events_attended_band Concert-Only Ticket Buyers
Lola
Representative customer
14,600 fans · 13.8%
Single-Game Family Outings
Paige
Representative customer
12,300 fans · 11.6%
App-Active Game-Day Regulars
Jamal
Representative customer
10,900 fans · 10.3%
Season Ticket Core
Gus
Representative customer
9,200 fans · 8.7%
Student Night Crowd
Zack
Representative customer
8,900 fans · 8.4%
Late-Arriving Downtown Workers
Ravi
Representative customer
8,400 fans · 7.9%
Merch-First Fans
Tess
Representative customer
7,600 fans · 7.2%
Lapsed Season Holders
Doug
Representative customer
6,800 fans · 6.4%
Resale-Market Opportunists
Nico
Representative customer
6,100 fans · 5.8%
Parking-Pass Early Arrivers
Carla
Representative customer
5,200 fans · 4.9%
Out-of-Town Visiting Fans
Hugh
Representative customer
4,700 fans · 4.4%
Kids Club Members
Abby
Representative customer
4,400 fans · 4.2%
Premium Club Seat Holders
Vivian
Representative customer
3,900 fans · 3.7%
Corporate Hospitality Guests
Elaine
Representative customer
2,900 fans · 2.7%
Industries · Sports & Entertainment
Teams, leagues and venues sit on ticketing, app, broadcast and merchandise data, and loyalty tiers flatten distinct fan journeys into a handful of buckets. Neuralift builds an AI model of your fan base from your own data, finds the segments inside it, and keeps it current through the season.
Every segment comes with a field guide: who they are, how you'd recognise them, and what sets them apart from everyone else.
Use the arrows, swipe, or pick a segment on the map.
Concert-Only Ticket Buyers
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Single-Game Family Outings
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
App-Active Game-Day Regulars
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Season Ticket Core
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Student Night Crowd
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Late-Arriving Downtown Workers
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Merch-First Fans
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Lapsed Season Holders
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Resale-Market Opportunists
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Parking-Pass Early Arrivers
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Out-of-Town Visiting Fans
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Kids Club Members
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Premium Club Seat Holders
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Corporate Hospitality Guests
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
This segment Everyone else
For brands the future is clear. Traditional segmentation told us what happened. AI segmentation discovery shows us who, where and how to lift our KPIs.
Sports and entertainment grow fan by fan: the family's second game of the season, the concert-goer in the app, the student who becomes a holder. Ask your model what each fan would come back for, and it comes back with the plays, ranked. Here's an example plan for Granite Pulse Arena, a Neuralift demo brand.
KPIs, in priority order
events_attended_band This use case Add one more event a season for fans at two to four. app_user Get more ticket holders using the app, where they buy food and their next ticket. avg_event_spend Lift spend per event through in-seat ordering and upgrades. parking_user Fill pre-paid parking, which means arriving early and spending more. Channels
Fans who come twice in 30 days keep coming
6.2games a season 2.8one-off attenders
App users spend more on the night
$58per event, app users $37non-app
Families who pre-park arrive earlier and eat on site
71 minearly, with pre-paid parking 24 mineveryone else
They miss the concourse but stay to the end
92%stay to the final buzzer 78%everyone else
Bring the use case that matters most. Neuralift ties what its deep learning discovers in your fan data to the KPIs behind it, and comes back with the fans to act on and the plays to reach them.
| Keyfan_id | Cleanedtickets_12m | |||||
|---|---|---|---|---|---|---|
| f_21408 | 41 | Premium | 0.38 | 0.00 | 0.84 | 2.1 |
| f_46901 | 0 | Casual | — | — | — | 6.4 |
| f_15807 | 6 | Regular | 0.92 | 0.81 | 0.40 | 1.2 |
| f_24312 | 2 | Premium | 1.00 | 0.00 | 0.50 | 3.6 |
| f_29114 | 8 | Regular | 1.00 | 0.00 | 0.25 | 0.8 |
| f_09611 | 78 | Superfan | 0.97 | 0.04 | 0.51 | 5.2 |
Data dictionary fan_value_band category · 4 bands
A new KPI: each fan's value band (Casual, Regular, Premium, Superfan) on attendance, per-cap, merchandise and broadcast together, across every account the fan holds.
Built from tickets_12mamountsource
Data dictionary attendance_rate float · 0 to 1
The share of games a fan held a ticket for that they actually scanned into, from ticketing joined to gate scans.
Built from eventgame_idacct_no
Data dictionary resale_share float · 0 to 1
The share of a fan's tickets bought on the resale market rather than from the primary box office.
Built from sourceeventamount
Data dictionary late_season_skew float · 0 to 1
How far a fan's attendance leans toward the end of the season, with game dates aligned to the season calendar.
Built from event_tsgame_idevent
Data dictionary app_sessions_wk float · sessions
Average app sessions a week, with duplicate events removed and app accounts joined to ticketing accounts.
Built from acct_nosourceevent_ts
Discovery starts by transforming the data you already have. Neuralift turns it into an AI-ready asset, engineers the features your use case needs, creates new KPIs and documents every one.
It fuels the deep learning, and it's yours to keep. Your data science teams can build on it, and so can your own agentic and AI applications.
The superfans paper, stories and the events where we meet teams and venues.
Case study Sep 22, 2026 Delaware North finds the customers legacy segmentation couldn't see Read the story 
White paper Jul 2, 2026 From customers to superfans with AI-native segmentation discovery Read the paper 
Event Jun 1, 2026 CRMC 2026 See the event A lookalike is only as good as its seed. Blog Apr 30, 2026 Fix the seed, fix the lookalike Read 
Event Sep 8, 2025 Disney Data & Analytics Conference 2025 See the event Our team is always happy to talk through your customer data challenges and opportunities. We usually respond within a day, and we love helping teams see what their customer data can tell them.