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.

Signals statement text

We observed behavior, not opinions: clicks, signups, checkout starts and payment attempts from real visitors.

Funnel summary 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
 ]
]

Behavioral read text

Visitors respond to a concrete, time-bound promise. Interest collapses when price appears.

Segment differences table

Segment	Signup rate A	Signup rate B
Mobile	9.1%	4.6%
Desktop	12.4%	5.9%

Friction points list

Price revealed only at checkout.
Account creation required before preview.

Next decision text

Whether to show price earlier and remove forced signup.

Recommended next experiment text

A vs A+visible price, 7 days, same budget.

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.