Faire Resource Hub
Last updated October 2026
Part of our Faire Resource Hub. For the full growth framework, start with How to Get More Orders on Faire.
Faire tells you, inside your own dashboard, what your ranking in the marketplace depends on. Most brands have never read the sentence.
It sits in small grey type above the first cards on the Analytics page:
“Your ranking in the marketplace is determined by your store conversion rate, sell-through rate, and average star rating.”
Faire, Analytics page, brand portal
If your sales are flat and you do not know why, that line is the right place to start. Not because it is the whole answer, and we will be careful about that in a moment, but because it tells you which three numbers Faire itself is pointing at.
This guide walks through what Faire actually measures, where each number lives, what Faire means by it, and how to read the dashboard without drawing the wrong conclusion from it.
How this guide was researched. Every screen, label, tooltip and metric description below was reviewed directly in the Faire brand portal on October 12, 2026, and checked against Faire’s current Help Center documentation. Faire changes this interface regularly, so treat the structure as accurate as of that date and confirm anything critical in your own account.
Throughout, we separate three things: what the dashboard shows, what Faire’s documentation says, and what Faire Growth Agency recommends. We are an independent consultancy. We are not affiliated with or endorsed by Faire.
What Faire Says Its Ranking Depends On
Faire names three brand-performance inputs in the dashboard: store conversion rate, sell-through rate and average star rating. Its documentation adds more factors, describes customer service metrics as a possible additional influence, and says search results consider over 100 factors in total.
There are two statements inside the portal. The first is the Analytics summary line quoted above. The second sits on the Customer service page, under the question “Does this affect my marketplace ranking?”
“Ranking is based on several factors including your store conversion rate, sell-through rate, and average star rating. Your customer service metrics may be an additional factor and improving on these metrics may improve your shop’s ranking.”
Faire, Customer service page, brand portal
Read the hedges, because they are doing real work. “Several factors including.” “May be an additional factor.” “May improve.” Faire is naming inputs, not publishing a formula.
Faire’s help documentation confirms the list is partial. It names product information, retailers’ prior activity, and brand performance, which it breaks into product conversion, review rating, return rate, cancellations and fulfillment timing. It calls product performance the most important factor in product ranking, says search results consider over 100 factors, and says ranking is weighted on broad patterns over weeks and months rather than individual actions.
What this does not mean. There is no published weighting, no scoring formula and no complete list. Any article that gives you percentages for Faire ranking factors has invented them. What you can take from this is narrower and more useful: conversion rate, sell-through rate and star rating are three things Faire has told you it looks at, and all three are visible in your own dashboard.
One oddity worth knowing, because it will confuse you otherwise. The Analytics summary sentence names conversion rate, sell-through rate and average star rating. The three cards directly underneath it show total sales, conversion rate and average rating. Sell-through rate is not on that card at all. It lives on its own page under Analytics. So the card does not actually display the three things the sentence above it describes.
Where Faire’s Analytics Actually Live
The Analytics menu holds the headline numbers plus two sub-reports. The rest is scattered across Products, Customers, Marketing, Orders and My shop. Knowing the map saves most of the time people waste looking for figures that are not where they expect.
| What you want to know | Where it lives |
|---|---|
| Sales, orders, average order value | Analytics, Sales overview |
| Traffic and conversion | Analytics, Shop overview |
| Faire Direct performance | Analytics, Faire Direct section |
| Sell-through and returns | Analytics > Sell-through |
| Reviews and service quality | Analytics > Customer service, My shop > Reviews and Top Shop |
| Product performance | Products > Performance |
| Customer behavior and segments | Customers |
| Advertising | Marketing > Promoted Listings |
| Email and promotions | Marketing > Campaigns, Automations, Promotions |
| What each order actually paid you | Orders > Payouts |
| Service issues and their cost | Orders > Issues |
That last pair matters more than most brands realize. We come back to it, because it is where the money questions get answered.
The Core Numbers, and What Faire Means by Them
Faire’s definitions are narrower than the words suggest. Total sales excludes shipping and tax. Shop visits counts unique signed-in retailers, not sessions. Conversion rate counts retailers, not orders.
These are Faire’s own definitions, from the dashboard tooltips and its Help Center.
The total gross product value of orders. Product value excludes shipping and taxes, and only items that were not canceled or returned are counted.
The total number of gross orders, based on the date you were notified of each order. Fully canceled orders are not included.
