If you still guess your Amazon keywords, you're leaving money on the table.
In the video above, I walk through how I use Jello SEO to go from a new product idea (an ergonomic office chair) to a listing-ready keyword plan—all powered by real Amazon search data, not vibes.
This article pulls out the workflow so you can steal it.
Step 1: Start with the product, not a seed keyword
Most Amazon research starts with a vague seed like "office chair" and explodes into noise.
Instead, I start with a plain-English prompt inside Jello:
I'm launching a new ergonomic office chair on Amazon. Find profitable keywords and Amazon search volumes I should target.
That single instruction gives Jello context on:
- Product type – ergonomic office chair
- Channel – Amazon (so it uses Amazon-specific endpoints)
- Goal – profitable, high-intent keywords with volume
You don't have to know which tool to run first. You just describe the outcome.
Step 2: Let Jello explore real Amazon searches for you
Under the hood, Jello connects to DataForSEO's Amazon endpoints, so when it runs something like an Amazon related keywords lookup, it's pulling:
- Actual Amazon search queries shoppers type
- Associated search volumes
- Variants and long-tails you wouldn't think of on your own
For the chair example, that quickly surfaces both:
- Problem-focused searches – "office chair for back pain", "best office chair for lower back pain"
- Solution-focused searches – "ergonomic office chair", "mesh chair", "high back office chair"
This is the first big unlock: you get a map of how shoppers describe the problem and the product, instead of guessing which angle matters.
Step 3: Separate magnets from support keywords
Once Jello has the keyword universe, I ask it to structure the plan:
From these Amazon keywords, suggest primary keywords for the title and secondary keywords for bullets and backend search terms.
The goal is to split keywords into two jobs:
- Primary magnets – the few phrases that belong in your title because they drive the most relevant volume
- Support keywords – phrases that should live in bullets, description, and backend search terms
For the chair, Jello recommends primary phrases like:
- "ergonomic office chair"
- "mesh office chair"
- "high back office chair"
…and then layers in problem language ("for back pain", "for long hours") plus feature language ("adjustable lumbar", "flip-up arms") as secondary targets.
Step 4: Turn the data into a listing you can actually ship
Data is useless if it doesn't change your listing.
This is where I have Jello do one more job: translate the research into concrete copy decisions, including example titles and bullet structures.
For example, it might recommend:
- Lead the title with "ergonomic office chair" (highest relevance + volume)
- Add 1–2 benefit phrases ("for back pain", "for long hours")
- Add 3–4 feature phrases ("mesh high back", "adjustable lumbar support", "flip-up arms")
The result is a title that:
- Reads naturally to humans
- Hits the most important Amazon keywords
- Sets you up to use the remaining phrases in bullets and backend fields without stuffing
Why this workflow beats manual brainstorming
You can absolutely guess your way to a list of Amazon keywords. But this workflow gives you three things guessing never will:
- Coverage – it explores the full breadth of how shoppers search, including long-tails and problem phrases
- Prioritization – it ranks by Amazon search volume so you know what actually moves the needle
- Execution – it hands you a structured plan you can paste into your listing editor today
If you're launching or optimizing Amazon products regularly, build this into your standard process:
- Describe the product and goal to Jello
- Let it pull real Amazon search data
- Have it split primary vs. secondary keywords
- Use its example titles and bullets as your starting point
You'll spend less time guessing and more time shipping listings that actually line up with how people search.