Shopify Affiliate Program Statistics: What 1,178 Real Programs Tell Us in 2026

Most affiliate marketing statistics come from surveys, affiliate networks, or numbers that have been copied from one blog to another for years.

We wanted something more useful.

So we looked at actual affiliate activity across Shopify stores using Affilitrak: real programs, real affiliates, real referral visits, and real orders.

The dataset covers:

• 1,178 affiliate programs
• 2,582 Shopify stores
• 8,695 affiliates
• 12,532 referred orders

The biggest finding is not about commission rates or cookie windows.

It is that most recruited affiliates never make a sale.

Only about 16% of recruited affiliates ever generate one.

That does not mean affiliate programs do not work.

It means affiliate growth is a numbers-and-selection problem.

Merchants need enough relevant affiliates entering the program to have a real chance of finding the people who can sell.

Then they need to help those people get active, identify the sellers, and give the winners more support.

The goal is not simply to get the largest possible roster.

And it is not to keep the roster artificially small.

It is to:

Recruit relevant affiliates → activate them → identify sellers → scale the winners.

Shopify affiliate program statistics at a glance

Here are the numbers that stood out most from the dataset:

• 61.3% of affiliate programs have never generated a referred sale.
• Only about 16% of recruited affiliates ever make a sale.
• Among stores with at least two affiliates who have generated sales, the best-performing affiliate accounts for a typical 54.5% of referred orders.
• 9.5 minutes is the typical time between an affiliate referral visit and the resulting order, based on 47 stores with at least five attributable orders.
• 93.1% of affiliate programs on stores with real orders have never changed the default 30-day attribution window.
• 10% is the most common commission rate, applying to 4,243 of 8,695 affiliates.
• The typical store with at least five commissioned orders had a realized commission rate of 10.0%.
• Higher commission rates were not associated with better affiliate performance across the programs we analyzed.
• Instagram is the most consistently identifiable affiliate traffic source, appearing at 30 of the 36 stores in our 200+ visit sample.
• Affiliate traffic is overwhelmingly mobile: 80% on average, with the typical store seeing 87% mobile traffic.
• Affiliates send 54.2% of referral visits to the homepage, compared with 37.2% to product pages.

The bigger story isn't any one of these numbers.

It's what they tell us about how affiliate programs actually behave.

1. Most affiliate sales happen within minutes

The typical store's affiliate sale happened 9.5 minutes after the referral visit.

That number comes from 47 stores with at least five attributable orders each.

The middle half of those stores fell between 5.7 and 32.3 minutes.

Pooling every order we could reconstruct, rather than weighting each store equally, shows the same two-mode shape from a different angle:

When affiliate orders happen after a referral

Time after referral	Share of orders
Within 5 minutes	51.4%
Within 1 hour	79.3%
Within 24 hours	84.6%
Within 7 days	89.4%
Within 30 days	96.8%

This does not look like one smooth pattern where orders gradually become less likely over time.

Instead, two different customer journeys appear in the measurable data.

Some customers buy close to the original referral.

Others return much later.

The pooled timing data reaches its 75th percentile at 22.7 minutes, then jumps to 7.9 days at the 90th percentile.

That gap suggests there is not much of a smooth middle between "buy now" and "come back later."

What does this mean for merchants?

It makes the first few days of an affiliate's activity more informative than you might think.

If an affiliate is sending genuinely interested buyers, you will often see some evidence relatively quickly.

That doesn't mean you should remove an affiliate because they haven't generated a sale in 48 hours.

Affiliate performance depends on audience size, content schedule, traffic source, and what the affiliate is actually promoting.

But it does suggest that waiting months for an affiliate to eventually start working probably isn't a great strategy.

There is usually an early signal.

The better question is what you do with that signal.

If an affiliate joins your program but isn't getting any clicks, they may need better promotional material.

If they're getting clicks but no sales, they may have an audience-product mismatch.

If they're generating sales, you have a completely different problem: figuring out how to help that affiliate do more.

An important limitation

We could only calculate the exact time between a referral and an order when we could match the customer's checkout activity to the resulting order.

That was possible for 2,712 of 12,532 eligible referred orders, or 21.6%.

The other orders weren't necessarily less legitimate.

We simply couldn't reliably reconstruct the exact timing for them.

