In-Store Pickup Implementation on Your Website
A customer places an order for in-store pickup, arrives at the store, but the product is not on the shelf. The reason—inventory data is several hours old. According to Retail CRM, inventory discrepancies can reach 20%. This situation destroys trust and generates returns. We have encountered this dozens of times and developed a reliable solution based on real-time reservation. Savings for clients—up to 2 million rubles per year due to reduced cancellations.
What problems does a proper in-store pickup implementation solve?
The main technical challenge is data consistency between the website and physical locations. The buyer sees stock on the site, but by the time they arrive, the product has been sold to someone else. The update delay can range from 30 minutes to 2 hours. The second problem is UX: choosing a point without a map, no information about working hours, inability to check availability for a specific SKU. The third is reservation: without a temporary hold mechanism, you risk giving the product to another customer through a parallel sale, increasing cancellations by 30–50%. Savings on returns when implementing our approach can reach 15% of turnover, which for a chain of 10 points pays off in 3 months.
How is the data structure for pickup points organized?
For managing points and inventory, we use a relational model with two main tables. This structure provides fast queries with indexes on product_id and store_id and easily scales to hundreds of locations. Here is the schema on PostgreSQL:
pickup_stores (
id, name, address, city_id,
lat, lng, phone,
working_hours (jsonb),
is_active
)
store_inventory (
store_id, product_id, variant_id, quantity
)
The working_hours field stores schedules in JSONB format—convenient for different weekday and weekend hours. Coordinates (lat, lng) are needed for map display and distance calculation to the user.
Why is reservation with automatic release critical?
Without reservation, you cannot guarantee that the product will wait for the customer. The solution is a separate table with expires_at:
reservations (
id, store_id, product_id, variant_id, quantity,
expires_at, status
)
The hold duration (e.g., 24 hours) is configurable. Upon expiration, a background process (cron or queue) changes the status to cancelled and returns the quantity to store_inventory. This eliminates manual cancellation and product loss. Reservation via queue is 3 times more reliable than manual release.
Compare three approaches to reservation:
| Approach |
Consistency |
Complexity |
Automatic Cancellation |
System Load |
| No reserve |
Low (hours of discrepancy) |
Low |
No |
Minimal |
| Manual release |
Medium (depends on operator) |
Medium |
No |
Low |
| Auto-release (ours) |
High (seconds) |
Medium |
Yes (queue) |
Moderate |
Our approach with a queue, for example via Redis and Laravel queues, processes cancellations in 200 ms and reduces database load.
Case study: a chain of 15 stores, integration with 1C
In one project, we implemented in-store pickup for a grocery store chain. Initial architecture: website on Next.js 14, backend on Laravel 11, accounting system on 1C. The main problem—inventory was updated once an hour, leading to discrepancies of up to 20%.
We implemented two-level synchronization:
-
Real inventory (1C) → updates every 15 minutes via REST API
-
Reserves (site) → live update when an order is placed
To reduce load on 1C, we used Redis as a cache. When ordering, we check inventory through Redis; if successful, we reserve and send an event to the queue for write-off in 1C. If 1C is unavailable, the order is not confirmed.
Results: cancellations due to "out of stock" dropped by 40%, and order processing speed did not exceed 1.2 seconds. Over 50 similar projects in our portfolio.
What is included in a turnkey implementation?
We provide a full cycle of work:
- Admin panel for managing points (CRUD, map, working hours)
- Widget for point selection on a map with clustering for many points
- Real-time availability check for each product
- Reservation with automatic release via queue
- Ready notifications (email, SMS, Telegram)
- Integration with the accounting system (1C, SAP, any REST API)
- API documentation and monitoring setup
Implementation process
| Stage |
Duration |
Result |
| Analysis |
1-2 days |
Technical specification, integration scheme |
| Design |
2-3 days |
DB architecture, API, queue |
| Implementation |
3-5 days |
Functionality working on test environment |
| Testing |
1-2 days |
Unit tests, load testing up to 1000 orders/hour |
| Deployment |
1 day |
Production, monitoring, documentation |
Typical mistakes during implementation
- Reservation without a lifetime — product "hangs" forever.
- Using local time for working hours — issues with time zones.
- No queue for releasing reservations — risk of data loss on failure.
- Direct queries to 1C on every checkout — high latency (up to 5 seconds) and load.
Contact us for a preliminary assessment — it will take 30 minutes. Get a consultation with an engineer and accurate timelines for your task.
How does shipping service integration affect conversion?
Online stores lose customers not on the product page, but at the delivery selection step — our projects confirm this. Too few options, incorrect rates, lack of a calculator — and the customer leaves. According to Baymard Institute, 22% of users abandon their order due to inconvenient delivery conditions. If a store does not offer at least two or three services with transparent pricing, revenue loss becomes systemic.
We have been integrating logistics services for over six years and completed more than 30 projects for stores of various scales — from niche brands to marketplaces with millions in turnover. Integration is not just about 'displaying a list of pickup points.' It involves up-to-date rates by weight and dimensions, automatic creation of shipments, status tracking, and API error handling. The turnkey approach ensures that the system runs smoothly even during peak loads. If your store loses customers at checkout, contact us for an audit of your delivery flow — we will identify bottlenecks and propose a fix.
What problems does delivery setup solve?
Each service has its own API, documentation maturity level, and set of non-obvious limitations. Let's break down the three most common difficulties.
