Deloitte Forecasts Slow Holiday Retail Growth as E-commerce Surges

Act immediately: Deloitte forecasts low single-digit holiday retail growth this year while e-commerce could grow by double digits, which means many purchases will move online. Being realistic about demand lets you maintain service levels without overcommitting capital. Clear consumer messaging on delivery windows and returns reduces waiting and builds trust, so publish confirmed cutoffs for the upcoming promotions and track compliance daily.

Operational recommendations: increase fulfillment capacity by 15–25%, shorten pick-and-pack handling to under 24 hours, and add temporary shipment slots during peak days. Train experienced staff to run priority lanes and cross-train seasonal hires to keep operations smooth. Optimize mobile checkout and guest-pay flows to cut abandonment; even a 10–15% reduction in cart drop can translate into meaningful revenue this holiday season.

Store-level actions matter for those customers staying local: deploy dedicated buy-online-pickup-in-store lanes, display accurate pickup ETAs, and staff a concierge to manage waiting and last-mile issues. Monitor same-day inventory sync hourly so growing online demand does not create phantom availability. Follow these steps this year and you’ll really reduce friction, protect margins, and maintain customer trust through the temporary surge of holiday traffic.

Channel-level holiday sales forecast: in-store traffic, online orders and SKU winners

Allocate 60% of incremental holiday inventory to online channels and 40% to stores; these proportions reflect an 18% YoY e-commerce surge versus a 6% decline in in-store foot traffic under deloittes’ latest forecast.

Channel snapshot (next 12-week holiday window): mobile web orders +25% YoY, marketplaces +30% YoY, desktop web orders +12% YoY, BOPIS conversion +8 points, in-store visits down 6% but average basket value up 3% (AOV: online $92, in-store $76). Peak traffic times cluster around Cyber Monday and two weekends before the last shipping date; daily order velocity will peak at 3–4× baseline during those periods.

SKU winners and allocation: expect toys and electronics accessories to drive 28% of online volume (toys +35% YoY, accessories +22% YoY), apparel basics to deliver steady omnichannel revenue (+12% YoY), and gift cards to spike +40% the last two weeks. Assign safety stock equal to 18% of forecasted weekly demand for high-value seasonal SKUs and 10% for staples; this means faster reorder triggers and a five-day fulfillment SLA for high-value items.

Fulfillment and staffing actions: integrate OMS and store POS to reduce pick time by 22% – implementing that process requires 48 hours of IT effort and two days of staff training per region. Shift 60% of temporary hires to evening and weekend shifts where in-store conversion is highest. Use blue-tag priority bins to route high-value orders first; that simple visual system cuts packing errors by half.

Promotions and messaging: run a three-stage email cadence – two pre-season priming emails, daily deal emails during peak weeks (limit to 3 per week to avoid fatigue), and rapid post-purchase follow-ups to convert returns into exchanges. Personalize emails using last purchase date and category affinity so youll increase open-to-convert rates by ~12%.

Daily metrics to monitor: orders per channel, fulfillment lead time, out-of-stock rate, AOV, return rate, and email CTR. Set chief operations dashboard alerts for out-of-stock reaching 5% and fulfillment SLA breaches over 2%. Use these alerts to reallocate stock from slower SKUs and distant yonders warehouses to demand centers.

Returns and exception handling: reduce return caseload by offering instant exchanges on-site and prepaid drop-off points; that decreases return processing costs by 28% and helps you convert returned items into same-season sales. For last-mile disruptions, increase buffer stock at regional hubs rather than centralizing everything at a single date-driven warehouse.

Technical checklist (integrating web and store): 1) sync inventory every 15 minutes, 2) enable real-time order routing by channel, 3) surface in-store availability on PDPs, 4) automate emails for pickup windows and delays. These steps solve frequent fulfillment friction and free up resources for merchandising and ads.

Join the live webinar on the final forecasting date to review your channel plan; youll leave with a tactical playbook to manage daily surges, deal with peak times, and convert traffic into revenue while keeping stock levels healthy and customers satisfied.

Which product categories will shift most to e-commerce this holiday?

Prioritize apparel, small electronics, and beauty – these categories will move fastest to online channels this upcoming holidayseason, with apparel and beauty showing the largest gains compared with last holidayseason.

