Executive Summary
Same-day online grocery is frequently discussed as a delivery-speed proposition. The evidence reviewed for this report supports a broader interpretation: profitability is the result of coordinated customer promise, demand and workforce planning, basket economics, assortment, inventory, picking, packing, slot management, routing, last-mile capacity, and service recovery.
RETHINK Retail's mechanic-by-mechanic session follows one item from inbound to the customer doorstep and connects planning, material flow, routing, and capacity. [1] This report develops that framing through a secondary-research synthesis of government evidence, peer-reviewed retail and operations research, university repositories, and a clearly scoped consulting analysis.
The central finding is that the scalable unit of same-day grocery is not the delivery window. It is the operating model that decides which orders can be promised, fulfilled, routed, recovered, and repeated at an acceptable contribution.
Research Finding Same-day online grocery becomes scalable when the retailer governs promise, basket, inventory, fulfillment, slots, routing, recovery, and contribution as one operating model. |
Research Methodology and Source Selection
This report is a secondary-research synthesis and proprietary operating-model analysis. It does not present a primary survey or claim statistically representative findings of its own. The Intent Amplify Research Desk reviewed public materials from the official campaign publisher, the U.S. Department of Agriculture, peer-reviewed journals, university research portals, and a consulting analysis with explicit operating assumptions.
Source selection followed a hierarchy: official campaign and government sources first; peer-reviewed journal evidence second; university repositories for publication metadata and accessible abstracts; and consulting evidence only where assumptions and use limits could be retained. Quantitative findings are not combined as if they share one retailer, geography, time period, or cost base.
The evidence base covers customer demand, delivery fees, assortment, substitution, fulfillment nodes, rapid order processing, time-slot management, dynamic routing, revenue models, failures, future customer behavior, perishability, inventory, and advance ordering. Sources were selected for direct relevance to the end-to-end economics rather than for the volume of statistics.
Source Tier | Examples | Use in This Report |
Tier 1: Official and government | RETHINK Retail; USDA Economic Research Service | Campaign scope, online grocery use, pickup and delivery modes, customer characteristics, and stated motivations. |
Tier 2: Peer-reviewed journals | EJOR; Journal of Retailing; Transportation Science; Management Science; IJPE | Customer choice, fees, assortment, substitution, rapid fulfillment, slots, routing, revenue models, failures, perishability, and order economics. |
Tier 3: Scoped operating analysis | McKinsey fulfillment analysis | Illustrative node and cost comparisons with assumptions and context retained; not used as a universal benchmark. |
Executive Findings
1. Same-day profitability is a systems problem. Demand, inventory, labor, fulfillment-node design, slot availability, routing, and recovery interact; improving one mechanic can transfer cost or risk to another. [2] [3] [4]
2. Customer demand should be segmented by mission and service mode. Nationally representative U.S. evidence shows meaningful use of both pickup and delivery and identifies convenience-related motivations, but it does not imply that every customer or basket requires the same promise. [6]
3. Assortment and substitution are economic controls. Customer choice, operating cost, purchase history, category attributes, and future behavior after failures all affect the value of availability decisions. [3] [5] [11]
4. Delivery slots, pricing, and routes should be designed together. Peer-reviewed research treats slot offers and same-day acceptance as capacity-allocation decisions with routing consequences. [7] [8] [9]
5. Commercial models cannot be separated from operations. Delivery fees, subscription or per-order revenue, geography, order frequency, perishability, replenishment, and fulfillment cost influence multichannel profitability. [3] [10] [12]
1. Same-Day Grocery Is Moving From Speed Claims to Operating Discipline
The same-day proposition expands the number of decisions that must be made under time pressure. An item must be available, reachable by a picker, protected through temperature zones, consolidated into the right order, staged, dispatched, routed, delivered, and reconciled before the promise can be considered complete.
McKinsey's fulfillment analysis compares store picking, micro-fulfillment, dark-store, and warehouse configurations and shows that picking and delivery can be major incremental cost drivers. [2] The illustrative assumptions should not be applied universally, but they demonstrate why speed cannot be evaluated separately from fulfillment-node design and last-mile economics.
