# Quantiiv

> Quantiiv is a restaurant decision-intelligence and pricing platform for multi-unit restaurant brands and franchise systems. It unifies item-level POS transaction data, loyalty and digital-order data, and market context into a governed data warehouse, then applies pricing science on top: store-level price elasticity, surgical pricing plans, honest measurement of what price and promotion changes actually did, menu intelligence, and customer analytics. Restaurants are art and science; Quantiiv is built by operators who know the art and bring the science.

Key facts:
- Audience: multi-unit restaurant brands, franchisors, and franchisees
- Core capability: item-level, store-level price elasticity measured from a brand's own POS history
- Pricing plans are surgical: item-by-item, store-by-store price files built to hit a target check comp with minimal customer impact
- Pricing and promotion impact is measured against what would have happened anyway, separating the effect of the change from the market and the season
- Customer analytics always disclose trackability (the share of sales attributable to identifiable customers)
- ROGER is Quantiiv's AI analyst: operators ask data questions by email and get analyst-grade answers
- ROGER respects permissioned user and location access, so franchisees can ask about only their authorized stores while brand teams retain system-wide visibility

## Solutions

Problem-specific pages, each answering a question restaurant operators ask:

### Pricing & Elasticity

Know how much pricing power your menu has, take price surgically, and measure what the increase actually did.

- [Price Elasticity](https://www.quantiiv.com/solutions/restaurant-price-elasticity): How much can we raise prices without losing traffic? Price elasticity measures how much demand for a menu item changes when its price changes. Most restaurant brands price off a single blended assumption, but elasticity is not one number. It varies by item, by store, and by market. Quantiiv models elasticity at the item and store level from your own POS transaction history, so you know exactly which items can carry a price increase and which ones will cost you traffic.
- [Menu Pricing Strategy](https://www.quantiiv.com/solutions/raise-menu-prices-without-losing-customers): We need to take price this year. How do we do it without customers noticing or traffic falling? The safest way to raise menu prices is many small, targeted increases instead of one visible across-the-board move. Customers do not react to all price changes equally, and the difference is measurable in your own transaction data. Quantiiv builds pricing plans that spread your target check growth across hundreds of small item-by-store decisions, weighted toward the items and stores your demand history says can carry them and away from the changes customers actually notice.
- [Zone Pricing](https://www.quantiiv.com/solutions/restaurant-zone-pricing): Should all of our locations really charge the same prices? Almost certainly not. Store-level elasticity data consistently shows that price sensitivity varies widely across locations, even within the same city. A single national or system-wide price list means your least sensitive markets are underpriced and your most sensitive markets are carrying increases they cannot afford. Zone pricing groups stores by demonstrated pricing power, not just geography or income demographics, and prices each zone to what its customers will actually support.
- [Price Impact Measurement](https://www.quantiiv.com/solutions/measure-price-increase-impact): We took a price increase and sales are up. How do we know the increase worked and did not just ride a good quarter, or quietly cost us traffic? Comparing sales before and after a price increase cannot answer whether it worked, because everything else moved too: seasonality, weather, the broader market, promotions, and traffic trends that were already underway. Answering it honestly means isolating what your pricing action did from everything happening around it. That is the question Quantiiv is built to answer, and the answer looks different depending on how the increase rolled out and what data you have.
- [Pricing Experimentation](https://www.quantiiv.com/solutions/restaurant-pricing-experimentation): We want to try a price change at some stores first. How do we set that up so the result actually means something? A pricing test is only as good as its design. Pick the wrong test stores, skip the control group, or read the results too early, and you get an answer that feels rigorous but is really noise. A proper pricing experiment needs three things: treatment stores that represent the system, true control stores matched on market, volume, and trend that receive no change at all, and a pre-committed read date long enough for customer behavior to settle. Quantiiv designs all three before a single price moves.

### Menu Intelligence

Understand what every item earns and carries, so menu changes are designed instead of debated.

