How to improve your Amazon ads efficiency by at least 10%

Let’s say you’re spending ₹4–5 lakhs/month on Amazon ads

Your ACoS looks okay. Conversion rate seems fine. But your gut tells you—you’re wasting some money on irrelevant traffic.

You’re not wrong.

At Atomberg, we figured out that some of our Amazon spend was going toward search terms that had no business seeing our ads. Stuff like:

• “cheap fan”

• “rechargeable fan”

• “usb fan under 1000”

None of them had anything to do with our category—premium BLDC ceiling fans. But we were still showing up. And paying for the clicks.

And not just Atomberg. I would have looked at Amazon accounts of at least 6-7 brands in the last few years at different scale. Every single one of them had the same issue

The fix? N-gram analysis.

The good thing is you can do it in less than an hour—even if you're not a performance marketing expert.

What’s N-gram analysis?

It’s just a fancy way of breaking down every search term that triggered your ads into smaller word patterns—called N-grams.

Say a customer searches for “cheap rechargeable fan for hostel room.” That one phrase can be broken down into:

• 1-grams: cheap, rechargeable, fan, hostel, room

• 2-grams: cheap rechargeable, rechargeable fan, fan for, hostel room

• 3-grams: cheap rechargeable fan, fan for hostel, etc.

You do this across all your search terms. And now, you have data not just at the phrase level—but across every pattern that keeps popping up.

That’s where the gold lies.

Why you can’t rely on the search term report alone

I know what you're thinking—“Why do I need all this? Can’t I just look at my search term report and manually negate the bad ones?”

Sure, you can.

But that’s like killing mosquitoes one at a time instead of draining the dirty water.

Here’s why that approach doesn’t scale:

1. Search terms ≠ keywords

   
   Amazon takes your broad or phrase match keywords and shows your ad on hundreds of long-tail search queries. One keyword = hundreds of possible triggers. Some convert. Most don’t. N-gram shows you the underlying common words across them all

2. Volume dilution hides the real waste

   
   Maybe “rechargeable fan for hostel” spent ₹300 and didn’t convert. You ignore it. But what if 12 other search terms also had ‘rechargeable’ in them—and together they burned ₹6,000 with zero sales?

   
   You only see the pattern when you zoom out with N-gram

3. Long-tail searches are infinite. N-grams are finite.

   
   There will always be a new long-tail variation. But if you know “rechargeable”, “cheap”, or “usb” consistently perform poorly—you can negate them once and stop 100 future variations from wasting your budget.

4. It’s not just about cleaning up

   
   N-gram analysis also tells you what’s working—what combinations of words are converting better than average

   
   Maybe “white ceiling fan”, “silent BLDC fan”, or “fan for living room” will have a ROAS of 5+. Those are your goldmine phrases. Double down on them.

Here's what you should do

1. Download 3 months of search term data

2. Split into unigrams, bigrams, trigrams

3. Create a pivot table with frequency, spend, orders, ROAS per N-gram

4. Mark:

   • High-spend, low-conversion N-grams for negation: e.g., “cheap”, “rechargeable”, “usb”

   • High-ROAS, consistent N-grams for boosting: e.g., “bldc”, “ceiling fan white”, “silent fan”

5. Add negatives at a broad match level across all campaigns

6. Create exact match campaigns for top-performing phrases

If you’re serious about Amazon—and especially if you’re in a high-intent, generic category like us—N-gram analysis isn’t optional. It’s table stakes.

It’s the difference between throwing money at clicks, vs engineering your way to better returns.

And unlike broader marketing strategy or ATL campaigns, this is something you can control, run every month, and see results immediately.

Try it. And if it helps, let your growth team know. They’ll probably ask why you didn’t do this earlier :)