Click-and-Collect / BOPIS That Actually Hits SLAs

Why this matters (facts)

Scale: U.S. click-and-collect sales were forecast to hit $109.36B in 2024, driven heavily by grocery, with 150.9M U.S. shoppers using the channel (eMarketer).

• eMarketer
• Fit Small Business

Conversion: Retail chains offering BOPIS/curbside or in-store stock status show higher conversion than the Top 1000 average (Digital Commerce 360).

• Digital Commerce 360

Seasonal reality: Curbside was used in 17.5% of online orders (for retailers that offer it) across the 2024 holiday season, peaking at 37.8% on Dec 23 (Adobe).

• Adobe Newsroom

What shoppers want from stores: Same-day service, fast checkouts, returns, and BOPIS are among the top reasons people will visit a store at all (NRF / WD Partners).

• National Retail Federation

Where BOPIS breaks

• No aging dashboard: orders “fall through the cracks.”
• Pickers hunt: poor maps, no pick path, backroom mysteries.
• Ad-hoc substitutions: manager gets dragged in for every call.
• Curbside is manual: staff guess plate/stall; customers wait.
• Messaging gaps: “ready” texts are late or never go out.
• Inventory confidence: phantom stock drives cancels/no-fills (a root cause of BOPIS attrition noted across industry coverage). NRF

The one-screen BOPIS workstation (what’s on it)

Queue by aging & promise window

• Orders sort by time-to-SLA; timers go yellow/red as risk rises.
• Filters for zones (grocery ambient/chill/frozen; hardlines aisles) and order size.

Pick paths by zone

• Exact bay/backroom location; batch compatible orders; suggest optimal path (cut walking).
• Backroom flag for items likely misplaced (last scan vs. on-hand delta).

Rules-based substitutions

• Brand/size/price constraints, shopper notes, allergy locks; one-tap approvals.
• If price delta > threshold, require supervisor; otherwise auto-confirm.

Auto-messages

• “Picking,” “Ready,” “On our way to curb,” “Picked up.” Two-way SMS for clarifications (e.g., substitution or stall change).

Curbside panel

• Plate capture, stall assignment, elapsed timer.
• Geo-pings on arrival (optional) → staff alerted with order details.

Exceptions rail

• Stockouts, late picks, no-shows, payment fails; quick actions and notes feed analytics.

Tech & data you actually need (no rip-and-replace)

• Light integrations to POS/inventory, order mgmt, and messaging (SMS/push). Flat-file drops work to start; move to APIs as you scale.
• Location graph: store → zone → bay → backroom locations.
• Rules engine: substitutions, SLA thresholds, price deltas, high-value manual checks.
• Device reality: run well on rugged handhelds and cheap tablets; offline caching for spotty Wi-Fi.
• Audit trail: picker ID, timestamps, photos (for substitutions), and curbside handoff logs.

McKinsey’s store-ops work consistently finds that digitizing core tasks and tightening handoffs reduces labor waste and improves service; similar principles apply to mid-/last-mile handovers.

• McKinsey & Company

Standard operating flow (what good looks like)

1. Triage: Orders land with a promise time. If SLA < X mins, they jump the queue.
2. Pick: Batch by zone, show pick path, confirm with scan.
3. Sub: If OOS, system proposes FPO or customer-preferred alternates within price band.
4. Stage: Seal totes by temp zone; stage location recorded.
5. Notify: Auto “ready” with pickup instructions; “running late?” nudge if customer ETA > N mins.
6. Arrive: Curbside screen shows car/plate/stall; handoff timer starts.
7. Handoff: Confirm ID or order code; complete; send receipt + 1-tap NPS.

KPIs to watch (store & hourly view)

ROI sketch

Cutting average pick time from 18 → 11 minutes across a 40-order morning saves ~280 minutes (~4.7 hours) per store per day. Add fewer cancels and faster curbside turns, and you’ve freed labor and protected revenue without new headcount.

60-day rollout (pilot that proves it)

Weeks 1–2: Configure & connect

• Import orders via OMS feed; expose inventory availability; set SLA thresholds; wire SMS.
• Map zones/bays/backroom; define substitution rules and price bands.

Weeks 3–4: Train & dry run

• 60-minute sessions for pickers/leads; two mock waves per store; verify timers and messages.
• Baseline metrics for two weeks: pick time, cancels, curb wait, calls.

Weeks 5–8: Live pilot in 3–5 stores

• Run the one-screen workstation; enforce scan-to-confirm and substitution rules.
• Weekly review of exceptions (top 10 OOS SKUs, longest waits, cancelled orders).

Go/No-Go: Expand when pick time ↓, SLA attainment ↑, cancel rate ↓, and curb wait ↓ vs. baseline.

Capacity & peak management (holiday-proof it)

• Staff forecast from order aging: schedule a “power hour” picker when red timers spike (historically 4–7pm or pre-holiday peaks).
• Staging math: ensure totes/slots exceed P90 peak order volume by zone (Adobe shows curbside spikes near deadline days).

• Adobe Newsroom

• Micro-SLA tiers: “Ready in 30/60/120 min” options at checkout balance capacity and expectation.

Inventory confidence (the hidden BOPIS killer)

• Cycle-count the top A SKUs daily; reconcile deltas nightly back to e-comm on-hand.
• Event hooks: when an item is transferred, returned, or substituted, write back immediately.
• Phantom-stock cleanup: flag SKUs with high BOPIS cancel/no-fill and trigger manager review.

NRF and partner research highlight that same-day services (incl. BOPIS) and returns are key to why shoppers choose a store; poor inventory accuracy undermines both.

• National Retail Federation

Substitutions that don’t create customer service tickets

• Constraint set: equal/greater pack size, same brand (or whitelisted alternates), price delta ≤ X%.
• Preference learning: remember customer declines/acceptances by SKU family.
• Comms: auto-text with “Approve / Pick something else / I’ll wait.”
• Escalation: only high-value deltas need a manager; everything else is picker-level.

Messaging that prevents lot-traffic jams

• “We’re picking.”
• “Ready — here’s stall map & number.”
• “We see you arriving — lane 2 is open.” (Geo-ping optional.)
• “Handoff complete — receipt + quick survey.”

Adobe’s 2024 data shows mobile now drives a majority of online revenue, so SMS/push has become the most reliable way to close the loop on pickup.

• Adobe for Business

Governance, safety & audit

• Photo on substitution (when policy requires).
• Handoff confirmation (order code/ID per policy; limit PII in notes).
• Incident log (late pickups, damaged items) with time/user stamps.
• Accessibility: ensure curbside flow works for customers without smartphones (printed QR or manual check-in lane).

What leadership should ask vendors (and us)

• Which KPI moves in 60–90 days and how will you measure it?
• What’s your read/write plan for POS/inventory and OMS — and the rollback?
• How do you enforce substitution rules without slowing pickers?
• How will you reduce calls/order and track deflection?
• How do you handle peak weeks (labor, staging, comms)?

Our process (how we’d deliver your pilot)

• Free 1-day assessment: map current flow, extract baseline metrics, identify top 5 failure points.
• Design (2 weeks): configure one-screen workstation, define substitution/alert rules, wire SMS.
• Pilot (4 weeks, 3–5 stores): instrument pick/curb SLAs, substitutions, and cancels.
• Review & scale: keep what moves numbers; defer what doesn’t.