Executive Findings
Finding 1: Peak experience is a cross-functional operating outcome. Visible customer friction can originate in inventory, workload, fulfillment, technology, decision rights, or exception recovery.
Finding 2: Staff experience is an operating variable. Repeated searches, approvals, interventions, and low-value alerts consume capacity that would otherwise support customers or fulfillment.
Finding 3: Store-as-hub models increase the value of reliable cross-channel evidence and explicit priority rules.
Finding 4: Automation is most useful when it reduces predictable decision and coordination work rather than simply increasing instrumentation.
Finding 5: Recovery design matters because peak exceptions cannot be eliminated. Evidence, ownership, communication, boundaries, and closure determine whether an exception becomes prolonged friction.
Finding 6: Read-back converts peak events into evidence. Without it, organizations can confuse activity with improvement.
Market Context
NRF forecasts U.S. retail sales growth of 4.4% in 2026 to $5.6 trillion (NRF, 2026). This is broad market context, not evidence about any individual retailer. Its relevance is that substantial retail demand increases the importance of operating efficiency when traffic and transactions concentrate.
Deloitte’s 2026 connected-store research describes physical stores as increasingly integrated with associate, customer, fulfillment, and enterprise capabilities (Deloitte, 2026a). Its store-modernization work similarly positions stores as service and fulfillment assets (Deloitte, 2026b). These findings support an evidence map that follows the customer promise across functions rather than evaluating each touchpoint in isolation.
Evidence Zone 1: Preparation and Scenario Readiness
RETHINK Retail emphasizes acting in August and early September. The significance is not the calendar date by itself; it is the testing window created before live demand concentrates.
Useful evidence includes whether priority journeys have been stress-tested, whether fallback paths are usable, whether authority boundaries are known, and whether the operating team can distinguish targets from observed outcomes.
A readiness test should follow a realistic scenario. For example: demand rises while a critical device is unavailable; pickup arrivals exceed staged-order readiness; inventory confidence deteriorates; or a recurring checkout intervention increases. The research question is where the decision path becomes ambiguous.
Evidence Zone 2: Product and Inventory Confidence
Availability is both a customer promise and a staff workflow. When digital inventory does not match what an associate can access, the employee must recover the gap in front of the customer.
Leaders should examine which inventory value is shown to customers, which value governs store action, how fresh the evidence is, how discrepancies are resolved, and which recovery options employees can use. No single technology should be assumed to guarantee accuracy.
Useful internal evidence can include availability discrepancies, failed picks, substitutions, repeated product-location requests, and service contacts related to unavailable items. These are candidate measures until the retailer verifies definitions and sources.
Evidence Zone 3: Store Fulfillment Capacity
RETHINK Retail highlights strong growth in store-fulfilled delivery. Walmart’s FY26 Q4 earnings presentation independently reported approximately 50% growth in store-fulfilled delivery, with expedited deliveries under three hours representing about 35% of store-fulfilled orders (Walmart, 2026). The underlying mechanism is that store proximity can create advantage only when inventory confidence, labor, staging, service quality, and delivery economics remain controlled.
Deloitte’s store-modernization research supports the broader structural point that stores increasingly perform fulfillment and service roles (Deloitte, 2026b). Evidence should therefore connect digital-order demand to store capacity. Relevant questions include whether picking displaces customer service, whether staging becomes constrained, and whether the fulfillment promise changes when capacity tightens.
Evidence Zone 4: Queue and Service Friction
Queues are visible symptoms, not diagnoses. Traffic, staffing, device availability, transaction complexity, price exceptions, payment issues, interventions, task allocation, and policy questions can all increase service time.
A useful evidence model connects the queue condition to its driver. When did the condition appear? Which operating variable changed? What action was taken? When did customer-facing impact recover? Did the response create a constraint elsewhere?
This logic applies beyond checkout to pickup, returns, service desks, fitting rooms, and high-touch product areas.
Evidence Zone 5: Staff Workload and Decision Latency
Scheduled labor is only one part of staff capacity. Workload also includes interruptions, manager calls, duplicate status updates, device issues, application switching, manual searches, and alerts.
RETHINK Retail highlights age verification as a major self-checkout intervention point. Diebold Nixdorf states that age verification can account for up to 22% of interventions by a shop employee; the figure should therefore be treated as sponsored-source evidence. Its mechanism is valuable because it shows that automation can still create concentrated human recovery work.
Deloitte’s store-labor research argues that technology creates value when work is redesigned and administrative burden is reduced (Deloitte, 2026d). Evidence should therefore identify where routine decisions stall and where employees become the manual integration layer between systems.
