Helium 10 Chrome Extension: Complete Tutorial | Vova Even

Install the extension and understand your free access

Start with Google Chrome on your computer. Create or sign in to your Helium 10 account, visit the official extension download page, and follow the Chrome Web Store installation prompts. Pin the extension to your browser toolbar so you can open it while researching products.

After installation, visit Amazon, choose the marketplace you actually intend to sell in, and search for a product. Click the Helium 10 icon and sign in if prompted. Check the extension settings before your first research session, including which information you want displayed on Amazon pages.

Helium 10's installation documentation explains the site permissions needed for its features. Review those permissions, especially if you want to use the extension on Alibaba or another supported website. An extension that cannot access the page may appear installed while its tools remain unavailable.

Read the official installation and navigation instructions.

The extension is available as a free download, and a free account lets you explore limited functionality. That does not mean every tool, export, or research allowance is unlimited. Check the access shown inside your account before planning a large research project. Current Review Insights access, for example, requires a paid subscription.

I recommend starting with one product and following the entire workflow. You will learn more by understanding one useful comparison than by opening dozens of searches without knowing what the columns mean. When a limit stops you, identify the specific feature you need before choosing a subscription.

You also do not need to connect your own Amazon sales account just to begin researching public product pages. Researching competitors and importing your own business performance are different activities. Keep those purposes separate when the platform offers additional account connections.

For a broader introduction, read my Helium 10 free account and trial guide.

Read the extra information on Amazon search pages

Before opening Xray, look at the information the extension adds around individual search results. In the lesson, these widgets help me scan kitchen scissors without opening every listing. Depending on the page and available data, you can see details such as the ASIN, category, Best Sellers Rank, fulfillment method, and seller count.

An ASIN is Amazon's identifier for a catalog item. Save it when you find a relevant competitor because product titles can change and similar names are easy to confuse. Also record the marketplace. A research note saying “the green scissors” is much less useful than an ASIN linked to the exact product you examined.

Best Sellers Rank, usually shortened to BSR, describes a product's sales rank within a category. A lower numerical rank generally indicates stronger sales performance relative to products in that category. However, a rank of 5,000 in one category does not mean the same unit sales as 5,000 in another. Avoid turning one BSR example from the video into a universal conversion formula.

Open the additional information panel where available. Product dimensions, weight, brand, review information, and listing dates can help you decide whether the item belongs in your comparison. These details are a convenient starting point, but the packaged dimensions and weight of your own product still need verification before you calculate fees.

One useful lesson from the video is that search results are messy. Office scissors appear while we are researching kitchen scissors. They share words and some physical characteristics, but their intended use and customer expectations differ. Including them without thought can distort the average price, competition, and demand you see later.

I also treat listing dates as context. An old catalog record does not prove that a product has sold continuously for that entire period. A new-looking result might belong to an established brand or a variation family. Open the listing and understand the product before making assumptions about how easy its results were to achieve.

Use this first scan to identify relevant products, obvious mismatches, and questions you want Xray to answer. You are preparing a comparison group. That small preparation step makes the larger dashboard much more meaningful.

Run Xray and build a useful competitor comparison

On an Amazon search-results page, open the extension and choose Xray. It brings multiple products into one table so you can compare them together. This is the central research step in the lesson: instead of guessing from thumbnails, you inspect demand, prices, reviews, and competition side by side.

Begin with the results in their search-page order. This helps you see which products occupy the prominent positions customers encounter. Then sort by estimated sales, revenue, review count, or price to answer specific questions. Each sort reveals a different relationship; none of them should become your only view of the market.

Clean the results before interpreting the averages. Remove irrelevant products from the working comparison, identify sponsored placements, and watch for repeated ASINs or closely related variations. Keep a note of what you excluded. A tidy table is helpful only if it still represents the market you intend to enter.

The sponsored-product example in the video reflects the extension interface at the time of recording. For your current session, check the labels, filters, and included rows instead of assuming sponsored products always affect totals in exactly the same way. The same ASIN appearing in more than one placement is not automatically an additional business selling extra units.

Helium 10's current Xray instructions explain that filtered results change the top-line metrics. This matters when you compare screenshots or exports: two people can research the same keyword and reach different averages because they included different products.

