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Amazon FBA
Reliable Amazon keyword and SEO data depends entirely on how carefully sellers select their niche and competitors before doing any analysis in Data Dive — the guest argues you must build a competitor set that is neither bloated with non-selling sellers nor too narrow to reveal true market share, then reverse-engineer how those competitors actually earn their sales before writing listings or launching PPC.
Selecting the right competitors correctly is the foundational first step before any keyword optimization work in Data Dive.
Data Dive's core premise is reverse-engineering how competitors get sales, what total market demand is, and what you and they are doing right or wrong.
Brandon runs both a broad niche (all styles/materials) and a narrow niche (only his own style) to separately track category-wide demand and his own market share.
Recommended competitor count is roughly 8-25, prioritizing quality (real sales) over quantity; small/new niches with only 4-5 sellers shouldn't be padded out.
The litmus test for including a competitor: are they making decent sales, do they rank for unique useful keywords, and does including them capture enough of the market.
Top sellers often earn sales via keywords you can't compete on: branded/off-Amazon-driven search (TikTok, Walmart, physical retail) and ultra-competitive generic terms they already dominate.
Variation-level (color/size) keyword data reveals which variants drive sales, but high search volume for a variant doesn't automatically justify launching it.
Best-selling listings usually have one hero variation that absorbs traffic/ad spend and converts shoppers into buyers of other variants once they land on the page.
You can niche down repeatedly (e.g., leather to buffalo leather) to isolate specific keyword sets and check whether expensive competitors' sales are brand-driven (a red flag) rather than keyword-driven (replicable).
The number one mistake sellers make is including too many low/no-sales competitors, which dilutes the keyword-relevancy percentage calculation and mis-buckets keywords.
Data Dive offers two niche-building workflows: automated Niche Dive (fast, about 95% sufficient, with a per-competitor fit score) and manual ASIN-tray curation (slower, more precise).
After a dive, keywords are auto-sorted into four buckets: trash can (irrelevant), master keyword list (at least 30% relevant and at least 450 searches per month), outliers, and residue.
Competitor strength on the master keyword list is graded very strong (at least 80% of search volume), strong (at least 60%), or weak/very weak.
The outlier bucket surfaces keywords one dominant competitor uniquely owns (e.g., Bagsmart and toiletries organizer), useful either for recognizing unbeatable dominance or spotting a viable keyword to target instead.
The residue bucket (everything beyond the top 500 keywords) still needs manual review for niche-specific relevant terms.
Search-volume shifts over time are mainly driven by seasonality or by Amazon's autocomplete/auto fill suggestions changing, and auto fill volume tends to be inflated relative to real demand.
The finalized master keyword list should drive listing copy, PPC campaign targeting, and ongoing rank tracking via the separate Rank Radar feature.
Reverse-engineering competitor sales data — The core premise of Data Dive: inferring how competitors actually earn their sales, total market demand, and search behavior from aggregated listing data. Apply: Before optimizing your own listing, analyze a curated set of competitors' keyword rankings and sales to figure out which keywords are actually driving their revenue.
Dual-niche method — Building two niches for the same product idea, a broad niche covering all styles/materials and a narrow niche containing only your own style, to separate category-wide demand from true market share. Apply: Track overall demand and style-level shifts in the broad niche, and measure competitive position against direct competitors only in the narrow, niched-down niche.
Litmus test for competitor inclusion — A three-part check for whether to add a competitor to a niche: are they making decent sales, do they rank for unique useful keywords, and does adding them help capture enough of the market. Apply: Before adding an ASIN to a niche, verify it passes all three checks rather than adding every competitor you can find.
Cross-referencing niched-down rank within the broader field — Checking where your narrow sub-niche's competitors (e.g., canvas bag sellers) actually place within the broader niche's top-20 ranking. Apply: Use the broad niche's top-20 list to spot rank gaps (e.g., sub-niche sellers ranked 3rd, 7th, 9th, 15th) showing where a new entrant could realistically slot in.