The operating question is therefore not how fast the retailer can deliver one successful order. It is whether the retailer can repeatedly accept the right order, allocate it to the right node, protect route capacity, recover exceptions, and retain contribution.
2. Customer Demand Requires Segmented Promises
USDA's 2024 analysis uses nationally representative U.S. data to describe who shops for groceries online, whether pickup or delivery is used, and why. [6] It provides evidence that online grocery demand is heterogeneous rather than one universal customer segment.
A profitable promise should distinguish urgent top-up, planned weekly shop, bulky basket, high-perishability basket, and assisted or convenience-led missions. Each mission creates a different relationship among assortment, fee, lead time, labor, route, and service recovery.
The implication is a service catalog: defined service areas, eligibility, basket and item rules, slot types, fees, workload assumptions, capacity thresholds, and escalation authority. The customer sees a simple choice; the operation sees the complete conditions behind it.
Table 1. Same-Day Grocery Decision Evidence
Business Decision | Required Evidence | Approval Output |
Launch or expand a service tier | Customer mission, geography, basket, fee, demand profile, inventory, workload, node, route, and contribution. | Approved service boundary, capacity rule, commercial terms, owners, and go/no-go thresholds. |
Open or close delivery slots | Pick and dock status, route density, marginal cost, traffic, courier supply, future demand, and service risk. | Slot inventory, price or fee, override authority, and audit record. |
Change assortment or substitution policy | Demand, margin, stock confidence, pick complexity, category context, acceptance, refunds, and future behavior. | Eligibility, substitution hierarchy, customer controls, and review thresholds. |
Invest in automation or a node | Order cohorts, volume and variability, assortment, utilization, labor, maintenance, replenishment, delivery radius, and total economics. | Capital decision, operating envelope, ramp plan, failure scenarios, and review date. |
Renew or redesign the same-day proposition | Contribution, service, failures, customer response, capacity, action closure, and investment progress. | Promise portfolio, operating roadmap, funding, owners, and next-period tests. |
3. Basket Economics Must Be Measured End to End
Order-level contribution is product gross margin plus allocated commercial revenue, less the incremental costs of payment, picking, packing, staging, delivery, refunds, substitutions, and service recovery. The record also includes promotions, credits, and the promised and actual service outcome.
Fernandes and co-authors examine delivery fees, assortment, network characteristics, customer choice, operational costs, and multichannel profitability in one analytical setting. [3] Belavina and co-authors compare per-order and subscription revenue models while accounting for geography, delivery expense, order frequency, and routing scale economies. [10]
The research implication is that an average channel margin is an incomplete decision tool. Retailers need cohort economics by mission, basket, geography, slot, node, and failure type, with assumptions and cost allocations visible.
4. Assortment and Availability Are Fulfillment Controls
Online availability is a promise about physical execution. A stock signal may be current in a system and still fail at the shelf, pick face, or substitution decision. The economic result includes picker search, short picks, basket change, refund, communication, recovery, and future customer response.
Hoang and Breugelmans show that substitution acceptance depends on customer purchase history and category attributes. [5] Amorim and co-authors find that out-of-stocks, partial fulfillment, refunds, and substitutions can affect subsequent online grocery behavior. [11]
Every same-day assortment should therefore have eligibility, inventory source, refresh rate, minimum confidence, replenishment owner, substitution hierarchy, customer-control rule, and failure threshold. Availability must be governed as part of the customer promise, not only as a merchandising attribute.
5. Node and Automation Choices Require Total-System Economics
Fulfillment nodes create different combinations of capital, labor, assortment, proximity, and delivery cost. Store picking may use existing inventory but face congestion and availability variation. Micro-fulfillment may improve local pick flow but depends on utilization and replenishment. Larger facilities can create scale but increase distance or transfer requirements.
McKinsey compares multiple fulfillment configurations, while Dethlefs and co-authors integrate stores and distribution centers in a rapid-fulfillment model that includes order-processing cost, routing, and delivery capacity. [2] [4]
The leadership requirement is a node portfolio rather than one universal answer. Each node should have an order-cohort role, operating envelope, service radius, assortment logic, capital and labor assumptions, transfer rules, failure modes, and review date.
