frequency_tier Celebration Diners
Bella
Representative customer
18,600 guests · 12.4%
Business Lunch Regulars
Grant
Representative customer
16,200 guests · 10.8%
Happy-Hour Bar Grazers
Kim
Representative customer
15,800 guests · 10.6%
Discount-Led Occasionals
Rick
Representative customer
14,900 guests · 10.0%
Family Sunday Diners
Paul
Representative customer
13,600 guests · 9.1%
Lapsed Weekend Regulars
Lynn
Representative customer
11,200 guests · 7.5%
Takeout Steak Night Households
Ben
Representative customer
10,300 guests · 6.9%
Loyalty Points Maximisers
Shane
Representative customer
9,900 guests · 6.6%
Wine-Led Premium Diners
Olivia
Representative customer
9,400 guests · 6.3%
Pre-Theatre Set-Menu Guests
Ivy
Representative customer
8,700 guests · 5.8%
First-Visit Tourists
Ellie
Representative customer
7,100 guests · 4.8%
Bar-to-Dining Upgraders
Rafael
Representative customer
5,600 guests · 3.7%
Gift-Card Redeemers
Nell
Representative customer
4,300 guests · 2.9%
Private Dining Bookers
Diane
Representative customer
3,800 guests · 2.5%
Industries · Restaurants & Leisure
Restaurant groups, venues and leisure operators move on visit cadence, basket mix and offer response, signals a rewards program flattens into points balances. Neuralift builds an AI model of your guest base from your own data, finds the segments inside it, and keeps it current as visits change.
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.
Celebration Diners
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Business Lunch Regulars
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Happy-Hour Bar Grazers
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Discount-Led Occasionals
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Family Sunday Diners
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Lapsed Weekend Regulars
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Takeout Steak Night Households
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Loyalty Points Maximisers
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Wine-Led Premium Diners
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Pre-Theatre Set-Menu Guests
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
First-Visit Tourists
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Bar-to-Dining Upgraders
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Gift-Card Redeemers
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
Private Dining Bookers
Representative customer Not a real person, Neuralift does not process or store PII
How you'd recognise them
This segment Everyone else
Neuralift provides a significant competitive edge by knowing your customers better through deep learning and analytic reasoning. Instruct Neuralift AI, upload your customer data and in hours you have a roadmap of Segmentation, Insights, Personas and Strategic (and Tactical) Opportunities that will lift your KPIs.
Restaurant growth lives in frequency: one more visit a quarter, a bar regular who books the dining room, a takeout household at a table. Ask your model what brings each guest back, and it comes back with the plays, ranked. Here's an example plan for Leo's Steakhouse, a Neuralift demo brand.
KPIs, in priority order
frequency_tier This use case Add a visit a quarter for Occasional guests: the step to Regular. rewards_member Sign up the regulars who aren't members yet, so the next visit can be asked for. aov_tier Lift spend per cover with cross-sell, not discounts. Channels
Bar-to-dining upgraders spend half as much again
$96per visit after upgrading $64before
Members come back sooner
38 daysbetween visits for members 71 daysfor regulars who aren't
They already order the dining-room menu
64%of their orders are dine-in dishes
Lunch regulars rarely come back for dinner
9%have dined after 6pm 41%everyone else
Bring the use case that matters most. Neuralift ties what its deep learning discovers in your guest data to the KPIs behind it, and comes back with the guests to act on and the plays to reach them.
| Keyguest_id | Cleanedchecks_12m | |||||
|---|---|---|---|---|---|---|
| g_44718 | 38 | Frequent | 0.89 | 0.05 | 0.00 | 0.18 |
| g_29306 | 3 | Occasional | 0.54 | 0.00 | 0.83 | 0.70 |
| g_52604 | 41 | Frequent | 0.81 | 0.92 | 0.10 | 0.04 |
| g_58117 | 22 | Regular | 0.62 | 0.00 | 0.40 | 0.15 |
| g_31802 | 4 | Occasional | 0.22 | 0.10 | 0.00 | 0.34 |
| g_33240 | 14 | Regular | 0.66 | 0.12 | 0.50 | 0.88 |
Data dictionary frequency_tier category · 3 tiers
A new KPI: each guest's visit frequency tier (Occasional, Regular, Frequent) across dining room, bar and takeout together, with split checks merged into one visit.
Built from checks_12mcheck_tsmember_ref
Data dictionary visit_regularity float · 0 to 1
How regular the gaps between a guest's visits are, from 0 (erratic) to 1 (clockwork), with split checks merged into one visit.
Built from check_tsmember_reflocation
Data dictionary bar_share float · 0 to 1
The share of a guest's checks rung up at the bar rather than in the dining room, with location codes cleaned across POS systems.
Built from locationoffercheck_ts
Data dictionary bundle_response float · 0 to 1
How often a guest takes a pairing or set-menu offer, relative to how often they redeem a straight discount or points reward.
Built from offeritemnet
Data dictionary premium_item_share float · 0 to 1
The share of a guest's spend on the premium end of the menu (prime cuts, the reserve list), with item names mapped to one menu across locations.
Built from itemnetqty
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.
Stories, papers and the events where we meet hospitality, loyalty and CRM teams.
Case study Sep 22, 2026 Delaware North finds the customers legacy segmentation couldn't see Read the story 
White paper Sep 15, 2026 From warehouse to winning 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 Sixteen CMOs had a CDP. One liked it. Blog Jan 7, 2025 You have a CDP. So what?! Read 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.