The average product value per order, calculated by dividing total sales by total orders.
The number of unique signed-in retailers who visited your shop or product pages.
The percentage of unique signed-in retailers who placed an order after visiting your shop or product pages.
Two consequences follow, and both catch people out.
First, sales are dated by when you were notified of the order, not when it shipped or was delivered. If you are trying to reconcile Faire analytics against your own shipping or accounting records, that difference alone will stop the numbers matching.
Second, the Shop overview section carries a note that changes how you can use everything in it: “Shop metrics are based on activity from signed-in retailers. Each retailer is counted once, no matter how many times they visit or order.”
Why Your Faire Conversion Rate Is Not an Ecommerce Conversion Rate
On a normal ecommerce site, conversion rate is orders divided by sessions. On Faire it is neither of those things.
Both sides of Faire’s calculation are unique retailers. The denominator is retailers who visited. The numerator is retailers who ordered. A retailer who visits nine times and places three orders counts once on each side.
Three things follow:
- You cannot compare your Faire conversion rate to your Shopify or website conversion rate. They are different calculations on different populations.
- You cannot work backwards from conversion rate to an order count, because orders are not the numerator.
- Logged-out browsing is invisible. A retailer who looks at your shop without signing in does not appear in Shop visits at all.
Shop visits multiplied by conversion rate gives you roughly the number of ordering retailers in the period, a useful figure Faire does not display anywhere. Orders divided by Shop visits is a different ratio, and usually higher, because one retailer can place several orders.
The Comparison Trap That Makes Q4 Look Like a Miracle
Faire offers two comparisons: Prior period and Year over year. Prior period anchors to the start of the previous period of the same type and spans the same number of elapsed days. With This quarter selected in October, that means your October is compared against early July.
This is the single most misleading thing on the Analytics page, and it is misleading precisely because it looks sensible.
We tested the behavior directly on October 12, 2026 and read the date ranges Faire prints in the chart legend. Here is what each setting actually compares.
| Date range | Compare to | Current | Compared with |
|---|---|---|---|
| This quarter | Year over year | Oct 1 to Oct 13, 2026 | Oct 1 to Oct 13, 2025 |
| This quarter | Prior period | Oct 1 to Oct 13, 2026 | Jul 1 to Jul 13, 2026 |
| This month | Year over year | Oct 1 to Oct 13, 2026 | Oct 1 to Oct 13, 2025 |
| This month | Prior period | Oct 1 to Oct 13, 2026 | Sep 1 to Sep 13, 2026 |
Date windows as printed in the Faire chart legend, observed October 12, 2026.
The rule is consistent. Prior period starts at the beginning of the previous period of the same type and runs for the same number of elapsed days. It is not the full previous quarter. It is not the end of the previous quarter. It is the opening days of it.
For a seasonal wholesale brand, that is a problem. In October, Prior period compares your first two weeks of holiday ordering against your first two weeks of July. Those are not comparable periods for almost any gift, jewelry, accessory or apparel brand, so the number looks like explosive growth and means very little. The same mechanism runs in reverse in January, when the opening of Q1 is measured against the opening of Q4 and a healthy brand appears to fall off a cliff.
FGA recommendation. For seasonal wholesale, Year over year is the more honest default on quarterly and monthly views. Use Prior period when you are measuring something that is genuinely sequential, such as the effect of a change you made last month, and when you have checked that the two windows are comparable.
One More Thing the Dashboard Does Quietly
When you change the Date range, the Compare to selector resets itself to Year over year. We observed this twice: set Prior period, change the range, and the comparison silently reverts without any notice.
So set the date range first, then the comparison, then read the legend underneath the chart. Faire prints both windows there in full, and it is the only reliable check.
One point of vocabulary: This quarter means quarter to date, not the whole quarter.
The FGA Performance Diagnostic
- Visibility
- →
- Engagement
- →
- Conversion
- →
- Order Value
- →
- Retention
- →
- Profitability
This is an original framework developed by Faire Growth Agency. It is not Faire’s framework, not Faire’s algorithm, not a ranking formula, not endorsed by Faire, and not a guarantee of results.
