Product Research - formerly known as Terapeak, and still referred to by that name in a lot of older content - is referenced throughout this blog as the tool for verifying real demand before committing to a category or product. This guide goes deeper: exactly how to read its core metrics correctly, the specific sell-through rate benchmarks worth knowing, and how to use its less-discussed Sourcing Insights feature to find genuinely underserved niches rather than just validating ideas you already have.
I run Sellhelm, eBay dropshipping software for US sellers, and Product Research is one of the most underused free tools available to eBay sellers - most either never open it, or open it once, misread the numbers, and never return. This guide is meant to fix both problems.
The short version
- Product Research shows up to three years of real eBay sales and pricing data for any search term, completely free to every seller.
- Sell-through rate is the single most important metric, calculated as sold divided by sold-plus-unsold-plus-currently-listed.
- Real benchmarks exist: roughly 10% or lower sell-through is generally weak, 15% ± 5% is mid-range, and 20%+ is generally strong.
- Sourcing Insights, available with a Basic Store subscription or higher, surfaces categories with high demand and comparatively low supply - genuinely different from simply searching a product you already have in mind.
- The tool is available on both desktop Seller Hub and the mobile app, making it practical to check a product idea on the spot rather than only during a dedicated research session.
1. What Product Research actually is
eBay's own Product Research page describes the tool's purpose plainly: determining what to sell, when to sell it, and at what price, using real marketplace data rather than guesswork. It's found in Seller Hub under the Research tab, free to every seller regardless of Store subscription tier for its core search function, and it draws on up to three years of actual eBay sales and pricing history for any search term you enter.
This is worth appreciating for what it actually represents: real, historical transaction data from the same marketplace you're selling on, not a third-party estimate or a general market research report - when Product Research shows a product sold 340 times in the last 90 days at an average price of $34, that's an actual count of real eBay transactions, not an inference.
2. The core metrics, and what each one actually tells you
A Product Research search returns several key figures worth understanding individually: sales trend over time (rising, flat or declining), average sale price, the full sold price range (showing how much price variation exists across sellers), average shipping cost charged, sell-through rate, and the total number of distinct sellers who've sold the item. Each tells a different part of the story - trend tells you about momentum, price range tells you about competitive positioning options, and sell-through rate, covered in depth next, tells you about the balance between demand and supply.
3. Sell-through rate: the metric worth understanding precisely
Sell-through rate is calculated as: Sold ÷ (Sold + Unsold + Currently Listed). In plain terms, it's the share of all the supply that's actually attempting to sell a given product that's succeeding, over the measured period. A search showing 100 sold, 50 ended unsold, and 50 currently listed has a sell-through rate of 100 ÷ 200, or 50% - a genuinely strong number by the benchmarks covered next.
Based on widely-referenced seller research into this metric, a useful set of rough benchmarks: many low-performing items show a sell-through rate of 10% or lower, many mid-performing items cluster around 15% (plus or minus roughly 5 percentage points), and many high-performing items show 20% or higher. These aren't official eBay-published thresholds, but they're a useful, realistic frame for interpreting a specific number rather than looking at "23% sell-through" with no sense of whether that's actually good.
4. Why sell-through rate needs to be read alongside seller count, not alone
A specific sell-through rate means genuinely different things depending on how many sellers are competing for that same demand, covered briefly in the categories guide and worth expanding on directly here. A 25% sell-through rate with only three active sellers suggests a genuinely strong, underserved opportunity - real demand, limited competition absorbing it. The same 25% sell-through rate with 200 active sellers tells a different story: real demand exists, but it's already being served by a crowded field, and a new entrant is fighting for a share of already-contested demand rather than finding a gap.
This is worth checking as a deliberate, two-factor read every single time - sell-through rate alone, without also checking the number of sellers currently competing for that same search term, is an incomplete picture that can lead to entering a category that looks statistically strong but is actually already saturated.
5. Using different date ranges to separate trends from noise
Product Research lets you adjust the time period examined, and it's worth deliberately checking more than one window rather than relying on a single default range. A shorter, recent window (the last 30-90 days) shows current, immediate performance, useful for catching a genuinely emerging trend or a recent shift in competition. A longer window (up to the full three years available) reveals whether a product's current performance is part of a stable, sustained pattern or a temporary spike - directly relevant to the "genuine category vs. a fad that's already peaked" distinction covered in the categories guide.
