# Pattern #111: Field Explanations

### Pattern #111  Tested 3 timesFirst tested by Online Dialogue Recently tested by Lars Skjold Iversen on Dec 31, 2019

Based on 3 Tests, Members See How Likely Version B Wins Or Loses And By How Much

LOSSES
-5
-4
-3
-2
-1
FLAT
+1
+2
+3
+4
+5
WINS

Measured by the sum of negative and positive tests.

A B

?

Progression

(1 tests)

-

?

Signups

(2 tests)

-

Engagement

?

Sales

(1 tests)

-

Revenue

-

Retention

-

Referrals

?

ANY PRIMARY

(3 tests)

# Pattern #111: Field ExplanationsWas Tested On Valkexclusief.nl by Online Dialogue

Isolated

Test #261 on Valkexclusief.nl by Online Dialogue    Sep 20, 2019 Test link

## Find Out How It Did

• Measured by moving to next step

• Measured by successful bookings

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."

## The Same Pattern Was Also Tested Here

Replaced

Isolated

Test #276 on Umbraco.com by Lars Skjold Iversen    Dec 31, 2019 Test link

## Find Out How It Did With 48,811 Visitors

• Measured by total signups

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").

Isolated

Test #105 on Inktweb.nl by Martijn Oud    Sep 23, 2019 Test link

## Find Out How It Did With 3,169 Visitors

• Measured by successful signups

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".

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.