6. Delivery Slots Require a Capacity and Pricing Framework
Delivery slots are inventory that expires. Opening a slot creates customer choice but also commits pick, pack, dock, vehicle, and route capacity. The value of the slot depends on the order profile, location, existing route, and later demand.
Agatz and co-authors formulate time-slot management as a balance between customer service and routing efficiency. [7] Klein and co-authors integrate differentiated slot pricing, customer choice, and anticipated routing cost. [8]
The operating response is a slot decision framework based on customer value, workload, marginal route cost, density, promised service, and capacity protection. Slot availability and price should change when the underlying capacity position changes.
7. Same-Day Routing Must Protect Future Flexibility
Same-day routing is dynamic because orders arrive while the delivery horizon is already unfolding. Accepting one order can reduce the ability to serve later demand, create a detour, consume vehicle slack, or increase lateness risk.
Ulmer's same-day delivery research examines anticipatory pricing and routing designed to preserve fleet flexibility and improve the set of orders served. [9] The findings are model-specific, but the operating principle is broadly useful: acceptance decisions should include the opportunity cost of future capacity.
The control system should therefore connect order release, pick completion, dock readiness, traffic, service time, vehicle or courier supply, cold-chain constraints, and remaining route flexibility. Overrides must be visible and attributable.
8. Fulfillment Failure Is a Customer and Economic Metric
A same-day failure has immediate and later effects. It can create refund, credit, redelivery, replacement, customer-service effort, waste, route disruption, and reduced confidence. A successful ticket closure does not necessarily restore the economics or the customer relationship.
Amorim and co-authors empirically examine the effect of fulfillment failures and retailer mitigation on future customer behavior. [11] Hoang and Breugelmans show that substitution policy effectiveness varies with customer and category context. [5]
Useful measures include failure rate by cause, substitution acceptance, refund and recovery cost, time to resolution, repeat-order timing, subsequent basket value, and recurrence. The recovery policy must be tested as carefully as the original service promise.
9. Profitability Requires Cross-Functional Governance
Profitability ownership is distributed across ecommerce, merchandising, supply chain, store or fulfillment operations, logistics, product, finance, analytics, and customer service. The customer experiences one promise even when the enterprise operates through many queues and systems.
A governance model should include one objective hierarchy, common order definitions, decision rights, capacity thresholds, exception playbooks, an order economics ledger, a test register, and a recurring review that closes actions.
The model must preserve constructive tension. Commercial teams should challenge overly restrictive capacity rules; operations should challenge promises without feasible resources; finance should challenge cost allocation; and customer teams should challenge recovery policies that protect the transaction but weaken future behavior.
10. Maturity Progression
Table. Same-Day Grocery Profitability Maturity
Maturity | Operating Pattern | Leadership Priority |
Speed-Led | One broad promise is marketed; costs and capacity are reviewed after demand arrives. | Define service tiers, eligibility, and complete order economics. |
Defined | Promise, assortment, node, slot, and recovery rules are documented. | Create repeatable workload, capacity, and exception controls. |
Connected | Ecommerce, supply chain, fulfillment, logistics, finance, product, and service share data and decisions. | Reduce handoff loss and maintain one order evidence chain. |
Measured | Contribution and service are managed by cohort; route, failure, and future behavior are visible. | Use granular evidence to adjust promises, capacity, and commercial policy. |
Adaptive | Pricing, slotting, routing, inventory, labor, and recovery improve through governed tests and closed-loop learning. | Scale profitable cohorts and retire rules or investments that do not meet thresholds. |
11. Research Desk Observation: Contribution Breaks at the Handoffs
The evidence base points to a consistent operating weakness: contribution is often lost between otherwise competent functions. Forecasting can be accurate at daily level while missing interval workload. Inventory can be correct in a system while unavailable to the picker. Pick productivity can improve while staging and dispatch become congested. Route efficiency can improve while customer recovery costs rise.
The most important handoffs are visible before launch. Commercial teams define the promise; planning converts demand to labor; merchandising and supply chain determine availability; fulfillment selects the node and process; product exposes slots; logistics commits route capacity; finance calculates contribution; and customer service manages the failure. When one handoff changes a rule without a traceable decision, the final economics can be accurate locally and misleading in total.
