Tools That Analyze the Hooks in Your TikTok and Meta Video Ads

The tools that analyze the hooks in your top TikTok and Meta video ads fall into three groups. Motion and Superads connect to your ad accounts and score each video's opening with a hook metric built from thumb-stop and early-view data. Segwise and Atria use AI to find and tag hooks in your own ads and tie them to results, with Segwise built for mobile apps and games. Crux tags creative elements across connected Meta, TikTok and Google accounts and adds competitor research. Foreplay surfaces top hooks in competitors' ads. If you want to break your own ad archive into scenes and query the first three seconds yourself, a developer API such as Mixpeek fits.

What "hook analysis" means in practice

The hook is the opening of a video ad, usually the first two or three seconds. Teams ask three different questions about it:

1. Is this hook holding attention? A metric question, answered from platform data like 3-second views over impressions.
2. What kind of hook is it? A tagging question: problem callout, product to camera, creator talking head, text-only open.
3. Which hook types win across hundreds of ads? A comparison question, answered by joining the tags to performance.

The first question needs a connection to the ad account. The second needs something that watches the video. The third needs both, plus a consistent tag set so two strategists label the same hook the same way.

The shortlist

Tool	Best for	What it does with hooks
Motion	DTC brands on Meta and TikTok	Hook score from thumb stop and first frame retention; AI tags that include hook and creative angle
Superads	Brands and agencies wanting a scored view	Hook Score for video ads; AI labels that include hook type
Segwise	Mobile apps and games	AI tags for hook headline, hook tactic and hook visual format, tied to ad network and MMP data
Crux	Performance teams that also want competitor research	AI tagging, creative deep-dives and custom breakdowns on connected ad accounts
Atria	DTC teams on Meta and TikTok	An AI analyst that finds the personas, hooks and messages that keep winning
Foreplay	Creative strategists doing research	Saved ads with transcription; a competitor tracker that identifies top hooks
Vidmob	Enterprise brands	Element-level creative analysis and creative scoring across major ad platforms

Which tools break an ad into hook, scenes and messaging?

Many creative analytics tools work at the level of the whole ad: they attach tags like "hook: problem callout" or "format: UGC" to an ad ID and report on those tags. That is the right shape for weekly reporting.

A per-scene breakdown is a different output. You get the ad cut at each shot change, with a start and end time on each piece, plus what was said, what text was on screen and what the shot shows. That lets you look at the hook as its own object, and also see what happens in the scenes after it: where the product first appears, when the offer lands, how long the creator stays on screen.

Ask any vendor you evaluate whether their tags are per ad or per time range, and whether you can export them with timestamps.

How creative strategists tag and compare hundreds of ads

The workflow that holds up at a few hundred ads looks like this:

• Fix the tag set first. Write down your hook types with a one-line definition and one example ad each. A tag set that drifts between people makes the comparison meaningless.
• Tag the hook separately from the body. An ad that opens on a creator and cuts to a product demo should carry both, at different times.
• Join to performance by ad ID. Keep spend, hook rate and conversions in the same table as the tags.
• Compare within a format. A UGC hook and a motion graphics hook rarely compete for the same placements.

If your ads live on Meta and TikTok and you want this done for you, Motion, Superads or Atria cover it. If you run an app, start with Segwise. If you want to save and study competitors' hooks, Foreplay or Crux.

Doing hook analysis on your own ad archive

Disclosure: I work on Mixpeek.

Mixpeek does not connect to your Meta or TikTok ad accounts and does not pull performance metrics. You bring the video files from your own object storage, and you join performance data yourself by ad ID. What it gives you is the per-scene breakdown described above, as an API you can query.

1. Cut every ad into scenes at ingest. The multimodal extractor splits on visual change with split_method: "scene". Scenes longer than max_segment_duration (30 seconds by default) are split further. Turn on transcription, OCR and descriptions so each scene carries what was said, the on-screen text and a description of the shot.

{
  "feature_extractor": {
    "feature_extractor_name": "multimodal_extractor",
    "version": "v1",
    "input_mappings": { "video": "video_url" },
    "field_passthrough": [{ "source_path": "metadata.ad_id" }],
    "parameters": {
      "split_method": "scene",
      "scene_detection_threshold": 0.5,
      "run_transcription": true,
      "run_ocr": true,
      "run_video_description": true
    }
  }
}

Each scene comes back as its own document with start_time, end_time, transcription, ocr_text, description and a thumbnail_url. If you would rather cut on a fixed clock, split_method: "time" with time_split_interval does that.

2. Pull just the hooks. In a retriever, an attribute filter on start_time keeps only the opening segments:

{
  "stage_name": "attribute_filter",
  "stage_type": "filter",
  "config": {
    "stage_id": "attribute_filter",
    "parameters": { "field": "start_time", "operator": "lt", "value": 3 }
  }
}

3. Ask questions a tag list cannot. An LLM filter judges each hook against a plain-language criterion. With include_media: true the segment's media goes to the model along with its fields, so the judgment is made on what the opening shows.

{
  "stage_name": "llm_filter",
  "stage_type": "filter",
  "config": {
    "stage_id": "llm_filter",
    "parameters": {
      "criteria": "Keep only hooks where a person holds the product up to the camera",
      "include_media": true,
      "include_reasoning": true
    }
  }
}

4. Make your hook types stick. A taxonomy matches each segment against a reference collection, such as one example clip per hook type, and attaches the label. With taxonomy_applications set to execution_mode: "materialize", every new ad gets labeled at ingest with the same definitions, so the tag set stops drifting between people.

The creative DNA template sets most of this up in one step: scene-level ad documents with brand and ad ID, a separate searchable collection for ad copy, and clusters that group scenes into recurring visual patterns.

On cost, the public rate for video search (scenes, speech transcript and descriptions) was 0.05 USD per minute of video when I checked on 10 November 2026, with on-screen text as a 0.10 USD per minute add-on (pricing). A library of 1,000 thirty-second ads is 500 minutes.

From there, export the labeled hooks with their ad_id, join them to the spend and hook-rate numbers from your ad platform, and compare hook types in whatever reporting tool you already use.