Flat sales are never one problem. They are a break somewhere in a chain, and a framework is useful because it tells you which number to check next instead of staring at the total. Work the stages in order. The first one that looks wrong is usually where the problem is, and fixing a later stage rarely helps while an earlier one is broken.
| Stage | The question | Where to look |
|---|---|---|
| 1. Visibility | Are retailers finding you at all? | Shop visits, product page views, ad impressions |
| 2. Engagement | Do they look at more than one thing? | Page views per product, ad click-through rate, abandoned cart segment |
| 3. Conversion | Do visitors become buyers? | Conversion rate, ad click-to-order rate |
| 4. Order Value | Are orders big enough to be worth filling? | Average order value, promotion AOV, units per order |
| 5. Retention | Do they come back? | Customers segments, reorder conversions, sell-through, reviews, customer service |
| 6. Profitability | Is any of it making money? | Payouts, Issues, ad spend, promotion depth |
A visibility problem is the most common assumption and often the wrong one. Retailers arriving and not ordering is a conversion problem. Retailers ordering on terms that leave nothing behind is a profitability problem. Both look identical on a flat sales line.
Product-Level Analytics, and One Ratio Worth Calculating
Products > Performance gives you page views, orders and units sold per product. Dividing units sold by orders gives you the average units per order for that product, which tells you whether retailers are testing it or committing to it.
Faire documents page views as the number of times retailers clicked on the product page, including views from both desktop and the mobile app. Note that this counts clicks, not unique retailers, which is the opposite of how Shop visits works. The two numbers are not on the same basis and should not be divided into each other.
Faire does not publish a definition of the Orders or Units sold columns on that page. There is no tooltip on either column header and the Help Center article does not define them. So we can tell you what the columns are called and not much more. In particular, Faire has not documented whether the product Orders figure excludes canceled orders the way Total orders does.
The average number of units per order containing that product. A product sitting near your case pack minimum is being tested. A product running well above it is being committed to. A bestseller by order count that sits at the floor on this ratio is a product retailers are willing to try and not yet willing to back.
Compare products against each other within your own catalog rather than against any external benchmark. The useful signal is the spread across your own range, not the absolute number.
This ratio is a Faire Growth Agency calculation, not an official Faire metric. Because Faire has not documented the precise definition of the product Orders field, treat the result as directional rather than exact.
What that page does not give you is revenue per product. There is no sales column. You can estimate it from units sold and your wholesale price, but that ignores promotions, returns and case packs, so label it an estimate. The listing-level work this feeds is covered in our guide to optimizing a Faire product listing.
What Your Orders Actually Pay You
Faire does not show profitability as a metric anywhere in Analytics. It does something better: Orders > Payouts itemizes every order from the retailer’s subtotal down to the amount that lands in your account, with each deduction on its own line.
This is the part of the portal most brands never open, and it answers what the Analytics page cannot. Total sales tells you what retailers agreed to pay, not what you received.
The Payouts page lists every order with its payout date, payout amount, order, customer, ship date and order total, across All, Paid and Unpaid tabs, with an export.
Click any payout amount and Faire opens a breakdown in two halves.
Item subtotal with the quantity. Then any promotion, shown as a named line with its percentage. Then shipping. The result is the order total, which is what the retailer pays.
Commission, with the rate applied. Ship with Faire. Insider free shipping, where you are covering it. Payment processing fee, with its percentage and flat fee. The result is the payout total, which is what you receive.
And then there is one sentence in that modal that is worth more than most analytics advice:
“Commission and payment processing fee are based on item subtotal, after discounts and promotions have been applied.”
Faire, payout details, brand portal
Faire’s published fee documentation describes the same calculation slightly differently, giving commission as order subtotal multiplied by the commission rate for that order type, and the payment processing fee as item subtotal multiplied by the processing rate. The wording differs between the two sources. The behavior both describe is the same in the way that matters: fees are charged on the discounted amount, not the list amount.
Rates Are Not One Number
Do not assume one commission rate applies. Faire’s cost documentation says it depends on the order:
- Faire Direct orders from customers you brought to Faire carry no commission.
- Marketplace orders carry a standard commission rate.
- A new marketplace customer’s first order carries a one-time additional new customer fee on top of that rate.
- The payment processing fee varies with the payout speed you have chosen, not with the order type. Faster payouts cost more.
So the only reliable way to know what an order cost you is to open its payout breakdown. The rate shown there is the one that was applied.
The Other Cost Line, on a Different Page
Orders > Issues lists orders that had customer service issues, with tabs for reported issues, late shipments and canceled orders. Its columns include issue type, items reported and, importantly, a deduction amount.
