The problem this solves
Everyone gets everything: the enterprise prospect and the student who downloaded one PDF receive the same sends, in the same language, with the same offer. The data to segment on either does not exist, is unreliable, or sits in fields nobody standardized - so segmentation stays a wish and results stay average, because average is what untargeted messaging earns.
How we work
Segmentation dies without data, so we start there: which properties the model needs - industry, size, language, role, behavior - which are populated and trustworthy, and how the gaps get filled through form strategy, enrichment, or inference from behavior. Then we define a segment model with the marketing team: few enough segments to serve properly, distinct enough to justify different treatment.
Implementation follows in HubSpot: active lists that maintain themselves as contacts change, personalization tokens with fallbacks so nobody gets an email addressed to a blank space, and smart content rules that adapt email and page sections by list membership or language where the use case earns the complexity.
We deliberately build personalization only where you have the content to feed it - a segmentation model bigger than your content production capacity is just a prettier way to send everyone the same thing.
Deliverables
- Data readiness audit of segmentation-critical properties
- Segment model agreed with the marketing team
- Self-maintaining active lists per segment
- Personalization tokens with tested fallbacks
- Smart content rules for email and page variants
- Playbook for extending segments and variants