SKP Suite
Behavior & Funnel Decision BriefSAMPLEReady
Sample report with illustrative demo values. Not a real study, customer or market claim.
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Behavior & Funnel Decision Brief

Behavior & Funnel Decision Brief

SAMPLE subject: landing page test for a meal-planning app

Winning behavior: clicking through the “5-minute plan” promise.

Confidence: MediumPrepared for: SAMPLE / EXEMPLUGenerated: 28/09/2026
Executive summary
  • A’s concrete time promise outperformed B at every funnel step (SAMPLE data).
  • The biggest drop-off for both is signup → checkout.
  • Payment counts are too small to support a pricing conclusion.
1,240 (SAMPLE)Visitors
€180 (SAMPLE)Budget
7Run time (days)
01

What happened

Across 1,240 SAMPLE visitors, A produced 62 signups vs 31 for B.

02

What it likely means

Speed is a stronger hook than health outcomes for this audience — likely, not proven.

03

Variant comparison

Events per variant (SAMPLE counts).

A · “Plan in 5 min”B · “Eat better”
Visit
620620
Click
186124
Signup
6231
Checkout
1911
Payment
63
04

Step conversion A (SAMPLE %)

Visit → Click
30%
Click → Signup
33%
Signup → Checkout
31%
Checkout → Payment
32%
05

Recommended action

Adopt A’s headline; test visible pricing next.

Next stepRe-run SKPSIS on pricing before scaling spend.

Methodology. Two landing variants split 50/50 on paid social traffic. Events tracked: click, signup, checkout start, payment intent. No personal data beyond consented analytics.

Research behind the method / SKPTST

Current methodological context. These references are not evidence for this historical run.

SKPTST: planned methodology. The engine and paid traffic acquisition are not active.

Plan allocation and sample size before testing

Kohavi, R.; Longbotham, R.; Sommerfield, D.; Henne, R. M. (2009). Controlled experiments on the web: survey and practical guide. Data Mining and Knowledge Discovery, 18, 140–181. Online: 30 July 2008; issue: February 2009.

Research finding
Controlled experiments use randomization, statistical power and variance considerations.

Define stopping rules before inspecting outcomes

Kohavi, R.; Deng, A.; Vermeer, L. (2022). A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments. Proceedings of ACM SIGKDD 2022, 3168–3177. Conference: 14–18 August 2022.

Research finding
Opportunistic stopping and post-hoc analysis can undermine A/B-test interpretation.

Separate a traffic pilot from proof of advertising impact

Lewis, R. A.; Rao, J. M. (2015). The Unfavorable Economics of Measuring the Returns to Advertising. The Quarterly Journal of Economics, 130(4), 1941–1973. Issue: November 2015. · Blake, T.; Nosko, C.; Tadelis, S. (2015). Consumer Heterogeneity and Paid Search Effectiveness: A Large-Scale Field Experiment. Econometrica, 83(1), 155–174. Issue: January 2015.

Research finding
Advertising returns can be difficult to estimate; the eBay experiment found effects differed across user groups.
The SKP suite
Adversarial research

Research designed to disagree with you.

Logical next step →Explore SKPSIS
Synthetic decision panel

Where ideas meet resistance.

Explore SKPTIK
Observe real behavior

Test behavior, not opinions.

This report