Buy Online, Pick Up In Store (BOPIS)

Buy Online, Pick Up In Store (BOPIS) is a retail fulfillment option where customers purchase online and collect their order at a physical store, combining digital conversion with in‑store pickup.

Last updated: October 04, 2026

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Quick answer / definition

Buy Online, Pick Up In Store (BOPIS)—also called click-and-collect—is a fulfillment method where a customer places and pays for an order online and then picks the item up at a retail location. It describes a customer journey and a fulfillment channel, and it's commonly used by omnichannel retailers, DTC brands with retail partnerships, and Shopify merchants who operate at least one physical pickup point.

What it is: an online purchase with in-store pickup fulfillment.
What it describes: customer intent, fulfillment flow, and a sales channel mix.
Where it's used: brick-and-mortar retailers, pop-ups, grocery chains, and omnichannel DTC brands.
Why it matters: it reduces shipping costs, shortens delivery time, increases conversion for intent-driven shoppers, and creates upsell opportunities at pickup.

Why it matters

BOPIS sits at the intersection of revenue, cost control, and customer experience. For ecommerce founders and merchants it matters because:

Revenue: accelerates purchase decisions for high-intent shoppers and creates in-store cross-sell/upsell opportunities at pickup.
Conversion rate: reduces top-of-funnel friction for shoppers who want instant gratification or to avoid shipping fees.
Customer acquisition & retention: supports local marketing, loyalty integration, and repeat purchases when pickup experiences are positive.
Profitability & operations: cuts last-mile shipping costs and returns when implemented correctly, but requires store-level processes and inventory accuracy.
Marketing performance: provides a trackable campaign outcome (local search, paid local ads, email) and a channel for measurement of offline impact.

Benchmarks vary widely by industry, store footprint, product size, and customer behavior—so treat adoption rates and uplift estimates as business-specific.

What is Buy Online, Pick Up In Store (BOPIS)?

BOPIS is a fulfillment option where the online checkout and payment are completed digitally, and fulfillment occurs at a physical pickup location. It includes several variations—BOPIS with scheduled pickup windows, immediate in-store pickup, and pickup at designated lockers or curbside locations.

What BOPIS includes:

Online order placement and payment.
Inventory reservation at the chosen pickup location (real-time or near real-time).
Notifications to customers that the order is ready (email, SMS, or app push).
In-store pickup process (counter pickup, locker, curbside handoff).

What BOPIS typically excludes:

Ship-to-store returns when those are handled as a separate return flow (though they often coexist).
Curbside-only models where the order is never intended to enter the store footprint (some businesses treat curbside as a distinct experience).

When businesses use BOPIS: to offer faster fulfillment without shipping, reduce fulfillment costs, drive store traffic for additional purchases, and improve conversion for items customers prefer to inspect before final ownership (e.g., apparel, electronics).

High BOPIS adoption may indicate strong local demand, effective inventory visibility, and successful local marketing; low adoption can point to poor messaging, limited pickup locations, inventory mismatches, or friction in pickup communications.

Important terminology:

Reservation: the process of allocating stock to the online order at the pickup location.
Ready-for-pickup window: the time between order confirmation and when staff mark the order available.
Hold time / pickup SLA: how long the location holds items before restocking or cancelling.
Click-and-collect / click & collect: synonyms commonly used outside the U.S.

Formula / calculation

BOPIS itself is a fulfillment method, not a single predefined metric. However, common measurable KPIs tied to BOPIS are:

BOPIS Adoption Rate = (BOPIS orders / Total online orders) x 100
BOPIS Conversion Lift = ((Conversion rate for sessions where BOPIS is offered - Conversion rate where it is not) / Conversion rate where it is not) x 100

Variables explained:

BOPIS orders: completed orders that used in-store pickup as the fulfillment method.
Total online orders: all completed online orders during the same period (include marketplace orders only if pickup is offered there).
Conversion rate: orders / sessions for the examined cohort.

Example (BOPIS Adoption Rate):

Monthly total online orders = 1,000
Monthly BOPIS orders = 150
BOPIS Adoption Rate = (150 / 1,000) x 100 = 15%

Note: define the denominator consistently—some analytics teams compare BOPIS orders to web-only orders, others include mobile app orders. Document your choice.