Check the current Xray navigation guide.

Revenue is not profit. A product generating a large sales total can still have expensive advertising, frequent returns, or weak margins. Likewise, a high average revenue can conceal a market where two brands dominate while most other products barely sell. Always move from the summary into the individual rows.

When you export a shortlist, include the keyword, marketplace, research date, and filtering notes. This turns a temporary browser view into a record that you or a teammate can revisit without reconstructing the entire search.

Judge demand, competition, and the Xray Success Score

In the kitchen-scissors lesson, several recognizable brands capture substantial sales. That is why I spend time on the distribution of demand rather than celebrating the market's total revenue. The practical question is whether a new product has a believable route to customers.

Look across the first page. Are several relevant products selling, or does almost everything go to one or two listings? Do less-established products attract meaningful demand? Are shoppers buying different designs, price points, and bundles? A market with several successful approaches gives you more to investigate than a market built around one household name.

Review count helps you understand the competitive landscape, but it is not a pass-or-fail rule. In the video, I discuss examples such as products with fewer than 100, 150, or 300 reviews. Treat those as research preferences within that discussion. A product with 80 reviews can still be difficult to compete with if its brand, pricing, or distribution is unusually strong.

The most interesting comparison is often between review count and estimated sales. If several relevant products with relatively few reviews appear to sell consistently, investigate what helps them compete. Their design, customer segment, offer, or presentation may explain more than the review total itself.

Seller count needs similar care. One seller can suggest a brand-controlled offer, while many sellers may suggest wholesale or resale competition. Neither pattern proves the business model. Check the brand, seller identities, and listing context before deciding what the numbers mean.

Review velocity, when available, describes changes in review counts over time. A low increase does not prove the seller lacks a review strategy. Sales volume, product age, variation changes, and customer behavior can all affect the pattern. Use it as supporting context rather than a shortcut to judging a competitor's competence.

Read how the Xray Success Score is calculated.

My final competition question is simple: what would make someone choose my product? If the answer is only “I can sell the same thing slightly cheaper,” the research is unfinished. Better materials, a more suitable design, clearer instructions, or a useful accessory might create a stronger reason, but each idea needs customer evidence and a realistic cost.

Use sales history, BSR, and price trends together

A single research session is a snapshot. Historical charts help you investigate whether today's picture is typical, seasonal, or unusual. In the tutorial, I switch between shorter and longer time ranges to understand how a product's estimated sales have moved.

Start with the longest useful history available, then narrow the period. A product may show a long-term increase while declining during the most recent month. That is not automatically a contradiction. You are looking at different windows, and the reason could be seasonality, availability, changing demand, or a temporary event.

Where the chart provides a seven-day moving average, think of it as a smoother view of daily estimates. It averages a rolling set of seven days so one unusually strong or weak day has less visual influence. It can make a pattern easier to inspect, but it is still based on the underlying data and does not remove estimation error.

Be precise about what a trend line says. A downward line inside a 30-day chart does not, by itself, prove that total sales were lower than in the previous 30 days. To make that comparison, examine both complete periods using consistent dates and definitions.

The same discipline applies to price and BSR history. Falling prices alongside weaker rank may suggest pressure, but the chart alone cannot tell you why it happened. Promotions, stock availability, changes to the offer, and broader demand can produce similar-looking movements. Write down the possible explanations and check other evidence.

The video refers to a list-price line and the actual selling-price line. Focus on what customers can purchase the product for, including the current offer context. A list price is a reference price and may be displayed to shoppers; it should not automatically be treated as a hidden backend figure.

For seasonal products, compare similar seasons across years where the data allows it. A December spike followed by a January decline has a different meaning from a steady year-round drop. Avoid guessing that every spike came from a holiday or promotion unless you can connect it to that event.

Helium 10 explains that Xray sales are estimates for a rolling 30-day period and that variation estimates depend on parent-level BSR data. Review its explanation of sales estimates.

That is why I would not order inventory based on one attractive daily number. I want a relevant competitor group, a history I can explain, and a conservative assumption about the share a new product might earn.

