Our Specialisations
I design MCAE systems that do not rely on static assumptions. Instead, they continuously reassess prospect eligibility as data changes, signals appear, or certainty degrades. By using gated entry logic, cascading suppression, and explicit distinctions between what is known, inferred, and unknown, the system corrects itself over time. This prevents silent decay, keeps audiences clean without constant manual intervention, and ensures automation reflects current reality rather than outdated input.
Stable automation depends on clarity over where truth lives. I design clear single-source-of-truth hierarchies across Salesforce and Marketing Cloud Account Engagement, ensuring each field, decision, and gate is driven by the most reliable and relevant signal available. Certainty is prioritised over convenience, while ambiguity is surfaced rather than concealed. When Salesforce data changes, MCAE responds automatically, keeping segmentation, journeys, and suppression aligned without creating conflict between systems.
Rather than building static lists, I design qualifier-first segmentation systems where audience membership is continuously conditional. Prospects must meet minimum criteria to enter and to remain in each segment. When information changes or degrades, they are automatically reclassified or removed. This approach prevents misaligned contacts from entering journeys prematurely and makes segmentation resilient to human error, partial data, and evolving organisational structures.
Traditional attribution models assume levels of visibility that no longer exist, particularly in sectors where tracking pixels, cookies, and passive surveillance are restricted or unreliable. I design attribution and automation systems that operate effectively under these conditions by explicitly accounting for blind spots such as blocked tracking, shared inboxes, and incomplete engagement data. Rather than overstating certainty, attribution is weighted and contextual, prioritising consent-based, observable actions over inferred behaviour. The aim is not perfect attribution, but decision-ready insight that remains ethical, compliant, and credible even when visibility is limited.
Buying decisions rarely sit with a single individual. I design journeys that respond to relational signals across contacts, colleagues, parent accounts, and master organisations. Engagement by one stakeholder can influence how journeys adapt for others, even when the original contact remains silent. By modelling tangential relationships and organisational context, journeys reflect real decision dynamics rather than isolated activity, allowing automation to support collective buying behaviour without relying on invasive tracking.