QSR Pricing Elasticity Studio
Understand how customers respond to menu pricing, combos, and promotions - and optimize revenue, orders, and margins.
Current State Overview
Before optimizing pricing, let's understand the current menu structure, pricing tiers, and key business metrics driving orders, revenue, and margins.
Loading Data
Initializing QSR demand, pricing, and elasticity datasets...
Current Price Architecture
Key Performance Metrics (Latest Week)
The Question
How much pricing room does QSR have before weekly orders, margin quality, or promo dependence start to break? The next steps move from the current state into the data, cohorts, and elasticity models that answer that question.
Data Explorer
This is the unified data foundation powering the pricing engine - combining menu, pricing, promotions, channels, and performance into a single, analysis-ready view.
Acquisition Elasticity
See how price changes impact weekly orders across customer occasions and order channels. Understand the revenue impact and identify where pricing can drive growth without hurting demand.
Churn Elasticity
See how price changes reduce repeat visits over time and impact long-term demand.
Order Channel Migration
Understand how pricing and fee changes shift orders between delivery and owned channels-and identify where to rebalance pricing to protect margin without losing volume.
Customer Cohorts & Elasticity
Understand how different customer cohorts respond to pricing - and identify where to increase price, protect volume, or drive premium spend.
Segment Elasticity Comparison
Compare price elasticity across different customer segments. Identify which segments are most and least price-sensitive.
Event Calendar
Track pricing actions, promotions, and seasonal events - and understand how they impact orders, revenue, and customer behavior. This creates the foundation for accurate elasticity modeling and scenario simulation.
AI Chat & Advanced Analytics
Chat with AI to get insights, ask questions about your data, and access advanced analytical tools.
QSR Pricing Studio
Item-level and channel-aware price simulation on top of the QSR operating panel.
Contribution margin follows the current seeded item margin rate. This is where channel and commodity enrichment should go next.
Choose a QSR item and run a scenario to see the readout.
- Loading QSR pricing guidance...
QSR Data Explorer
Foundation Tables
Dataset Snapshot
Loading data...
Select a QSR foundation table from the left sidebar to inspect the modeled pricing inputs.
Scenario Engine - 3 Elasticity Models
Simulate QSR pricing scenarios across traffic acquisition, repeat-visit loss, and order-channel migration models
- Loading acquisition elasticity summary...
- Loading recommended actions...
Adjust Price
How To Read This View
This readout is always scoped to the selected visit mission and order channel. An elasticity of 1.19 means demand is moderately price sensitive, with value-led ladders reacting faster than premium baskets.
Projected Weekly Orders by Product Ladder
Elasticity by Product Ladder
| Ladder | Elasticity | Sensitivity | Projected Change |
|---|---|---|---|
| Value & Personal Meals | -2.5 | High | 0% |
| Core Pizza Orders | -1.9 | Medium | 0% |
| Premium & Shareables | -1.6 | Low | 0% |
Model 1: Acquisition Elasticity
What it captures: How QSR weekly order traffic responds when effective check changes within a chosen channel and visit mission.
Key Insight: Stable channels can absorb more price, while digital and value-led missions usually react first when the check moves too far.
Pre-Built Scenarios
Note: Detailed cohort analysis tables will appear below after running a scenario simulation.
Simulate Price Increase
Why Time-Lagged?
Guests usually do not react immediately. Some absorb the move for a few meal cycles, while others change behavior once coupons, bundles, or app offers stop offsetting the increase. Peak repeat loss typically appears 8-12 weeks after a price move.
Revenue Note: The cumulative revenue line accumulates over time. With extreme price increases or highly elastic cohorts (Deal Hunter), it can peak then decline as customer loss outweighs price gain.
Cumulative Repeat-Visit Loss Over Time
Retention Forecast & Contribution Impact
Repeat-Loss Impact by Time Horizon
Model 2: Churn Elasticity (Time-Lagged)
What it captures: When QSR guests trim visit frequency after menu price increases or value support expires.
Key Insight: Visit loss usually peaks 8-12 weeks after a price move, once meal cycles and promo offsets wear off.
Pre-Built Scenarios
Note: Detailed cohort repeat-loss heatmaps will appear below after running a scenario simulation.
Adjust Channel Checks
Migration Dynamics
When carryout and pickup keep a clear value edge, more QSR traffic migrates out of delivery. If that edge disappears, guests fall back to delivery or leak into other channels.