Connect product research to Cerebro and buyer intent

A promising product needs ways for customers to find it. In the lesson, kitchen scissors and kitchen shears illustrate how more than one relevant search phrase can describe essentially the same product. Different phrases may also reveal different requirements, such as heavy-duty use, cleaning, or storage.

Select genuinely comparable ASINs when you move from Xray into Cerebro. A popular product in a neighboring category may bring in many keywords that look impressive but do not match your offer. The purpose of competitor research is to discover relevant demand, not to collect the longest possible keyword list.

Cerebro can support ASIN-based keyword research. In the older video, I also move into Magnet for keyword-based discovery. Helium 10 announced in January 2026 that Magnet had been integrated into Cerebro, bringing both approaches into the same tool. During a staged rollout, labels or access points may differ between accounts.

Read the Magnet-to-Cerebro update.

In the tutorial, I mention looking for several keywords with meaningful monthly search volume. The example of ten keywords with more than 5,000 searches is a preference, not a requirement every viable product must meet. A smaller, highly relevant market may fit a seller better than a large market with expensive competition.

Do not simply add every keyword's search volume and call the result your potential customer count. Related searches can overlap, and one shopper can search several times. Search volume is not unique buyers, orders, or a promise that your listing will receive traffic.

Instead, group phrases by intent. A shopper searching for poultry shears may need a different cutting capability from someone searching for everyday kitchen scissors. A dishwasher-related phrase raises questions about materials, maintenance, and instructions. Your listing should match the product you can actually deliver.

This is also where research starts shaping your product brief. If customers repeatedly look for a protective cover, a particular grip, or easy cleaning, investigate whether those needs are underserved. Keywords tell you what people search for; reviews help explain what they struggle with after buying.

Keep your first keyword shortlist manageable. A dozen relevant phrases with clear notes about customer intent can be more useful than hundreds of loosely related terms. Expand the list when you understand which customer problem your product is meant to solve.

Find suppliers on Alibaba without confusing price with cost

The sourcing section connects your Amazon research to possible suppliers. From Xray, the video opens a supplier-search panel on Alibaba. Current Helium 10 materials describe this route as Find Suppliers on Alibaba. It is useful for discovering options while the product idea is still fresh.

Explore the official Alibaba Supplier Finder overview.

In the lesson, I experiment with filters such as unit price, minimum order quantity, supplier location, account age, and supplier type. Available filters may change, but the purpose stays the same: narrow the list to suppliers who might fit your product, budget, and order size.

Minimum order quantity, or MOQ, is especially important. A low advertised unit price might apply only to a quantity much larger than you can fund. Ask whether the MOQ applies to the overall order, each color, each size, or a customized version. A test order and a fully branded production order may have different terms.

Manufacturers and trading companies can both be worth considering. A manufacturer produces goods, while a trading company may coordinate products from other factories. Direct production does not automatically guarantee the best total outcome. Communication, consistency, sampling, packaging, and the ability to deliver your specification also matter.

Likewise, supplier ratings and verification labels are screening signals. They do not replace checking the product, discussing requirements, and reviewing the supplier's actual proposal. An older marketplace account may be reassuring, but age alone does not establish whether a factory can produce your version reliably.

The video's most useful sourcing warning is about displayed prices. Treat the Alibaba listing price as an invitation to obtain a quote. Confirm the material, size, finish, packaging, order quantity, and shipping terms. Otherwise, you may compare a basic unbranded item with a finished product that costs substantially more.

For kitchen scissors, I would turn the research into specific questions: What material is used? How is the hinge constructed? Does the quoted product include a blade cover? What packaging protects the item in transit? Can the supplier produce samples that match the final specification?

Then request comparable quotations. If one quote includes packaging and another excludes it, their unit prices are not directly comparable. Keep a simple record of what is included, what remains uncertain, and which assumptions must go into your profitability model. This makes the supplier conversation part of the research instead of a separate guessing exercise.

Use Demand Analyzer and ASIN Grabber for focused follow-up

Research can also begin on Alibaba. You might discover an interesting product from a supplier and want to know whether shoppers search for something similar on Amazon. In the lesson, I open the extension from Alibaba and use Demand Analyzer to investigate that question.