Evidence Zone 6: Digital-to-Store Handoffs
Hybrid journeys create handoffs that become fragile under pressure. Pickup is a clear example because inventory, picking, staging, arrival recognition, customer communication, and physical handoff must align.
Deloitte’s omnichannel post-purchase analysis emphasizes consistent execution after the initial purchase decision (Deloitte, 2026c). The evidence map should therefore follow the promise across systems: when was the commitment made, when was the order actually ready, what did the employee see, and what recovery existed when states did not match?
The purpose is to identify where the customer experiences an internal boundary.
Evidence Zone 7: Technology and Device Resilience
Uptime alone does not describe workflow resilience. Leaders also need to understand business impact, fallback availability, detection time, action time, and recovery time.
A device failure with a clear local fallback can create less customer impact than a minor application issue that produces uncertainty across many employees. Technology evidence should connect technical state to operating consequence.
Deloitte’s connected-store research supports the importance of integrated operating capabilities (Deloitte, 2026a). The relevant question is not simply whether a component is monitored, but whether monitoring changes an authorized action.
Evidence Zone 8: Safety and Protection Boundaries
Peak traffic and workflow changes can alter the context in which service and protection decisions are made. A shared evidence model should distinguish routine service conditions from material safety, privacy, fraud, or loss events.
The thresholds must come from retailer policy and authoritative risk evidence; this report does not invent them. The design requirement is clarity about what evidence changes the owner, what action becomes restricted, and how the service team is informed.
Controls should also be tested for usability. A control that exists on paper but cannot be executed under realistic workload should not be treated as proven operating resilience.
Evidence Zone 9: Intentional Automation
RETHINK Retail’s intentional-automation pillar directs attention to predictable work. The strongest automation candidates have clear evidence, stable rules, frequent repetition, and verifiable outcomes.
Deloitte’s 2026 analysis of AI and frontline capacity adds an important qualification: released capacity becomes value only when work is deliberately redesigned (Deloitte, 2026e). Evidence should therefore show not only that a task became faster, but what happened to the released time and whether exceptions or workload moved elsewhere.
Automation evidence should answer: What manual step disappeared? What decision became faster? What new exception appeared? What did staff do with the released capacity? What customer or operating outcome changed?
Evidence Zone 10: Exception Recovery and Read-Back
Exceptions reveal the quality of the operating model. Common examples include unavailable products, delayed pickup, device or payment issues, returns, service recovery, and material incidents.
For each recurring exception, capture the condition, authoritative evidence, owner, routine action, material boundary, customer communication, closure, and outcome. Compare the predicted result with the observed result.
Recovery should be measured economically as well as operationally when reliable data is available. The accessible public sources reviewed for this report did not independently substantiate the source-specific operating-income figure, so it has been removed rather than treated as an outcome benchmark.
A Peak Evidence Record
For each material event, capture: workflow; customer promise; observed condition; timestamp; authoritative evidence; staff action; decision owner; exception class; customer communication; safety or protection relevance; action start; recovery point; recurrence; and follow-up rule.
Not every field will apply to every workflow. The purpose is comparable evidence, not administrative volume. A small set of high-quality records is more useful than a large set that does not change decisions.
Analyst Interpretation
Cross-Zone Evidence Patterns
The ten evidence zones become more useful when leaders examine how signals interact rather than treating each category as a separate diagnostic. Peak friction often appears as a chain. An inventory discrepancy can trigger a failed pick; the failed pick can increase associate search time; the search can delay pickup readiness; the delay can create a customer-service contact; and the contact can require manager intervention if the employee lacks a clear recovery option. Each event is observable on its own, but the operating cause becomes clearer when the sequence is reconstructed. This is why the evidence map should connect events through time, ownership, and consequence rather than simply aggregate counts by function.
A second pattern is evidence asymmetry. Customers, associates, store leaders, and central teams may all be looking at different versions of the same operating state. A customer may see an item as available while the associate cannot locate it. A store dashboard may show orders as ready while staging congestion makes the handoff unreliable. A system may report device availability while employees are using a slower fallback process. These differences do not automatically indicate data failure. They indicate that leaders need to know which source is authoritative for each decision and whether that source is timely enough for the operating moment. The most useful evidence hierarchy is therefore decision-specific, not merely system-specific.
