Your free shipping threshold could be a conversion blocker. Many brands pull a random number out of a hat or add 10-15% to their AOV or average product price — if this sounds like you, this approach could be sabotaging your growth. But how can you know if this is the case, and if it is, then how do you find the right threshold?

These two questions will be answered throughout this guide, so that you can confidently set a threshold that is validated by empirical data. The end result of the right free shipping threshold: More revenue out of every user and an increased bottom line.

Before you get started make sure to grab this free report developed by our team at Enavi. Our intra-site funnel report allows you to focus your CRO efforts on a clear metric on fire. Conversion rate alone is useless, but the intra-site funnel report breaks your CVR down into 4 key behaviors in order to identify where users are dropping off across the funnel. We will reference this throughout the guide as it is key measuring your online store performance.

A few tips while doing your analysis:

1. Use larger sample sizes, which may require longer data ranges.

2. Analyze with seasonality in mind.

3. This is high effort, and only high reward if there is a real data-backed problem.

How to identify if your free shipping threshold is a problem?

The best way to maximize your CRO efforts is to solve data backed problems. The last thing you’ll want to do is go chasing a problem that doesn’t actually exist. Of by the end of this section you can have at least 2 data points pointing to the free shipping threshold as a potential problem then it’s worth it for you to move on to the next step. Throughout this guide we'll be referencing a real case study as an example.

1. Analyze average order value (AOV)

The simplest thing you can do is look at your AOV in a histogram chart. This will help you see the percentage of orders that qualify for free shipping and where the majority of your orders are in terms of AOV. This in itself won’t indicate if you have a problem, but it can help provide the context needed to further analyze.

Data points to gather:

• What percentage of orders qualify for free shipping?

• What AOV range has the bulk of orders?

• How does AOV fluctuate throughout the year?

In our example case 17.49% of orders qualified for free shipping, while the vast majority of orders are between $51-$100.

Depending on your AOV shipping costs can inflate this significantly, so removing shipping along with taxes can give your better data on your AOV that is effecting Free Shipping.

In our example when we remove shipping cost from AOV we can see that it dips down to $101.25 from $110.46. That is nearly a 10% impact on AOV from the $9.21 in shipping cost alone.

We can also see that AOV doesn’t fluctuate that much overtime. There is no more than a 10% swing depending on the season.

2. Analyze checkout abandonment rates

For most stores it is the Checkout to Purchase rate that will be the most effected by the Free Shipping Threshold. The baseline target for this metric is 45-60% of users that Checkout should purchase. If you are seeing that less than 45% of users that checkout complete their purchase, this is a clue that your shipping threshold could be negatively impacting your conversion rate.

Data points to gather:

• Where is your Checkout to Purchase rate?

• How does your Checkout to Purchase rate vary by cart total?

You can take this a step deeper by analyzing the impact of cart size on Checkout to Purchase rate. Start by segmenting users with carts that qualify for free shipping and carts that don’t. What is the difference in Checkout to Purchase between users who qualified and didn’t qualify for free shipping?

In the example below we can see that visitors who qualify for free shipping are purchasing at a 65.06% rate, while users who don’t qualify for free shipping are purchasing at 60.62% rate. This means users that qualify for free shipping are 7.07% more likely to purchase. If we assume that visitors that didn’t qualify for free shipping would have had the same Checkout to Purchase rate as the free shipping group if they also qualified for free shipping, it would be worth $198,825.93 in additional monthly revenue.

Of course, it’s probable that visitors with a larger cart size are more likely to purchase and cart size could be a barometer for intent, so we can’t fully expect the lower cart sizes to increase Checkout to Purchase rate by 7.07%, but even if there was just a 3.5% increase that would still be worth an additional 6 figures in monthly revenue.

Bonus: If you have access to micro checkout events (for example through a third-party app like Elevar) you can take this analysis a step deeper to see whether visitors are dropping off once they get to the shipping step — this can provide even more context.

