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E1 · Reported onlyreported loser

Does a 90-Day Plan-Change Guarantee Badge Increase Click-Throughs?

Hypothesis

On a plan selection page where the offer includes a no-charge plan-change guarantee, users may not be aware of the protection and fear locking into the wrong plan. A more prominent badge calling out the guarantee should reduce that fear and improve click-through into enrollment.

Copy & MessagingProduct Listing PageEnergy & Utilitiesbenefit badgeguarantee copytrustplan selection pageambiguity vs clarity

Evidence: E1 Reported only

An outcome is reported, but there is not enough disclosed information to check whether the result is reliable.

32/100
Record completeness
Sample size
Not disclosed
Test duration
Not disclosed
Primary metric
next-step click-through
Statistical result
Not disclosed
Why this rating

Use for inspiration or follow-up, not as proof.

Integrity: Caution. Analysis: Unknown.

Missing for a higher tier: Control description, Variant description, Positive sample size, Positive duration, Valid arm counts, Expected allocation, and more.

Caution: Stored caution integrity status, Analysis method not independently specified.

Read the evidence-rating methodology

Key Learning

Making the guarantee more visible did not make it more persuasive. The behavioral indicators suggest the badge attracted attention and then created unanswered questions: visitors spent longer on the page, exits increased, and engagement shifted toward the FAQ. A comparable implementation used a fuller explanation, while this version relied on a short label. The practical lesson is to explain eligibility, timing, and fees beside the claim. A guarantee can increase uncertainty when its prominence grows faster than its supporting context.

How to Apply This to Your Site

The reported outcome labels this treatment a loser. The measured outcome reportedly underperformed control. The change was tested on a product listing page page in the energy & utilities industry. Diagnose the mechanism and context before testing the opposite; a losing result does not prove that every related treatment will fail.

Before you test: use your own baseline, business MDE, traffic, and risk tolerance to set the sample and stopping rule. Declare whether the analysis is fixed-horizon, sequential, or Bayesian before launch, and log campaign, targeting, instrumentation, and concurrent-experiment changes while it runs.

What Was Tested

Test on a plan selection / pricing page where a benefit guarantee was already documented in policy but not visually prominent. The variant added a callout badge stating the guarantee.

Methodology

Primary Metric
next-step click-through
Reported Lift Range
-2% to -4% (directional)
Analysis Method
Unknown
Integrity Review
Caution

Build On These Learnings

Save your plans, results, context, and limitations so the next decision can use the strongest available evidence without treating every outcome as universal.

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