There are also customer journeys that this type of measurement can miss.

For example, someone might click an affiliate link on their phone and complete the purchase later on their laptop.

Those longer journeys are harder to reconstruct, which means the orders we can measure are likely to skew toward the faster ones. The real median is probably somewhat higher than 9.5 minutes, and the true split between the two modes is likely less lopsided than the measurable data suggests.

The finding that survives that caveat is the shape rather than the exact mix:

Two distinct purchase behaviours are visible, and a substantial share of the purchases we can reconstruct happen very close to the original referral.

2. Most merchants never change their 30-day attribution window

If you expected merchants to spend a lot of time adjusting their affiliate cookie duration, the data says otherwise.

93.1% of programs on stores with at least one paid Shopify order have never changed the default 30-day attribution window.

That is 729 of 783 programs. The remaining 54 had changed it.

Looking at all 1,178 programs with at least one affiliate, 94.3% had left the default unchanged.

Of the 54 programs that did change the window, 12 moved to 60 days and 9 to 90 days, making those the two most common alternatives.

The 30-day default also looks fairly sensible when compared with the measurable order data.

Moving from a seven-day window to a 30-day window captures roughly 7.4 additional percentage points of orders.

There are still some purchases after 30 days, but they represent only 3.2% of the attributable orders in this dataset.

So the lesson isn't that attribution windows don't matter.

It's that 30 days already captures the overwhelming majority of affiliate purchases we observed.

If you're choosing an attribution window for your own program, the right answer will depend on your sales cycle, product type, and customers.

But there's no obvious reason to assume that a much longer window is automatically better.

3. 10% is the most common affiliate commission rate

So what are Shopify merchants actually paying their affiliates?

For most merchants, the answer is 10%.

The most common affiliate commission rates

Commission rate	Affiliates
10%	4,243
15%	1,171
5%	585
30%	382
20%	366
25%	281

There's an important detail behind these numbers.

The commission rate isn't always individually assigned to each affiliate.

In fact, 59% of affiliates are paid according to a commission tier.

In other words, a merchant might have different groups of affiliates with different commission levels, rather than manually setting a rate for every person.

Looking only at stores with at least five commissioned orders, the typical realized commission rate was also 10.0%.

So 10% is not just a default that merchants leave untouched.

It is also the rate merchants commonly use in practice.

599 of the 1,178 programs, just over half, have configured at least one rate that differs from the software default, and 228 programs run more than one distinct commission rate at the same time.

The takeaway is pretty simple:

10% is a reasonable description of the current Shopify affiliate norm, but it isn't a universal rule.

Many merchants use different rates for different affiliates or tiers. If you are setting yours now, how to choose your affiliate commission rate turns this data into a decision.

4. Higher commissions don't automatically produce better affiliates

This is probably one of the most interesting findings in the dataset.

You might assume that a merchant offering a 20% commission would naturally get better affiliate performance than one offering 5%.

But we couldn't find evidence that this is generally true.

We compared commission rates with how much affiliate sales each program was actually generating, across the 471 programs with at least three affiliates.

The result was essentially a flat line: Spearman ρ = −0.032, with a 95% confidence interval spanning zero.

Programs paying higher commissions did not consistently generate more sales per affiliate than programs paying lower commissions.

We then repeated the analysis on a narrower group: the 283 programs that have configured at least one non-default commission rate and also have at least three affiliates. These are the programs where a merchant has actually made a deliberate decision about what to pay, rather than accepting whatever the software installed with.

That result was also close to zero:

• Spearman ρ = −0.070
• 95% confidence interval: −0.185 to 0.047
• Programs analyzed: 283

Neither result shows a meaningful relationship.

This does not mean higher commissions hurt performance.

It also does not mean raising a commission will never help a specific merchant recruit or retain a valuable affiliate.

It means something narrower and more useful:

Commission rate by itself did not explain why some affiliate programs performed better than others in this dataset.

If you are paying 10% and getting no sales, moving to 20% may not fix the actual problem.

You may have:

• a recruitment problem
• an activation problem
• affiliates with the wrong audience
• affiliates who do not know what to promote
• referral visits landing on the wrong page
• a store that does not convert the traffic being sent

Commission affects the economics of each sale.