- [Menu Rationalization](https://www.quantiiv.com/solutions/menu-rationalization): Our menu has gotten too big. Which items can we actually remove without losing the customers attached to them? The danger in menu rationalization is not cutting an item, it is cutting a customer. An item with modest sales can still be the only reason a loyal group visits, the anchor of a high-value basket, or the veto-breaker that lets a group choose your brand. Quantiiv evaluates every removal candidate on three questions the sales report cannot answer: who buys it, what else is in their basket, and where their spend goes if it disappears.
- [Menu Engineering](https://www.quantiiv.com/solutions/menu-engineering-analytics): Which items actually drive our revenue and margin, and how should the menu change? Menu engineering answers three questions with data: what each item contributes, what role it plays in baskets and visits, and what would happen if you changed its price, placement, or existence. The classic popularity-versus-margin matrix answers only the first, and only partially. Quantiiv builds the full picture from item-level POS history: item and category performance with trend, basket pairings and attachment, substitution relationships, and price sensitivity, all normalized across locations and channels.
- [LTO & New Item Analysis](https://www.quantiiv.com/solutions/restaurant-lto-analysis): Our LTO sold well. But did it grow the business, or did it just cannibalize the core menu? An LTO's own sales number is the least informative fact about it. The questions that decide whether it worked are comparative: how much of its volume came out of the core menu it sat next to, whether it brought in customers who would not have visited otherwise, what it did to check sizes, and whether anything it built survived after it left the menu. Quantiiv answers each one from transaction data, separating the LTO's genuine contribution from the sales it merely relabeled.
- [New Item Incrementality](https://www.quantiiv.com/solutions/new-menu-item-incrementality): Our new item is selling. Is it actually growing the business, or just moving sales around the menu? A new menu item is incremental only if the total business grew beyond where it was already heading — beyond its pre-launch trend and its normal seasonal pattern — and that growth traces to the item rather than to momentum that was coming anyway. The item's own sales figure cannot answer this, and neither can its category's: a category can grow while the rest of the menu shrinks by the same amount. Quantiiv measures what a launch really added, net of what it took from the menu around it.

### Customer Analytics

See who comes back, who leaves, and which customers and habits are worth investing in.

- [Customer Analytics](https://www.quantiiv.com/solutions/restaurant-customer-analytics): Who are our customers, are they coming back, and which ones are actually worth investing in? Most restaurant brands can quote their sales to the dollar and cannot say what share of last year's customers ever returned. Customer analytics closes that gap: retention and churn by cohort, lifetime value by segment, visit frequency and habit formation, and where new customers come from and which ones convert into regulars. Quantiiv builds this from the customer identity you already have, loyalty accounts, digital orders, and payment signals, tied to item-level transaction history.
- [Loyalty Analytics](https://www.quantiiv.com/solutions/restaurant-loyalty-program-analytics): Our loyalty program costs real money in discounts and technology. Is it actually changing customer behavior, or rewarding visits we would have gotten anyway? A loyalty program pays for itself only if it changes behavior: more visits, bigger checks, or longer customer lifetimes than would have happened without it. Platform dashboards cannot answer that, because members were already your better customers before they joined; comparing members to non-members flatters the program by construction. Quantiiv measures loyalty impact from transaction behavior: how frequency and spend change after enrollment, whether reward economics are sized sensibly, and whether points and offers are building habits or subsidizing them.

### Locations & Franchise

Diagnose store performance fairly and give every operator evidence they can act on.

