Experience that is stated, not inflated
Nicholas Eisel’s stated career record covers 24 years, 245+ engagements and $200M+ in client revenue. These are career-history figures, not independently audited ConversionMax-only totals. His work includes conversion optimization, Google and Meta PPC, digital marketing leadership and fractional CMO roles.
How to read the approved historical examples
One anonymized ecommerce example shows annual recurring revenue rising from $400k to $2.6M and conversion rate from 0.8% to 3.1% while he was VP of Marketing (Aug 2017–Jan 2024). A separate paid-media example shows ROAS rising from 0.8x to 3.2x and sales conversion rate from 1.4% to 2.9% while he was Senior Online Marketing Manager (Feb 2018–Feb 2020). Multiple initiatives contributed during both periods.
What the figures do not establish
These before-and-after figures do not isolate CRO or prove randomized-test causation. They do not show typical performance or predict client results. Traffic, offer, seasonality, implementation, measurement and concurrent work can affect outcomes.
How evidence should inform a conversation
This work fits businesses that can define a problem, investigate it, measure it and support implementation. Review experience alongside fit, method, measurement and business constraints—not as proof that a past result will repeat.
Questions to ask about any result
Ask what was measured and changed, what else changed, whether the comparison was experimental or observational, and who owned implementation. Keep dates, definitions and limitations clear.
Common questions
Are these ConversionMax case studies?
No. They are anonymized historical examples from prior roles and engagements, not independently audited ConversionMax-only performance claims.
Can the same results be expected?
No. Results depend on client context, traffic, offer, implementation and measurement, and multiple initiatives contributed.
Does data-driven attribution prove a channel caused the sale?
No. GA4 data-driven attribution uses converting and non-converting path data and a counterfactual modeling approach to distribute credit. It estimates contribution under its model; it is not a randomized test of your specific campaign. Treat its allocation as one decision input and use a suitable incrementality test when the question is whether advertising caused additional outcomes. Sources and context