But it is not a substitute for finding relevant affiliates and helping them become active sellers.

5. Most affiliate programs never generate a sale

This is one of the numbers that changes how you should think about affiliate programs.

61.3% of programs with at least one affiliate have never generated a referred sale.

That's 722 out of 1,178 programs.

Only 456 programs in the dataset had generated at least one referred sale.

And even among those 456, the results were heavily concentrated.

285 had only one affiliate who had ever generated a sale.

That does not mean merchants should recruit fewer affiliates.

It means that signing affiliates up is only the beginning.

The real journey looks more like:

signed up → activated → first referral visit → first sale → repeat sales

Most programs appear to lose affiliates somewhere along that path.

At the same time, only around 16% of recruited affiliates ever made a sale.

That means recruitment is part of the discovery process.

You need enough relevant people entering the program to have a realistic chance of finding the smaller group who can sell.

The useful question is not only: How do I recruit more affiliates?

It is: How do I recruit enough relevant affiliates, help them get active, and identify the people who can actually sell?

6. One affiliate usually does most of the selling

The concentration becomes even clearer when we look at the best-performing affiliate in each store.

Among stores with at least two affiliates who had generated sales, the top affiliate accounted for a median 54.5% of referred orders.

Top affiliate's share of orders, by program size

Selling affiliates	Top affiliate's share
2+	54.5%
3+	44.6%
5+	35.1%
10+	26.4%

Here's why that last comparison matters.

For each store, we compared the top affiliate's actual share against the share they would have received if every selling affiliate in that store had produced an equal number of orders. We then took the median of those per-store ratios.

That is not the same as dividing the two medians in the table. A store in the "2+" group might have 2 selling affiliates or 20, and its even split would be 50% in the first case and 5% in the second. The multiplier reflects each store's own roster, which is why the "2+" row shows 3.5× rather than the 1.09× you would get by dividing 54.5% by a flat 50%.

Among stores with at least 10 selling affiliates, the top affiliate's share is more than eight times an even share.

Notice what happens as programs grow: the top affiliate's raw percentage falls, from 54.5% down to 26.4%, but their advantage over an even split widens, from 3.5× to 8.4×.

Bigger programs spread orders across more affiliates, and the leader still pulls disproportionately far ahead.

The same pattern appears when we look at revenue rather than order count. The top affiliate's median share of referred revenue runs 59.7% in stores with at least 2 selling affiliates, 49.5% at 3 or more, 38.7% at 5 or more, and 23.3% at 10 or more.

So this isn't simply one affiliate generating lots of cheap orders.

The concentration exists in the money being generated too.

What should merchants do with this?

The conclusion is not "find one good affiliate and stop recruiting."

You cannot discover a top affiliate if they never enter your program.

Instead, affiliate growth needs two parts:

1. Build a sufficiently large pool of relevant potential sellers.
2. Give more attention to the affiliates who prove they can produce results.

Once a strong affiliate emerges, help them promote more products, create more content, send visitors to better landing pages, or earn more through performance-based incentives.

Recruitment creates the pool. Measurement and activation help you find and scale the winners.

7. Affiliates that are going to sell usually show signs early

We also looked at how long it takes affiliates to generate their first sale.

There are two different ways to measure this, and they answer different questions. Both are below, and they are not interchangeable.

The first asks: out of everyone recruited, how many have sold by a given point?

This analysis uses 8,687 affiliates, a slightly different population from the 8,695 affiliates in the commission-rate analysis.

How quickly affiliates make their first sale

Time since joining	Affiliates who had made a first sale
1 day	4.8%
7 days	9.1%
30 days	13.1%
90 days	15.7%

By 90 days, 15.7% had made a first sale.

The eventual seller rate also stayed close to 16% when we looked at affiliates with different amounts of time in the program:

• all affiliates: 15.8%
• affiliates with at least 30 days: 16.6%
• affiliates with at least 90 days: 16.8%
• affiliates with at least 180 days: 16.4%

That suggests the low selling rate is not simply caused by counting brand-new affiliates who have not had enough time.

Most affiliates really do not make a sale.

The second question is different: among the affiliates who eventually do sell, how quickly did they get there?