A product looking strong only in the most recent 30 days, with no comparable historical pattern in the longer view, deserves more caution than one showing consistent performance across the full available history - the short window alone can't distinguish a genuine, sustained shift from a temporary spike that's likely to fade before a new listing built around it fully ramps up.
6. Sourcing Insights: the underused feature that flips the research direction
Most sellers use Product Research the way this guide has covered so far - starting with a specific product idea and checking whether real demand supports it. Sourcing Insights, a related feature available to sellers with a Basic Store subscription or higher, covered in the Store tiers guide, works in the opposite direction: it surfaces categories showing high demand relative to comparatively low supply, without requiring you to already have a specific product in mind. This is genuinely valuable for the category-exploration phase covered in the categories guide, since it can reveal a promising, under-served niche you wouldn't have thought to search for directly.
Using Sourcing Insights effectively means treating its suggestions as leads worth investigating further with the standard Product Research search covered throughout the rest of this guide - a category flagged as high-demand, low-supply is a starting point for deeper research, including checking whether a genuine, compliant wholesale supplier actually exists for that specific niche, covered in the suppliers guide, not an automatic green light to list immediately.
7. Using Product Research to validate a price point, not just a product
Beyond validating whether a product has demand at all, Product Research's sold price range is directly useful for the market-checking step covered in the pricing guide - comparing a formula-derived target price against the actual range other sellers have successfully sold the same or a similar product for reveals whether your target profit is realistic for that specific market, before a single listing goes live. A formula-derived price sitting well above the observed sold price range is a clear signal to revisit either the target profit or the sourcing cost before committing, exactly the check that guide recommends running as a matter of course.
8. Using Product Research to study what's actually working, not guess
Beyond the aggregate numbers, it's worth actually opening a handful of the specific sold listings a search surfaces - not to copy them, which crosses into the intellectual property concern covered in the listings guide, but to study the pattern of what a genuinely successful listing in that specific search actually does differently from a mediocre one: how complete are its item specifics, how many photos does it use, how is the title structured. This is the exact research habit covered in the listings guide's discussion of learning from competitors, and Product Research's sold-listing data is the most direct, real-world source for it - you're studying listings that have actually, demonstrably converted, not just ones that happen to rank well today.
9. Using the mobile version for on-the-spot checks
Product Research is available on the eBay app, not just desktop Seller Hub, which makes it practical to check a potential product idea the moment it occurs - while browsing a supplier's catalog, noticing a product in daily life, or reviewing a competitor's listing - rather than needing to wait for a dedicated desktop research session. Building the habit of a quick, on-the-spot check whenever a product idea surfaces, rather than trying to remember it for a later, more thorough session, catches more genuine opportunities than relying purely on scheduled research time, and it costs nothing beyond the minute or two it takes to run the search.
10. Using historical data to plan around seasonality
Product Research's up-to-three-year history makes genuine seasonal patterns directly visible - a category showing the same rise-and-fall shape repeating at the same time each year, covered as a category-selection consideration in the categories guide and directly relevant to the Q4 planning covered in the holiday guide. Checking a product's multi-year trend specifically for this pattern, rather than only its most recent performance, is worth doing before committing significant catalog depth to a product that might turn out to be sharply seasonal in a way that wasn't obvious from a shorter, more recent view alone.
11. What Product Research can't tell you - genuine limitations worth knowing
Product Research shows what has already happened on eBay - it can't predict future demand with certainty, and a strong historical pattern is a genuinely useful signal, not a guarantee that continues unchanged. It also can't tell you anything about supplier availability, cost, or reliability for a specific product - the demand-side research covered throughout this guide is only half the picture, and needs to be paired with the supplier-side vetting covered in the suppliers guide before a product decision is genuinely complete.
It's also worth remembering the data reflects eBay's own marketplace specifically - a product's performance on eBay doesn't necessarily predict its performance on a different platform, relevant if you're also considering the multi-platform question covered in the platform comparison guide.