- [Location Performance](https://www.quantiiv.com/solutions/restaurant-location-performance): One of our locations is falling behind. Is it the market, the operation, the menu, or something else, and what do we do about it? An underperforming location is a symptom with a dozen possible diseases: traffic loss versus check decline, one daypart versus all of them, dine-in versus delivery, a local competitor, an operational change, or a market-wide shift the store never caused. Quantiiv diagnoses store performance systematically, decomposing the gap into its components and comparing the store against a fair peer benchmark rather than the system average, until the explanation is specific enough to act on.
- [Franchise Analytics](https://www.quantiiv.com/solutions/franchise-analytics): How do we get real visibility across franchisees, give each operator access to only their stores, and turn the data into action? Franchise analytics software should give the franchisor system-wide truth without exposing every operator to every store. Quantiiv normalizes performance across the whole system, then applies permissioned access so each franchisee can see and ask questions about only the locations they are authorized to manage. That creates one analytical foundation with the right view for every user, rather than separate reports that drift apart.
- [Labor Analytics](https://www.quantiiv.com/solutions/restaurant-labor-analytics): Labor is our biggest controllable cost. Are we actually scheduling to demand, and which stores have it right? Most restaurant schedules are last week's schedule with edits, which means labor drifts away from demand one small decision at a time. Labor analytics puts the two back side by side: transaction demand by store, day, and daypart against the hours deployed to serve it. The output is specific, not philosophical: which stores and shifts run heavy, which run dangerously lean, and what the efficient stores in your own system do differently.
- [3rd-Party Delivery Profitability](https://www.quantiiv.com/solutions/third-party-delivery-profitability): Between the commissions and the packaging, is third-party delivery actually making us money? Third-party delivery is profitable when its orders are incremental, customers you would not have served otherwise, and a margin problem when it mostly shifts your own dine-in and pickup business into a channel that pays a commission on every order. The commission is certain; the incrementality is not, and it varies enormously by store, market, and daypart. Quantiiv measures it from your transaction data: what happened to your direct channels as 3PD grew, how baskets and margins differ by channel, and which stores' delivery business is genuinely additive.

### Discounts & Promotions

Separate incremental sales from margin giveaways across every offer you run.

- [Discount Effectiveness](https://www.quantiiv.com/solutions/restaurant-discount-effectiveness): We discount heavily and sales respond. But are the discounts creating new business, or just giving margin away on visits that would have happened anyway? A discount only works if it changes behavior: a visit that would not have happened, a basket that would have been smaller, a lapsed customer who came back. Redemption volume proves none of that. Most discount programs, measured honestly, are a mix of genuinely incremental sales, subsidized visits that were coming anyway, and pulled-forward demand that borrows from next week. Quantiiv separates the three from transaction data, offer by offer, so you know which promotions earn their cost and which just relabel existing revenue at a lower margin.

### Digital & Online Ordering

Find where your ordering funnel leaks and which marketing dollars actually produce orders.

- [Online Ordering Analytics](https://www.quantiiv.com/solutions/restaurant-online-ordering-analytics): We drive traffic to our ordering site. Where do we lose people, and is our digital marketing actually producing orders? Every online ordering flow is a funnel: visitors arrive, browse the menu, build a cart, start checkout, and pay, and at each step some share disappears. Small leaks compound: a modest improvement in checkout completion is often worth more than a large increase in ad spend, because it converts demand you already paid for. Quantiiv instruments the funnel from web analytics and ties it back to POS transaction truth, so conversion is measured against orders that actually happened, not just analytics events.

### Diagnostics & Measurement

When the number moves and nobody knows why, decompose it until the cause is actionable.

- [Sales Decline Diagnosis](https://www.quantiiv.com/solutions/restaurant-sales-decline-diagnosis): Comps are negative and every meeting produces a different theory. What is actually driving the decline? A sales decline is an aggregate, and aggregates hide their causes. The fastest way to a real answer is decomposition: split the decline into traffic versus check, then by daypart, channel, location, category, and customer segment, and compare each piece against the market's own movement. Done systematically, a vague negative comp almost always resolves into something specific: a traffic problem concentrated in weekday lunch, a check problem from mix trading down, three locations dragging the average, or a market-wide downturn the brand is actually weathering better than its peers.
- [Comp Sales Done Right](https://www.quantiiv.com/solutions/restaurant-comp-sales): Our comp number drives every board conversation. Are we even calculating it right? Comparable-store sales, comps or same-store sales, measure growth from stores open long enough to have a fair year-over-year comparison, stripping out the noise of openings and closings. The definition sounds simple; the implementation is where brands go wrong. Which stores qualify, how long they must be open, how remodels, transfers, and temporary closures are handled, and whether the calendar is aligned week-for-week all change the number, sometimes by more than the trend being reported.

### Market & Competitive Context

Know what surrounds your stores and judge every location against what its market actually allows.

- [Competitive Density](https://www.quantiiv.com/solutions/restaurant-competitive-density): How much competition does each of our locations really face, and is it showing up in our numbers? Every store's performance is judged against expectations, and expectations without competitive context are half blind. A store holding flat in a trade area that added three relevant competitors is outperforming; a store growing modestly in a market competitors abandoned is coasting. Quantiiv builds the competitive picture around every location: which nearby restaurants actually compete for your customers, at what distances, and how that pressure differs across the fleet.