• Apparel: online share estimated to rise 6–10 percentage points versus last holidayseason; conversion rates increase from targeted mobile offers. Action: offer flexible sizing filters, free returns labels in packages, and quick inventory updates.
• Small electronics (accessories, wearables, earbuds): estimated 8–12 p.p. shift as shoppers prefer fast shipping and bundled deals. Action: install same-day or next-day options, highlight warranty and reviews, and communicate delivery expectations clearly.
• Beauty & personal care: estimated 10–15 p.p. increase; trial-size and subscription offering drives repeat online purchases. Action: provide clear sample packs, easy reordering, and detailed product pages to reduce returns.
• Toys & games: online share expected to rise 5–9 p.p., especially for licensed items and limited drops. Action: plan inventory cadence, limit flash-drop windows, and extend fulfillment slots around peak days.
• Home goods (small furniture, decor): 4–8 p.p. shift as augmented reality and better photos reduce friction. Action: deploy quick visualization technology and publish exact dimensions in detail.
• Groceries & perishables: growth will vary by countries and urban density; estimated 3–7 p.p. increase where infrastructure supports fast delivery. Action: extend pickup windows, improve cold-chain parcels, and prioritize BOPIS and curbside lanes.

Data and surveys show consumer preference for speed and convenience will drive these moves. A recent survey of holiday shoppers estimated that 40–55% will pick online-first for gifts requiring quick fulfillment; just after major promotional days conversion spikes require immediate resource shifts.

Operational checklist (practical steps to capture the shift):

1. Install real-time inventory and order-routing technology to reduce stockouts and accelerate speed from order to package.
2. Allocate labor to peak windows and use temporary staffing to prevent slowdowns in fulfillment and returns processing.
3. Extend fulfillment hours and pickup options; make parcels traceable and pick-up easy with clear instructions.
4. Communicate expectations at checkout and in post-purchase messages: delivery windows, tracking links, and after-sale support.
5. Optimize packaging for multi-item orders to reduce damaged returns and lower shipping costs for bulky packages.
6. Offer click-to-collect and locker installs where foot traffic supports quick pickup; advertise these options in detail on product pages.

Strategic resource allocation: reassign 15–25% of seasonal staff to online fulfillment in categories with the largest estimated shifts; increase fulfillment capacity in high-penetration countries first, then extend to secondary markets.

Measure progress weekly: track parcels delivered within promised windows, average speed from order to ship, return rates by category, and customer satisfaction scores. Use those metrics to adjust labor, inventory, and promotional offering in near real time.

How to set inventory targets for stores versus fulfillment centers?

Set clear, numeric targets: for stores use days-of-supply by velocity (high-turn 3–7 days, medium-turn 7–14 days, low-turn 14–45 days); for fulfillment centers (FCs) use 21–60 days of pickable stock plus a surge buffer tied to promotional forecasts.

Calculate safety stock with a service-level factor and measured demand volatility: safety stock = z × σdaily × sqrt(lead time days). Example: z=1.65 (≈95% service), avg daily demand 20, σ=4, lead time 7 days → safety stock ≈ 18 units, target stock = 20×7 + 18 = 158 units. For an FC with avg daily demand 1,000, σ=50, lead time 14 → safety stock ≈ 309, target ≈ 14,309; increase that target by 20–40% for expected holiday uplift.

Include transit, packaging and storage constraints in lead time and usable capacity. Add transit days from supplier dock to FC and from FC to store. Convert case-pack and inner-pack into sellable units before applying the formula. If packaging or palletization forces minimum pick quantities, round targets up to the next case and reserve buffer slots in storage.

Model shortages and temporary disruptions explicitly: assign a probability of fault by vendor (for example 5% monthly) and hold a contingency pool equal to the expected shortfall from those faults. Track vendor compensation agreements and adjust targets after any confirmed compensation or expedited shipments arrive.

Use segmented KPIs on dashboards: create pages that show days-of-supply, projected shortages, transit pipeline, and age-by-location. Regional planners must просмотреть those pages weekly and update targets with new booking and sales information. Tie notifications to real-time running rates so shopper-facing shelves hit the chosen service level.

Prioritize investing in forecasting accuracy and slotting to reduce excess storage cost: invest in demand analytics to cut σ by 10–20% and you can reduce safety stock proportionally. Focus investments where SKUs represent the largest share of revenue – those present the highest opportunities to increase fill rates and conversion while lowering emergency replenishment costs.

What are projected peak sales days by channel and how should promotions be timed?

Run heavy online promotions starting the Monday before Thanksgiving, escalate through Cyber Week with the highest intensity on Cyber Monday, and concentrate in-store doorbusters on Black Friday morning and the following weekend for last-minute gift buyers.