Helium 10's documentation describes opening an Alibaba search, launching the extension, and selecting the Amazon demand-analysis option. Confirm the target Amazon marketplace before interpreting the figures. Demand in the United States should not automatically be treated as demand in the United Kingdom or another country.

Read the Alibaba Demand Analyzer walkthrough.

The tutorial shows search-volume history, related keywords, and links into additional research tools. Use those routes to test whether the supplier's product matches an actual customer search. A product name supplied by a factory may differ from the words customers use when shopping.

You may also notice that the numbers differ from an earlier Xray search. Check the underlying product set before assuming something is wrong. In the video, a route into Black Box applies a different keyword and title-based comparison. A narrower group of products naturally produces different totals and averages from a broad Amazon results page.

Keep the question consistent: which products are included, which keyword is being analyzed, and which marketplace is selected? Without those details, two dashboards can look contradictory when they are simply describing different groups.

ASIN Grabber serves a simpler purpose. It collects product identifiers and available listing information into a list you can use for further research or hand over to a teammate. In the video, I demonstrate collecting a group of ASINs instead of copying each identifier manually.

Before exporting, check relevance and duplicates. A long list containing office scissors, kitchen scissors, sponsored repeats, and unrelated accessories will create work later. Add a short note explaining why each shortlisted product matters: direct competitor, premium alternative, unusual bundle, or a useful example of customer complaints.

If you are delegating the next step, make the task concrete. Ask your teammate to compare five relevant products, identify recurring complaints, and record current offers. Sending a large spreadsheet without a research question usually produces more data without a clearer decision.

Use the Profitability Calculator with complete assumptions

Open a specific product page to use the Profitability Calculator. The lesson demonstrates changing inputs and watching the estimated margin and ROI respond. This is valuable because a promising market can become unattractive as soon as you replace rough assumptions with realistic costs.

Begin with your intended selling price. A competitor's price is a reference, but your own product may need different packaging, materials, or positioning. If your calculation works only at a premium price, explain why customers would pay it. Do not quietly assume you can charge more while offering the same experience.

Next, verify packaged dimensions and weight. A competitor's listing data is a useful placeholder during early research, not a final specification for your product. Packaging changes can affect shipping and fulfillment costs, especially when they move the product into another fee classification.

Enter the supplier's confirmed product cost and a realistic shipping allocation. Include the expenses needed to get a sellable unit ready and delivered into your chosen fulfillment arrangement. Depending on the order, that can involve packaging, inspection, freight, import costs, preparation, labeling, and inbound charges. Record each cost once so you do not accidentally count the same expense twice.

The tutorial changes estimated storage time to show how slower inventory movement can reduce the result. Apply that idea to your own forecast. A large order may offer a lower manufacturing price while creating more storage exposure and keeping cash committed for longer.

Amazon fees depend on the marketplace, product characteristics, fee category, and current rules. The video uses historical examples, including a referral percentage and fulfillment amounts. Do not reuse those figures as today's fee schedule. Cross-check your scenario with Amazon's own calculator before committing to an order.

Check Amazon’s official fee-estimation guidance.

Advertising belongs in the analysis too. A profit estimate before advertising is not the amount you necessarily keep after acquiring customers. Add an advertising allowance, along with a reasonable provision for returns, discounts, and other costs relevant to your model. If a field cannot represent an expense clearly, keep a separate worksheet beside the calculator.

I like testing at least three scenarios. The base case uses your best-supported assumptions. The weaker case lowers the selling price or raises acquisition costs. The stronger case shows what might happen if execution goes well. Your first inventory decision should not depend entirely on the strongest case being correct.

Finally, separate unit economics from business cash needs. Samples, photography, development, and initial launch work can require cash before the first sale. A healthy per-unit result does not automatically mean the business can fund production, replenishment, and unexpected delays.

Understand margin, ROI, and advertising headroom

Margin and return on investment answer different questions. Margin compares profit with selling revenue. ROI compares profit with a defined investment base. You need to know what costs are included and what denominator is being used before comparing either figure.

Here is a simplified illustration using invented figures. It is not an Amazon fee quotation, a supplier offer, or a prediction for kitchen scissors. The purpose is to show how quickly an attractive pre-advertising result can change when you include the rest of the assumptions.