### Data Foundation

One governed source of truth across every POS system, location, and channel.

- [POS Data Normalization](https://www.quantiiv.com/solutions/restaurant-pos-data-normalization): Every location configures the POS differently and every report tells a different story. How do we get one source of truth? Multi-unit restaurant data is messy in a specific, predictable way: the same product lives under a dozen POS names across locations, sizes ride as modifiers at one store and separate items at another, categories drift with every menu update, and franchisees improvise their own conventions. Analytics built on that raw layer produces confident numbers about fragments, which is why reports disagree and why brands quietly stop trusting them. The fix is normalization: every POS record mapped to one clean item, category, and location structure, maintained as governed infrastructure rather than cleaned ad hoc per report.

## Guides

- [Restaurant Menu Pricing: The Complete Guide](https://www.quantiiv.com/guides/restaurant-menu-pricing): How to take price without losing customers — pricing methods, elasticity, surgical increases, zone pricing, testing, and honest impact measurement.

## Glossary

Plain-English definitions of restaurant pricing and analytics terms:

### Pricing & Elasticity

- [Price Elasticity](https://www.quantiiv.com/glossary/price-elasticity): Price elasticity measures how much demand for a menu item changes when its price changes. An elastic item loses meaningful volume when its price goes up; an inelastic item holds its volume, so a price increase converts almost entirely into revenue. In restaurants, elasticity is not one number: the same item can be elastic in one store and inelastic in another, which is why item-level, store-level measurement matters.
- [Pricing Power](https://www.quantiiv.com/glossary/pricing-power): Pricing power is the amount of price a menu can absorb before customers meaningfully change their behavior — visiting less often, trading down, or leaving. A brand's pricing power is the sum of many small, item-level facts: some items can carry several increases without volume loss, others punish a fifty-cent move. Locating where that power actually lives on the menu is the core job of pricing analytics.
- [Zone Pricing](https://www.quantiiv.com/glossary/zone-pricing): Zone pricing (also called tiered or market pricing) is the practice of charging different menu prices in different markets or store groups instead of one uniform price everywhere. Stores are grouped into zones based on what their markets can support — costs, incomes, competition, and measured price sensitivity — and each zone gets its own price file.
- [Dynamic Pricing](https://www.quantiiv.com/glossary/dynamic-pricing): At Quantiiv, dynamic pricing means setting the optimal price for every store-product combination based on customers' maximum willingness to pay. The price can be revisited as new evidence changes that estimate, but it does not move minute by minute or rise when demand spikes. We do not advocate Uber-style surge pricing, demand-based increases, daypart pricing outside a transparent program such as happy hour, or different prices across a brand's owned channels. Third-party delivery marketplaces are the exception: brands may charge more there to offset commissions and other channel-specific costs.
- [Menu Price Optimization](https://www.quantiiv.com/glossary/menu-price-optimization): Menu price optimization is the process of setting each menu item's price using measured customer demand response, rather than applying uniform percentages or cost-plus markups. The output is a specific price file — item by item, and for multi-unit brands, store by store — designed to hit a revenue or check target with the least possible customer impact.
- [Price Testing](https://www.quantiiv.com/glossary/price-testing): Price testing is trying a price change in a subset of stores and measuring customer response before rolling it out system-wide. A valid test compares test stores against carefully matched control stores — locations with similar volume, market, and trend — so that the difference in performance can be attributed to the price change rather than to weather, seasonality, or local noise.
- [Cost-Plus Pricing](https://www.quantiiv.com/glossary/cost-plus-pricing): Cost-plus pricing sets a menu item's price by marking up its ingredient cost to hit a target food-cost percentage — for example, pricing a dish with $3 of ingredients at $10 for a 30% food cost. It is the restaurant industry's default method because it is simple and protects margins on paper, but it prices from the kitchen's perspective and ignores what customers are willing to pay.

