At the heart of every A/B test lies a hypothesis, guiding the experiment and providing a clear framework for analysis. In this article, we'll explore examples tailored for e-commerce websites of hypotheses using the if-then-because format. We recommend that you take insights from your business and customer data to formulate similar hypotheses, then use Compose to create and link hypotheses to your experiments.
The if-then-because format
If [change or variation is implemented]
Then [expected outcome will occur]
Because [rationale or reasoning behind the expected outcome].
This format helps articulate the specific change being tested, the anticipated impact on user behavior or metrics, and the underlying rationale driving the hypothesis. The examples below show the original insight followed by the if-then-because hypothesis.
eCommerce hypothesis examples
- We offer free shipping on orders over $50 but AOV is significantly lower. If we add messaging regarding the free shipping threshold to the cart, Then we anticipate a rise in the average order value and an uptick in the overall conversion rate, Because more customers will see the free shipping incentive and encourage customers to add more items to their cart to qualify for the offer.
- Our add-to-cart conversion rate from the PDP is low. If we switch from dropdowns to variant selectors, then customers can engage with the PDP and add to cart more frequently because they can see the available options on page load and complete the process in fewer clicks.
- Our email marketing campaigns are performing great, but we aren't getting many new sign-ups on the website. If we make it an automatic pop-up, then there will be an increase in sign-ups because we will ensure that users see it.
- Bounce rate on the homepage is high for returning customers. If we implement a personalized recommendation engine on the homepage based on previous browsing and purchase history, Then we predict an improvement in the click-through rate on recommended products and an enhancement in overall user satisfaction, Because personalized recommendations enhance the relevance of product offerings, leading to a more tailored shopping experience and increased engagement.
- We frequently have products with high inventory that need to be moved. If we introduce a limited-time discount code prominently displayed at checkout, Then we hypothesize an increase in the conversion rate at the checkout stage and a reduction in cart abandonment rates for these products, Because scarcity and urgency tactics have been shown to incentivize impulse purchases and create a sense of immediacy among shoppers.