12. Building a consistent research routine rather than one-off checks
The real value of Product Research compounds when it's used consistently as part of an ongoing routine, not just once when first choosing a category. A reasonable habit: checking Product Research for your existing top-performing products periodically to confirm demand remains stable, running it against any new product idea before committing real listings, and periodically browsing Sourcing Insights for new category leads worth investigating, all as part of the same broader quarterly business review covered in the Store tiers guide.
A seller who checks Product Research only once, at the very beginning of choosing a first category, misses the ongoing value the tool provides as market conditions shift over time - the same category that looked strong a year ago may show meaningfully different sell-through and competition numbers today, and only a genuinely recurring research habit catches that shift before it's reflected in declining sales you're left to explain after the fact.
13. A worked example: using the tool to compare two product candidates
Consider a seller deciding between two candidate products in the same broader category. Product A shows a 22% sell-through rate over the last 90 days, with 15 active competing sellers and a stable, consistent sales pattern across the full three-year history available. Product B shows a higher 35% sell-through rate, but with only 40 total sales recorded in the last 90 days against 6 active sellers, and a sales pattern that shows a sharp, recent spike with no comparable activity in the prior two years.
Product A, despite its lower headline sell-through rate, represents the more reliable opportunity: a proven, stable pattern with enough competing sellers to confirm the category is genuinely established rather than a fluke, and a track record long enough to trust. Product B's higher rate is worth investigating further rather than dismissing outright - the small sample size (40 sales) makes the percentage statistically less stable, and the sharp recent spike with no prior history raises the same fad-versus-genuine-trend question covered in the categories guide. A seller reading only the headline sell-through percentage would have picked Product B; a seller reading the full context this guide has covered correctly identifies Product A as the safer, better-supported choice.
14. Using Product Research before committing to a specific supplier's catalog
Before fully committing to a new supplier relationship, covered throughout the suppliers guide, it's worth running every seriously-considered product from that supplier's catalog through Product Research individually, rather than assuming a supplier's overall category focus guarantees demand for every specific item they carry. A wholesale catalog often contains a genuine mix of consistently strong sellers and much weaker, slower-moving products within the same broad category - Product Research is the tool that distinguishes between them before you commit shelf space, in the dropshipping sense of catalog attention and listing effort, to a specific product that might turn out to be one of the weaker ones.
This is a genuinely practical, product-by-product filter worth applying systematically when first evaluating a new supplier's catalog - rather than listing everything a new supplier offers and waiting to see what sells, checking each candidate product against real demand data first concentrates your initial listing effort on the products most likely to actually perform.
15. Checking whether demand varies meaningfully by region within the US
While Product Research's data isn't broken down granularly by US region within a single search, it's worth being aware that some product categories carry genuine regional demand variation - outdoor and seasonal products tied to climate, for instance, or products with regional cultural relevance - that an aggregate, nationwide figure can mask. For a seller noticing an aggregate sell-through rate that seems inconsistent with their own specific sales pattern, considering whether a regional demand factor might explain the difference is worth a moment's thought, even though Product Research itself won't directly surface this distinction the way it does for time-based trends.
16. Checking Product Research's projections against your own actual results over time
Once you have genuine sales history of your own for a specific product, it's worth periodically comparing your own actual sell-through and pricing performance against what Product Research shows for that same search term in the broader market - a meaningful, sustained gap between your own performance and the aggregate market data is worth investigating specifically, since it can reveal something genuinely correctable (a weaker listing than competitors, covered in the listings guide, or a pricing gap relative to the market, covered in the pricing guide) rather than simply accepting underperformance as unexplained bad luck.
This comparison also works as a genuine feedback loop on your own research process: a seller who consistently finds their own results tracking closely with what Product Research predicted is calibrating the tool's use well; one who finds a persistent, unexplained gap either has a specific, fixable listing or pricing problem, or is misreading the research data in a way worth revisiting against the guidance covered throughout this guide.
17. Using the tool across a diversifying, multi-category catalog
As a catalog expands into additional categories, covered in the scaling guide, it's worth maintaining the same Product Research discipline for each new category independently, resisting the temptation to assume success in one category transfers directly to research confidence in an entirely different one. The specific sell-through benchmarks and competition levels genuinely vary by category, and a seller who's built strong research instincts in one category still needs to run the same rigorous check in a new one rather than assuming their accumulated experience substitutes for checking the actual, current data in the new space.