The calculation is $24 minus $6, minus $7, minus $1, minus $4, leaving $6 per unit before fixed overhead. The contribution margin after advertising is therefore $6 divided by $24, or 25%. It is not final business net margin because fixed overhead and any other omitted expenses still need accounting for.

If we define the investment base as the $6 landed inventory cost, the same $6 contribution produces an illustrative inventory ROI of 100%. That definition is explicit here. A calculator or another seller may use a different cost base, so a displayed ROI should never be compared without checking its meaning.

The video discusses 100% ROI as an attractive target. Treat it as a preference in that example, rather than a minimum every business must achieve. Turnover speed, risk, overhead, and cash requirements affect whether a product makes sense. A percentage by itself does not tell you how quickly money returns or whether you can finance the next order.

Our example also shows $10 of contribution before advertising. If advertising consumes the entire $10, nothing remains for fixed overhead or profit under these assumptions. That makes the number useful as a planning boundary, even though actual advertising performance will vary.

Now reduce the selling price to $22 while holding these simplified dollar costs constant. Contribution after advertising falls to $4. A small price change has removed one-third of the previous contribution. Real fees may also change with price, but the exercise shows why testing weaker scenarios matters.

Reinvestment can help a business grow, as I explain in the lesson, but it does not guarantee that cash will double repeatedly. Inventory needs time to sell, costs change, and demand is uncertain. Use the calculator to challenge your assumptions before those assumptions become a purchase order.

Read Inventory Levels as a limited visibility tool

Inventory Levels is another product-page feature covered in the video. It can provide information about available seller offers and stock visibility, but I would treat it as supporting evidence. It is not a complete audit of everything a competitor owns.

The lesson demonstrates a seller with a maximum order quantity. In that situation, the visible number can reflect the purchase limit instead of total stock. If you see ten units, that does not necessarily mean the seller has only ten units remaining. A missing or unavailable result is also not proof of a stockout.

The official Inventory Levels guide notes that stock information can be obscured. Check the availability and limitations shown for the particular listing you are researching.

Read the Inventory Levels documentation.

Even when the visible quantity changes, the difference is not automatically sales. Inventory may move between fulfillment locations, become temporarily unavailable, be replenished, or change because of the offer being displayed. The video uses a warehouse-transfer example to explain why an apparent stock drop can mislead you.

If you observe a decline from 650 to 630 units, record it as a change in visible availability. Then compare other evidence, such as the sales estimate, offer status, and historical pattern. Do not report twenty confirmed customer orders from that observation alone.

The same caution applies to listings with several sellers. Total product demand does not tell you how much each seller receives, and offers do not necessarily share sales equally. Price, availability, and the offer customers encounter can change the distribution.

For private-label research, I would usually spend more time understanding customer needs, realistic costs, and competitive demand. Inventory visibility can add context, but it should not become the deciding factor when the rest of the business case is weak.

Turn Review Insights into a better product brief

Review research is one of the most practical parts of this tutorial. Sales estimates suggest whether customers are buying. Reviews help you understand what happens after the purchase: what works, what disappoints, and what buyers wish someone had designed differently.

In the original lesson, I filter lower-rated reviews with photos and examine complaints about rust, broken parts, and disappointing performance. I also inspect repeated phrases and customer questions. That older extraction screen should not be treated as an exact description of today's interface.

Read the current Review Insights guide.

The research method still applies. Start with one recurring problem and inspect the available context. A rust complaint suggests questions about materials, handling, storage, care instructions, or quality checks. It does not prove a particular root cause by itself. Turn the complaint into something you can investigate with samples and the supplier.

When a customer reports a broken hinge, ask what they were trying to cut and whether the product was intended for that task. Your improvement might involve a stronger component, a different product design, clearer usage limits, or a combination of these. Promising stronger performance without testing it would create another disappointed customer.

Positive feedback matters just as much. If customers repeatedly appreciate sharpness, comfort, or easy cleaning, preserve those strengths while you improve weaknesses. Product research is not only a hunt for defects. It also shows what buyers value enough to mention after using the item.

