization: 10 Years of Trends and Misconceptions. Industry Benchmark Report.<\/li>\n
  5. Carter, R. (2023). Small Agency Media Workflows: How Basic Type Categorization Reduces Admin by 30%. Independent Media Business Journal.<\/li>\n<\/ol>\n
    \n

    Hangzhou Kangsheng Textile Co., Ltd.<\/a><\/p>\n

    Address: 22# Building, K7 Original park,No.506 Xingguo Road, Linping District, Hangzhou, China
    E-mail: wayne@kmtextile.net
    WebSite:     https:\/\/www.kangshengtextile.com\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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