When eventual sellers make their first sale

Time since joining	Share of eventual sellers
Within 1 day	28.9%
Within 7 days	46.5%
Within 30 days	69.8%
Within 90 days	89.8%

These two charts use different denominators, and it is worth being explicit about that because the numbers look like they should reconcile and they don't.

The first is measured against affiliates at risk at each point. The second is measured only against affiliates who eventually sold. Dividing 9.1% by the roughly 16% eventual seller rate does not reproduce 46.5%, and it shouldn't: those are not the same population.

Read together, they say something useful. Almost half of the affiliates who will ever sell for you do it in their first week, but that group is a small slice of everyone you recruit.

That makes early activity worth paying attention to.

If someone joins your program and does nothing for several weeks, waiting longer may not be the best response.

Give them something specific to promote.

Help them choose a product.

Give them content or links they can use.

Show them what is already selling.

Ask whether their audience is a fit for your store.

The program needs an activation process, not just a signup form.

8. Instagram is the biggest affiliate traffic source

We also looked at where affiliate referral traffic actually comes from.

The analysis covers stores with at least 100 referral visits during the measurement period.

The average share of referral visits looked like this:

Where affiliate referral traffic comes from

Traffic source	Share of referral visits
Instagram	28.8%
No identifiable referrer	25.5%
YouTube	15%
Facebook	14%
Other websites	11%
TikTok	2%
X	2%

Instagram also appears more consistently than any other source. Narrowing to the 36 stores with at least 200 referral visits, where source data is most reliable, Instagram showed up at 30 of them.

That consistency is what makes it interesting, not just the average share.

YouTube is the clearest contrast. Its average share is similar to Instagram's, but it appears at only 14 of those same 36 stores, and its median share across stores is zero. In other words, a minority of stores are effectively YouTube programs while most get none at all. An average alone would hide that.

A surprising amount of affiliate traffic has no identifiable source

The second-largest category is traffic with no identifiable referrer.

It accounts for around 25.5% of affiliate referral visits on average.

That can include things like:

• DMs
• Group chats
• Link-in-bio traffic
• Direct link sharing
• Private communities
• Other situations where the original source isn't passed through

This is easy to underestimate because analytics software can't always tell you where the person originally came from.

But it isn't a tiny edge case.

For many affiliate programs, a meaningful amount of traffic is happening in places where traditional referral analytics can't identify the original source.

TikTok isn't dominating this dataset

TikTok gets a huge amount of attention in affiliate marketing.

But in this dataset, it represented only around 2% of referral visits on average.

That doesn't mean TikTok doesn't work.

It means the average across these Shopify programs doesn't support the idea that TikTok is the dominant affiliate channel.

The right channel will depend heavily on the affiliate, the product, and the audience.

9. Affiliate traffic is overwhelmingly mobile

This one is hard to miss.

Affiliate referral traffic is overwhelmingly mobile.

Here's the data:

Affiliate referral traffic is mostly mobile

Device	Share of referral traffic
Mobile	80%
Desktop	16.4%

The typical store was even more mobile-heavy: 87.0% mobile and 8.5% desktop, with the remainder tablet or unidentified.

That has a very practical implication.

Your affiliate program might be managed from a desktop computer.

Your affiliates might create their content on laptops.

Your merchant dashboard might be desktop-first.

But the people clicking those affiliate links are overwhelmingly using phones.

So your affiliate landing experience is a mobile experience.

That means things like:

• page speed
• mobile navigation
• product-page clarity
• checkout friction
• how quickly the offer is visible
• whether the affiliate's link takes someone to the right place

can all affect affiliate performance.

Mobile optimization isn't just a general Shopify best practice here.

It's part of affiliate optimization.

10. Affiliates usually send people to the homepage

Here's another result we didn't expect.

You might expect affiliates to send traffic directly to the product they're recommending.

Instead, the majority of affiliate referral visits land on the store's homepage.

Here's the data across 63 stores:

Where affiliates send their visitors

Landing page	Share of referral visits
Home page	54.2%
Product page	37.2%
Collection page	6%
Content / blog	0.8%

More than half of affiliate traffic goes to the homepage.

That isn't necessarily wrong.

If an affiliate is introducing someone to an entire brand, the homepage can make sense.

But if an affiliate is recommending a specific product, sending someone to a generic homepage adds another step between the recommendation and the product they were interested in buying.

