Hey everyone! 👋 I've seen a lot of buzz about Suno here, especially around its AI music generation. I decided to go all-in for a month, using it almost daily for various marketing video projects and social media content. I'm a big believer in tracking performance, so I kept a simple log of my attempts.
My main goal was to see if it could reliably produce usable background tracks and short jingles. Here's a breakdown of my attempts, categorized by my use case and whether the output was a "hit" (usable as-is or with minor edits) or a "miss" (completely off-brief or unusable quality).
| Use Case | Prompt Detail Level | Attempts | Hits | Misses | Hit Rate | Notes |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **Upbeat Social Media Clip** | Basic (genre, mood) | 12 | 5 | 7 | 42% | Often got the vibe right, but structure was sometimes chaotic. |
| **Corporate Explainer Video** | Detailed (BPM, instruments, no vocals) | 8 | 6 | 2 | 75% | Excelled here! Consistent, instrumental, on-theme. |
| **Short Brand Jingle** | Very Detailed (lyric snippet, style references) | 10 | 2 | 8 | 20% | Struggled with lyrical cohesion and memorable melodies. |
| **Ambient/Background Loops** | Basic (genre, "looping") | 15 | 11 | 4 | 73% | A strong point. Great for filler audio beds. |
| **Genre Experimentation** | Fun, exploratory prompts | 20 | 9 | 11 | 45% | Mixed bag. Some delightful surprises, many odd misses. |
**Key Takeaways:**
* **Be specific for functional music:** For corporate/podcast backgrounds, Suno is fantastic. The more constraints you give (instrumentation, "no drums," "calm"), the better it performs.
* **Vocal tracks are a gamble:** My jingle attempts had a very low hit rate. The AI often interprets lyrics in a strangely literal or awkward melodic way.
* **Iteration is required:** You rarely get a perfect track on the first try. Using the "Custom Mode" and regenerating sections was crucial for my "hits."
* **The "vibe" is easier than "structure":** It captures a genre's sound well, but crafting a song with a clean intro, verse, and outro often needs multiple prompts and edits.
For my workflow in marketing automation, it's become a solid tool for generating unique, royalty-free background scores. I wouldn't rely on it for a client's main jingle without a lot of time for iteration, though. The data table helped me see where to focus my efforts.
Has anyone else tracked their usage like this? I'd love to compare notes, especially on the vocal generation side.
Happy reviewing!
Happy reviewing!
Your corporate explainer video hit rate (75%) aligns with my own results when the prompt is tightly constrained to instrumental parameters. The system seems deterministic when you feed it clear, non-subjective specs like BPM and instrument lists.
The jingle failure rate doesn't surprise me. These models still can't handle the abstract concept of "memorable" or coherently develop a short melodic motif from a lyric snippet. It's a structural/genuine creativity gap, not a prompt engineering problem.
I'd be curious if you tracked time-to-hit. For my team, the time spent iterating on 10 jingle prompts to get 2 hits often exceeded the cost of licensing a stock track, which changes the ROI calculation significantly.
shift left or go home
Your data on corporate explainer videos is the key finding here. It confirms that these systems function best as constrained audio assemblers, not creative partners. The high success rate with detailed instrumental prompts suggests the real use case is for generating specific, functional assets where the creative brief is essentially a technical spec sheet.
This makes me question where the break-even point is for a team. If you need five usable social clips, your log shows you'll likely run twelve generations. The subscription cost and, more critically, the time spent curating and iterating across those twelve attempts has to be compared against the flat cost and immediate usability of a stock music library. For one-off projects, the calculus might tilt towards traditional sources.
Has anyone done a formal time/cost comparison on generating versus sourcing, factoring in the human hours for prompt refinement and output sifting? The raw hit rate is useful, but the operational overhead could be the deciding metric for business adoption.
Support is a product, not a department.
This is such a useful, data-driven breakdown. You're hitting on a core principle that applies way beyond AI music - the more your request resembles a structured data input (like your BPM and instrument list), the more reliably an automated system can output something usable.
It's like the difference between asking a data pipeline for "sales numbers" versus a specific query for "last month's North American subscription revenue, broken out by product SKU." Precision gets you what you need. Your 75% hit rate on explainer videos is probably the ceiling for this kind of tool right now.
Makes me wonder if the "creative" uses like jingles will always be hit-or-miss, or if they'll just need a new kind of prompting language we haven't figured out yet. Thanks for putting in the legwork!
ship it