Patron Intelligence Engine (PIE)
A data intelligence engine that turns raw patron behaviour into clear, actionable next best actions for revenue growth.
01 — The Problem
Too much information. Not enough translation.
Most organisations segment their audience using whatever data is easiest to access. That usually means basic attendance history, crude frequency bands, and limited or disconnected data sources.
- — Direction of travel — are they becoming more or less engaged?
- — Price sensitivity
- — Programme affinity
- — Full behavioural context
02 — The Insight
Segmentation shouldn't be driven by available data. It should be driven by the decisions you're trying to make. The goal isn't to describe the audience — it's to define which groups matter most, what action each group requires, and how that action drives revenue.
Instead of asking
How do we segment our audience with the data we have?
We reframed it as
What decisions are we trying to make, and which cohorts help us make them?
03 — The Build
How it works.
An engine designed to convert behavioural data into structured, decision-ready intelligence.
01
Deep data pre-processing
Combines ticketing, behaviour, and engagement data. Calculates affinity, frequency trends, and value signals. Tracks direction of travel — not just current state.
02
Strategic cohort design
Defines a small set of high-value, actionable cohorts — At-Risk Subscribers, Reactivation Targets, High Affinity / Low Attendance, Advance Buyers (Full Price vs Needs Incentive). Built around decisions, not descriptions.
03
Next best action framework
Each cohort is linked to a clear strategic action — retain, convert, incentivise, expand, reactivate. Moves teams from analysis to execution.
04
Engine, not interface
Designed to plug into other systems. Powers tools like the Recommendations Engine and Digital Consultant. Operates invisibly, but critically.
04 — The Output
What users actually get.
Instead of a long list of segments, organisations get a small number of clearly defined cohorts, a shared understanding of what each group needs, and a practical framework for who to target, how to communicate, and what outcome to aim for. It becomes a decision-making layer, not a reporting layer.
- — Decision-led — not data-led
- — Actionable — every cohort has a next move
- — Composable — powers other systems
05 — The Impact
What changed.
01
Replaces guesswork with structured strategy
for audience and revenue teams
02
Aligns marketing, programming, and revenue thinking
around shared cohorts
03
Enables more precise and effective communication
across every channel
04
Makes advanced audience intelligence accessible
without a large data team
The shift
"What does our data say?"
"What should we do next?"
06 — The Future
Where this goes next.
PIE becomes the foundation layer across multiple systems.
It can be extended to
- — Powers personalised recommendations at scale
- — Feeds AI-driven insight tools like the Digital Consultant
- — Enables automated decisioning and next best action systems
Long term, tools like this could
- — The standard way organisations understand their audiences
- — Not just analyse them — act on them
- — An intelligence layer, not a reporting layer