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Most Amazon sellers misuse broad match — and it's costing them every month.
They treat it as a selling tool. It's not. It's a discovery tool.
Your only job with broad match:
→ Find converting search terms
→ Move them to exact match
→ Negate everything else
No harvest and negate system = wasted spend. Simple as that.
Broad match isn't the problem. Running it without a system is.
#AmazonPPC #AmazonAdvertising #PPCStrategy #AmazonSeller #AmazonUK
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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You can't CRO your way out of a bad product.
Most brands hire conversion optimizers when they should be calling customers.
I've audited stores converting at 0.7% who wanted heatmaps, A/B tests, and a new checkout flow. The actual problem showed up in 3 customer interviews: the product didn't do what the page promised. No amount of UX work fixes that.
CRO is a multiplier. It amplifies whatever signal your product is sending. Good product, sharp positioning, clear messaging → CRO compounds. Bad product, vague positioning, confused messaging → CRO just makes the friction more efficient.
The brands scaling in 2026 do the boring work first:
Product-market fit before funnel optimization. Talk to 20 customers. Find out what's actually being said in DMs, returns, and reviews. Fix the gap between expectation and reality.
Clear positioning before clever headlines. "Who is this for and what specifically does it solve" beats every hero copy variant you can test.
Real differentiation before social proof badges. "As seen in Forbes" doesn't help if your product is the same as 12 cheaper versions on Amazon.
Once those three are solid, CRO does compounding work. Every test you run lifts the whole machine because the machine actually works.
If you're trying to scale a store that hasn't sorted product-market fit yet, more conversion tactics won't save you. They'll just speed up the rate at which you lose money on ads.
Get the product right first.
The rest is amplification.
Sharing my work on AI-powered receipt processing as part of HCM expense tracker module. Would welcome feedback to make it more user-friendly.
Using Amazon Nova Lite, a multimodal LLM for receipt data extraction.
I build and optimize RAG pipelines for production — retrieval, ingestion, token efficiency, the full thing.
Recently cut latency 40%, token costs 60%, and ingestion from 70s→27s in a live system. Also have a merged PR in Haystack (25k+ stars).
Open for remote contracts. If you're building something with LLMs or RAG, feel free to reach out.
Check it out: https://rag.allcognix.com/
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