A profitable operation therefore needs an Order Economics Ledger. For every material cohort, it records promise, basket, inventory outcome, node, handling effort, route, delivery, recovery, customer response, and contribution. The ledger creates the evidence chain required to identify where margin moved and what decision should change.
12. Same-Day Grocery Operating Archetypes
Same-day grocery networks do not need to converge on one physical design. They do need to make each design's economics and operating boundary explicit. Four archetypes provide a practical way to explain where inventory sits, how labor is deployed, what route pattern is expected, and which order cohorts fit.
Table 4. Same-Day Grocery Operating Archetypes
Archetype | Operating Pattern | Evidence Required |
Store-Led Network | Existing stores pick and dispatch local orders, using proximity and current inventory. | Store availability, congestion, labor, pick productivity, staging, service radius, and route economics. |
Micro-Fulfillment Network | Local automation or dedicated capacity supports dense, repeatable order profiles. | Volume, assortment, replenishment, utilization, maintenance, exception handling, and local delivery cost. |
Dedicated Fulfillment Network | Dark stores or larger facilities concentrate inventory and process control. | Capital and labor, order density, linehaul or delivery distance, cutoff, assortment, and transfer economics. |
Hybrid Orchestrated Network | Orders are allocated dynamically across stores and facilities according to service and contribution. | Current inventory, node workload, transport capacity, marginal cost, allocation logic, override record, and reconciliation. |
A retailer may use more than one archetype by geography, demand density, customer mission, or assortment. The control is to define the handoff and allocation rules so that an order is not routed to a convenient node that is uneconomic for its basket, distance, or service requirement.
13. The Intent Amplify Same-Day Grocery Profitability Operating Model™
Intent Amplify Same-Day Grocery Profitability Operating Model™
Eight operating layers connecting the customer promise to order-level contribution
01 | Customer Promise & Demand Shape Define the service promise by mission, geography, basket, time window, and willingness to pay; translate forecast demand into workload and capacity. |
02 | Basket, Price & Fee Economics Track contribution by basket after product margin, pricing, delivery fees, membership economics, promotions, payment, picking, packaging, and delivery. |
03 | Assortment, Availability & Substitution Design an online assortment that can be fulfilled reliably; govern inventory accuracy, availability disclosure, substitution logic, and refund exposure. |
04 | Inbound, Inventory & Replenishment Synchronize supplier arrivals, replenishment, perishability, shelf life, storage, and order demand so promised stock is physically available. |
05 | Picking, Packing & Material Flow Choose the right node and degree of automation; manage pick path, batching, ergonomics, cold chain, consolidation, staging, and handoff. |
06 | Slotting, Dispatch & Real-Time Routing Offer delivery slots and dispatch decisions that reflect marginal route cost, loading time, traffic, order density, and remaining fleet flexibility. |
07 | Last-Mile Capacity & Service Recovery Plan couriers and vehicles by interval; protect on-time delivery, temperature, substitutions, communication, refunds, and exception recovery. |
08 | Profitability Control & Continuous Learning Use order-level P&L, promise accuracy, capacity utilization, failure cost, cohort behavior, and test results to improve the operating model. |
Figure 1. Intent Amplify Same-Day Grocery Profitability Operating Model™ - Eight-Layer Architecture
14. Same-Day Grocery Profitability Scorecard™
Intent Amplify Same-Day Grocery Profitability Scorecard™
Table. Intent Amplify Same-Day Grocery Profitability Scorecard™
Domain | Executive Assessment Question | Ready-State Evidence |
Service Promise Discipline | Is the speed promise segmented by customer mission, geography, basket, fee, and operational feasibility? | Promise catalog, service-area rules, lead-time tiers, price/fee logic, and named approval owners. |
Demand & Workforce Planning | Can forecast demand be translated into inbound, pick, pack, dock, and courier requirements by interval? | Forecast hierarchy, order-profile assumptions, workload plan, staffing model, error bands, and capacity thresholds. |
Basket Economics | Does each order type have a transparent contribution view after all incremental costs and commercial offsets? | Order-level P&L, margin waterfall, fee and membership treatment, promotion cost, and breakeven thresholds. |
Assortment & Availability | Can the online range be fulfilled with accurate stock, controlled substitutions, and visible customer choices? | Assortment rules, inventory accuracy, availability disclosure, substitution policy, and refund/short-pick tracking. |