There is also an interesting signal in the checkout data.

Collection pages started checkout for 8.2% of visits, compared with 5.0% for homepages and 4.5% for product pages in this dataset.

The sample isn't large enough to turn those numbers into universal conversion benchmarks.

But the ordering is interesting.

It suggests that merchants should test where affiliate traffic lands rather than assuming the homepage is always the best destination.

For example, an affiliate recommending "the best skincare products for dry skin" may be better served by a relevant skincare collection than by the store's homepage.

What the data says about running an affiliate program

The individual statistics are interesting.

The combination is more useful.

Three patterns show up repeatedly.

1. Most affiliates won't sell, which is why volume matters

Only about 16% of recruited affiliates ever generate a sale.

That is not an argument for recruiting fewer people. It is the opposite. A one-in-six hit rate means a merchant with a handful of affiliates has probably not recruited enough of them to have found a seller yet, no matter how carefully they picked.

Recruitment is how you take draws. The question isn't whether to recruit more, it's whether the people you're recruiting are relevant enough for the hit rate to hold.

2. Affiliates who work tend to show signs early

Among the affiliates who eventually sell, 46.5% make their first sale within seven days.

Combined with the short time between referral and purchase, that suggests the early stage of an affiliate relationship is unusually informative.

The first week shouldn't be passive.

It's an opportunity for merchants to help affiliates get activated.

3. Results concentrate, so measurement matters as much as recruitment

Most stores don't have a balanced affiliate roster. One or two affiliates often account for a large share of the results.

That doesn't mean the goal is a small roster. You cannot promote a top affiliate you never recruited.

It means the job has two halves that both have to work: bring in enough relevant affiliates to find the sellers, then measure well enough to spot them and give them more support once they appear.

What about commission rates?

The data gives merchants a useful reality check here too.

10% is clearly common.

But paying more doesn't automatically produce more sales.

That doesn't mean merchants should always pay 10%.

It means you shouldn't assume that commission rate is the first lever to pull when your program isn't working.

If you have 50 affiliates and zero sales, increasing the commission from 10% to 20% doesn't fix the fact that those affiliates aren't sending buyers.

The bigger questions are:

• Are you recruiting the right people?
• Are they actually promoting you?
• Do they know what to promote?
• Are you helping them get their first sale?
• Does the traffic they're sending match your product?
• Does your store convert the traffic they're sending?

Those questions are much closer to the actual bottleneck.

Methodology

This analysis covers 2,582 merchant stores after removing app-owner accounts and 140 Shopify Partner development stores.

The development stores were excluded because they contained 5,413 test-pattern referred orders, which would materially distort the results.

Of the remaining stores:

• 1,337 have at least one paid Shopify order
• 1,178 have at least one affiliate
• 456 have generated at least one referred sale

Each store in this dataset runs a single affiliate program, so store counts and program counts are interchangeable within the 1,178.

The sub-populations used in this analysis

Several findings use narrower groups than the full 1,178. For clarity:

• 783 programs have at least one affiliate and sit on a store with at least one paid Shopify order. This is the base for the attribution-window finding.
• 599 programs have configured at least one commission rate that differs from the software default.
• 471 programs have at least three affiliates. This is the base for the main commission-versus-performance correlation.
• 283 programs are in both of the two groups above: at least one non-default rate configured, and at least three affiliates. This is the base for the narrower correlation.
• 228 programs run more than one distinct commission rate at the same time.

How we calculated "typical store" numbers

When we describe a "typical store," we give every store equal weight.

That means a small store with 10 orders counts just as much as a large store with 10,000 orders.

We do this because otherwise a handful of very large merchants can dominate the results.

For example, one large merchant accounts for 51% of referral visits and 19% of orders in the combined dataset.

Where we use all orders or visits together, we use those numbers to describe the overall dataset rather than what a typical merchant experiences.

Referral visits

Referral visits are deduplicated by store, affiliate code, visitor, and 24-hour period.

They should therefore be thought of as visitor-days rather than raw clicks.

Click-to-order timing

The click-to-order analysis requires us to match a checkout-start event to the resulting order.

That was possible for 2,712 of 12,532 eligible referred orders, or 21.6%.

Because of this limitation, we don't publish a conventional affiliate conversion rate from this dataset.

