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.

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 "fake" control impression and B is the real 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 between to the two groups of visitors is the campaign 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.

Because analytics are pulled directly from Shopify analytics.

When a site visitor arrives on site, we identify them by Shopify session ID and we randomly select them for either group A or group B (if you're running an AB test). From there, you can either show two different campaigns (1 to group A and 1 to group B) and track both of their behavior (add to carts, orders, size of order, etc.) or you can show nothing (control group) to group A and show the campaign to group B (test group) to again track the same behavior on both groups.The latter is our more popular option and the primary way we prove ROI during the trial.During an initial trial period, we'll gather data across 5-10 campaigns. Some will do well and some won't - that's just AB testing! Our goal is to prove an overall 5-15% conversion lift to show you how you can lean into the tests that did work and continue to use those learnings to drive CRO in the long term.

For ROI, we don't attempt to track revenue across multiple campaigns as there will most definitely be overlap (a site visitor may see a badge on the PLP and then also a message in cart and both would be connected to the same order for example). In order to prove ROI, we calculate revenue lift on one campaign (let's say for example that group B converted 10% higher and had a $2 higher AOV and thus drove $4,000 more revenue). We then divide that revenue lift by the monthly price of Because (dependent on your plan).

You can cross reference all of your data inside your Shopify analytics, but it's difficult to directly compare as you won't have visibility into group A versus B as mentioned above and there's also a lot more advanced rules layered on top of the Because data (for example one rule set could be -> site visitor has >$50 in their cart, is in Klaviyo segment X, is in the United States, and is on Product Page Y). You should be able to get a general comparison though by comparing Because to Shopify.

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).