Read phrases in context. “For the price” could introduce praise or disappointment. “Comes apart” could describe convenient cleaning or an unwanted failure. Word frequency is a prompt to inspect the underlying feedback, not a complete interpretation of it.

Questions can reveal missing information too. If buyers repeatedly ask whether a cover is included, whether the item suits a particular task, or how it should be cleaned, your product brief and listing need to address those points accurately. Check the live listing and available feedback rather than assuming the old questions-export feature still exists.

I would organize the findings into three columns: observed concern, proposed response, and evidence needed. For example, a storage concern might lead to testing a fitted cover. A packaging complaint might lead to a revised protective insert. A cleaning question might lead to clearer instructions supported by product testing.

Compare several relevant competitors before committing to an improvement. One unusual complaint can be an isolated experience. A repeated issue across similar products deserves closer attention, particularly when the proposed solution is affordable and useful to your intended buyer.

Use the research to improve the listing and the offer

The video ends by revisiting product-page charts and shortcuts into other Helium 10 tools. These connections help you carry the research forward. You can move from a product's history into keyword analysis, fee calculations, or work on the listing itself without losing the original question.

In the recorded interface, Listing Optimizer opens Scribbles. If your account presents Scribbles, Listing Builder, or a different route today, follow the available workflow. The important task is to turn your own verified product benefits and relevant keywords into a clear listing. A competitor's listing is research material, not copy to reuse.

Look for genuine presentation weaknesses. In the lesson, I mention listings with few images or poorly explained benefits. That can suggest room for improvement, but a weak-looking listing may still sell because customers already trust the brand. Better images alone do not guarantee you will win its customers.

Build your offer around something specific. A protective cover should fit properly. A multipack should make sense for the customer's use. A gift box should be worth its extra cost. A premium material claim should be supported. Each improvement needs to survive both the customer test and the profitability calculation.

Then align the images, written copy, and product experience. Show what is included, explain the intended use, and answer the recurring questions you discovered. If the customer expects one capability and receives another, more traffic will simply expose the mismatch faster.

For a wider view of the toolkit, explore my complete Helium 10 beginner tutorial. Use the extension to identify the next useful task, then move into the appropriate tool when you are ready to do it.

A repeatable research session from idea to shortlist

Here is how I would bring the lesson together for one product idea. Start by writing down the intended customer, marketplace, and use. “Kitchen scissors for everyday meal preparation” is a clearer starting point than “something in the kitchen category.” It gives you a way to decide which products belong in the comparison.

Search the main phrase on Amazon, inspect the extra listing information, and run Xray. Keep a relevant group of competitors, record the date, and review how estimated sales are distributed. Look for enough demand to investigate and a plausible opening for your product.

Next, examine historical sales, rank, and pricing. Record anything you cannot explain: a sudden spike, a long decline, repeated unavailability, or a large change in price. Follow up on those questions before relying on the current snapshot.

Move into keyword research and customer feedback. Identify how shoppers describe the product and what they want improved. Write a short differentiation statement that a customer would understand. If you cannot explain the benefit without saying “better quality,” make the idea more specific.

Only then develop a supplier shortlist and obtain comparable quotes. Replace estimated dimensions, costs, and packaging assumptions as better information arrives. Recalculate profitability under weaker as well as stronger conditions.

Finish with a decision: continue researching, request samples, pause the idea, or reject it. Add the reason and the next piece of evidence required. A rejected product is still a useful result when the research prevents an expensive mistake.

Keep the record short enough to maintain. A useful shortlist includes the ASINs studied, relevant keywords, observed customer problem, proposed improvement, cost assumptions, unresolved questions, and next action. This gives your next research session a starting point and makes collaboration much easier.

Helium 10 Chrome extension: common questions

Start with one product and make the next decision clearer

The Helium 10 Chrome extension becomes useful when each feature answers a question. Xray helps you compare the market. Historical charts put the current figures in context. Supplier research and the calculator test whether the product could work commercially. Customer feedback helps you decide what deserves improving.

Choose one product and follow the full process before researching ten more. Keep the assumptions visible, replace them with evidence as you learn, and leave yourself a clear next action. That is how I would turn this tutorial into a practical research habit.

Vova :)