Hey fellow Amazon sellers,

I’ve been testing Amazon’s new demographic analytics features while building my Q4 2025 product line, and it completely shifted how I approach product selection—no more chasing saturated niches that everyone else using the same filter tools is also targeting. This is part 3 of my product selection framework series, and today we’re diving into actionable, data-backed steps you can implement today.

Define your target audience first, then map their unmet needs

Use Amazon’s new Amazon Standard Identification Number (ASIN)-Level Demographic Insights to find underserved audience segments

Build small product adjustments to serve those segments

This approach aligns with Amazon’s customer-obsessed ranking logic, avoids low-price competition, and unlocks higher-margin opportunities most sellers miss.

Why Traditional Data-Only Product Selection Fails

Nearly every popular product research tool (Helium 10, Jungle Scout, etc.) relies on the same set of filters to surface “low-competition” niches. This method has clear benefits:

It is fully data-driven, with no bias from personal product preferences

It uses a standardized process that any team member can replicate

It is fast, requiring no deep industry expertise to surface initial product ideas

But after testing this method for 4 years, I’ve found 3 critical flaws that lead to crowded, low-margin products 80% of the time:

Commoditized results: Every seller uses the same tools and filters, so everyone ends up competing for the exact same 10-20 niche segments

Seller-centric logic: This approach prioritizes what is easy for sellers to source, not what customers actually want, which directly conflicts with Amazon’s core ranking priority of customer satisfaction

Backward-looking data: All filter data is based on products that already exist, so you are always chasing past trends, not identifying future demand

The Core Framework: Audience First, Product Second

The most consistent rule of eCommerce is that all demand comes from customers, not category data. Your first step in product selection is to define who your customer is, what needs they have, and then build a product to meet those needs—not the other way around.

This framework relies on two consistent truths about consumer behavior:

Universal, unchanging consumer psychology: All customers, regardless of location or demographic, want:

More product choices (driven by loss aversion and desire for the best fit)

Faster delivery (driven by impulse purchase behavior)

Better value for their money

Higher-quality service (both functional product value and emotional experience value)

These drivers are consistent across every Amazon market, so you only need to adapt how you meet them, not rewrite the rulebook.

Variable audience needs: Different demographic groups have drastically different priorities for the same product category. Once you segment these audiences, you can find gaps that generic category filters completely miss.

Actionable Implementation: Use Amazon’s ASIN-Level Demographic Insights

Last quarter, Amazon rolled out an update to its Demographic Insights feature (available in Seller Central > Brands > Brand Analytics for all Brand Registry-enrolled sellers) that makes this audience-first approach far easier to execute. Previously, demographic data was only available at the category level, which was too broad to be useful. The new update provides data down to the individual ASIN level, with 6 core segmentation dimensions:

Age

Household income

Education level

Gender

Household composition

Product category

After testing this across 7 different categories, the 3 highest-impact segmentation dimensions for product expansion are household income, gender, and age. Below are concrete use cases for each:

  1. Household Income Segmentation

A customer’s household income directly maps to the price point they are willing to pay, and every category has clear income tiers that are often overlooked by sellers who default to the lowest, most crowded price band.

Take the portable charger category as an example:

Price Segment

Product Specs

Share of Market Volume

Share of Competing Sellers

$20-$30

5,000-10,000 mAh

65%

82%

$50+

30,000-60,000 mAh

21%

12%

Key takeaway: The $20-$30 segment is completely saturated, with 82% of sellers fighting for 65% of market share. The $50+ high-capacity segment, by contrast, serves high-income frequent travelers who prioritize long battery life over low cost, and has far less competition.

When you price and position your product for a specific income tier, Amazon’s algorithm will match your Listing to users with that spending power, letting you completely avoid the low-price “race to the bottom” in most categories.

Note: Adjust these price points to match the average category values for your specific market (US, EU, JP, etc.) as needed.

  1. Gender Segmentation

Gender is the lowest-effort, highest-ROI way to differentiate your product from generic category competitors, with two easy entry points:

Color: Male users consistently prefer neutral, muted tones (black, metallic gray, navy blue, mocha brown), while female users often prefer a wider range of colors, pastels, or color-block designs. For example, in the yoga mat category, pastel-colored mats targeted at female users sell for 15-20% more than generic black or gray mats, with no extra production cost.

Design/Features: Beyond color, you can adjust product features to match gender-specific needs: for example, women’s hiking boots with narrower foot beds, or men’s skincare products formulated for thicker skin.

Note: While gender identity exists on a spectrum, Amazon’s current demographic data uses binary male/female segments for targeting purposes. Always avoid stereotypical or exclusionary language in your Listing copy.

  1. Age Segmentation

Age is a powerful segmentation dimension, but requires careful cultural adaptation for US/EU markets to avoid backlash:

⚠️ Warning for Western markets: Never explicitly market a product as “for seniors” or “for elderly users” in your Listing or A+ Content. Western consumers prioritize independence, and framing a product as designed for older users is often perceived as patronizing, leading to lower conversion rates and even negative feedback.

For example, instead of selling a “senior phone with large buttons”, position the same product as a “large-display accessible phone with simplified interface” — this targets the same user group without alienating them.

For other age segments, you can tailor features directly to their priorities:

18-24 year old users prioritize trendy, social media-friendly designs (e.g., phone cases with built-in mounts for content creation)

35-54 year old users prioritize durability and functionality over flashy features

Users 55+ prioritize ease of use and clear, simple instructions

Quick Action Plan You Can Test Today (No Fancy Paid Tools Required)

Ready to put this framework to work without wasting hours on overcomplicated research? This is the exact 4-step workflow I use for my own launches, and you can run through the whole thing in an afternoon:

Head to Seller Central > Brand Analytics > Demographics and pull demographic data for the top 3 competing Amazon Standard Identification Numbers (ASINs) in your target category.Pro tip: This data is 100% free for all sellers enrolled in Brand Registry — no extra subscriptions needed.

Scan the data to spot income, gender, and age segments that the top-performing Listings are completely ignoring. For example: if you’re in the portable charger category and all top 3 listings only target 25-34 year old middle-income women, you might find that 35-44 year old high-income women who travel internationally are barely served, or male buyers in the same 25-34 age bracket who prioritize rugged, outdoor-ready designs.

Brainstorm 2 to 3 low-cost product tweaks (custom colorways, small feature additions, minor size adjustments) that would cater directly to that underserved group. Rule of thumb: Stick to changes that cost less than $0.75 per unit to implement to avoid eating into your profit margins.

Validate demand for your adjusted product by checking search volume for relevant Keywords using your tool of choice (Helium 10, Jungle Scout, or even the free Amazon Search Term Report all work great here). For example, if you’re targeting frequent international travelers, you’d look up volume for terms like “high-capacity 20000mAh portable charger for international travel”.

I’ve used this exact process to launch 3 products across home goods and outdoor categories in the last 6 months. All of them hit average Advertising Cost of Sale (ACOS) under 18% and profit margins above 40% — and the best part? None of these underserved segments showed up in generic product research tool filters, so we had almost zero direct competition for the first 3 months of launch.

Next week, I’m breaking down how to layer this demographic data with TikTok and Instagram trend insights to spot high-potential demand before it even registers in Amazon’s search volume data. No guesswork required.

Have you ever skipped the saturated main category to target a small, underserved demographic instead? Drop your wins (or even your failed attempts) in the comments — I’d love to hear how it worked out for you!

Have you tested Amazon’s ASIN-level demographic insights for product selection? What’s the most underserved audience segment you’ve found in your category? Drop your experiences below — I’d love to hear what’s worked for you!