SKP report — Appendix

Appendix

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.

Small real-traffic sample. Directional signal, not definitive validation.

Evidence & sources

Source	Type	Note
Analytics events	Behavioral	SAMPLE export
Ad platform	Traffic	Spend and reach (SAMPLE)

Uncertainty notes

Sample size too small for fine segment claims.
Paid traffic may differ from organic visitors.

What happened text

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

What it likely means text

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

Variant comparison funnel

Events per variant (SAMPLE counts).

A · “Plan in 5 min”B · “Eat better”

Visit

620620

Click

186124

Signup

6231

Checkout

1911

Payment

[
 [
  "Visit",
  620,
  620
 ],
 [
  "Click",
  186,
  124
 ],
 [
  "Signup",
  62,
  31
 ],
 [
  "Checkout",
  19,
  11
 ],
 [
  "Payment",
  6,
  3
 ]
]

Step conversion A (SAMPLE %) bars

Visit → Click

30%

Click → Signup

33%

Signup → Checkout

31%

Checkout → Payment

32%

[
 [
  "Visit → Click",
  30
 ],
 [
  "Click → Signup",
  33
 ],
 [
  "Signup → Checkout",
  31
 ],
 [
  "Checkout → Payment",
  32
 ]
]

Recommended action text

Adopt A’s headline; test visible pricing next.

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.