How it works (practical 6-step flow)

Customer chooses BOPIS at checkout.
What happens: customer selects a nearby pickup location and time window. What you track: pickup location selected, chosen pickup window, and device used. Why it matters: location selection drives inventory reservation and pickup staffing decisions.
System verifies inventory and reserves stock.
What happens: the backend checks store-level inventory and reserves items for the order. What you track: inventory deduction/reservation events and potential fulfillment failures. Why it matters: inaccurate reservations cause cancellations and customer dissatisfaction.
Order fulfillment and staging at store.
What happens: a store associate picks and stages the order for pickup, tagging it with order ID. What you track: pick time, staging time, and exceptions (out-of-stock, damaged).
Customer notified when ready.
What happens: automated SMS/email/app notification instructs pickup procedure. What you track: deliverability, open rates, and customer pickup ETA. Why it matters: clear communication reduces missed pickups and staff time.
Customer arrives and verifies identity.
What happens: customer presents order confirmation and ID or verification code. What you track: pickup completion time and no‑show rate. Why it matters: verification balances speed and loss prevention.
Order closes; measure add-on behavior.
What happens: staff complete the pickup transaction and record any additional purchases. What you track: attach rate (in-store add-on purchases), returns, and pickup SLA compliance. Why it matters: provides data to measure incremental revenue from BOPIS traffic.

Key components / factors

Inventory accuracy: Real-time or near-real-time store inventory is critical—errors cause cancellations and damage trust.
Pickup locations & density: More convenient locations increase adoption; a sparse footprint lowers the addressable audience.
Product type & size: Low-cost, high-turnover items and urgent-need categories (groceries, baby supplies) have higher BOPIS suitability than bulky freight items.
Messaging & UX at checkout: Clear pickup options, pickup estimates, and pickup fees (if any) influence conversion.
Pickup SLA & speed: Faster readiness times increase customer satisfaction and reduce cancellations.
Notifications & verification: Reliable SMS/email/app alerts and simple verification codes cut friction at handoff.
Staffing & in-store processes: Defined workflows for picking, staging, and holding orders reduce time-per-pickup labor costs.
Local marketing & paid search: Local inventory ads, store pickup tags in Google Merchant Center, and geo-targeted paid media affect how many local shoppers see BOPIS availability.
Device & traffic source: Mobile users often prefer BOPIS for speed; paid search and local listings can drive higher-intent BOPIS sessions.
Analytics & attribution: Tracking pickup conversions correctly across web, app, and ad platforms is necessary to measure ROI accurately.

Example (realistic scenario and calculations)

Situation: A regional retailer receives 1,000 online orders per month. They already offer BOPIS but adoption is low.

Baseline: total online orders = 1,000 / month
Baseline BOPIS share = 10% → BOPIS orders = 100
Average order value (AOV) for BOPIS orders = $60
In-store attach rate (additional impulse purchases at pickup) = 20% with avg add-on $25

Baseline revenue from BOPIS orders: 100 × $60 = $6,000

Baseline add-on revenue: 100 × 20% × $25 = $500

Total BOPIS-related revenue = $6,500

Action taken: improve pickup messaging, add a clear "Pick up today" badge in search and product pages, and implement a 2-hour express pickup option. After changes, BOPIS share rises to 18% (180 orders).

New BOPIS revenue: 180 × $60 = $10,800

New add-on revenue: 180 × 20% × $25 = $900

Total new BOPIS-related revenue = $11,700

Impact:

Incremental revenue = $11,700 - $6,500 = $5,200 / month
If average gross margin is 50%, incremental gross profit = $2,600 / month
Incremental operational cost: assume $1.50 handling per additional pickup order × 80 extra orders = $120 / month
Net incremental gross contribution ≈ $2,480 for the first month (gross margin minus handling cost). If implementation cost (software, signage) was $3,000 one-time, payback would occur in roughly ≈1–2 months at that rate.

Notes: these are illustrative numbers. Results will depend on AOV, attach rate, margin, and implementation cost. Always run the calculation using your own metrics.