### Menu Intelligence

- [Menu Engineering](https://www.quantiiv.com/glossary/menu-engineering): Menu engineering is the practice of classifying menu items by popularity and profitability to decide what to promote, reprice, rework, or remove. The classic framework sorts items into four quadrants: stars (popular and profitable), plowhorses (popular, low margin), puzzles (profitable, low volume), and dogs (neither). It turns the menu from a list of recipes into a portfolio with explicit roles.
- [Menu Mix (PMIX)](https://www.quantiiv.com/glossary/menu-mix): Menu mix — commonly called PMIX, for product mix — is the breakdown of what customers actually order: each item's share of units sold or sales dollars. A brand's average check and margin are as much a function of mix as of prices, because customers shifting between items changes profitability even when no price moved and traffic held flat.
- [Menu Rationalization](https://www.quantiiv.com/glossary/menu-rationalization): Menu rationalization is the systematic process of deciding which menu items earn their place and which should be removed, using data on sales, margin, operational cost, and — critically — what each item carries with it: the attach sales, the customers who visit specifically for it, and where its volume would go if it disappeared.
- [Contribution Margin](https://www.quantiiv.com/glossary/contribution-margin): Contribution margin is the dollar amount a menu item leaves behind after its direct costs: selling price minus ingredient (and, done rigorously, direct preparation) cost. It is the number a menu should be managed on, because percentages mislead — a burger at 35% food cost contributing $6 per sale is worth more to the business than a side at 20% food cost contributing $1.60.
- [Cannibalization](https://www.quantiiv.com/glossary/cannibalization): Cannibalization — also called internal cannibalization — is when a new item, promotion, price change, or new location pulls sales away from a brand's own existing items or stores rather than generating new demand. A limited-time offer that sells ten thousand units has not added ten thousand sales if most of its buyers would otherwise have ordered a full-price item — the launch number is gross, and the business impact is net.

### Customer Analytics

- [Customer Trackability](https://www.quantiiv.com/glossary/customer-trackability): Customer trackability is the share of a restaurant's sales that can be attributed to identifiable customers — loyalty members, digital-order accounts, and recognizable payment cards. It is the denominator behind every customer metric: retention, frequency, and lifetime value are only measured on the trackable portion, so any customer analysis that doesn't disclose its trackability is describing a sample of unknown size.
- [Guest Retention Rate](https://www.quantiiv.com/glossary/guest-retention-rate): Guest retention rate is the share of a restaurant's customers who come back within a defined window — for example, the percentage of this quarter's identified guests who visit again next quarter. It converts the vague worry 'are we losing customers?' into a measurable number that can be tracked over time, compared across stores, and decomposed by customer segment.
- [Customer Lifetime Value (CLV)](https://www.quantiiv.com/glossary/customer-lifetime-value): Customer lifetime value (CLV) is the total contribution a guest is expected to bring over their entire relationship with a brand. For restaurants it is built from three measurable components: how often the guest visits, what they spend per visit, and how long they stay a customer. A guest visiting twice a month at a $14 check who stays three years is roughly a thousand-dollar relationship — which reframes what one bad visit or one good save is worth.
- [Visit Frequency](https://www.quantiiv.com/glossary/visit-frequency): Visit frequency is how often a guest visits within a period — visits per month or per quarter, measured on identified customers. Brand-level sales moves that get blamed on 'losing customers' are frequently a frequency story instead: the same guests coming slightly less often, a shift that is invisible in traffic counts but obvious in identified-customer data.
- [Attach Rate](https://www.quantiiv.com/glossary/attach-rate): Attach rate is how often one item is purchased alongside another — the share of entrée orders that add a drink, a side, or a dessert. It measures the basket-building behavior of the menu: some items earn little on their own but reliably pull high-margin attachments with them, which makes their true value invisible in a simple item sales report.

