Pattern #17: Expensive First

Pattern Author: Jakub Linowski - Founder @ GoodUI

Based on 6 Tests, Members See How Likely This Pattern Will Win Or Lose And Its (?) Median Effect

Almost Certain Loser
-5
-4
-3
-2
-1
0
+1
+2
+3
+4
+5
Almost Certain Winner
Expensive First

  1. Replace: Ordering Of Plans By Most Expensive First Anchoring

    The pattern simply suggests to place the most expensive plans or products first. This way the most expensive items are subtly recommend. More so, perhaps as customers scan their options, the less expensive plans are also anchored with the higher priced ones that they first noticed.

Median Effects

?

Progression

Ex: First Action

?

Leads

Ex: Leads, Quotes

?

Signups

Ex: Trials

?

Engagement

Ex: Future Action

?

Sales

Ex: Transactions

(6 tests)

?

Revenue

Ex: AOV, LTV

(6 tests)

?

Retention

Ex: Return Visits

?

Referrals

Ex: Social Shares

Tests

Pattern #17: Expensive First
Was Tested by Jakub Linowski

Test #324 Tested on An Anonymous Site Oct 30, 2020

Find Out How It Performed With 31,768 Visitors

Product Desktop, Mobile

  • Measured by total sales   |   p-val (?)

  • Measured by total revenue   |   p-val (?)

Zoom In
  • (?)% ((?) successes out of (?) visits)
  •  
A
  • (?)% ((?) successes out of (?) visits)
  •  
B

This experiment tested the order of purchase plans. The control version sorted the purchase options by the least expensive while the variation sorted them by the most expensive first. Impact on sales and revenue was measured.

Get Access To See The Test Results

The Same Pattern Was Also Tested Here

Test #289 Tested on Prepagent.com by Arthur Sparks Arthur Mar 23, 2020

Find Out How It Performed

Pricing Desktop

  • Measured by total sales   |   p-val (?)

  • Measured by total revenue   |   p-val (?)

Zoom In
  • (?)% ((?) successes out of (?) visits)
  •  
A
  • (?)% ((?) successes out of (?) visits)
  •  
B

In this experiment, the order of pricing plans was rearranged as to show the most expensive one first.

Get Access To See The Test Results

Test #254 Tested on Volders.de by Alexander Krieger Alexander Aug 16, 2019

Find Out How It Performed

Signup Desktop, Mobile

  • Measured by any successful cancellation transactions   |   p-val (?)

  •  

Zoom In
  • (?)% ((?) successes out of (?) visits)
  •  
A
  • (?)% ((?) successes out of (?) visits)
  •  
B

In this experiment, 4 things were adjusted in the variation: the highest pricing plan was shifted to the left, it was set as the default one, the recommendation was also adjusted to point to the highest plan, and one benefit from the lowest plan was removed (customer support).

Get Access To See The Test Results

Test #253 Tested on Volders.de by Alexander Krieger Alexander Aug 08, 2019

Find Out How It Performed

Signup Desktop, Mobile

  • Measured by any successful cancellation transactions   |   p-val (?)

  •  

Zoom In
  • (?)% ((?) successes out of (?) visits)
  •  
A
  • (?)% ((?) successes out of (?) visits)
  •  
B

In this experiment, two pricing plans were inverted to show the most expensive plan first (in the variation).

Get Access To See The Test Results

Test #130 Tested on Kenhub.com by Niels Hapke Niels Dec 07, 2017

Find Out How It Performed

Pricing

  • Measured by total purchases of any plan   |   p-val (?)

  •  

Zoom In
  • (?)% ((?) successes out of (?) visits)
  •  
A
  • (?)% ((?) successes out of (?) visits)
  •  
B

In this experiment, the plans were sorted by the most expensive first, left to right (variation).

Get Access To See The Test Results

Test #70 Tested on Uptowork.com by Kuba Koziej Nov 01, 2016

Find Out How It Performed

Pricing

  • Measured by any plan purchases   |   p-val (?)

  •  

Zoom In
  • (?)% ((?) successes out of (?) visits)
  •  
A
  • (?)% ((?) successes out of (?) visits)
  •  
B

This test simply ordered the pricing plans from highest to most expensive. Overall sales decreased by an insignificant -1% with a possible +14% increase to the most expensive plan. The net effect was an insignificant 0.9% increase the the premium plans.

Get Access To See The Test Results

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.