3. Review customer feedback

Pay attention to customer feedback and complaints regarding shipping costs. If customers are consistently expressing dissatisfaction with shipping charges, this can be an additional data point to indicate that your free shipping threshold could be a problem. You can do this by looking at support tickets and reviews (especially third-party reviews). Make sure you have a good sample size and don’t let one loud voice push you to chase a problem that doesn’t actually exist.

4. You’ve gathered the data, but is it really a problem?

Now you have context as to how many orders qualify for free shipping and how that affects checkout abandonments, but is this a significant enough problem to invest more time and resources into?

• Do less than 65% of your orders qualifying for the current free shipping threshold?

• Are users with cart totals that don’t qualify for free shipping significantly less likely to checkout (>5% less likely)?

• Do you frequently receive an outsized number of complaints on your free shipping threshold?

If you answered Yes to at least 2 of these, you may a problem and should look at testing a different free shipping threshold.

If the above isn’t true for you then testing a lower Free Shipping Threshold is probably not worth addressing at this point. As we all know, there are plenty of problems for to be fixed in e-commerce, so prioritizing these is essential.

How to know what free shipping thresholds to test?

If at this point you’ve identified that your free shipping threshold might be a problem or you’re interested in introducing a free shipping threshold, you’ll need to decide on what new thresholds to test. We’ve heard a lot of conversional wisdom or “best practices” around this, like you should set your free shipping threshold slightly above your average product price. The problem with this approach is that every brand’s product is different, every brand’s catalog is different, and every brand’s customers are different. So there is no “best practice” approach to finding the perfect threshold, but there is a way to do this that is driven by your unique data.

1. Analyze your product pricing

The first thing you’ll need to understand is the feasibility of building a cart that unlocks free shipping. If it is challenging to easily reach the free shipping threshold with your product prices and catalog, that can lead visitors to abandon cart. There a are few different ways to approach this and gather the unique context about your brand in order to make a data informed decision.

Average product price ordered

We want to know what is the average product price ordered after discounts. Many stores may have there average product price at $90, but users are commonly applying a 10-20% discount, so the actually price they are ordering at is $85.

Data points to gather:

• What is your average product price?

• What is your net average product price?

In the example case, there is a fairly large difference between the average product price and the average product price ordered. It’s only important to factor in discounts, because that can influence your order value.

Average net products per order

Certain products naturally lend themselves to customers purchasing multiples or exploring similar items within the same category. Understand your current average products per order and combining that with your average product price can help indicate whether it is likely for most shoppers to reach a given threshold.

Data points to gather:

• What is your average products per order?

In our example the average net products per order was 1.2.

If you sell apparel be weary of users buying multiple sizes of one product or multiples of similar products and then returning most of them and only keeping one product. This is where calculating your net average products per order rate is super important.

2. Analyze your catalog in order to understand natural possibilities to achieve the threshold

Do you have easy or natural cross-sells?

Understanding the composition of your product catalog is crucial to understanding whether you can actually even use your free shipping threshold to improve your average order value. If you have a natural upsell that allows visitors to increase their basket size to reach the free shipping threshold, then that can be a great opportunity to increase AOV. For example if you sell shoes, an easy cross sell could be socks.

In our example there were no natural cross sells and with the current free shipping threshold users would have to buy 2 or 3 products to reach the threshold—as we can see by looking at the average items per order this was not happening.

3. Putting all the data together to identify the threshold to test

Now you have even more context and information. So what do we do with all of this data to decide what variant thresholds to test?

The following scenarios should account for the majority of cases and help you decide what to do next:

In our example we can see that the average product price order was $85 after discounts, users were hardly ordering multiple products, and the vast majority of orders were between $75-$100. With this in mind we decided to test a threshold of $75, which is half of the current $150 free shipping threshold and about 13% less than the average product price of $85.

How to run the test using Intelligems?