18. Common Product Research mistakes
- Reading sell-through rate in isolation without checking the number of competing sellers alongside it.
- Only checking a short, recent date range, missing whether a product's current performance reflects a stable pattern or a temporary spike.
- Never using Sourcing Insights, missing genuinely underserved category leads that wouldn't have been found by searching a product already in mind.
- Treating strong Product Research data as a complete green light without separately confirming genuine supplier availability and cost.
- Using the tool once at the start and never returning to it, missing how market conditions shift over time for products already in an active catalog.
19. Using Product Research to diagnose an underperforming existing listing
Beyond its role in choosing new products, Product Research is directly useful for diagnosing a specific listing already in your catalog that isn't performing as well as expected, covered as a review process in the listings guide. Running the same search term your underperforming listing targets and comparing your own price, and the general market's sold price range and sell-through rate, reveals concretely whether the issue is pricing (a price meaningfully out of step with what's actually selling), competition (a search term that's become considerably more crowded since the listing was first created), or something specific to the listing itself if the aggregate market data still looks favorable despite your own listing's weak performance.
This diagnostic use of the tool - not just for new product decisions, but for ongoing troubleshooting of an existing catalog - is exactly the kind of recurring, practical application that turns Product Research from a one-time research step into a genuine, ongoing management tool for the entire catalog.
20. How Product Research fits alongside the other tools covered on this blog
It's worth seeing Product Research as one piece of a larger toolkit rather than a standalone solution - it answers the demand question, but the sourcing question depends on the supplier vetting covered in the suppliers guide, the pricing question depends on the formula covered in the pricing guide, and the ongoing accuracy question depends on the stock and price monitoring covered in the overselling guide. A product decision genuinely well-supported by evidence checks all of these, not just the demand data Product Research provides on its own - the tool is necessary for a sound decision, but it was never meant to be sufficient by itself.
20. Closing out a month of building a real, evidence-based operation
This guide caps a month of posts covering nearly every operational dimension of running a genuine US eBay dropshipping business - sourcing, pricing, fulfillment, customer service, seller standards, scaling, and now, the research discipline underneath the decisions that start it all. None of these guides work in isolation; they're designed to reinforce each other, the way this specific guide connects directly back to the categories guide, the suppliers guide, and the pricing guide covered throughout its own sections.
The throughline across all of it, worth restating plainly one final time: a genuinely sustainable eBay dropshipping business is built on verified facts, not assumptions - a genuinely compliant wholesale supplier, a price calculated from real fees rather than a guessed markup, a category chosen from real demand data rather than a hunch, and a fulfillment process built on honest, verified promises rather than optimistic ones. Every guide across this month has been one piece of that same underlying discipline, applied to a different part of the business.
Where to go from here
Product Research is the evidence-gathering foundation underneath nearly every other decision covered on this blog - category selection, pricing validation, listing optimization research - and it's free, already available in every seller's account. The categories guide covers how this data feeds into the broader category-selection process, and it's worth making this tool a genuine, recurring habit rather than a one-time check.
The bottom line: Product Research is one of the most valuable free tools available to any eBay seller, and most sellers use a fraction of what it actually offers - reading sell-through rate alongside competitor count, checking multiple date ranges, and using Sourcing Insights to find opportunities rather than only validate ones you already had in mind, turns a tool most sellers glance at once into a genuine, ongoing research habit that improves every product and pricing decision built on top of it.
Questions people ask
What is a good sell-through rate on eBay?
Rough, widely-referenced benchmarks suggest 10% or lower is generally weak, around 15% (plus or minus 5 percentage points) is mid-range, and 20% or higher is generally strong - though this should always be read alongside how many sellers are competing for that same demand.
Is eBay Product Research free to use?
Yes, its core search function is free to every eBay seller through Seller Hub’s Research tab and the eBay mobile app, showing up to three years of real sales and pricing history for any search term.
What is Sourcing Insights on eBay?
A related feature, available with a Basic Store subscription or higher, that surfaces categories showing high demand relative to comparatively low supply - useful for discovering a promising niche without already having a specific product in mind.
Can Product Research tell me if a product will sell well in the future?
It shows historical, real marketplace data, which is a genuinely useful signal but not a guarantee - a strong past pattern is worth more confidence than a short-term recent spike, but neither predicts future demand with certainty.