21:02
Amazon PPC
Chris Rawlings argues that effective Amazon PPC starts with a specific keyword-research process that segregates a product's keywords by buyer/shopper intent — using Helium 10's Cerebro to pull and filter a competitor-based keyword pool, then AI-assisted clustering to group terms by intent — producing a 'master list' that becomes the raw material for intent-specific ad campaigns.
The best Amazon PPC 'always starts with banging keyword research,' aimed at avoiding wasted ad spend from skipped research.
The process works for both new product launches and relaunching a dead/unranked product; a separate video/process is claimed for mining keyword data from an already-live listing's search term reports.
The core technique — segregating keywords by buyer intent — is presented as the video's key differentiator from other keyword-research training.
The end deliverable is a 'keyword research master list' containing single-keyword (alpha) campaigns, intent-segregated groups, competitor keywords, branded keywords, negative keywords, and terms better suited to Store traffic vs. product-page traffic.
The goal is not to capture every possible keyword (even ones searched by a single person), but to narrow down to roughly 50-150 keywords worth building campaigns around.
Competitor products must be vetted for genuine niche/offer fit before pulling their keyword data, not just selected because they rank for the same keyword.
Lower-budget sellers may need to select less competitive, lower-volume alpha keywords and niche their listing assets (images, title, copy) accordingly, since they can't outcompete for top-volume terms.
Intent-segregated campaigns let a seller measure CTR, conversion rate, and impressions per intent segment, revealing whether the listing communicates properly to a given buyer motivation.
Some search terms indicate storefront/browse intent rather than intent to buy one specific unit, and should be routed to a Sponsored Brand campaign instead of Sponsored Products.
The simplified process taught in the video is described as similar to, but less sophisticated than, the process run inside the speaker's own portfolio/agency (Sophie Society).
Buyer/Shopper Intent Segregation — The video's core framework of splitting a product's filtered keyword list into groups by the shopper's underlying buying motivation (e.g., decorative vs. heavy-duty) rather than treating all keywords the same. Apply: After filtering the raw keyword pull, cluster the remaining terms by intent and build one campaign per intent group so per-segment CTR, conversion, and impressions can be tied back to specific listing assets.
Helium 10 X-Ray — A Helium 10 Chrome extension feature that overlays ranking/sales data directly on a live Amazon search-results page in the same order as the organic results. Apply: Run X-Ray on the target search term with 'hide sponsored products' enabled to identify true organic page-one competitors before running Cerebro on them.
Competitor Selection/Vetting — Manually choosing a set of about 9 competitor ASINs that genuinely match the product's niche and offer, not just ones that share keyword rank. Apply: Exclude products that rank for the same keyword but serve a different sub-niche (e.g., a commercial utility mat vs. a home welcome mat) before pulling keyword data on the vetted set.
Cerebro (reverse-ASIN keyword tool) — Helium 10's tool that pulls every keyword the selected set of competitor ASINs ranks for on Amazon. Apply: Run Cerebro against the 9 vetted competitor ASINs to generate the raw, unfiltered keyword pool (8,000 terms in the demo) as the starting point for the master list.
Cerebro Filter Stack — A combination of Cerebro filters — Match Type = Organic, minimum search volume 500, minimum 3 ranking competitors, and maximum competitor rank 50 — used to cut an oversized raw keyword pull down to a workable size. Apply: Apply all four filters together to shrink a list like 8,000 keywords down to roughly 50-150 relevant, high-traffic terms (144 in the demo).
CSV Export/Import to Google Sheets — Moving filtered Cerebro results out of Helium 10 into a Google Sheets workbook for manual processing and sorting. Apply: Export Cerebro's filtered results as CSV, use File > Import > Upload > Replace current sheet in Google Sheets, then sort by search volume descending via Data > Sort sheet.
Alpha Keywords — A small set (about 5 in the demo) of the highest-volume, most-relevant terms marked for their own single-keyword exact-match campaigns. Apply: After sorting by search volume, mark chosen terms with an 'X' in a helper column, use Create Filter to isolate them, and build a dedicated exact-match campaign per term; lower-budget sellers should pick lower-volume, less-competitive alpha terms and niche their listing assets to match.
Phrase Negations — A manually curated list (roughly a dozen to two dozen terms) of search terms that are clearly irrelevant to the product but keep surfacing in the keyword pull. Apply: Add these terms as negative phrase targets in campaigns so Amazon doesn't waste spend serving ads against them, while still leaving room for Amazon to discover other relevant long-tail terms.
AI-Assisted Intent Clustering (ChatGPT two-prompt method) — A technique for offloading buyer-intent classification of keywords to ChatGPT instead of manually tagging every term. Apply: Upload a CSV of remaining keyword+search-volume data and ask ChatGPT to identify top keyword families by buyer intent, then ask it to output a deduplicated CSV assigning each keyword to exactly one intent category using a stated priority order from most-specific to most-generic intent.
Intent-Based Column Filtering in Google Sheets — Re-importing the AI-segregated CSV into the original workbook and filtering by the intent column to view one shopper-intent category at a time. Apply: Use Data > Create a Filter on the intent column (e.g., 'decorative aesthetic') to pull that group's keywords into the master list.