Categorizing competitor traffic sources — Splitting a top seller's traffic into branded/off-Amazon-driven search, highly competitive generic keywords they already dominate, and keywords you could realistically win. Apply: Before assuming you can replicate a top seller's rank, check whether their volume comes from their own brand name or off-Amazon channels versus generic keywords you could actually compete for.
Variation-level keyword aggregation — Data Dive's aggregation of color/size-specific search terms (e.g., blue toiletry bag) and their search volume across competitors. Apply: Check which color/size variants competitors rank for and how much of their sales/traffic ties to a specific variation before deciding which variants to launch.
Hero variation strategy — The observation that best-selling listings typically funnel ad spend and ranking effort into one generic, low-cost hero variation that then converts shoppers to other variants once on the page. Apply: Identify which single color/variant a competitor is pushing hardest and consider adopting a similar hero-variant approach rather than spreading traffic evenly across all variants.
Iterative niching down — Repeatedly narrowing a niche (e.g., toiletry bags to leather to buffalo leather) to isolate progressively more specific keyword sets. Apply: When evaluating a premium or specialized product idea, niche down step by step to see keyword sets and competitor strength unique to that exact sub-segment.
Keyword relevancy bucketing algorithm — Data Dive calculates a keyword's relevance to a niche based on what percentage of the chosen competitor set ranks on page 1 for it. Apply: Trust keywords where most/all chosen niche competitors rank page 1 as core to the niche, and treat keywords where only a few rank as likely pulling in unrelated product types.
Niche Dive (automated niche builder) — An algorithm that compares a selected hero listing against related search terms/subcategories to auto-select around 15 closest-fit competitors, each given a percentage fit score. Apply: Use Niche Dive as the fast default way to build a niche (sufficient about 95% of the time), reviewing each suggested competitor's fit score before finalizing the set.
Manual competitor-selection workflow — The slower, more selective alternative to Niche Dive where the seller manually judges whether candidate ASINs truly belong in the niche (e.g., nylon vs. canvas bags, or excluding a hanging cosmetic bag). Apply: When Niche Dive's automated picks look questionable, manually include/exclude competitors based on direct listing comparison.
Sort-by-sales competitor selection — A step in building a niche where candidate competitor products are sorted by sales volume to prioritize the fastest-moving sellers. Apply: When selecting which ASINs to add to a niche, sort candidates by sales first so top performers' data isn't overlooked.
Reverse ASIN lookup — Data Dive's process of pulling every keyword each selected competitor is indexed for, described as similar to Helium 10's Cerebro feature. Apply: Run a reverse ASIN lookup across the chosen competitor set to generate the full pool of keywords for bucket sorting, even ones ranking as low as page 10-20.
Four-bucket keyword sorting system — Data Dive sorts all pulled keywords into four buckets: trash can (irrelevant), master keyword list (at least 30% relevant and at least 450 monthly searches), outliers, and residue (everything beyond the top 500 keywords). Apply: Focus listing/PPC decisions on the master keyword list, mine the outlier bucket for single-competitor-dominated terms, and manually scan the residue for missed niche-relevant terms.
ASIN tray manual curation — A Chrome-extension feature for manually browsing competitors/subcategories/keywords and adding promising ASINs to a tray before naming and diving the curated set. Apply: Use the ASIN tray as the more methodical backup method when Niche Dive doesn't fit a product type, building the niche competitor by competitor across multiple subcategory pages.
Post-dive ongoing niche curation — The practice of manually adding missed competitor ASINs or removing irrelevant ones (e.g., underpriced sellers) from an already-built niche. Apply: Periodically review an existing niche's competitor list and adjust it as new sellers emerge or existing ones prove irrelevant, checking their data before removing.
Compare feature (niche tracking over time) — A Data Dive function that re-runs or compares a niche over time (e.g., month over month) to show movement in sales, search terms, and search volume. Apply: Re-dive or compare an existing niche periodically to see which competitors are gaining/losing ground and which search terms shifted significantly.
