Path Finder

The problem

Brand tracking programmes generate mountains of survey data and two kinds of output. Tracker dashboards show every score moving and explain none of it. Academic SEM tools can establish cause and effect, but they live on an analyst's desktop, speak in path coefficients, and say nothing about money. Between the two sits the question every CMO asks: which lever do we pull, and what is it worth?

The build

Path Finder answers that question with a tested model. The workflow is hypothesis-first: you state how you believe the brand works as a tiered structure - concrete attributes feeding brand perceptions, feeding equity dimensions, feeding the KPI you care about - and then test that structure against the data.

An AI agent accelerates the slow part. It reads the survey items, proposes tier assignments and causal paths, and hands them to the analyst for review. Nothing enters the model without a human approving it.

Once a model passes validation, the what-if laboratory opens. Move any driver by a few points and watch the effect propagate through the model to the KPI, expressed in percentage points and in money. Scenarios can be saved, compared, and exported. Multiple brands and tracking waves are first-class, so competitor benchmarking and wave-over-wave movement come built in.

The workflow

The statistical engine is R running seminr for PLS-SEM, with bootstrap significance testing and fixed seeds so every result reproduces exactly. A Python and Flask layer orchestrates the workflow, Vue and D3 drive the interface, and the whole platform runs on Docker.

The outcome

Our brand equity engagements now end with a working simulator the client can run. The model is defensible in front of a sceptical finance director: every path is tested, every validity check is visible, and every recommendation carries a number. Path Finder is in active development and in use on live engagements. If you want to see it on your own tracking data, book a demo.