Food & Beverage
F&B Customer & Experience Analytics
F&b customer analytics in restaurants and QSR often splits across loyalty apps, POS tickets, NPS and rating by outlet, and a helpdesk for complaints, so nobody sees repeat visit rate next to average ticket size trend in the same week. GMs run on gut feel and monthly exports while delivery ratings diverge from dine-in and the same dish scores differently across locations.
FireAI unifies check-level spend, visit identifiers, feedback scores, and complaint category analysis so experience leaders ask why repeat visit rate lags in a city, how average ticket size trend moves after a menu or price change, and which outlets drive NPS and rating by outlet and dish. Service teams get complaint category analysis tied to time, channel, and item before social spikes or refunds pile up.
The domain is built for f&b customer analytics, repeat visit rate by segment, average ticket size trend, NPS and rating patterns, and complaint root themes that operations and brand can act on together. See how it works: get a demo.
Repeat visit rate by segment
Repeat visit rate by segment is the loyalty pulse: without linking visits to a stable guest or household key, you only see check counts, not who came back. Segments from CRM, app login, or card hash rarely join to ticket trends in the same view as f&b customer analytics needs.
FireAI de-duplicates guests where your systems allow, tags segments such as new, lapsed, high-frequency, and delivery-only, and computes visit cadence and repeat visit rate on rolling windows. You see which regions, dayparts, or formats retain guests after a first visit or after a bad rating week.
How FireAI solves the problem: It aligns loyalty, POS, and channel events on one time line so repeat visit rate by segment is comparable across cities and not double-counted when the same person orders on app and walks in under different keys.
What FireAI tracks:
- Second and third visit rate within 30, 60, 90 days by segment and format
- Share of revenue from repeat guests versus new trial
- Cohort view after menu launches, promos, or NPS and rating by outlet events
- Delivery-only versus dine-in repeat paths when you split channels
Marketing and operations use repeat visit rate with average ticket size trend to judge whether acquisition spend fixes a top-of-funnel issue or a retention gap.
Loyalty and repeat health
Average ticket size trend analysis
Average ticket size trend is easy to read wrong when mix shifts, delivery fees, or a bundle promo move the number without a real price or attach-rate change. A rising average can hide lower traffic, and a fall may reflect more snack-dayparts rather than value erosion.
FireAI decomposes average ticket into cover count, add-ons, beverage attach, and channel mix for f&b customer analytics. You set baselines by format and daypart, then watch week-over-week average ticket size trend with alerts when a region diverges from the band peers run.
How FireAI solves the problem: It keeps ticket and line-item grain so average ticket size trend analysis explains which SKU groups or modifiers drive the move, not only the headline number.
What FireAI tracks:
- Average and median ticket by outlet, daypart, and channel
- Modifier and combo attach rate next to the same trend line
- Compare to last year and to plan for pricing or promo reviews
- Split dine-in, takeaway, and aggregator orders when data allows
Finance and GMs use average ticket size trend with repeat visit rate to decide if margin actions or traffic programs come first.
Ask FireAI about spend per visit
See how your team can ask questions in plain language and get instant analytics answers.
NPS and rating by outlet and dish
Nps rating outlet and dish feedback lives in survey tools, app stars, and Google in parallel, so brand teams cannot see whether a low NPS in one city is service, food quality, or wait time. Dish-level comments cluster in text files nobody joins to sales mix.
FireAI standardizes NPS, CSAT, and public rating feeds where you connect them, and links survey tags to outlet, shift, and menu items when the payload allows. F&b customer analytics shows NPS and rating by outlet and dish with peer bands so weak outlets stand out before the monthly business review.
How FireAI solves the problem: It brings structured scores and free-text themes into one place so you prioritize fixes by revenue-weighted impact, not only the noisiest comment thread.
What FireAI tracks:
- NPS and average star by outlet, region, and format
- Dish or category mentioned in negative feedback with volume and trend
- Correlation to repeat visit rate and average ticket where keys join
- Time-to-respond and closure when feedback links to a ticket id
Brand and operations use NPS and rating by outlet and dish with complaint category analysis to close the loop from signal to kitchen and training.
Experience scores at a glance
Complaint category analysis
Complaint category analysis fails when helpdesk types are too generic ("food") or when delivery platforms use different reason codes than your own app. The same problem shows up in social, email, and store managers Whatsapp, so root themes never roll up to f&b customer analytics.
FireAI classifies and clusters complaints with rules you own, then maps to outlet, time, and item when possible. You see count, severity, and resolution time by category, and how complaint category analysis trends against NPS and rating by outlet the following week.
How FireAI solves the problem: It unifies multichannel cases into a single category spine so legal, ops, and training see the same top five themes with trend, not a different slide per channel.
What FireAI tracks:
- Volume and TAT by category: quality, service speed, wrong order, hygiene, refund
- Repeat issue rate for the same guest or same outlet in 30 days
- Link to staff shift or production batch when operational data is available
- Cost of comps and refunds as a share of category volume
Leadership uses complaint category analysis with repeat visit rate to judge whether problems are local execution or menu and process design.