Benchmark / what is a good metric?

There is no universal “good” BOPIS adoption rate—benchmarks vary by category, geography, and store density. As guidance:

Low adoption: single-digit percentages may indicate usability or availability issues.
Reasonable adoption: double-digit percentages are common where stores are convenient and messaging is strong.
High adoption: rates above ~20% typically reflect either very convenient pickup networks or categories where immediate pickup is a major value proposition.

Important caveats: industry, product size, store footprint, and local competition strongly affect what’s achievable. Use your own historical data and cohort tests for reliable benchmarks.

How to improve / optimize BOPIS (prioritized tactics)

Fix inventory accuracy first (highest impact).
What to change: implement real-time inventory sync or frequent store-level sync, and monitor reservation failures. Why it works: prevents cancellations and builds trust. How to implement: integrate POS and ecommerce inventory or use scheduled syncs with fast reconciliation. Monitor: reservation failure rate and cancellation rate for BOPIS orders.
Make pickup options visible and persuasive in search and PDPs.
What to change: surface "Pick up today" badges, show expected ready time and pickup fees. Why it works: increases perceived convenience and conversion. How to implement: A/B test badge text, position, and microcopy. Monitor: click-through rate on pickup options and conversion uplift.
Speed up pickup SLA.
What to change: offer express pickup where possible (e.g., 2-hour ready). Why it works: many customers value speed as much as cost. How to implement: dedicate a rapid-pick zone and optimize picks. Monitor: pickup readiness time and customer satisfaction.
Improve notification and verification flows.
What to change: reliable SMS with click-to-open verification codes, one-tap confirmations, and clear pickup instructions. Why it works: reduces no-shows and speeds handoffs. How to implement: integrate SMS provider and link codes to order IDs. Monitor: pickup completion rate and no-show rate.
Train staff & standardize in-store processes.
What to change: define pick, stage, hold, and handoff procedures and set KPIs per store. Why it works: consistency improves speed and reduces errors. How to implement: short SOPs and weekly store performance dashboards. Monitor: average pickup handling time and error rate.
Measure attach rate and test in-store offers.
What to change: test non-discounted add-ons (samples, small accessories) presented at pickup. Why it works: captures high-intent customers physically present. How to implement: track add-on attach rate and test different offers per store. Monitor: incremental revenue per pickup and conversion of in-store offers.
Optimize local marketing and ads for BOPIS.
What to change: run local inventory ads and localized paid search with pickup annotations. Why it works: drives the right intent traffic. How to implement: use merchant center features and geo-targeted bids. Monitor: orders attributed to local ad campaigns and cost per BOPIS order.

Best practices

Document a precise definition of what counts as a BOPIS order in analytics; be consistent across platforms.
Show store-level inventory and pickup windows on product pages to reduce friction at checkout.
Offer clear pickup instructions and one-click verification codes to speed handoffs and reduce staff time.
Set a realistic hold policy (e.g., 48–72 hours) and communicate it in order confirmations to lower no-shows.
Measure and report pick-to-pickup time and pickup completion rate per store; use these as operational KPIs.
Track in-store attach rate for BOPIS customers and test localized merchandising for higher attach rates.
Segment analytics by device and traffic source—mobile users and local search traffic often behave differently.
Integrate loyalty programs at pickup (points, instant redemption) rather than defaulting to discounts.

Common mistakes to avoid

Poor inventory definition across channels.
Why it happens: disconnected POS and ecommerce systems. Why harmful: leads to cancellations and returns. Correct approach: reconcile inventory rules and clearly flag which inventory is available for pickup.
Counting unconfirmed or cancelled pickups as BOPIS orders.
Why it happens: inconsistent analytics definitions. Why harmful: inflates adoption and misleads operational planning. Correct approach: count only completed pickups or completed pickup handoffs, and report cancellation/no-show rates separately.
No measurement of add-on behavior.
Why it happens: separate POS and ecommerce analytics. Why harmful: misses the primary revenue benefit of BOPIS. Correct approach: track attach rate and AOV change for BOPIS cohorts.
Treating curbside as identical to in-store pickup.
Why it happens: operational convenience. Why harmful: curbside workflows, verification, and labor differ. Correct approach: segment curbside vs in-store in analytics and operations.
Failing to test messaging and timing.
Why it happens: assumption that "pickup is self-explanatory." Why harmful: missed conversion and readiness improvements. Correct approach: A/B test badges, lead times, and notification cadence.