Those deductions are real money leaving the business, and they appear neither on the Analytics page nor in the original order’s payout breakdown. A true cost picture needs this page in it.
Working Out Contribution After Product Costs
Here it is in full, with round illustrative numbers. These are not real rates or real figures. Use the rates on your own payout breakdowns.
Item subtotal $1,000. Promotion at 10% off, minus $100. Shipping $0. Order total $900.
Commission at an illustrative 15% of the discounted $900, minus $135. Payment processing at an illustrative 3.5% plus $0.30, minus $31.80. Insider free shipping you are covering, an illustrative $40. Payout total $693.20.
Cost of goods on those units, say $300. Faire never sees this number and cannot calculate it for you.
$693.20 minus $300 equals $393.20. That is 39% of the $1,000 you listed the goods at, and 44% of the $900 the retailer actually paid.
Now run the same order without the promotion, holding the same illustrative $40 of Insider shipping. At a $1,000 subtotal the illustrative commission is $150 and processing is $35.30, so $1,000 minus $150 minus $35.30 minus $40 gives a payout total of $774.70, against $693.20 with the promotion.
The discount took $100 off the invoice but cost you $81.50, because commission and processing fell with it. That is the practical consequence of the after-discounts rule, and the correct way to price a promotion. The sticker cost of a discount is not its real cost.
FGA recommendation. Before you set promotion depth for a season, run two or three representative orders through this calculation at the depth you are considering. It takes ten minutes and it is the difference between discounting on instinct and discounting on arithmetic.
What This Is Not
An itemized payout is not a profit and loss statement, and treating it as one will flatter your business.
A payout breakdown covers one order and the Faire-side costs attached to it. A real P&L for your wholesale channel also has to absorb the things Faire never sees:
- Cost of goods, including freight in and duty
- Advertising spend, which sits in Promoted Listings and not in any order
- Issue deductions, which sit on the Issues page
- Labour, pick and pack, packaging, warehousing and overheads
- Samples, trade shows, photography and the cost of acquiring the account
What Faire gives you is the complete Faire-side cost of an order, reliably and per order. That is genuinely valuable, and it is most of the hard part. The rest has to come from your own books.
Reading Promoted Listings Without Fooling Yourself
Faire reports advertising return separately for new customers and returning customers. The new-customer figure is a projection. The returning-customer figure is based on orders that actually happened. They should not be read the same way.
Promoted Listings carries the most detailed reporting in the portal: Overview, New customers, Returning customers, Products, and a Customer insights tab marked Beta. Each headline figure splits in two, and that split is the thing to understand.
Faire describes first-year value as projected first-year sales from customers acquired through each month’s ads, based on your brand’s average order value and reorder rate. Faire states that after twelve months the estimate is replaced with actual sales. So the new-customer ROAS on a recent month is a forecast built on your own historical behavior, not a record of money received.
The returning-customer side is built on reorder conversions that already happened. It is the harder of the two numbers and the one to lean on when you are deciding whether advertising is working.
Two consequences follow. Recent months look softer and noisier than mature ones because their projections have not resolved. And a brand with a short Faire history has a thinner basis for its own projection, since the projection leans on its own order value and reorder rate.
Sales This Month Can Belong to Last Quarter’s Ads
The monthly ad sales chart carries three series, and the third is easy to miss: new customer ads, returning customer ads, and previous months’ ad acquisitions.
That third series is why monthly ad sales and monthly ad spend never line up. A customer acquired in June can order in October, and that October revenue is attributed back to the June acquisition. Comparing this month’s ad sales against this month’s ad spend is therefore not a return calculation.
One more timing detail, from Faire’s own tooltip: ad spend updates on the 4th, 11th, 18th and 25th of the month, while impressions, clicks and conversions update daily. Mid-month, your spend figure is stale and your performance figures are current, so any ratio between them is distorted. Read return on finished months.
Before you increase a budget, it is worth reading our guide to why brands do not get orders on Faire, because advertising a listing that cannot convert is the most expensive mistake available on the platform.
Faire Direct Numbers Need Care
The Faire Direct section uses metric names that look familiar and are calculated differently. Average order volume there is per retailer. Average order value elsewhere is per order.
The Faire Direct cards cover Faire Direct sales, ordering customers, average order volume and Faire Direct savings. Two cautions.
First, average order volume is Faire Direct sales divided by the number of ordering customers, which Faire describes as average order volume per retailer. The main Average order value on the same page is total sales divided by total orders. Same vocabulary, different denominator, two sections apart.