Doing so would understate the true rate.

Instead, we use the timing data and checkout-start data where the underlying information is reliable enough to support a useful comparison.

First-sale timing

The two first-sale charts in section 7 use different denominators and should not be reconciled against each other.

The cumulative curve is measured against affiliates who had been in the program long enough to reach each point. The eventual-seller curve is measured only against affiliates who ever made a sale.

Comparing commission rates and performance

We tested whether programs paying higher commissions tended to generate more orders per affiliate.

The answer was essentially no meaningful relationship.

We used Spearman rank correlation because affiliate performance varies enormously between programs. Some affiliates generate hundreds of orders while others generate none, and a rank-based test is not distorted by those extremes the way an ordinary correlation would be.

The full result: Spearman ρ = −0.032, 95% CI −0.122 to 0.059, across 471 programs with at least three affiliates.

On the narrower rate-aware subset: Spearman ρ = −0.070, 95% CI −0.185 to 0.047, across 283 programs.

Both confidence intervals span zero, so neither shows evidence of a relationship in either direction.

The important takeaway for everyone else is simply:

"Higher commission rates did not consistently translate into better affiliate performance in this dataset."

What we learned from 1,178 Shopify affiliate programs

The data doesn't suggest that merchants should simply recruit as many affiliates as possible.

It suggests something more useful.

Affiliate programs need enough relevant affiliates entering the funnel to discover the small percentage who can actually sell.

Only about 16% of recruited affiliates in this dataset ever generated a sale.

That means a merchant who recruits three affiliates should expect fewer than one seller on average. Run the probability rather than the average and it gets starker: if each recruit were an independent one-in-six chance, a three-affiliate program would have roughly a 59% chance of having no sellers at all. In practice affiliates recruited by the same merchant from the same channel are not fully independent, so the real figure is likely higher still. Either way, the point holds: three draws is not enough draws.

That reframes the 61.3% of programs that have never generated a referred sale. A large share of them are not failing programs. They are programs that have not yet recruited enough people for the odds to work.

Recruiting more relevant affiliates increases the number of opportunities to find those sellers.

But recruitment is only the beginning.

Once sellers emerge, their contribution is often heavily concentrated.

Among stores with at least two selling affiliates, the top affiliate generated a median 54.5% of referred orders.

As programs get larger, the top affiliate's percentage falls, but their share remains disproportionately large compared with an even split.

That creates a four-part job for merchants:

Recruit enough relevant affiliates. Activate them. Identify the ones who sell. Scale the winners.

That's a very different objective from simply maximizing affiliate count.

And it's also different from keeping a small roster of hand-picked affiliates.

The data gives merchants some useful benchmarks along the way:

• 30 days is the dominant attribution window
• 10% is the most commonly configured commission rate, and also the typical realized rate
• Around 16% of recruited affiliates ever generate a sale
• Instagram is the most consistently identifiable traffic source
• The homepage receives more than half of affiliate referral visits
• 80% of affiliate referral traffic is mobile on a store-weighted basis
• 61.3% of programs in the dataset had never generated a referred sale
• And one affiliate can account for a surprisingly large share of a program's results

None of these numbers tells you exactly how to run your program.

But together, they give you a much better starting point.

The question to ask yourself isn't simply "How many affiliates do I have?"

It's "How many relevant affiliates am I bringing into the program, how many are becoming active, which ones are actually selling, and how do I help those affiliates sell more?"

The data suggests that the strongest affiliate programs need a system for doing both: creating a large enough pool to discover sellers, then concentrating attention on the affiliates who prove they can produce results.

That's why we're building the Affilitrak marketplace, to help merchants reach affiliates in their product niche instead of waiting for the right ones to find them.

If you're running an affiliate program and want to see how your own numbers compare, Affilitrak is free to install and gives you the tracking needed to see exactly which affiliates, links, and referrals are producing sales.

Start free with Affilitrak.

How to cite this research

We refresh these numbers as more Shopify stores run affiliate programs through Affilitrak.

Last updated: August 17, 2026

To cite this analysis:

Affilitrak (2026). Shopify Affiliate Program Statistics: What 1,178 Real Programs Tell Us in 2026. https://affilitrak.com/blog/shopify-affiliate-program-statistics