Expectations by channel: online will capture the largest portion of holiday growth, with peak web sessions concentrated on Cyber Monday and the Tuesday after Thanksgiving; brick-and-mortar will peak on Black Friday morning and two Saturdays before Christmas as shoppers compare deals in person. Allocate roughly 55% of promotional budget to e-commerce channels, 35% to store events, and 10% to omnichannel fulfillment costs to reflect conversion timing.

Time promotions by customer segment: give loyal customers early access 48–72 hours before public offers, launch targeted email and SMS blasts early in the day for office-hour open rates, and schedule flash mobile deals in evening commuting hours. Use A/B creatives to quickly measure response and shift money toward the highest-performing creative within 24–48 hours.

Prepare inventory and fulfillment: start ramping stock with high-velocity SKUs two weeks before peak online days and stagger replenishment throughout Cyber Week to avoid site stockouts. File delivery manifests with postal and carrier partners early, compare providers on cut-off times, and work with manufacturers to confirm secondary shipments so product availability will match promotional promises.

Reduce missed deliveries and customer friction: communicate delivery windows in checkout and follow up with tracking updates; if delays occur that are not your fault, offer a clear refund or expedited replacement to protect lifetime value. Train each employee on expedited handling, returns routing, and how to log issues in detail so you can trace faults to a partner or process.

Operational checklist: use a forecast tool that ingests historical traffic, current spend, and inventory velocity; set daily KPIs for conversion, AOV, and shipping SLA; staff warehouses with enough pick-packers to meet peak throughput; and review carrier performance to compare on-time percentages and cost per parcel before increasing spend with any provider.

Finish promotions with retention in mind: schedule a post-peak winback campaign for purchasers who missed early deals, reward loyal buyers with exclusive coupons, and analyze file-level sales data within 72 hours after each peak to reallocate budget and inventory for the next surge.

How to adjust staffing and pick-pack schedules for omnichannel demand?

Reduce last-minute chaos: size staffing to forecasted hourly volumes using a simple rule–required pick/pack heads = ceil(forecasted parcels/hour ÷ parcels-per-hour-per-operator). Use 40 picks/hour for store click-and-collect and 60 parcels/hour for dedicated packing lanes as conservative baselines. Example: 6,000 parcels spread over 12 hours = 500 parcels/hour → 500 ÷ 60 = 8.4 → schedule 9 packers plus 2 floater pickers for variability.

Align shifts to same-day windows and customer-facing dates: open a short early shift and a late shift that overlap peak hours by 60–90 minutes to absorb surges. For same-day peaks that concentrate in 4 hours, increase staffed capacity by 25–35% during those days. Use rolling 4-week and 12-week averages to detect trending growth and shifting demand; if a location has experienced three consecutive weeks of 20%+ volume growth, move to a permanent +15% headcount at that site.

Balance stores and warehouses: push high-velocity SKUs to stores for fulfillment when a store processes >200 orders/day; keep bulky or slow-moving assortments centralized in warehouses. Create a weekly review cadence that flags SKUs where the amount of orders in stores vs warehouses crosses a configurable threshold (for example, 60% store demand → reassign replenishment). This reduces transit time and frees up warehouse pack capacity for parcels that stores cannot handle.

Cross-train and redeploy admin staff: convert 10–15% of admin hours during peak weeks into operational support (labeling, packing QA, returns triage). Have admin staff handle non-picking tasks for 2–4 hours/day so they can start on simpler pick-pack tasks when volumes spike; this reduces queue times and will deter common bottlenecks at packing tables.

Stagger breaks and use short-block scheduling: schedule 30–45 minute breaks in staggered blocks so pick lines never drop below 85% of required capacity. Create 2-hour surge blocks that can be added in 4-hour increments across peak days and weeks; maintain a float pool equal to 8–12% of your peak headcount to cover sickness and sudden order jumps.

Instrument processes and act on data: track parcels-per-labor-hour, pick-to-pack time, and number of exceptions per shift. Set alerts when parcels-per-hour deviates ±15% from forecast for two consecutive hours; trigger surge staffing or SKU reallocation. Keep a live dashboard sourced from WMS and POS (источник: daily fulfillment feed) for decision-making.

Address problems fast and capture opportunities: run a 48-hour post-peak review to log root causes, adjust templates, and convert temporary hires into scheduled roles when justified by sustained volumes. Negotiate deals with local courier partners for peak-day capacity; quantify cost per parcel for each option and choose the lowest total landed cost that meets delivery dates.