Buy Online, Pick Up In Store (BOPIS) vs related concepts

BOPIS vs Curbside Pickup

BOPIS: customer picks up inside the store or designated locker after online payment; typically involves entering the store or counter pickup.
Curbside Pickup: customer remains in vehicle; order is brought to the car—often treated as a separate operational flow.
Key difference: operational workflow and verification differ—curbside emphasizes contactless handoff and may require different staffing and signage.

BOPIS vs Ship-to-Store

BOPIS: inventory is reserved and fulfilled from a local store location for immediate pickup.
Ship-to-Store: inventory is shipped from a warehouse to the store after the order is placed—pickup is delayed until the item arrives at the store.
Key difference: timing and inventory source—BOPIS aims for quicker availability by using store stock; ship-to-store relies on centralized inventory and introduces additional transfer time.

BOPIS vs Ship-to-Home

BOPIS: removes or reduces last-mile shipping cost and offers faster local pickup.
Ship-to-Home: traditional ecommerce delivery to customer address; better for customers who can’t visit stores or for bulky items.
Key difference: fulfillment destination—BOPIS trades shipping convenience for in-person pickup convenience and potential in-store upsell.

When should you track BOPIS?

Who should track it: any retailer with both an online presence and at least one pickup location—especially DTC brands with retail partnerships and storefronts.
Stage of growth: track from the moment you offer it; early tracking helps fine-tune processes before scaling.
Review frequency: weekly operational reviews (per store) and monthly strategic reviews (chain-level KPI trends).
Segments to analyze: store/location, product category, traffic source, device, time of day, and first-time vs returning customers.
Metrics to view alongside BOPIS: cancellation/no-show rate, pickup readiness time, attach rate, AOV, and cost-per-pickup.

Related ecommerce metrics

Conversion rate: shows how BOPIS availability affects the likelihood a session converts.
Average order value (AOV): BOPIS often changes AOV through attach purchases at pickup.
No-show / cancellation rate: measures how often reserved BOPIS orders are not picked up.
Pickup readiness time: average time between order placement and when the order is ready for pickup; operational performance indicator.
Pick-to-pickup labor time: operational cost metric for staffing and efficiency.
In-store attach rate: percentage of BOPIS customers who buy additional items during pickup.

FAQs

How is BOPIS different from click-and-collect?
They're the same concept; "click-and-collect" is a commonly used synonym, especially outside North America.
How should I measure BOPIS adoption?
Use BOPIS Adoption Rate = (BOPIS orders / Total online orders) × 100 and segment by store, product, and traffic source for actionable insights.
Why are my BOPIS orders being cancelled?
Common causes: inventory sync issues, long pickup SLAs without clear communication, or manual picking errors. Start by auditing inventory reservations and store-level pick accuracy.
Should I charge for pickup?
Charging can reduce adoption. Many merchants prefer free pickup and instead optimize attach rate and operational efficiency to cover costs. Test both approaches and measure impact on conversion and AOV.
How do I track in-store add-ons from BOPIS customers?
Integrate POS with ecommerce analytics, tag transactions as "BOPIS pickup" at point of sale, and reconcile daily to attribute attach revenue to BOPIS orders.
Is curbside part of BOPIS?
Curbside is a variant of BOPIS operationally. Treat them as separate fulfillment channels in analytics because verification, staffing, and pick flows differ.
What are the main analytics pitfalls?
Mixing reserved-but-cancelled orders with completed pickups, ignoring refund/return activity, and failing to segment by pickup type are common mistakes—define and report consistently.
Which customers prefer BOPIS?
High-intent shoppers who value immediacy, shoppers avoiding shipping fees, and local customers are the primary adopters—segment by repeat vs new and by traffic source to refine targeting.