- The Baymard Institute reports an average cart abandonment rate of 70.22%. The top reason is extra costs that are too high, named by 40% of US shoppers.
- The page lists 9 fixes you can ship before Friday, November 27, 2026. Each one names the Baymard reason it addresses, or is labeled a hypothesis.
- Price comparison, the discount code field and stock uncertainty may play a role in sale week. They are hypotheses, so check them against your own data.
- A checkout checklist with 8 points and tests with the exit-intent layer help you confirm the fixes before the peak.
To reduce cart abandonment on Black Friday, fix the reasons shoppers leave: show the end price early, offer guest checkout, name the delivery date, put trust cues next to the payment step, shorten the form and explain returns. The Baymard Institute reports an average abandonment rate of 70.22%, and the biggest reason is extra costs that are too high.
Black Friday 2026 is Friday, November 27. This guide shows the Baymard reasons in a table, what may differ in sale week, 9 fixes you can ship before the start, a checkout checklist with 8 points and tests you can run with Varify.
Why carts get abandoned
The Baymard Institute reports an average cart abandonment rate of 70.22%. In its list of reasons (US shoppers, retrieved October 20, 2026) the top one is extra costs that are too high, named by 40% of shoppers. Slow delivery follows with 20%, distrust of payment data with 19%, an account requirement with 18%, a long checkout with 17%, unclear returns with 13% and a total cost that is not visible with 12%.
| Reason for abandoning a cart | Share of US shoppers |
|---|---|
| Extra costs too high (shipping, taxes, fees) | 40% |
| Delivery was too slow | 20% |
| Did not trust the site with payment data | 19% |
| Site wanted me to create an account | 18% |
| Checkout was too long or complicated | 17% |
| Returns policy was not satisfactory | 13% |
| Could not see the total order cost upfront | 12% |
Source: Baymard Institute, cart abandonment rate, retrieved October 20, 2026. Average abandonment rate: 70.22%. Shoppers could name more than one reason.
Most of these reasons are things you control. You can show costs earlier, offer guest checkout, name the delivery date and shorten the form. Fix them before Black Friday on Friday, November 27, 2026. For fixes that apply all year, see abandoned cart best practices.
What is different on Black Friday
The Baymard reasons hold all year. Three further effects may play a role in the sale week. All three are hypotheses, so check them against your own data:
- Hypothesis: price comparison. Shoppers open several shops and compare the end price. A cost that appears late loses the comparison.
- Hypothesis: the discount code field. A visible code field makes shoppers leave to search for a code. Apply the offer automatically where you can.
- Hypothesis: stock uncertainty. Shoppers wonder whether an item will still be there. A clear, truthful stock note reduces the guesswork.
Search interest for Black Friday terms rises from October and peaks in calendar weeks 46 and 47 (Varify analysis of Google Trends, 2022–2024). Finish the fixes by the end of week 46 and freeze the checkout in week 47.
9 fixes you can ship before November 27
Each fix names the Baymard reason it addresses. Fixes 3, 4 and 9 have no Baymard reason behind them. They are hypotheses for sale week and need a test.
- Show the end price early. Show shipping and taxes on the product page and in the cart, before the last step. Addresses: extra costs too high (40%) and total cost not visible (12%).
- Offer guest checkout. Make "buy without an account" the visible first option. Addresses: account required (18%).
- Apply the sale code automatically. Let the cart apply the offer, so shoppers do not leave to search for a code. Addresses: no Baymard reason. Hypothesis for sale week.
- Show a stock note from live data. Tell shoppers when an item is reserved in the cart, or that stock is low, only when your inventory system says so. Addresses: no Baymard reason. Hypothesis for sale week.
- Name the delivery date. Show the delivery date or the last order date for Christmas on the cart. Addresses: delivery too slow (20%).
- Put trust cues next to the payment step. Payment methods, security signs and contact. Addresses: distrust of payment data (19%).
- Cut the checkout form. Remove every field that is not needed to deliver and charge. Addresses: checkout too long (17%).
- Explain the returns for sale items. One line with the conditions, one click to the details. Addresses: returns unclear (13%).
- Use an exit-intent layer with a helpful note. When a shopper is about to leave the cart, show shipping, delivery and returns in two lines. Addresses: reminds of the points above. Hypothesis.
Where to start. Begin with the fixes that address the largest shares in the Baymard list: fix 1 (extra costs, 40%), fix 5 (slow delivery, 20%) and fix 6 (distrust of payment data, 19%). They need little build effort, and most of them are a line of text and a small block on the cart page. Fixes 2 and 7 touch the checkout itself, so plan them early and test them carefully. The guide to checkout optimization covers the checkout in more depth. Open the cart on a phone first, because the first screen is the smallest there.
Have the wording of any stock note, countdown or deadline checked legally before the start. For the checklist of the whole shop, see how to prepare your online shop for Black Friday.
Checklist: checkout before Black Friday
Work through this list on a phone and a desktop, with a real test order.
- Total price visible. The cart shows the full total with shipping and taxes.
- Guest checkout works. A test order without an account goes through on a phone and a desktop.
- Sale code applies. The discount shows as planned, alone and with other offers.
- Delivery date shown. The date or the last order date is visible before payment.
- Payment methods tested. Every method completes a test order.
- Trust cues in place. Security signs and contact sit near the payment step.
- Short form. Every field has a reason, and error messages are clear.
- Returns explained. The terms for sale items are one click away.
Test your fixes with Varify
Do not guess which fix works for your shop. Test it:
- Prompt-Based Experimentation (PBX). Describe the fix in plain language, for example a delivery date line on the cart page. In the Visual Editor, "Ask AI" builds the variation, writes the code and sets up the experiment, without a developer. You can refine a variation as often as you like. See the PBX examples and the PBX docs.
- Exit-intent layer (Campaign Booster). A ready-made layer for the moment a shopper is about to leave. See the exit-intent layer guide. The Black Friday Campaign Booster page shows how to set it up without a developer.
- Visual Editor and Code Editor. Fine-tune checkout hints, delivery dates or trust cues in a variation of the cart page by hand.
- Shopify checkout app. For changes inside the Shopify checkout, see the page about the Shopify checkout app and the guide to Shopify checkout A/B testing.
- Purchase as a goal. Use the GA4 integration or the Shopify goal purchase to measure how many carts become orders.
- Preview link and QA mode. Check every variation and every goal before the start. See the quality assurance guide.
Use the test runtime calculator to see whether you have time for a test before the sale. To choose the tests, see what to A/B test before Black Friday.
What not to do
- A pop-up at the first click. A layer that appears before the shopper has seen the offer gets in the way. Trigger it only when the shopper is about to leave the cart.
- False scarcity. Stock labels and countdown timers need real data behind them. Have them checked legally.
- A forced account. Baymard lists the account requirement as a reason for 18% of shoppers. Offer guest checkout.
- Late surprises. Extra costs are the largest reason in the Baymard list (40%). Show them early.
- Last-minute checkout changes. Freeze the checkout in week 47. Test changes in a quiet period.
Test your checkout fixes before the sale.
Exit-intent layer, Prompt-Based Experimentation, preview link and QA mode. Measure purchases per variation.