Second, do not divide Faire Direct sales by Total sales and call it channel share. Faire describes Total sales as gross product value, explicitly excluding shipping and taxes. It describes Faire Direct sales simply as total revenue for all Faire Direct orders, and does not say whether that uses the same basis. Until Faire reconciles those two labels, the ratio is an approximation at best.
If you want a defensible split, the Analytics page has one already: the Marketplace versus Faire Direct orders card, which Faire defines as the percentage of your order volume placed through each channel. That is a documented metric with a stated basis. Use it.
We also cannot tell you whether Faire Direct traffic is counted in Shop visits. The tooltip and the section note both describe signed-in retailers visiting your shop or product pages, and neither mentions channel. Faire has not documented it, so neither will we.
Timing Traps That Make the Numbers Look Wrong
Different parts of the portal refresh on different schedules and cover different windows. Most of the apparent contradictions people find between pages are timing, not errors.
Faire documents that analytics update hourly. The page prints its own last-updated stamp, which is worth reading before you conclude anything changed.
Faire states that sell-through data is only available on orders placed more than 60 days ago. The preset windows all end roughly 60 days before today. Nothing you sold this month is in here, by design.
Updates on the 4th, 11th, 18th and 25th. Everything else in Promoted Listings updates daily.
Customer service rates cover a rolling three months. Top Shop uses fixed quarterly review periods. The same underlying behavior can produce two different-looking rates on two pages at the same moment, and the goal thresholds differ between the two programs as well.
The Customer service page states that if you have not received messages or fulfilled orders in the last three months, some metrics will not appear. A blank is not necessarily a zero, and period-level review figures can read as blank while the lifetime figures underneath them are populated.
One more: review metrics include published and unpublished reviews but exclude deleted and removed ones, and the rating retailers see on your shop page is your lifetime rating, not the period figure on the card.
What Faire Does Not Show You
Knowing the gaps is as useful as knowing the metrics, because it stops you looking for things that are not there.
| Not reported | Closest available substitute |
|---|---|
| Revenue per product | Units sold multiplied by wholesale price, as an estimate only |
| New versus returning customers overall | Reported for advertising only, in Promoted Listings |
| Repeat purchase rate, reorder rate, lifetime value | Customers list filter counts on total orders and last ordered |
| Traffic sources, search impressions, ranking position | Nothing. This is the largest single gap in the dashboard |
| Product-level organic conversion rate | Nothing directly comparable |
| Cart abandonment rate | Abandoned cart is a customer segment, not a rate |
| Net sales or margin as a metric | Derivable per order from Payouts, as above |
| Export of the main Analytics dashboard | No export exists. Record the figures yourself, or export the sub-reports |
That last row matters if you want a trend. Products > Performance, Sell-through, Promoted Listings, Orders, Payouts, Issues and Customers all export. The headline Analytics page does not, so month-on-month history of your conversion rate has to be written down.
A note on the Customers export. It contains retailer contact details. Treat it as personal data, keep it out of shared drives and spreadsheets that circulate, and do not export it when a filter count on screen would answer the question.
Your 20-Minute Monthly Faire Review
Same date range, same comparison, same order, every month. Consistency is what makes the numbers mean something, not the depth of any single session.
- Set the view first. Date range, then comparison, then check the chart legend to confirm the two windows. Year over year for seasonal categories.
- Sales, orders and AOV. Which of the three moved? Orders up with sales flat is an order-value problem, not a traffic problem.
- Shop visits and conversion rate. Visits up with conversion down means the storefront or the assortment is not closing what the traffic is bringing.
- Products > Performance. Sort by page views. Look for products with attention and no orders, and check units per order on your top sellers.
- Customers. Check the lapsed segments at 60 and 180 days. Growth there is the earliest warning you get.
- Promoted Listings. Read the returning-customer return on finished months only.
- Payouts. Open two or three orders and read the breakdowns. Ask whether the promotion depth still makes sense.
- Issues and Customer service. Any deductions, any rate drifting toward its goal line.
- Write down the headline figures. There is no dashboard export. Your own record is the only history you will have.
Frequently Asked Questions About Faire Analytics
Why is my Faire conversion rate so different from my website conversion rate?
