Pattern #111: Field Explanations

Pattern Author: Online Dialogue

Based on 3 Tests, Members See How Likely This Pattern Will Win Or Lose And Its (?) Median Effect

Almost Certain Loser
Almost Certain Winner
Field Explanations
  1. Form Field Explanations Explaining

    This pattern suggests to add explanations to certain fields that explain why some of them are being asked for (ex: email addresses or phone numbers).

Median Effects



Ex: First Action

(1 tests)



Ex: Leads, Quotes



Ex: Trials

(2 tests)



Ex: Future Action



Ex: Transactions

(1 tests)






Ex: Return Visits



Ex: Social Shares


Test #276 Tested on by Lars Skjold Iversen Lars Dec 31, 2019

Find Out How It Performed With 48,811 Visitors

Home & Landing Desktop, Mobile
  • Measured by total signups   |   p-val (?)

In this experiment, the idea was to move away from copy that was focusing on the needs of the company ("we need your email") towards copy that hinted at a customer benefit ("create your trial").

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Test #105 Tested on by Martijn Oud Sep 23, 2019

Find Out How It Performed With 3,169 Visitors

Signup Desktop, Mobile
  • Measured by successful signups   |   p-val (?)

In this experiment, onhover tooltip explanations were added to selected fields (Firstname, Lastname, Phone, Email and Password). One translation example of the Firstname tooltip was the following "Enter your first name (or letter) so that we can address you in a more personal way".

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Test #261 Tested on by Online Dialogue Online Sep 20, 2019

Find Out How It Performed

Checkout Desktop
  • Measured by moving to next step   |   p-val (?)

  • Measured by successful bookings   |   p-val (?)

In this experiment on Valk Exclusief's web site, a reason was provided for why the e-mail address is being collected. Google translation of the added text is as follows: "If your e-mail address is not yet known to us, we will ask you to add some missing information. Then you immediately benefit from our benefits such as the ValkLoyal savings program."

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For each pattern, we measure three key data points derived from related tests:

REPEATABILITY - this is a measure of how often a given pattern has generated a positive or negative effect. The higher this number, the more likely the pattern will continue to repeat.

SHALLOW MEDIAN - this is a median effect measured with low intent actions such as initiating the first step of a lengthier process

DEEP MEDIAN - this is derived from the highest intent metrics that we have for a given test such as fully completed signups or sales.