Seasonality vs. auto-fill diagnostic — A framework attributing major search-volume shifts to two causes: seasonal demand spikes (e.g., Valentine's Day gift for men) or changes in Amazon's autocomplete/auto fill suggestions, which tend to show inflated volume relative to real demand. Apply: When a keyword's volume moves sharply, check whether it's a seasonal, predictable pattern or an autocomplete artifact before building a listing/PPC campaign around it.
B button brand-keyword filter — A quick filter in Data Dive that isolates keywords containing brand names within a niche's keyword list. Apply: After a dive, click the B button first to quickly identify and exclude brand-name keywords you can't compete on before further cleanup.
Outlier bucket analysis — The bucket of keywords (and their search volume) that one competitor uniquely dominates, separate from the shared master keyword list. Apply: Use a large outlier bucket (e.g., 66 keywords, 1M+ search volume) to recognize when a single competitor is dominant, or mine it for adjacent viable keywords worth moving into the master list.
Residue bucket review — The leftover pool of keywords beyond the top 500 (or below the 30% relevancy cutoff), still containing potentially relevant niche-specific terms. Apply: Manually sort and scan the residue bucket (sorted by relevancy) for terms specific to your exact product variant (e.g., waterproof for a nylon bag) and promote relevant ones into the master keyword list.
Rank Radar — A separate Data Dive feature (covered in a prior video) for tracking PPC performance, Search Query Performance (SQP) data, competitor keyword rankings, and rank progress over time. Apply: After finalizing a master keyword list, use Rank Radar to monitor PPC and organic rank performance against those keywords over time.
Including sellers who make zero sales doesn't just add noise, it mathematically corrupts the relevancy percentage that decides which keywords land in the important buckets, so more competitors is actively worse under this system.
A competitor's dominance can be traced back to being fundamentally unbeatable (brand recognition, off-Amazon retail presence) versus beatable (pure on-Amazon keyword/listing strength), letting a seller answer whether they can actually beat a competitor directly from data rather than gut feeling.
Auto-fill keywords systematically overstate real demand because their volume comes from ease of clicking a suggestion rather than deliberate typing, so a listing built around them at launch can end up anchored to keywords that quietly go dead later.
The hero variation concept frames a single cheap, generic-colored SKU as the deliberate traffic/ad-spend magnet for an entire multi-variant listing, converting browsers into buyers of pricier or less-common variants after they land on the page.
The outlier bucket can be read two opposite ways depending on context: as proof a competitor is unbeatable (broad dominance across many exclusive keywords) or as a map of adjacent keywords a smaller seller could realistically target instead.
«Analyzing the right niches and the right competitors is what makes your product launch data reliable and your decisions profitable.»
— 00:00
«The entire premise of Data Dive is to try to reverse engineer how the competitors are getting their sales, what the total demand of the market is, how users are searching, and then what your competitors are doing right and what they're doing wrong and ultimately what you're doing right and doing wrong so that you can try to push it higher.»
— 02:43
«The litmus test to whether you should include someone or not is are they making a decent amount of sales?»
— 05:18
«Some of the best sellers are often on keywords you cannot compete on.»
— 07:40
«Just because someone searches it 100 times a month doesn't mean you should launch it.»
— 10:45
«I think the number one mistake I see people make is that they include too many sellers in their niche, and those sellers don't have good data.»
— 13:56
«That's the mistake that a lot of software out there make... they try to analyze an entire niche from one keyword. And that's just not the reality of it. We know that people get their sales from hundreds or even thousands of different keywords.»
— 16:56
«Get into the mindset of thinking, how are they making their sales?»
— 18:58
«Can I beat Bagsmart with my own toiletry bag? No, I really cannot, and I need to understand that I cannot beat them because otherwise I'll be wasting money trying to beat them.»
— 28:29
«...coupon code orange click for 10% off the first 6 months.»
— 31:53
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This is a sponsor-guest tutorial by Data Dive's co-founder, structured as an educational deep-dive on niche/competitor selection methodology but also functioning as a product demo, with the discount code repeated at the start, middle, and end.
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