Migration Flow Diagram
Channel Mix Shift (Delivery → Pickup)
| Transition | Probability | Weekly Orders | Revenue Impact |
|---|---|---|---|
| Delivery → Carryout / Pickup | 8.2% | ~850 | +$2,550 |
| Carryout / Pickup → Delivery | 3.5% | ~420 | -$1,260 |
| Delivery → Dine-In / Other | 4.2% | ~430 | -$2,574 |
| Carryout / Pickup → Dine-In / Other | 3.8% | ~460 | -$4,134 |
Channel Mix Over Time
Model 3: Order Channel Migration
What it captures: How QSR guests switch between delivery and carryout / pickup when relative price gaps change.
Decision Takeaway: Preserve enough owned-channel value to win carryout and pickup migration without creating excess delivery leakage or total traffic loss.
Pre-Built Scenarios
Note: Use the flow diagram and mix shift cards above as the primary migration readout for QSR.
Decision Engine - Auto-Ranking
Top 3 Recommendations
Simulation Results
Revenue Impact
Order Impact
Cohort-Level Acquisition Response
| Cohort | Size | Traffic Elasticity | Order Lift @ P-5% | Order Lift @ P+5% | Recommendation | Confidence |
|---|
Cohort Repeat-Loss Uplift Heatmap (Δ vs baseline)
| Cohort | 0-4 Weeks | 4-8 Weeks | 8-12 Weeks | 12+ Weeks |
|---|
Elasticity Analysis
Scenario Comparison
Compare multiple scenarios side-by-side
KPI Comparison
Multi-Dimensional Trade-offs
Customer Cohorts & Elasticity
Understand how different customer cohorts respond to pricing - and where to protect traffic versus expand margin.
- Loading cohort insights...
Cohort Elasticity Heatmap
3-Axis Cohort Map
Cohort Scatter: Customers vs Elasticity
Channel View
Bar: Elasticity by channel
Heatmap: Channel × Price-Tier Sensitivity
Price-tier summary (elasticity & mix)
Cohort Elasticity Comparison
Loading cohort comparison insight...
Waiting for segment comparison data.
Elasticity Comparison Chart
Event Calendar
Loading...Last 12 Months
| Date | Event | Campaign / Window | Channels | Output | Notes |
|---|
Campaign Output Summary
Ask Questions About The QSR Elasticity Story
Ask about scenarios: interpret results, compare options, or suggest new scenarios.
QSR Elasticity Assistant
Ask about scenarios, compare trade-offs, explain the current chart readouts, or turn the pricing evidence into a recommendation.
Edit Scenario Parameters
Methodology: Current State KPIs
Data Sources
- brand_market_channel_week_panel.csv - weekly QSR channel orders, sales, margin, and check signals by market
- brand_market_product_channel_week_panel.csv - weekly QSR item demand, realized price, promo, and elasticity rows by market and channel
- promo_calendar.csv - generated promotion pressure and campaign windows used for context
KPI Formulas
Weekly Orders = Sum of orders across the latest QSR week
Built from the current store-channel operating panel for the active QSR view.
Weekly Net Sales = Sum of net_sales across the latest QSR week
Anchors the operating snapshot to the same weekly cut as orders and margin.
AOV = Weekly Net Sales / Weekly Orders
Calculated from the latest weekly QSR operating panel.
Contribution Margin Rate = Contribution Margin / Weekly Net Sales
Uses the latest modeled QSR week to show margin posture after fulfillment and promo pressure.
Key Assumptions
- All data is modeled to reflect a realistic QSR seasonal operating pattern.
- Baseline readout emphasizes the latest week with recent-period smoothing where helpful.
- Channel mix reflects delivery, carryout, pickup-app, and dine-in behavior.
- Currency: USD
- Publicly anchored operating assumptions are used where direct internal data is not available.
Methodology: Acquisition Elasticity
Elasticity Model
Traffic acquisition elasticity measures how weekly QSR orders respond to effective price changes by channel, visit mission, and product ladder.
% Change in Orders = Elasticity Coefficient x % Change in Effective Check
Example: If elasticity = -1.9 and effective check increases by 10%, scoped weekly orders decline by about 19%.
Calibration Inputs
| Driver | How It Is Used | Why It Matters |
|---|---|---|
| Latest 8 weeks of channel orders | Sets baseline weekly traffic by channel | Keeps the simulation anchored to current QSR operating scale. |
| Item-level elasticity priors | Roll up into value, core, and premium ladders | Lets the model show where demand softens first when the check moves. |
| Cohort coefficients | Adjust slope by visit mission | Family, value-led, and lapse-risk guests do not respond the same way. |
Adjustment Factors
Base elasticity is modified by:
- Visit mission: Value-led and lapse-risk guests carry steeper response curves.
- Order channel: Delivery, carryout, pickup, and dine-in each start from different baseline checks.
- Product ladder: Value bundles, core pizzas, and premium items move at different rates.
- Observed volatility: Recent week-to-week order variation sets the displayed confidence band.