Inventory & Replenishment | Are inbound timing, shelf life, replenishment, and freshness aligned to same-day demand? | Supplier and inbound plan, freshness controls, replenishment triggers, waste measures, and inventory aging. |
Picking & Packing | Are pick path, batching, packing, consolidation, staging, and handoff designed for cost, quality, and speed? | Engineered standards, units per labor hour, error and damage rates, temperature controls, and queue visibility. |
Automation & Material Flow | Is automation deployed where volume, density, assortment, and process stability justify it? | Node business case, throughput envelope, utilization, exception handling, maintenance plan, and human-work design. |
Slot & Route Economics | Do slot availability, pricing, dispatch, and routing protect route density and remaining capacity? | Marginal-cost logic, slot controls, route plan, loading assumptions, travel-time feedback, and dispatch audit trail. |
Last-Mile Reliability | Can the operation meet the promise and recover transparently when stock, capacity, traffic, or quality conditions change? | On-time and in-full measures, courier plan, cold-chain evidence, exception playbooks, communication, and recovery cost. |
Profitability Governance | Do leaders review contribution, service, failure cost, customer behavior, and improvement actions together? | Executive dashboard, decision rights, experiment register, action owners, investment gates, and closed-loop reviews. |
15. Board-Level Evidence and Decision Metrics
• Percentage of same-day orders governed by a defined promise tier, eligibility rule, and named decision owner.
• Demand forecast accuracy by interval and the resulting labor, dock, slot, and courier variance.
• Order-level contribution by mission, basket, geography, slot, node, service tier, and failure type.
• Inventory accuracy, short picks, substitution acceptance, refunds, and future customer behavior by category.
• Pick, pack, staging, automation, and node utilization within their designed operating envelopes.
• Slot yield, marginal route cost, stop density, on-time and in-full delivery, and remaining capacity flexibility.
• Failure and recovery cost, repeat-order timing, customer communication, and recurrence by root cause.
• Improvement actions completed, investment gates met, and operating changes verified through cohort evidence.
Implementation Roadmap
Phase | Leadership Objective | Completion Evidence |
1. Baseline | Score the ten readiness domains and identify loss-making cohorts and handoff failures. | Approved scorecard, order economics ledger, issue register, owners, and priorities. |
2. Define the Operating Core | Standardize promise tiers, order definitions, inventory rules, fulfillment node roles, slot logic, recovery, and decision rights. | Promise catalog, cost dictionary, capacity model, RACI, and exception playbooks. |
3. Pilot the Complete Model | Use one geography or customer mission to test the model from demand through recovery. | Cohort scope, governed launch, daily evidence, root-cause log, and decision record. |
4. Scale Profitable Cohorts | Expand only where contribution, service, and capacity meet agreed thresholds. | Expansion gates, investment plan, staffing and node readiness, and verified economics. |
5. Institutionalize Learning | Embed cohort reviews, experiments, customer behavior, and action closure in planning. | Test register, completed actions, updated rules, roadmap, and board-level evidence. |
16. Recommendations With Owners and Completion Evidence
Table. Recommendations With Owners and Completion Evidence
Recommendation | Primary Owner | Completion Evidence |
Create a segmented service-service catalog. | Chief Ecommerce / Commercial Leader | Mission, geography, basket, fee, eligibility, capacity, and approval rules published. |
Build an interval-level demand-to-capacity model. | Planning / Operations | Forecast, workload conversion, staffing, dock, slot, courier, error bands, and thresholds approved. |
Implement the Order Economics Ledger. | Finance / Analytics | Cohort contribution including margin, handling, delivery, failure, recovery, and future behavior. |
Connect assortment, inventory, replenishment, and substitution. | Merchandising / Supply Chain | Eligibility, stock confidence, refresh, replenishment, substitution hierarchy, and exception ownership. |
Integrate slot release, pricing, dispatch, and routing. | Product / Logistics | Shared capacity view, marginal route logic, override rights, and audit trail. |
Govern failures as economic and customer events. | Customer Operations / Finance | Root cause, recovery cost, communication, subsequent behavior, recurrence, and closed actions. |
17. Strategic Takeaway: Scale Contribution, Not Speed Alone
The strategic objective is not to make every grocery order same-day. It is to scale the ability to identify which order can be accepted and fulfilled at the promised service and acceptable contribution.