Performance-to-Asset Diagnostic — Using per-intent-campaign CTR, conversion rate, and impressions as a diagnostic signal for whether listing assets (thumbnail, title, secondary images, A+ content, brand story, top reviews) properly communicate to that buyer segment. Apply: If an intent-segmented campaign underperforms relative to others, revise the specific listing assets tied to that intent (e.g., add decorative-use imagery) rather than adjusting only keywords or bids.
Storefront vs. Product-Page Intent Routing — Recognizing that some search terms (e.g., home decor, front door products) indicate an intent to browse a brand's storefront rather than buy one specific unit. Apply: Route keywords identified as browse/storefront intent into a Sponsored Brand campaign pointing to the Store instead of a Sponsored Products campaign.
Sponsored Products Manual Exact-Match Campaign Build — The concrete campaign-creation steps for turning a keyword grouping into a live exact-match campaign. Apply: In Sponsored Products, select the product, choose Manual Targeting > Keyword Targeting > Enter List, deselect Phrase and Broad match types, select Exact only, paste the keyword group, and click Add Keywords.
Down-Only Bid Strategy from Sales Data — Guidance to calculate bids from the seller's own sales/conversion data rather than defaulting to Amazon's suggested bid, using a 'down only' bidding setting. Apply: Calculate a bid manually whenever sales data exists, apply it under down-only bidding, and only fall back to Amazon's suggested bid if no data is available.
Placement Adjustments — Optional percentage bid boosts applied to specific ad placements (e.g., top of search) within a campaign. Apply: Only add modest placement adjustments (roughly 10-20%, max 30%) when a placement is already known to perform well or is a ranking priority; otherwise leave it blank.
Campaign Planner Budgeting — A budgeting workflow that builds every campaign first before allocating daily spend across them. Apply: Create all campaigns in a campaign planner, determine total daily ad budget, then allocate that budget across each campaign (e.g., $20/day per campaign in the demo).
Broad-Match Campaigns with Phrase Negation — A companion campaign type using the same keyword list in broad match instead of exact match, paired with the phrase-negation list. Apply: Launch the same keywords as broad match while pasting the phrase-negation terms into Negative Keyword Targeting > Negative Phrase, letting Amazon discover new converting search terms while blocking known-irrelevant ones.
Manually hoarding thousands of hyper-granular keywords is reframed as 'saying I'm smarter than Amazon' — the video argues sellers should deliberately leave Amazon leeway to discover its own long-tail keywords rather than trying to out-target the algorithm.
Per-intent PPC performance data (CTR, conversion, impressions) is used as a diagnostic for listing quality: if a specific intent segment (e.g., 'decorative aesthetic') underperforms relative to others, that's read as a signal the listing's assets (thumbnail, title, secondary images, A+ content, brand story, reviews) fail to communicate to that buyer segment — turning ad data into a listing-optimization signal rather than just a bidding lever.
The ChatGPT clustering step forces every keyword into exactly one intent bucket (even when a term plausibly fits several) by having the seller specify a priority order from most-specific to most-generic intent, resolving overlap deterministically rather than through manual judgment calls.
Competitor selection is explicitly niche-filtered (e.g., excluding a commercial/business utility mat from a home welcome-mat keyword pull despite shared keyword rank) so the base keyword pool doesn't get contaminated by off-niche products before filtering even begins.
The same negative-keyword logic is applied twice for different purposes: phrase negations protect exact-match/alpha campaigns from wasted spend, while in broad campaigns they're reused specifically to preserve Amazon's discovery leeway on new search terms while still blocking terms the seller's own product knowledge says are irrelevant.
«I'm one of the single top spenders of Amazon ads on the platform with over $3 million in monthly Amazon PPC ad spend.»
— 00:19
«The best Amazon PBC always starts with banging keyword research.»
— 00:22
«Helium 10, if you're watching this, be nice, dude.»
— 02:51
«It is still the most trusted platform to do keyword research external to Amazon itself on the marketplace right now.»
— 02:54
«It's basically you saying, 'I'm smarter than Amazon.'»
— 06:12
«We're trying to find like 50 to 150 keywords that we can focus on with our campaigns that are the most important, that are the most relevant, and have the most traffic.»
— 06:44
«This is really a secret that you may not find in other keyword research trainings.»
— 12:07
«This is the magic, guys. Seriously, this is what most people don't take the time to do. But if you do it, it can supercharge your profitability.»
— 16:41
«That way in your broad campaigns, Amazon still has the leeway to find other high-erforming search terms for you, but it's not going to show you for search terms that it might think are relevant, but you know based on your own product knowledge it's not relevant for.»
— 19:38
«It's the most well-attended Amazon PPC focused digital event in the world right now.»
— 20:28
Reception
Viewers overwhelmingly praise the tutorial as clear and time-saving for keyword research, with only a handful of minor gripes about pricing, a broken link, and tool preferences.
A dense, step-by-step protocol video that names and demonstrates a specific sequence of tools and techniques (Cerebro filtering, alpha keywords, phrase negations, AI-assisted intent clustering, campaign build-out) rather than offering abstract PPC theory; its central claimed differentiator is the buyer-intent segregation framework used to structure both campaigns and listing-optimization diagnostics.
21:02