You can use Intelligems’ shipping threshold test type to easily split test multiple different shipping thresholds. Intelligems will handle showing the correct shipping rates at checkout, and can also dynamically render your free shipping progress bar or callouts on free shipping across the site so they are accurate per test group. Want to learn more? Grab some time with the Intelligems team here.

You should plan to run the test at least one business cycle, or ideally two. If your average time to purchase is three weeks long you should run the test for at least 3 weeks. When it comes to sample size you will most likely see a lift smaller than 10%, so you need at least 5,000 orders across both the control and the variant to have enough confidence in the data.

How to analyze the test results?

After the test has run to significance we can now validate the results. It’s incredibly important to make sure that you have a large enough sample size size to have confidence in the data. If you don’t then you should let your test run another business cycle.

1. Primary KPIs

Our primary KPI for this test is Revenue per Site Visitor.

In our example, conversion rate was up 6% in the test group, and Revenue per visitor was up 2.03% (both with 100% confidence).

2. Intra-site funnel impact

It’s important to measure the impact of your free shipping threshold across the intra-site funnel. It’s most likely going to have it’s greatest impact on Checkout → Purchase, but can affect other aspects of the funnel. With this context you can further validate the impact.

As we can see in our example users across the funnel were positively impacted all leading to that 6% increase we see in conversion rate.

3. Impact on AOV and items per order

Does your free shipping threshold make an impact on AOV? By looking at average units per order and average order value we can understand this.

In our example we can see that there is barely a difference in average units per order—it’s pretty clear that the current free shipping threshold does nothing to influence that!

When we drill further down into AOV, we can see that the majority of the AOV difference isn’t from product revenue, but the additional $4.14 that users are paying for shipping on the control.

4. Shipping breakdown

Intelligems will surface some incredible data around shipping that would be time consuming to query in GA4. All of these data points will help you gather context to help support the primary KPI (RPV) and help to make your final business decision.

Percent of Orders with Free Shipping

Understanding how many orders qualified for free shipping can be super insightful. This can help inform expectations on what shipping costs you will have to cover to help make your business decision and if maybe it’s worthwhile to explore completely remove your free shipping threshold if 75%+ of orders are already qualifying for free shipping.

Shipping Revenue per Order

This can give you more context as to what users are paying in terms of shipping costs.

In our example there is a massive difference in the number of orders qualifying for free shipping 60% vs 17.4% when comparing the variant threshold to the original.

Making the business decision

After doing all of these analysis and having empirical today to prove out the potential problem of your current free shipping threshold and validate the solution of your new free shipping threshold, you will need to make the business decision of where to place your free shipping threshold. Not only should you look at conversion rate, average revenue per user and how that impacts your top line revenue, but what this means to your bottom line. With this decision there are so many layers to think about, below are just a few:

• What is your shipping cost as a result of the additional orders that qualified for free shipping?

• How many new customers were generated from this and what is the potential LTV of this cohort?

• What is the bottom line impact and can we afford these new orders at this AOV?

In our example there was an additional +2,490 orders per month which was roughly +658 new customers per month with a revenue total of +$92,915 per month. There was also an additional +3,590 orders that the brand had to cover free shipping on which resulted in an additional +$28,720 in shipping cost. All of these played into the final decision of whether to implement the new shipping threshold or not.

Conclusion

Setting the right free shipping threshold can significantly impact your e-commerce revenue. Instead of relying on arbitrary figures or generic industry practices, it's crucial to leverage empirical data to validate your threshold decisions. By following the steps outlined in this guide, you can confidently identify potential issues with your current threshold and determine the optimal threshold for your business.

Author: Anthony Morgan, Founder and CEO of Enavi. I went to school for music and stumbled my way into ecommerce… I’ve had a chance to do almost everything both in-house and as an agency from packing shipments to Facebook ads, but Customer-First CRO is what stuck. Why? Because we squeeze more revenue out of every user by solving data-backed problems with customer research. Better efficiency = more profitable.