Retailers that govern promise, basket, inventory, node, slot, route, recovery, and learning through one operating model can add speed selectively without making cost and service risk invisible.
Register for the Mechanic-by-Mechanic Session Explore the RETHINK Retail session that follows one grocery item from inbound to the customer doorstep and examines demand and workforce planning, material flow, real-time routing, and last-mile capacity. |
Continue the Same-Day Grocery Profitability Journey
Move from executive education to operating assessment through one consistent content and decision path.
Stage | Asset or Offer | Purpose |
Top of Funnel | Download the Same-Day Grocery Profitability Checklist | Identify the first gaps across promise design, basket economics, inventory, fulfillment, routing, and governance. |
Middle of Funnel | Download the Same-Day Grocery Profitability Playbook | Apply the eight-layer operating model, control questions, implementation roadmap, and scorecard. |
Decision Stage | Access the Same-Day Online Grocery Economics 2026 Research Report | Review methodology, independent evidence, operating implications, maturity progression, and executive findings. |
Commercial Stage | Request a Same-Day Grocery Profitability Assessment | Evaluate order-level economics, service promise, fulfillment-node design, inventory, fulfillment, last mile, and decision controls. |
Activation Stage | Align ecommerce, operations, supply chain, logistics, finance, product, and strategy leaders on priorities and next actions. |
About Intent Amplify
Intent Amplify combines market intelligence, buyer-signal interpretation, content-led engagement, and precision GTM execution to help B2B organizations turn complex market themes into measurable pipeline programs. [13]
Research and Citation Governance
Official publisher, government, peer-reviewed journal, university repository, and clearly scoped consulting sources are used for campaign framing, customer demand, fulfillment economics, inventory, delivery-slot design, routing, and service-recovery claims. Quantitative findings retain their geography, date, method, and sample or modeling limits. Proprietary frameworks and recommendations are presented as Intent Amplify analysis, not as independent market findings. All URLs were checked as accessible public sources on the revision date.
References
[1] RETHINK Retail. What Profitable Same-Day Online Grocery Actually Takes: Mechanic by Mechanic - official webinar page. https://rethink.industries/video/what-profitable-same-day-online-grocery-actually-takes-mechanic-by-mechanic/. Accessed July 29, 2026. Official campaign source following one grocery item from inbound to the customer doorstep and framing the session around demand and workforce planning, automated material flow, real-time routing, and last-mile capacity. Promotional performance percentages are not used in this suite.
[2] McKinsey & Company. Achieving profitable online grocery order fulfillment. McKinsey & Company, May 18, 2022. https://www.mckinsey.com/industries/retail/our-insights/achieving-profitable-online-grocery-order-fulfillment. Accessed July 29, 2026. North American operating analysis identifying picking and delivery as major incremental fulfillment-cost drivers and comparing store picking, micro-fulfillment centers, dark stores, and warehouse configurations. Its basket economics are treated as illustrative, not universal.
[3] Fernandes, Daniela; Neves-Moreira, Fábio; Amorim, Pedro; and Fransoo, Jan C. Optimizing Online Grocery Service: from Customer Understanding to Multichannel Profitability. European Journal of Operational Research, accepted/in press, January 29, 2026. https://research.tilburguniversity.edu/en/publications/optimizing-online-grocery-service-from-customer-understanding-to-/. Accessed July 29, 2026. Peer-reviewed research using transaction data from a major grocery retailer to examine delivery fees, assortment size, network characteristics, customer choice, operational costs, and multichannel profitability.