### Measurement & Market

- [Same-Store Sales (Comps)](https://www.quantiiv.com/glossary/same-store-sales): Same-store sales — comps, or comparable sales — measure sales growth at locations open long enough (conventionally at least a year) to be compared against their own prior-year performance. By excluding newly opened and closed stores, comps isolate whether the existing business is actually growing, rather than whether the brand is simply adding units.
- [Check Average](https://www.quantiiv.com/glossary/check-average): Check average is sales divided by transaction count — the average amount a customer spends per visit. It is one of the two levers of sales (the other is traffic), but it is a composite: check moves not only when prices change, but when mix shifts, attach behavior changes, party sizes move, or channels with different basket sizes grow or shrink.
- [Sales Decomposition](https://www.quantiiv.com/glossary/sales-decomposition): Sales decomposition is the discipline of breaking a sales change into its component causes — traffic versus check, price versus mix within check, channel shifts, daypart shifts, store groups, and market backdrop — until the driver is specific enough to act on. 'Sales are down 4%' is a symptom; 'lunch traffic at urban locations is down 9% since the March price increase' is a decision.
- [Incrementality](https://www.quantiiv.com/glossary/incrementality): Incrementality is the portion of a promotion's sales that would not have happened without it. A redeemed offer is not automatically an incremental sale: a discount used by a guest who was coming anyway, on an item they would have bought anyway, is a margin giveaway wearing a marketing costume. Incrementality separates the demand a promotion created from the demand it merely subsidized.
- [Counterfactual Baseline](https://www.quantiiv.com/glossary/counterfactual-baseline): A counterfactual baseline is an estimate of what would have happened without the change being measured — what sales would have been if the price increase, promotion, or menu change had never shipped. It is the honest denominator for impact measurement, replacing the misleading defaults: last year's number, the week before, or the system average, all of which bundle the change's effect together with seasonality, weather, and market trend.
- [Trade Area](https://www.quantiiv.com/glossary/trade-area): A trade area is the geographic zone a restaurant location actually draws its customers from — typically defined by drive time or observed customer origin rather than a simple radius. It is the store's real market: its population, incomes, daytime traffic, and competitive set, which together determine what performance is achievable at that address.
- [Average Unit Volume (AUV)](https://www.quantiiv.com/glossary/average-unit-volume): Average unit volume (AUV) is a restaurant brand's average annual net sales per location: total sales divided by the number of locations open for the full period. It is the standard shorthand for a concept's unit-level strength — the number franchise buyers, lenders, and analysts reach for first — because it compresses the entire system's performance into one comparable figure.

### Data Foundation

- [Item-Level POS Data](https://www.quantiiv.com/glossary/item-level-pos-data): Item-level POS data is the transaction-line detail a point-of-sale system records: every item on every check, with its price, modifiers, discounts, channel, and timestamp. It is the difference between knowing a store sold $8,200 on Tuesday and knowing exactly what was in every basket — and it is the raw material for every serious restaurant analytics question, from elasticity to attach rates to customer behavior.
- [Menu Mapping](https://www.quantiiv.com/glossary/menu-mapping): Menu mapping is the normalization of menu items across POS systems, locations, and channels so that the same real-world item is recognized as the same item everywhere — whether it was rung in as 'CHZBRGR', 'Cheeseburger Combo', or a delivery-platform variant. It sounds clerical; it is actually the load-bearing step under all menu and pricing analytics.
- [Restaurant Data Warehouse](https://www.quantiiv.com/glossary/restaurant-data-warehouse): A restaurant data warehouse is a single governed repository that unifies a brand's data — item-level POS transactions, loyalty and digital-order data, labor, and market context — across every location, POS platform, and channel. Its job is to make the brand's numbers agree with each other: one definition of an item, a store, a customer, and a metric, so every downstream analysis starts from the same truth.