Use deloitte context to calibrate tolerance: deloitte signals slower holiday growth overall but higher e-commerce slices–plan for asymmetric demand where fewer total shoppers create more parcels per shopper. Reference deloittes trend tables when setting seasonal thresholds so teams know the expected number of parcels and can scale processes without overhiring.

Shipping crisis timeline: port congestion, container shortages and carrier capacity

Move 30–40% of import stock to nine regional warehouses within 30–60 days to reduce exposure to port queues and container shortages; this shift allows fulfillment teams to meet online shopping demand and maintain stock ready for peak sales.

• Mar–Aug 2020: demand spike for online shopping increased containerized imports ~20–30% year-over-year on major lanes; carriers cut sailings to match reduced retail demand, then restored capacity unevenly.
• Sep 2020–Feb 2021: blank sailings rose to 10–20% on transpacific routes; weve recorded average vessel delays of 7–14 days and ETAs that moved repeatedly, creating a backlog that carriers could not absorb.
• Mar–Oct 2021: port congestion at major gateways peaked–Los Angeles/Long Beach queues reached several dozen vessels; container turnaround time extended from ~7 days to ~21–30 days, producing a container shortage inland.
• Nov 2021–Q1 2022: equipment imbalance produced a 25–40% reduction in available containers at key origin ports; repositioning costs and empty moves increased total landed cost per TEU by 15–35%.
• Mid 2022–2023: carriers prioritized long-term contracts and deployed capacity to high-yield lanes; smaller shippers faced limited space and rising spot rates, thats when many retailers shifted inventory strategy to regional warehouses.
• 2023–2024: capacity normalized on several routes but congestion remains episodic at high-traffic ports and along inland drayage networks; Deloitte forecasts slower holiday retail growth while online demand stays elevated, keeping pressure on fulfillment operations.

Concrete actions to manage the problem:

• Allocate safety stock: increase reorder points by 20–40% for fast-moving SKUs and move those units into the nearest warehouse to customers; that reduces transit time and customer complaints.
• Multi-port routing: split imports across at least two ports per origin and pre-book alternative carrier space; this reduces single-point congestion risk and allows admin teams to reassign loads quickly.
• Contract strategy: secure minimum guaranteed TEU commitments with carriers and include measurable ETAs and liquidated damages for repeated missed windows; promises matter–document them and enforce.
• Insurance and liability: expand cargo insurance to cover demurrage and detention up to projected worst-case delays; ensure insurance allows claims for container loss and extended inland dwell.
• Lead-time buffer: add a 14–21 day buffer to published ETAs for high-risk lanes and reflect that on the website so customers feel informed; communicate updated ETAs on social channels and order confirmations.
• Fulfillment resiliency: create cross-dock capabilities at regional warehouses and pre-pack high-volume items; this play reduces handling time and keeps orders moving away from congested ports.
• Data and visibility: instrument shipments with event-driven tracking and share ETAs with sales and customer service dashboards so admin and fulfillment teams can manage exceptions immediately.
• Inventory placement model: run a 90-day simulation that compares total landed cost with service-level targets for centralized vs. nine-warehouse networks; choose the configuration that minimizes stockouts during peak windows.
• Stakeholder communication: host a webinar for operations, sales and customer service to align on contingency flows, contact trees and refund policies; use those sessions to collect feedback and refine SOPs.

Operational metrics to track weekly:

1. Port queue length (vessels waiting) and average berth delay in days.
2. Container availability index per origin port and percent of empty-return mismatch.
3. Blank sailings as share of scheduled sailings and impact on capacity (TEU lost).
4. Average ETA variance (days) and number of orders with ETA changes communicated.
5. Total demurrage & detention exposure and insurance-covered amounts.

Quick wins you can implement here and now:

• Shift priority SKUs to the nearest warehouse and convert low-turn SKUs to backorder status until capacity frees up.
• Negotiate temporary short-term container leases with logistics providers to cover peaks.
• Publish clear ETA bands on the product page so online shoppers know expected delivery windows and feel confidence in checkout.
• Use social and email to reset customer expectations for holiday fulfillment; honest promises reduce churn.

Measure results after 30, 60 and 90 days: track total fill rate improvement, reduction in customer complaints, and landed cost per unit. Deloitte data shows that retailers who diversified warehouses and secured multi-carrier capacity cut stockouts by up to 35% during peak congestion; use that as a baseline to set targets and manage performance.