Recommendations Engine
A personalisation engine that turns every patron's unique trail of behaviour into recommendations that feel genuinely individual.
01 - The Problem
Reasonable recommendations aren't personal ones.
Most venues recommend performances using a narrow set of inputs - staff knowledge and simple "people who bought this also bought that" logic.
- - Thousands of patrons receive recommendations that are merely reasonable rather than genuinely personal
- - Marketing teams spend valuable time deciding what to promote instead of focusing on strategy and creativity
02 - The Insight
Every patron leaves behind a unique trail of interests - not just the shows they've attended, but the genres they gravitate towards, how adventurous they are, how recently they've engaged, and how similar their behaviour is to thousands of other patrons. The challenge isn't finding data. It's turning all of those signals into recommendations that feel personal.
Instead of asking
Which show should we promote this week?
We reframed it as
Which performances is this individual most likely to enjoy right now?
03 - The Build
How it works.
The Recommendations Engine combines multiple sources of intelligence to build a personalised recommendation for every patron.
01
Behavioural intelligence
Ticket purchase history, booking frequency, attendance patterns, and patron affinity scores generated by PIE.
02
Event intelligence
AI-generated event summaries, genre and thematic similarities, and similarity between productions.
03
Recommendation logic
Multiple recommendation algorithms, intelligent ranking and weighting, and continuous refinement as new data arrives.
04
A ranked, personal portfolio
The result is a ranked portfolio of performances that is unique to every individual.
04 - The Output
What users actually get.
For every patron, the system produces a personalised list of recommended events. These recommendations aren't simply "similar shows" - they're chosen because the engine believes they represent the strongest opportunity to deepen that patron's relationship with the venue. They can power personalised emails, customer portals, digital consultants, future website experiences, and audience insight tools.
- - Personalised emails
- - Customer portals
- - Digital consultants
- - Future website experiences
- - Audience insight tools
05 - The Impact
What changed.
01
More relevant customer communications
grounded in individual behaviour, not broad segments
02
Increased patron engagement
through recommendations that feel genuinely personal
03
Better discovery of lesser-known performances
beyond the obvious hits
04
Reduced manual campaign planning
freeing marketing teams for strategy and creativity
05
Improved commercial performance
driven by relevance, not volume
The shift
"Generic campaign marketing"
"Genuine personalisation - helping patrons find experiences they'll value"
06 - The Future
Where this goes next.
The Recommendations Engine is designed to become the recommendation layer across an organisation. Powered by PIE, it can support a continuous personalisation model rather than a campaign-based one.
It can be extended to
- - Automated "next best performance" recommendations
- - Cross-venue discovery
- - Personalised season planning
- - Subscription suggestions
- - Dynamic website content
- - AI-powered audience assistants
Long term, tools like this could
- - Recommendations become continuous, not campaign-based
- - The question shifts from "what should we market today?" to "what should this patron discover next?"
- - Personalisation becomes an always-on layer, not a periodic activity