CRM complexity grows quickly when systems, data and processes stop working together.

ChargedEV partnered with me to simplify and modernise its CRM ecosystem while supporting the company’s continued growth.

The business was operating across multiple HubSpot portals and several disconnected systems, which had created duplicate processes, fragmented customer data and unnecessary manual work.

My role was to help turn that environment into a single, scalable CRM platform.

I consolidated two HubSpot portals into one environment, managed the migration from HubSpot Enterprise to Professional, redesigned the CRM architecture, and migrated and validated business-critical data.

From there, I built automated workflows for sales and operational processes, created custom reports and management dashboards, and connected HubSpot with systems including Jira, Confluence, WooCommerce, Aircall, Calendly, Typeform and Companies House.

The engagement ultimately covered 670+ pieces of CRM work, spanning migration, automation, reporting, integrations and ongoing optimisation.

The result was a much more connected CRM ecosystem, with cleaner data, fewer manual processes and a stronger foundation for the business to continue scaling.

Projects like this are a good reminder that CRM transformation isn’t simply about moving data from one system to another. The real value comes from simplifying the architecture and making the different parts of the business work together.

Strong systems thinking here—consolidating the CRM and validating the migration, rather than just moving data, is what makes the work genuinely scalable.

What makes an A/B test readout useful to a product team?

My preferred first page answers four questions:

What changed, and by how much?

How uncertain is the estimate?

Did an important guardrail get worse?

What decision does the evidence support, and what remains unresolved?

A result can be statistically significant and still too small to matter. An inconclusive result can still leave a meaningful gain or loss plausible. The decision needs more than a green badge.

I built an independent A/B analyzer to make these checks easier to inspect. The project includes a live demo, downloadable reports and documented limitations: https://contra.com/p/mgoXb3qY-ab-test-analyzer-correct-p-values-power-and-fdr

If you work on product or CRO, what do you wish analysts put on the first page more often?

This is a strong framing of experiment readouts: separating signal, uncertainty, guardrails, and the actual decision keeps the team honest. The reminder that significance is not the same as usefulness is especially important.