Because they measure different things. A website conversion rate is usually orders divided by sessions. Faire’s is unique signed-in retailers who ordered, divided by unique signed-in retailers who visited, with each retailer counted once no matter how many times they visit or order. Faire’s figure also excludes anyone browsing while logged out. The two numbers are not comparable, and a Faire rate that looks implausibly high next to an ecommerce rate is usually just the different denominator.
What does Faire say affects my marketplace ranking?
Faire names store conversion rate, sell-through rate and average star rating in the dashboard, and describes customer service metrics as a factor that may additionally influence ranking. Its documentation adds product information, retailer activity, return rate, cancellations and fulfillment timing, and states that search results consider over 100 factors. No weighting is published.
Can I export Faire analytics?
Partly. Products > Performance, Sell-through, Promoted Listings, Orders, Payouts, Issues and Customers all have export controls. The main Analytics dashboard does not, so trend history has to be recorded manually.
Why do the same numbers differ between two Faire pages?
Usually the window. Customer service runs on a rolling three months while Top Shop runs on fixed quarters. Sell-through excludes anything sold in the last 60 days. Ad spend updates four times a month while clicks update daily. Check which period each page is using before assuming an error.
How does Faire calculate average order value?
Total sales divided by total orders, where total sales is gross product value excluding shipping and taxes. The Faire Direct section’s average order volume is a different calculation, measured per ordering retailer rather than per order.
Where can I see what an order actually paid me?
Orders > Payouts. Clicking a payout amount opens a breakdown showing the item subtotal, any promotion, shipping, the order total, then commission, shipping charges and the payment processing fee, down to the payout total.
Are Faire commissions charged before or after a discount?
After. Faire’s payout breakdown states that commission and the payment processing fee are based on the item subtotal after discounts and promotions have been applied. One practical effect is that a discount costs you less than its face value, because the fees fall with it.
Does Faire show my profit?
No. It shows every cost on its own side of the transaction, per order, which is most of the work. Cost of goods, labour, overheads and acquisition costs are never visible to Faire and have to come from your own accounts.
Flat Sales Are a Diagnosis, Not a Verdict
Most brands looking at a flat sales line are looking at the one number that cannot tell them anything. It is a result. The explanation is always one stage earlier.
Faire gives you more than people assume: what it looks at when it ranks you, plus traffic, conversion, product-level attention, customer behavior, service quality and the full cost of every order. What it withholds is traffic sources, product revenue, a repeat purchase rate and an export of the headline numbers. Knowing which gaps exist stops you hunting for them.
Work the stages in order, set your comparison honestly, and read the payout breakdowns before you decide whether a quarter was good. The brands that grow on Faire are rarely the ones with the most data. They are the ones who looked at the right number next.
- How to Get More Orders on Faire, the cornerstone guide this sits underneath.
- Why Am I Not Getting Orders on Faire?, for working through a slow period stage by stage.
- Faire SEO, for the visibility stage.
- How to Optimize a Faire Product Listing, for the listing-level work.
- Faire Product Photography, for what retailers need to see before they order.
- Faire Storefront Optimization, for the store-level view.
- Faire Collections and Merchandising, for assortment and shopping paths.
- Faire October 2026 Trends, our latest monthly market intelligence report.
Want a Second Read on Your Faire Numbers?
At Faire Growth Agency we help consumer product brands work out what their Faire data is actually telling them, and what to do about it across storefront, listings, search visibility, merchandising and wholesale growth.
The Monthly Faire Storefront & SEO Audit gives you an expert review of your storefront and product listings every month, with ready-to-use titles and descriptions and a prioritized plan for the season ahead.
Each monthly audit includes:
- A storefront and SEO report with prioritized recommendations
- A product optimization spreadsheet
- Recommended titles and descriptions written for wholesale buyers
- Seasonal buying recommendations
- Delivery within 24 to 48 hours, Monday through Friday
If you would like us to make the listing updates for you, the Faire Product Optimizer add-on implements the titles and descriptions you approve each month. For hands-on help across your whole Faire business, see Faire Management.
Sources: the Faire brand portal, reviewed October 12, 2026, and Faire’s Help Center documentation on shop performance analytics, product performance analytics, search and ranking, customer service metrics and selling costs. Metric definitions and quoted wording are Faire’s. Frameworks, derived ratios, illustrative calculations and recommendations are Faire Growth Agency’s own. Illustrative figures are not drawn from any brand’s account. Faire Growth Agency is not affiliated with or endorsed by Faire.