## Insights (blog)

- [Why Across-the-Board Price Increases Quietly Lose Traffic](https://www.quantiiv.com/posts/across-the-board-price-increases-lose-traffic): The most common way restaurants take price — a flat percentage across the menu — is also the most expensive. It over-prices the items customers care about and under-prices the ones that could carry more, so the margin gained on one side leaks out the other as lost visits.
- [How to Unify Data from Multiple POS Systems](https://www.quantiiv.com/posts/how-to-unify-data-from-multiple-pos-systems): Multi-location restaurant brands often inherit several POS systems, leaving sales, labor, and menu data fragmented. A POS-agnostic data layer unifies those systems without disrupting restaurant operations.
- [AI for Restaurants: Practical Uses, Real Examples, and How to Get Started](https://www.quantiiv.com/posts/ai-for-restaurants-practical-uses-real-examples-and-how-to-get-started): AI is now accessible to independent restaurants and small groups through affordable, practical tools. This guide covers real applications across guest service, kitchens, staffing, marketing, and a 90-day rollout.
- [The Risk of Uninformed AI in Restaurant Analytics](https://www.quantiiv.com/posts/the-risk-of-uninformed-ai-in-restaurant-analytics): AI can process restaurant data quickly, but speed amplifies mistakes when the system lacks restaurant context. Domain-aware analytics protects operators from polished but fundamentally flawed conclusions.
- [Why Your Operating Hours Might Be Costing You Money (And How to Find Out)](https://www.quantiiv.com/posts/why-your-operating-hours-might-be-costing-you-money-and-how-to-find-out): Late-night or early-morning sales do not automatically make those hours profitable. Hourly contribution margin reveals which operating hours create value, which lose money, and where strategic exceptions matter.
- [The Macro Context That Changes Everything](https://www.quantiiv.com/posts/the-macro-context-that-changes-everything): A restaurant traffic decline can signal a problem or an outperformance, depending on the market around it. Industry, category, economic, local, calendar, and weather context turn raw numbers into insight.
- [New Store Analysis — A Different Playbook Entirely](https://www.quantiiv.com/posts/new-store-analysis-a-different-playbook-entirely): New restaurant performance cannot be judged against mature-store averages. The useful signals are ramp trajectory, comparison with stores at the same lifecycle stage, and expectations adjusted for the market.
- [The "Dead Item" That Shouldn't Die — Understanding Penetration vs. Habituation](https://www.quantiiv.com/posts/the-dead-item-that-shouldnt-die-understanding-penetration-vs-habituation): A low-volume menu item is not necessarily a dead item. Comparing how many customers buy it with how often those customers return to it separates products nobody wants from niche winners with loyal demand.
- [Why "Average Ticket" Analysis Is Rarely Useful (And What to Do Instead)](https://www.quantiiv.com/posts/why-average-ticket-analysis-is-rarely-useful-and-what-to-do-instead): Restaurant average ticket is an outcome, not an explanation. Price changes, product mix, attachment, channel shifts, and catering can all move it, so operators need to decompose the number before treating growth as a win.
- [The Right Way to Judge a New Product Launch (And Why Most Brands Get It Wrong)](https://www.quantiiv.com/posts/the-right-way-to-judge-a-new-product-launch-and-why-most-brands-get-it-wrong): Total units sold cannot tell you whether a new restaurant product succeeded. Trial, repeat behavior, incrementality, customer composition, and a normalized launch window reveal whether the item created durable demand.
- [From Multiple POS Systems to 1 Source of Truth: Building Great Harvest's Unified Data Foundation with Quantiiv.](https://www.quantiiv.com/posts/from-multiple-pos-systems-to-1-source-of-truth-building-great-harvests-unified-data-foundation-with-quantiiv): Great Harvest needed a single analytical view across more than one hundred independently operated franchise locations using multiple POS systems. Quantiiv built a governed, POS-agnostic data foundation that preserved local flexibility while enabling systemwide analysis.
- [Solving the 3 Burger Problem - The Importance of Flexible Database Architecture](https://www.quantiiv.com/posts/solving-the-3-burger-problem-the-importance-of-flexible-database-architecture): When the same menu item has different names across locations or POS systems, reporting fractures and analytics become unreliable. A flexible mapping layer creates one consistent menu view without forcing every restaurant into the same operating system.
- [Meet ROGER: The Restaurant AI Analyst Who Lives in Your Inbox](https://www.quantiiv.com/posts/meet-roger-the-restaurant-ai-analyst-who-lives-in-your-inbox): ROGER is Quantiiv's email-based AI analyst for restaurant operators. It answers questions from governed warehouse data, cites its sources, and returns the underlying data so teams can act without living in dashboards.
- [The Understated Importance of POS-Agnostic Data Infrastructure](https://www.quantiiv.com/posts/the-understated-importance-of-pos-agnostic-data-infrastructure): POS-agnostic infrastructure gives restaurant brands one governed data foundation across corporate, franchise, acquired, and pilot locations. It protects historical data, makes reporting consistent, and keeps POS vendors replaceable.

