Food & Beverage
F&B Menu & Product Analytics
F&b menu analytics breaks when contribution margin, raw popularity, and kitchen load live in different exports for the same menu code. Menu performance analysis devolves to gut when nobody agrees whether a dish is a star on revenue or a dog on time and waste. Bestseller dead item identification stalls when long-tail SKUs with zero search still carry prep slots because nobody retires the code in POS. Combo upsell tracking inflates when bundled discount is booked as hero sales while attachment and cannibalized full price lines sit untagged. New menu item analytics rarely survives week four when pre-lift targets never linked to post-launch depletion, training gaps, and platform listing lag.
FireAI unifies check-level line detail, where available standard recipe and portion cost, and outlet or format tags so f&b menu analytics answers which items sit in the menu performance analysis quadrants you define after margin and hours of cover, which SKUs need bestseller dead item identification to clear dead stock and prep time in the same review, how combo upsell tracking splits true attachment from one-off discount chasing on the same daypart, and what new menu item analytics should show for trial rate, repeat, and full margin at week one versus week eight on dine-in, delivery, and in-store formats.
The domain covers menu mix engineering, long-tail health, bundle economics, and new dish readouts, through chat, dashboards, and causal chains product and commercial teams can share with finance. See how it works: get a demo.
Menu performance analysis (BCG matrix)
Menu performance analysis only works when margin and volume share a grain and a week. A classic BCG view fails if stars are just high revenue without line-level contribution or if dogs hide inside family bundles you never decompose.
FireAI plots dishes into quadrants you configure using contribution per cover, material margin after waste, and prep minutes where the data links. Menu performance analysis highlights outlet and format differences so a star in fine dine is not forced into QSR without a test.
How FireAI solves the problem: It versions segment rules for menu performance analysis so the same f&b menu analytics board compares week to week on one definition, not four screenshots.
What FireAI tracks:
- Contribution margin and volume index by SKU, outlet, and channel where tagged
- Movement between quadrants after price, portion, or placement changes
- Peer bands within city and format to spot local stars and dogs early
- Mix shift when a category gains menu share but loses aggregate margin
Leaders use menu performance analysis with f&b menu analytics to fund engineering time on winners and deprioritize codes that only look busy on gross.
Menu mix and BCG view
Bestseller vs dead item identification
Bestseller dead item identification needs velocity, margin, and opportunity cost, not just rank by units. A top seller on discount can be a margin leak while a slow code still earns on catering pull-through you never connect.
FireAI flags dead items using min covers, min revenue, and margin floors you set, then enriches with days since last sell and open orders on the same ingredient. Bestseller dead item identification surfaces SKUs to retire, to shrink prep, or to merge into a new composite code.
How FireAI solves the problem: It pairs bestseller dead item identification with menu performance analysis so the cut list and the invest list meet in the same f&b menu analytics review.
What FireAI tracks:
- Sell-through, margin, and rank by outlet, daypart, and channel
- Dead tail list with rupee and prep minutes tied to each code
- Cannibalization signals when a new LTO takes share from an older hero
- Correlation to inventory risk when dead items share lots with high-turn SKUs
Product teams use bestseller dead item identification to free line space for new menu item analytics without a blind SKU count target.
Ask FireAI about bestsellers and dogs
See how your team can ask questions in plain language and get instant analytics answers.
Combo and upsell performance tracking
Combo upsell tracking becomes theater when the POS records a bundle as one line and nobody sees which component drove the click. In-house upsell to drinks can look strong until attach rates ignore table training variance across shifts.
FireAI decomposes bundles where the stack exposes components, and uses tagged modifiers or training cohorts for the rest. Combo upsell tracking reports incremental check margin after discount, cannibalized full price lines, and repeat attach when data allows.
How FireAI solves the problem: It ties combo upsell tracking to f&b menu analytics so LTOs do not stack discounts that turn stars into margin leaks.
What FireAI tracks:
- Uplift versus control for each combo and upsell path you tag
- Attach rate by shift, server band, and outlet when identifiers exist
- Cannibalization of a la carte on the same visit or short window after
- Platform versus dine-in performance for the same offer family
Commercial teams use combo upsell tracking with menu performance analysis to fund bundles that build contribution, not only ticket.
Causal chain: deep combo to margin
New menu item launch performance
New menu item analytics too often means a one-slide deck on day one. Finance wants payback, ops wants runability, and marketing wants trial without a common window on the same item master.
FireAI tags launch windows from calendar and first-seen-in-POS, sets trial and repeat reads by channel, and bridges to menu performance analysis when the item graduates from test list to core. New menu item analytics includes margin after ramp waste and training error where your tags support it.
How FireAI solves the problem: It keeps new menu item analytics on a clock so f&b menu analytics retires failed tests before they eat eight weeks of line space.
What FireAI tracks:
- Trial rate, second visit purchase, and mix share by week after launch
- Channel split for dine-in, delivery, and QSR for the same LTO
- Margin bridge from recipe drift, intro pricing, and promo stack
- Cannibalization of adjacent SKUs in the same section
Teams use new menu item analytics with bestseller dead item identification to trade in long-tail codes for launches that show repeat and margin.
Ask FireAI about new dishes
See how your team can ask questions in plain language and get instant analytics answers.