Scenario Simulation
- Calculate effective-check change for the selected channel.
- Apply ladder-level elasticity to projected weekly orders.
- Aggregate the ladder outputs into total channel orders and net sales impact.
- Show a calibrated confidence interval from recent operating-panel volatility.
Model Implementation
Engine: Client-side elasticity simulator using QSR channel and item panels.
Primary inputs: Channel mix, average check, item-level elasticity priors, and cohort coefficients.
Outputs: Weekly order change, net sales impact, ladder-level sensitivities, and a volatility-based confidence interval.
Key mechanics:
- Uses the latest QSR order panel instead of a generic benchmark-only curve.
- Maps item rows into value, core, and premium ladders before simulation.
- Applies cohort modifiers to show how value-led or loyal guests react differently.
- Anchors the uncertainty band to recent week-to-week traffic volatility.
Why this approach? It stays aligned to the visible QSR panel while keeping the scenario math auditable in-browser.
Methodology: Churn Elasticity
Repeat Loss Model
Repeat-visit loss elasticity models how QSR guests trim frequency over time after menu price increases or value support roll-off.
Additional Repeat Loss follows the calibrated price-change curve and time-horizon response profile.
Example: The same price move can look manageable in Week 4 and materially worse by Weeks 8-12 once guests cycle through fewer occasions.
Time-Lagged Effects
Repeat loss does not happen instantly. The model spreads impact across meal cycles:
| Period | Modeled Weight | Interpretation |
|---|---|---|
| Weeks 0-4 | 0.15 | Early reaction from the most price-sensitive guests. |
| Weeks 4-8 | 0.25 | More households feel the new check after one or two reorder cycles. |
| Weeks 8-12 | 0.30 | Peak repeat-loss window in the current QSR calibration. |
| Week 12+ | 0.30 | Long-tail erosion or stabilization among remaining guests. |
Cohort-Specific Repeat Loss
Different customer segments have different repeat-loss sensitivities:
- Coupon-driven guests: Highest near-term visit-loss risk when value support fades.
- Family ritual loyalists: Lower immediate risk, but still soften over time if price moves stack up.
- Premium pizza loyalists: Better short-run resilience on higher-ticket items.
- Channel switchers: More likely to change order behavior before fully lapsing.
Retention Tactics
The step is designed to inform selective mitigation rather than blanket discounting:
- Keep value support visible: Preserve a clear entry point on sensitive menu ladders.
- Target bounce-back offers: Re-engage lapse-risk guests instead of discounting the full base.
- Protect high-repeat missions: Test price first where weekly routine demand is more resilient.
Model Implementation
Engine: Client-side time-lagged repeat-loss simulator using the QSR item panel.
Primary inputs: Menu-ladder baseline contribution, cohort repeat-loss elasticity, and the time-lag distribution.
Outputs: Visit-loss curve, retained menu units, and cumulative contribution impact.
Key mechanics:
- Starts from observed QSR value and core/premium menu ladders.
- Spreads the impact across Weeks 0-4, 4-8, 8-12, and 12+.
- Adjusts the curve by cohort so lapse-risk and loyal guests diverge realistically.
- Translates the volume change into cumulative contribution, not just visit loss.
Why this approach? It shows when a price move still looks good early but degrades once frequency erosion compounds.
Methodology: Order Channel Migration
Migration Model
Models QSR order movement between delivery and carryout / pickup when relative check gaps change, with spillover into other channels.
Migration Rate = Base Flow + Price Move Response + Relative Gap Response
The live model compares the new delivery-versus-carryout gap to the current QSR baseline and applies cohort asymmetry factors.
Migration Patterns
- Base reverse flow starts near 1.4% of weekly carryout / pickup orders.
- It rises when the carryout / pickup effective check moves up faster than delivery.
- Coupon-driven and lapse-risk guests show the strongest fallback behavior.
- Base owned-channel gain starts near 3.0% of weekly delivery orders.
- It accelerates when delivery gets more expensive or the owned-channel value edge widens.
- Channel switchers and value-led guests produce the biggest mix shift.
Simulation Logic
- Read the latest delivery and carryout / pickup checks from the QSR store-channel panel.
- Recalculate the current gap after the user changes either channel check.
- Apply cohort asymmetry, upgrade willingness, and downgrade willingness modifiers.
- Estimate weekly migration and leakage rates for both directions.
- Translate the flow into projected channel share and revenue impact.
Elasticity Cross-Effects
Migration is driven by the relative channel-value signal:
Gap Signal = (Baseline Delivery Gap - New Delivery Gap) / |Baseline Delivery Gap|
A stronger owned-channel value edge increases delivery-to-carryout migration; a weaker edge does the opposite.
