12 A/B Testing Hypotheses to Run on Your Forms Today

Online forms are often overlooked when it comes to experimentation and optimization. This is a mistake. Forms are where the customer makes the final decision to commit; they hand over their precious personal or financial details. They’ve signed up for your product demo and are most likely in buyer mode. If you’ve overlooked something that is causing them to abandon it could be very costly. This makes forms a high value area for A/B testing and experimentation.

To truly understand what’s happening, you’ll need to delve deeper with form analytics software. Especially if you have a relatively low traffic site or app. Tools like Zuko Analytics provide granular, field-level data, helping you pinpoint exactly where users are encountering friction in your form. Zuko can easily and seamlessly integrate with tools like ABsmartly as well, to add an extra level of insights to experimentation. 

Where to Begin Testing

If you already use form analytics, start by identifying high-impact areas that are causing the most drop-offs. Testing solutions for these issues should be your first priority. However, if you don’t have form analytics data we’ve provided some inspiration below based on hypotheses & tests that have found success in the past:

1. Inline Validation

Hypothesis: Adding inline validation will reduce errors and increase conversions.

Inline validation gives users immediate feedback as they complete the form, helping them catch and correct errors in real time. Studies have shown this can result in significant improvements, with some reporting a 22% increase in conversions. If your form currently only validates inputs after submission, testing inline validation is a must.

An example of inline validation - triggering error messages immediately after input

2. Instructional Microcopy

Hypothesis: Adding instructional microcopy will reduce mistakes and abandonment.

Preventing errors is just as important as fixing them. Test the effectiveness of short, helpful instructions next to fields, ensuring users understand exactly what information is required. Experiment with different wording to see what minimizes field abandonment.

A simple example of using copy to clarify exactly what is needed from the user

3. Clearer Error Messages

Hypothesis: Rewriting error messages to be more helpful will reduce drop-offs.

When form analytics show high abandonment rates on a specific field, unclear or overly technical error messages are often to blame. Testing error message copy—making it clearer, friendlier, or more detailed—can guide users to resolve issues and complete the form.

An unclear error message where clearer messaging could be tested

4. Single-Step vs. Multi-Step Forms

Hypothesis: Breaking the form into multiple steps will improve completion rates.

Multi-step forms can outperform single-page forms because they make the process feel less overwhelming. Test both formats to see which works better for your audience, and consider experimenting with how sections are divided.

5. Progress Indicators

Hypothesis: Adding a progress bar will encourage users to complete multi-step forms.

Progress bars reassure users by showing how much of the form is left to complete. Test different styles, such as percentages, step numbers, or labeled sections, to see which format is most effective at improving completion rates.

A straightforward progress bar for a financial form

6. Question Order

Hypothesis: Reordering fields to start with easier questions will increase engagement.

The sequence of questions can influence whether users stick with the form. Starting with simple questions can build momentum, but there are exceptions—such as insurance forms, where users often prefer to get a quote before providing personal details. Experiment with different orders to find the optimal flow.

7. Optimizing Individual Fields

Hypothesis: Removing or simplifying unnecessary fields will boost conversions.

Fields like “Phone Number” often create hesitation. Test whether removing or simplifying specific fields impacts conversion rates. However, don’t assume fewer fields always mean better results—some fields add credibility, so their removal could have unintended consequences.

8. Social Proof and Trust Signals

Hypothesis: Adding trust badges or testimonials will increase user confidence.

Trust indicators like security badges or customer testimonials can reassure users, particularly on checkout forms. Test the placement, type, and wording of these elements to identify what builds the most trust with your audience.

9. Save and Resume Functionality

Hypothesis: Allowing users to save their progress will increase completion rates for lengthy forms.

For long or complex forms, providing a save-and-resume option can reduce abandonment. Test whether adding this feature encourages users to return and finish the form.

10. Payment Method Options

Hypothesis: Adding additional payment methods will reduce drop-offs during checkout.

A lack of preferred payment options is a common reason for abandonment. Test whether introducing alternatives like PayPal or Klarna increases conversions. Baymard research shows 7% of users abandon checkout when their preferred method isn’t available.

11. Terms and Conditions

Hypothesis: Revising the presentation of consent boxes will increase conversions.

Consent boxes and privacy disclaimers can be a sticking point. CXL research showed a potential 19% difference in variants. Test variations in wording, placement, or design to make them less intimidating while remaining compliant with legal requirements.

12. Call-to-Action Optimization

Hypothesis: Changing the button design, copy, or placement will increase click-through rates.

From “Red Button vs. Green Button” debates to testing CTA text like “Submit” vs. “Get Started,” small changes to your form’s call-to-action can yield surprising results. Experiment with design, wording, and positioning to find the combination that drives the best engagement. 

We hope these 12 practical A/B testing hypotheses are helpful on your experimentation journey to make better, faster data-driven decisions. It’s all about testing, learning and sharing. Form analytics fits nicely into the 4th pillar of experimentation culture “Invest in the Right Tools and Technologies“. Thank you to Alun for this helpful guest blog. For any questions, please reach out to…