A third pattern is capacity displacement. Peak interventions rarely consume time in isolation. When an associate handles a repeated checkout exception, someone else may cover a service task. When a manager resolves routine approvals, supervisory attention is diverted from staffing, safety, customer recovery, or store coordination. When digital fulfillment expands, pick activity can compete with replenishment and in-store service. The research implication is that leaders should avoid measuring productivity only within the workflow being optimized. Evidence should also show what work moved, what was delayed, and whether the intervention created a secondary constraint. This is especially important when automation appears to release capacity; the value of that capacity depends on where it is reinvested.
The fourth pattern is recovery quality. Two stores can experience the same exception and produce different customer outcomes because the recovery path differs. One team may have trusted evidence, clear authority, and a tested fallback. Another may need several searches, approvals, or handoffs before acting. The relevant measure is therefore not only incident frequency but decision latency and recovery duration. This distinction matters for technology, inventory, staffing, and service events alike. A low-frequency issue with a long and uncertain recovery path can create more visible damage than a more frequent issue that employees resolve quickly and consistently.
Finally, evidence should be interpreted at the level at which the decision can change. Enterprise averages can conceal local operating variation, while isolated store anecdotes can overstate a problem that is not systematic. A useful review moves between levels: event, workflow, store, region, and enterprise. Repetition across comparable conditions strengthens the case that a pattern is structural. Variation across stores may instead point to differences in staffing, layout, local process, technology adoption, or management practice. The evidence map should therefore support comparison without assuming that the same intervention belongs everywhere. The aim is to identify which mechanisms repeat, which are context-specific, and which require additional evidence before leaders change policy, staffing, technology, or fulfillment rules.
The useful research unit for peak experience is the operating event: what the customer experienced, what the operation observed, who decided, what action followed, and what changed afterward.
This approach prevents common analytical errors. A queue is not automatically a staffing problem. Repeated manager calls are not automatically an authority problem. A device alert is not automatically a customer-impact event. Each is an observation that requires evidence about the mechanism.
The evidence map should therefore be used to generate hypotheses, not predetermined conclusions. If the evidence points away from the initial explanation, the explanation should change.
Research Limitations
This report does not include primary interviews, a proprietary survey, target-account behavioral data, retailer-specific operating telemetry, or verified performance outcomes. Public market context is not evidence about individual retailers. Source-specific sponsored figures are disclosed.
Access the full Report: The Holiday Season Playbook: Delivering Exceptional Customer and Staff Experience During Peak Trading
Sources
- RETHINK Retail and Diebold Nixdorf (2026) The Holiday Season Playbook: Delivering Exceptional Customer and Staff Experience During Peak Trading. Available at: https://intentamplify.com/landing-page/report/the-holiday-season-playbook-delivering-exceptional-customer-and-staff-experience-during-peak-trading/ (Accessed: August 25, 2026).
- Diebold Nixdorf (2024) Diebold Nixdorf Sets Out to Combat Shrink in Retail with New AI-powered Offering. Available at: https://s27.q4cdn.com/808990265/files/doc_news/Diebold-Nixdorf-Sets-Out-to-Combat-Shrink-in-Retail-with-New-AI-powered-Offering-2024.pdf (Accessed: August 25, 2026).
- Walmart Inc. (2026) FY26 Q4 Earnings Presentation. Available at: https://fortune.com/company-assets/1854/quartr/slides-ad2ae-2026-02-19-12-36-02.pdf (Accessed: August 25, 2026).
- National Retail Federation (2026) NRF Forecasts 4.4% Annual Retail Sales Growth with New Economic Model. Available at: https://nrf.com/media-center/press-releases/nrf-forecasts-4-4-annual-retail-sales-growth-with-new-economic-model (Accessed: August 25, 2026).
- Deloitte (2026a) The Connected Store. Available at: https://www.deloitte.com/us/en/Industries/consumer/articles/connected-store-retail-digital-transformation.html (Accessed: August 25, 2026).
- Deloitte (2026b) Future-proof Your Stores. Available at: https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/industries/consumer/2026/future-proof-your-stores-2026.pdf (Accessed: August 25, 2026).
- Deloitte (2026c) Omnichannel Post-Purchase Experience. Available at: https://www.deloitte.com/us/en/industries/consumer/articles/omnichannel-post-purchase-strategy.html (Accessed: August 25, 2026).
- Deloitte (2026d) Store Labor Modernization and Workforce Management. Available at: https://www.deloitte.com/us/en/industries/consumer/articles/retail-labor-optimization-workforce-management.html (Accessed: August 25, 2026).
- Deloitte (2026e) AI Is Freeing Up Frontline Retail Capacity. Available at: https://www.deloitte.com/ca/en/Industries/consumer/perspectives/frontline-retail-ai-capacity-value.html (Accessed: August 25, 2026).