Model Implementation
Engine: Client-side migration simulator calibrated from QSR channel averages and cohort coefficients.
Inputs: Delivery check, carryout / pickup check, baseline gap, and cohort migration modifiers.
Outputs: Weekly transition rates, projected channel share, Sankey flows, and revenue impact.
Supported Channel Structures:
- Delivery: Premium off-premise basket.
- Carryout / Pickup: Owned-channel value destination.
- Dine-In / Other: Leakage bucket for demand leaving the two primary channels.
Live Coefficient Structure:
delivery_to_carryout base = 3.0%before price and gap adjustments.carryout_to_delivery base = 1.4%before reverse-flow adjustments.delivery leakage base = 1.0%andcarryout leakage base = 0.8%.- Cohort asymmetry and migration willingness factors scale those base flows.
Why this approach? It keeps the migration math transparent and directly tied to the visible QSR channel panel.
Methodology: Customer Cohorts
Segmentation Framework
3-axis behavioral segmentation framework creating 250 modeled segment combinations (5 visit missions x 5 repeat behaviors x 5 basket builds x 2 demand ladders).
Three Segmentation Axes
- Game-Day First Try: Trial driven by sports and shareable-meal occasions.
- Habitual Value Seeker: Repeat ordering anchored to established dinner habits.
- Group Occasion Buyer: Larger-party and family-sharing missions.
- Digital Promo Explorer: App, social, and creator-led demand.
- Deal-Seeking Customer: Visits start when bundle value is clearly visible.
- Family Ritual Loyalist: High repeat rate in recurring QSR meal routines.
- Value Bundle Shopper: Responds strongly to clear meal-deal ladders.
- Coupon-Driven Customer: Promo-first and highest repeat-loss risk.
- Occasional Indulger: Lower-frequency ordering tied to sporadic cravings.
- Channel Flexible Customer: Moves between delivery and owned channels as the value gap shifts.
- Single Pizza Order: Simple pizza-led order with limited attachments.
- Multi-Item Builder: Builds larger checks with sides, wings, or desserts.
- Bundle Buyer: Prefers boxes, meal deals, and clear value ladders.
- Premium Add-On: Trades into premium crusts or premium extras.
- Side Sampler: Uses lower-cost attachments to round out the order.
Segment Elasticity Calculation
Segment Elasticity = Base Tier Elasticity × Axis1 Modifier × Axis2 Modifier × Axis3 Modifier
Example: Delivery coupon-driven guest, channel-switch prone, bundle buyer:
-2.1 (base) x 1.3 (promo-led mission) x 1.2 (coupon-driven) x 1.1 (bundle basket) = -3.60 (highly elastic)
Data Sources
- segments.csv: 250 cohort definitions with characteristics
- segment_kpis.csv: Historical performance by cohort
- segment_elasticity.json: Calibrated elasticity parameters
Methodology: Segment Elasticity Comparison
Comparative Analysis
Compares price sensitivity across modeled QSR guest segments to identify where price can move and where value support still matters.
Analysis Methods
Visual matrix showing elasticity coefficients across segments.
- Color scale: Green (inelastic -0.5 to -1.5) → Red (highly elastic -3.0+)
- Sorting: By elasticity magnitude or revenue contribution
- Filtering: By tier, acquisition channel, engagement level
2D visualization: Elasticity (X-axis) vs Revenue Impact (Y-axis)
- Quadrant I: High elasticity, high revenue → Risky to increase prices
- Quadrant II: Low elasticity, high revenue → Safe to increase prices
- Quadrant III: Low elasticity, low revenue → Low priority
- Quadrant IV: High elasticity, low revenue → Discount opportunity
Multi-dimensional view plotting segments across three axes:
- Radius: Elasticity magnitude
- Angle: Revenue contribution
- Color: Repeat-loss risk level
Filtering & Aggregation
Analysis supports multiple filter combinations:
- By Menu Ladder: Compare within Entry & Value or Core & Premium ladders
- By Visit Mission: Game-day trial, weekly routine, group occasion, digital discovery, or value-triggered traffic
- By Basket Build: Single pizza, multi-item, bundle-led, premium add-on, or side sampler orders
- By Repeat Risk: Coupon-driven and lapse-risk pockets versus stable loyalist pockets
Export Capabilities
- CSV Export: Full cohort data with elasticity and KPI metrics
- SVG Export: Vector graphics for presentations
- JSON Export: Raw data for further analysis
Key Insights
- Segments with elasticity above 1.5 are highly price-sensitive
- Segments with elasticity below 1.0 are lower sensitivity (selective price increase opportunity)
- Focus price optimization on high-revenue, low-elasticity segments
- Consider targeted discounts for high-elasticity, low-engagement segments