[4] Dethlefs, Christian; Ostermeier, Manuel; and Hübner, Alexander. Rapid fulfillment of online orders in omnichannel grocery retailing. EURO Journal on Transportation and Logistics, 11, Article 100082, 2022. https://portal.fis.tum.de/en/publications/rapid-fulfillment-of-online-orders-in-omnichannel-grocery-retaili/. Accessed July 29, 2026. Peer-reviewed study of integrated rapid fulfillment across stores and distribution centers, including order-processing cost, vehicle routing, and delivery capacity. The reported cost result is specific to the study design.
[5] Hoang, Dong; and Breugelmans, Els. "Sorry, the product you ordered is out of stock": Effects of substitution policy in online grocery retailing. Journal of Retailing, 99(1), 26-45, 2023. https://www.sciencedirect.com/science/article/pii/S002243592200046X. Accessed July 29, 2026. Open-access research based on computer-simulated purchase experiments with more than 3,000 UK households across five categories, showing that substitution acceptance depends on customer purchase history and category attributes.
[6] U.S. Department of Agriculture, Economic Research Service. Who Shops for Groceries Online? Economic Research Report No. 336, September 2024. https://www.ers.usda.gov/publications/pub-details/?pubid=110065. Accessed July 29, 2026. Nationally representative U.S. analysis of online grocery use, pickup and delivery methods, shopper characteristics, and stated motivations, using the 2022 Eating and Health Module of the American Time Use Survey.
[7] Agatz, Niels; Campbell, Ann M.; Fleischmann, Moritz; and Savelsbergh, Martin. Time Slot Management in Attended Home Delivery. Transportation Science, 45(3), 435-449, 2011. https://pubsonline.informs.org/doi/10.1287/trsc.1100.0346. Accessed July 29, 2026. Peer-reviewed research on selecting delivery-slot offers that balance cost-effective routing with acceptable customer service.
[8] Klein, Robert; Neugebauer, Michael; Ratkovitch, Dimitri; and Steinhardt, Claudius. Differentiated Time Slot Pricing Under Routing Considerations in Attended Home Delivery. Transportation Science, 53(1), 236-255, 2019. https://pubsonline.informs.org/doi/10.1287/trsc.2017.0738. Accessed July 29, 2026. Peer-reviewed research integrating customer choice, differentiated slot pricing, and anticipated routing cost for an e-grocery setting.
[9] Ulmer, Marlin W. Dynamic Pricing and Routing for Same-Day Delivery. Transportation Science, 54(4), 1016-1033, 2020. https://pubsonline.informs.org/doi/10.1287/trsc.2019.0958. Accessed July 29, 2026. Peer-reviewed same-day delivery study examining anticipatory pricing and routing to protect fleet flexibility and improve the set of orders that can be served.
[10] Belavina, Elena; Girotra, Karan; and Kabra, Ashish. Online Grocery Retail: Revenue Models and Environmental Impact. Management Science, 63(6), 1781-1799, 2017. https://pubsonline.informs.org/doi/10.1287/mnsc.2016.2430. Accessed July 29, 2026. Peer-reviewed comparison of per-order and subscription revenue models, with geography, delivery expense, order frequency, and routing scale economies included in the analysis.
[11] Amorim, Pedro; Eng-Larsson, Fredrik; and Rooderkerk, Robert P. Navigating online order fulfillment failures: Impacts on future customer behavior and the role of retailer mitigation. Journal of Retailing, 101(3), 382-408, 2025. https://pure.eur.nl/en/publications/navigating-online-order-fulfillment-failures-impacts-on-future-cu/. Accessed July 29, 2026. Peer-reviewed empirical research showing that out-of-stocks, partial fulfillment, refunds, and substitutions can affect the timing and value of subsequent online grocery orders.
[12] Siawsolit, Chokdee; and Gaukler, Gary M. Offsetting omnichannel grocery fulfillment cost through advance ordering of perishables. International Journal of Production Economics, 239, Article 108192, 2021. https://ideas.repec.org/a/eee/proeco/v239y2021ics0925527321001687.html. Accessed July 29, 2026. Peer-reviewed modeling of advance-order information, perishability, replenishment, cart value, handling time, pick rate, inventory, spoilage, availability, and fulfillment economics.
[13] Intent Amplify. About Intent Amplify. https://intentamplify.com/about/. Accessed July 29, 2026. Official company description used for the About Intent Amplify section.