## Press & Articles

Verified external sources. These links go to the original publishers:

- [Restaurant Price Cuts Make Headlines. But Here’s Why the Hangover Lasts Years](https://www.qsrmagazine.com/story/restaurant-price-cuts-make-headlines-but-heres-why-the-hangover-lasts-years/) (QSR Magazine, 2026-06-17): A Quantiiv perspective on why broad restaurant price cuts can reset guest expectations for years, and why operators should build demand and use pricing power surgically instead.
- [Digital Transactions Covers Quantiiv’s Virtual Executive for Restaurants](https://www.digitaltransactions.net/quantiivs-virtual-executive-for-restaurants-popmenu-and-spoton-create-a-unified-commerce-platform/) (Digital Transactions, 2026-04-15): Digital Transactions examines how ROGER connects restaurant data through an email-based interface, interprets business context, and returns clear guidance for menu, pricing, and customer decisions.
- [Restaurant Business Profiles Quantiiv’s Email-Based AI Analyst](https://www.restaurantbusinessonline.com/technology/ex-starbucks-staffers-launch-ai-analyst-you-can-email) (Restaurant Business, 2026-04-13): Restaurant Business takes an independent look at how ROGER turns a restaurant’s unified data into plain-language answers and recommendations delivered through email, and how operators use it in practice. Publisher access may be required.
- [Quantiiv Introduces AI Decision Partner That Thinks Like a Restaurant Executive](https://www.businesswire.com/news/home/20260413492171/en/Quantiiv-Introduces-AI-Decision-Partner-That-Thinks-Like-a-Restaurant-Executive) (Business Wire, 2026-04-13): Quantiiv announces ROGER, an AI decision partner built for restaurant operators. The release explains how it unifies fragmented business data and returns clear interpretation and recommended action in plain language.

## Main pages

- [Home](https://www.quantiiv.com): Restaurant data warehouse and intelligence platform built by operators
- [Applied Pricing & Elasticity](https://www.quantiiv.com/applied-pricing): The flagship pricing science offering
- [Solutions](https://www.quantiiv.com/solutions): All problems Quantiiv solves, organized by topic
- [How We Work](https://www.quantiiv.com/how-we-work): The three layered engagement modes — Software, Sessions, and Strategy
- [About](https://www.quantiiv.com/about): Why Quantiiv exists and what it believes — built by restaurant operators
- [Competitive Edge](https://www.quantiiv.com/newsletter): Quantiiv's free monthly newsletter on restaurant pricing and analytics
  - [July 2026: Earning Traffic in an Era of Selective Spending](https://www.quantiiv.com/newsletter/2026-07-competitive-edge): Consumers are becoming more selective as inflation outpaces wages, raising the value of pricing discipline, customer analytics, and stronger occasions.
  - [June 2026: Will a Wobbled Consumer Still DoorDash?](https://www.quantiiv.com/newsletter/2026-06-competitive-edge): Economic pressure is widening the gap between consumers, while DoorDash's growth shows that convenience can still earn a premium.
  - [April 2026: Price Cuts Make Headlines. The Data Tells a Different Story.](https://www.quantiiv.com/newsletter/2026-04-competitive-edge): Restaurant traffic showed early signs of stabilization, while PepsiCo's results challenged the headline case for broad price cuts.
  - [August 2025: Competitive Edge: August 2025](https://www.quantiiv.com/newsletter/2025-08-competitive-edge): August 2025 restaurant brief: traffic gained momentum as inflation and wellness trends reshaped consumer behavior, earnings, and operators’ pricing outlook.
  - [July 2025: Competitive Edge: July 2025](https://www.quantiiv.com/newsletter/2025-07-competitive-edge): July 2025 restaurant brief: resilient spending met softer traffic and inflation uncertainty, sharpening the case for proactive, customer-aware pricing.
- [Press & Articles](https://www.quantiiv.com/press): Verified company announcements, independent coverage, and contributed articles
- [Contact](https://www.quantiiv.com/contact): Book a demo

## Full content

- [llms-full.txt](https://www.quantiiv.com/llms-full.txt): The full text of Quantiiv's evergreen content in one file

## Contact

- Website: https://www.quantiiv.com
- Email: info@quantiiv.com