Our script is 63kb.

First, since we load after the initial page load, we shouldn’t impact your site's accessibility score. With that said, you are more than welcome to check on a tool like this - with and without Because.

Second, our no-code editor gives you full control over the design of your campaigns, which essentially gives you ultimate control over ensuring that your campaigns are compliant- in line with your current site.

By default, we have a functionality called "inherit styling" which inherits styling from your site (sizes, fonts, colors, padding, etc.). If your site is already compliant, and you choose to keep your campaigns selected to "inherit styling" (rather than overriding), and if you keep your colors consistent with WCAG 2.1 guidelines, then all of your Because campaigns should be compliant.

We have an AB testing functionality that’s typically used for new campaigns to measure the impact on your site. You can either test one campaign against another to see which copy, design, colors, etc. perform better, or you can test a campaign against a blank control group which will simulate a “with” and “without” Because experience to track data across both the control and test groups. All AB tests will report out in real time on impressions (views), add to carts, orders, conversion rate lift, add to cart rate lift, AOV, and clicks (if applicable).Let's say for example that site visitor A and site visitor B both go to your cart on July 12th. Both Visitor A and Visitor B have $50 in their cart and both are in the same Klaviyo segment (aka they have the same rules). Visitor A sees no Because campaign (normal cart) and Visitor B sees the Because campaign (cart + Because).

We track 1 impression for each visitor on that first view (A is the control impression and B is the test impression). 1 visitor could have multiple impressions if they visit the cart and qualify for the campaign multiple times.

After their first impression, we then track everything each visitor does next. Maybe Visitor A does not purchase, but  Visitor B does. Maybe Visitor B had a cart size of $70 (the AOV on the campaign). Visitor B would have 1 impression, $70 AOV, and $70 in revenue. Visitor A would have 1 impression and no revenue. This becomes more interesting when you look at a higher volume of visitors and see the commonality in their behavior (impact on conversion rates and AOV) because the ONLY difference in site experience between the two groups of visitors is the Because campaign that one group saw and the other did not - since the test was run in the exact same time period (promotional or not), and visitors had to pass the exact same rules (be on the same page, have the same amount in cart, be in the same Klaviyo segment, etc.)

The final point to note is that there is no add to cart rate for cart campaigns because the impression happens after add to cart, but you'll see add to cart rate on all campaigns that are not cart campaigns.

Currently, we track whether a customer who saw a campaign converted during the same Shopify session, but we do not yet offer the ability to track specific sales back to individual products displayed in a campaign. For example, if a campaign promotes a specific product like a pair of shoes, we can track if the customer converted during that session, but we do not track whether they purchased that exact pair of shoes.

However, we're excited to announce that this feature is coming in 2025, allowing for more granular tracking of campaign-specific product sales.

It’s important to note that our attribution model is not tied directly to the specific product shown in a campaign. When a customer views a campaign on a product page (e.g., a review for a specific product), we track whether that customer made a purchase during the same session—not necessarily whether they purchased the product shown in the campaign.

For example, if a customer sees a review for Product A but decides to purchase Product B during the same session, we still attribute that conversion to the campaign because it influenced their overall shopping journey. The revenue attributed reflects all purchases made during that session, even if the purchased items differ from the product highlighted in the campaign.

This helps us provide a broader view of how your campaigns are influencing conversions across the entire shopping experience, even when customers choose different products.

Here’s a deck that explains how to use Image Testing visually! The below will still be helpful, though, so still make sure to read!

Where? We can support image testing on your home page hero image, product page images (sometimes carousels depending on the site), and collection page banner image (if applicable). We cannot support image testing on individual product image tiles on the PLP.

How? First, prepare your images. You can either choose to upload a loading state image into your theme to replace the existing image, or you can use the current image as the control. If you’d like to avoid a flicker from one image to the next on the 2nd image (see first image quickly and then within 0.2 seconds, load the second image), you’ll need to upload a loading state image into your theme. This is the more popular option, but you can do either. Next, prepare your two images you want to test. Most merchants text a product image versus a lifestyle image or two different designed images with different badges/messaging to see what resonates better.

How? Second, add the Because image testing script into your theme. Ask one of our team members and we’ll give you the snippet of code to add into your site! This is the only snippet of code Because requires - no other content requires code (badges, banners, etc.)

How? Third, create an image testing campaign inside Because. Ask us to turn on Image Testing permission if you don’t already have permission. Once you have the permission, you’ll create two separate campaigns (one for each image) if you plan to go the path of “no flicker.” If you don’t mind the flicker, you’ll leave your current site as is with the current image (the control) and you’ll upload the new image as the second image (the test). Once inside the editor, you’ll have the option to upload your image. Only one image per campaign!

How? Create an AB test. Go into your AB testing dashboard and click “Create AB Test.” From there, you’ll select a “campaign vs. campaign” test if you’re uploading two images (no flicker) or a “campaign v. control” test if you’re only uploading one image (will have a flicker). We recommend setting your AB test to 50-50 % split to get the best initial look at performance (add to cart rate, conversion rate, AOV, and revenue lift). Keep in mind if you set up a split different from 5-50%, revenue lift is unreliable because one had a higher chance of converting than the other based on the % of traffic.

REMINDER: All AB tests have to follow the exact same rules! So, you can’t AB test one image with one set of rules against a different image with a different set of rules. This same limitation applies across all Because campaigns (even outside of images) to ensure the integrity of your data (make sure you’re comparing apples to apples).