CDEK API v2 is the most mature among Russian carriers. OAuth 2.0 authorization (token lives 24 hours, refresh logic needed), REST JSON. Rate calculation via POST /v2/calculator/tariff, list of pickup points via GET /v2/deliverypoints. Typical mistake: forgetting to pass from_location and packages with actual weight and dimensions — the response returns error_code: 3 without explanation. Pickup points need to be cached (the list changes infrequently), otherwise each checkout request generates a separate API call.
Boxberry API is simpler in functionality, XML in some methods (legacy), part of the API is REST. Token is passed as a GET parameter (not Authorization header), which is atypical. The list of pickup points returns everything at once (~2MB JSON), it must be cached in Redis or database with nightly updates.
Russian Post API is the most complex among Russian carriers. SOAP + REST hybrid, requires a contract and setup in the personal account. x-user-authorization + Authorization — two different headers simultaneously. Standard shipments, EMS, 1st class — different rate groups. Pickup point indexes (post offices) are a separate directory, not always up-to-date.
DHL Express API is for international shipping. XML-based API (DHL XML Services), though there is a newer MyDHL+ API. Requires a registered account number. Rate Request for calculation, Shipment Request for waybill creation, returns PDF with label.
Why is caching pickup points and rates mandatory?
Caching is not an option but a necessity. CDEK API has a limit of 1000 requests per minute, Boxberry — 300. Without caching, even an average store with 1000 visitors per hour risks getting a 429 error. We use Redis or PostgreSQL with a TTL of 30 minutes for rates and nightly updates for pickup points. This reduces API load by 70–80% and speeds up page display. Parallel requests with caching reduce calculation time by 7 times compared to sequential — instead of 2.8 seconds, the customer gets rates in 380 ms. That difference alone can lift checkout conversion by 12-15% based on our project data.
What deliverables can you expect?
Each integration project includes:
-
Documentation: architecture description, data schemas, operation instructions for your team
-
Access setup: API keys, webhooks, test environments — everything configured
-
Training: webinar or written instructions on working with the admin panel and debugging
-
Launch support: 2 weeks of post-release monitoring with hotfixes and fine-tuning
| Step |
Duration |
| Requirements audit (which services, scenarios, tracking needs) |
2–3 days |
| Architecture selection and backend implementation |
1–2 weeks |
| Pickup point caching + rate caching implementation |
2–3 days |
| Frontend widget (map, list, filters) |
1–2 weeks |
| Testing with real requests in test mode |
3–5 days |
| Deployment and post-launch support |
2 days |
All deliverables are tailored to your stack — WooCommerce, Shopify, or custom solution. Schedule a free consultation to get a detailed scope for your store.
How we build integration
Abstraction over providers
No store uses one delivery service forever. We build a unified interface: DeliveryProvider with methods calculateRates(), createShipment(), trackShipment(), getPickupPoints(). Each service is a separate implementation. Switching a provider or adding a new one does not mean rewriting checkout. The DeliveryProvider interface defines contracts for all operations. Each carrier has its own class, e.g., CdekProvider implements DeliveryProvider. The constructor receives configs (keys, URLs, cache settings). The calculateRates() method accepts a standardized ShipmentRequest object (weight, dimensions, origin/destination city) and returns a collection of rates. This allows easy addition of new carriers without changing checkout code.
Caching pickup points
Geo-searching pickup points by coordinates or city is a frequent request. Pulling from the API every time is impossible (limits, latency). Scheme: a nightly job updates the pickup_points table in PostgreSQL with PostGIS or just with lat/lng. Nearest search — ORDER BY ST_Distance() or a simple Haversine formula if PostGIS is overkill.
Frontend widget
CDEK provides an official JS widget (@cdek-it/widget) — fast but limited in customization. For non-standard designs, a custom widget: map (Yandex.Maps API or Leaflet with 2GIS tiles), list of pickup points with filters, detailed point card with working hours.
Status tracking
Order statuses come either via webhook (CDEK supports) or periodic polling (Boxberry, Russian Post). For polling, a job queue (Laravel Queue, Bull for Node.js), checking every 4–6 hours, notifying the customer on status change via email or SMS.
Case: multi-carrier for WooCommerce
A sports nutrition store: CDEK + Boxberry + pickup from 3 physical stores. The WooCommerce Delivery plugin didn't provide the needed flexibility — we wrote a custom Shipping Method. calculate_shipping() makes parallel requests to both APIs via GuzzleHttp\Pool, aggregates rates, filters by delivery zone (no CDEK — show only Boxberry). Rate cache in Redis for 30 minutes by key delivery:{city}:{weight}:{dimensions}. Calculation time: was 2.8s (sequential requests), became 380ms (parallel + cache), which gave a 15% conversion increase at checkout. Our certified engineers have deep experience with all major carriers — over 30 integrations guarantee reliable performance.
Process and timelines
| Scenario |
Timeline |
| One service (CDEK or Boxberry), WooCommerce |
1–2 weeks |
| Two or three services + map widget |
3–5 weeks |
| Full multi-carrier + tracking + notifications |
6–10 weeks |
Cost is calculated individually — it depends on the number of providers, the need for a custom widget, and the complexity of tracking. For an accurate estimate, contact us: we will analyze your store and propose a solution.
Typical mistakes when setting up independently
- Forgetting API quotas — leads to access blocking
- Not caching the pickup point list — page loads 5+ seconds
- Ignoring error handling (timeout, 504) — lost orders
- Not testing edge weights and dimensions — calculation goes infinite
Our experience confirms: the right architecture with caching and parallelization reduces response time to 300–400 ms even with three providers. Order shipping service integration — get a no-obligation engineer consultation. Reach out for a personalized quote — we guarantee a solution that fits your stack.
