Make Decisions Based on Evidence, Not Opinions
Deploy an enterprise experimentation platform that brings scientific rigor to marketing decisions. Test hypotheses with proper statistical methodology, reach conclusions faster, and build a culture of data-driven optimization.
Most Marketing Tests Are Wrong
Tests run without calculating required sample size, leading to inconclusive results and wasted time?
Winners declared prematurely before reaching statistical significance, resulting in false positives?
Multiple comparisons and peeking at results early inflate error rates and produce unreliable findings?
Enterprise A/B Testing Capabilities
From simple split tests to complex multivariate experiments, our platform handles every testing scenario with statistical precision.
Classical A/B Testing
Frequentist hypothesis testing with proper sample size calculations, significance thresholds, and multiple comparison corrections. Gold standard methodology that eliminates false positives.
- →Automatic sample size calculation based on minimum detectable effect
- →Sequential testing with proper alpha spending to enable early stopping
- →Bonferroni correction for multiple comparisons across variants
- →Confidence intervals for effect size estimation, not just p-values
- →Detailed power analysis to avoid underpowered experiments
- →Guardrail metrics monitoring to detect negative side effects
Scientific Testing Methodology
Our proven framework ensures every experiment delivers reliable, actionable insights
Hypothesis Formation
Define clear, testable hypothesis with predicted effect direction and magnitude. Identify primary success metric and guardrail metrics to monitor for negative effects.
Outcomes
- ✓Hypothesis documented with measurable prediction
- ✓Success metrics and guardrails defined
- ✓Minimum detectable effect established
Experimental Design
Calculate required sample size for desired statistical power. Design randomization approach and variant allocation. Configure tracking and data collection.
Outcomes
- ✓Sample size calculated for 80% power
- ✓Experiment configuration completed
- ✓Tracking validated and tested
Test Execution
Launch experiment with proper traffic allocation. Monitor data collection and sample balance. Check for validity threats like sample ratio mismatch.
Outcomes
- ✓Experiment running with balanced traffic
- ✓Data collection validated
- ✓Sample size accumulating toward target
Analysis & Decision
Perform statistical analysis when sample target reached. Calculate confidence intervals and p-values. Make implementation decision based on results and guardrail metrics.
Outcomes
- ✓Statistical analysis completed
- ✓Winner identified with confidence level
- ✓Implementation recommendation made
Medical Trials Company
A clinical trials company needed to acquire patients for their studies efficiently and at scale. Patient recruitment is one of the most difficult challenges in the clinical trials industry, with most sites struggling to meet enrollment targets.
TMG developed a comprehensive marketing campaign paired with a scheduling process specifically designed for patient acquisition. The system combined targeted digital outreach with streamlined conversion workflows to move prospects from awareness to enrolled participants.
"TMG developed a marketing campaign and a scheduling process that was so successful, we ended up as the top producing site in the country. I can't say enough good things about TMG."
A/B Testing Platform FAQs
Common questions about implementing rigorous experimentation
Start Testing with Statistical Rigor
Schedule a consultation to learn how our experimentation platform can help you make reliable, data-driven decisions.