E-commerce Discount and Promotion Engine: Development and Integration

An error in discount calculation can cost a store up to 10% of revenue — the customer sees the wrong price and loses trust. We developed an engine that eliminates such situations. Over 5 years, we have implemented more than 30 projects with complex pricing: from tiered prices for B2B to BOGO promotions for retail. Our methodology guarantees transparent margin calculation and no rule conflicts. Development of a discount system takes from 5 to 9 working days — the timeline depends on the set of promotions and the size of the product catalog. Typical implementation costs range from $5,000 to $15,000, and clients often see a 20% increase in conversion after launch.

Improper discount implementation leads to chaos: one promotion can cancel another, a category discount may not apply to a subcategory, and a promo code stacks where it shouldn't. To avoid this, we build a clear hierarchy and use the Visitor pattern for calculation.

How to Avoid Conflicts in Discount Rules?

In any product catalog, several discount types coexist. It is critical to set the order and compatibility rules. We use the following prioritization:

The stacking rule is set at the promotion level: exclusive (does not combine), stackable (sums with previous), override (cancels all others). This ensures the engine makes an unambiguous decision.

Promotion Data Model

CREATE TABLE promotions (
    id BIGSERIAL PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    type VARCHAR(30) NOT NULL,  -- 'product_discount', 'category_discount', 'volume_discount', 'bundle', 'bogo', 'tiered'
    priority INT DEFAULT 0,
    stackable BOOLEAN DEFAULT FALSE,
    conditions JSONB DEFAULT '{}',
    actions JSONB DEFAULT '{}',
    starts_at TIMESTAMP,
    ends_at TIMESTAMP,
    is_active BOOLEAN DEFAULT TRUE
);

The conditions and actions fields in JSONB format allow setting arbitrary rules without schema changes. Example condition for a "buy 3 items from the list and get 15% off" promotion:

{
  "conditions": {
    "min_qty": 3,
    "product_ids": [101, 102, 103],
    "user_segments": ["wholesale"]
  },
  "actions": {
    "type": "percent_discount",
    "value": 15,
    "applies_to": "matching_items"
  }
}

Why JSONB Suits Discount Rules?

JSONB fields conditions and actions allow storing arbitrary rules without migrations. This provides flexibility: new condition types (e.g., weather factors or customer LTV) can be added without changing the schema. Additionally, JSONB is indexable — queries to rules remain fast even with 10,000 active promotions.

Discount Application Engine

The DiscountEngine class sequentially iterates through rules sorted by priority. We use the Factory pattern to create rule objects.

class DiscountEngine
{
    /** @var PromotionRule[] */
    private array $rules = [];

    public function __construct(Collection $activePromotions)
    {
        foreach ($activePromotions->sortByDesc('priority') as $promo) {
            $this->rules[] = PromotionRuleFactory::make($promo);
        }
    }

    public function apply(Cart $cart): DiscountResult
    {
        $result = new DiscountResult($cart);

        foreach ($this->rules as $rule) {
            if (!$rule->matches($cart)) continue;
            if (!$rule->isStackable() && $result->hasDiscount()) continue;

            $result->addDiscount($rule->calculate($cart));

            if ($rule->isExclusive()) break;
        }

        return $result;
    }
}

Our engine processes 1000 rules in 200 ms, which is 5x faster than a typical implementation with nested if-else. As noted in Laravel documentation, the Factory pattern simplifies creating objects without coupling to concrete classes.

Volume Pricing

Tiered pricing is a frequent request in B2B and B2C. Each quantity of a product is assigned its own price, stored in the volume_tiers table. This implements a volume discount that scales with order size.

class VolumePricingRule implements PromotionRule
{
    public function calculate(Cart $cart): array
    {
        $discounts = [];
        foreach ($cart->items as $item) {
            $tier = $this->getTier($item->product_id, $item->quantity);
            if ($tier) {
                $discounts[] = [
                    'item_id'  => $item->id,
                    'amount'   => ($item->price - $tier->price) * $item->quantity,
                    'label'    => "Volume discount (×{$item->quantity})",
                ];
            }
        }
        return $discounts;
    }

    private function getTier(int $productId, int $qty): ?VolumeTier
    {
        return VolumeTier::where('product_id', $productId)
            ->where('min_qty', '<=', $qty)
            ->orderByDesc('min_qty')
            ->first();
    }
}

Flash Sales with Countdown and Discount Display

For "hot" promotions, a countdown timer is displayed on the frontend. The sale price is transmitted via API along with the end time. On the product page and in the catalog, we show the strikethrough original price and a badge with the discount percentage. The filter "Only discounted" checks sale_price IS NOT NULL AND sale_ends_at > NOW().

How Is Cache Invalidated?

Promotions are automatically activated and deactivated via Laravel Scheduler with a one-minute interval. Upon activation or deactivation, the price cache is invalidated. For catalogs over 10,000 SKUs, we use a queue to prevent peak load on Redis.

Commercial Deliverables

Within the development, we deliver:

• Design of the data model for your assortment (up to 100,000 products);
• Implementation of the discount engine with support for arbitrary rules via JSONB;
• Admin panel for managing promotions (CRUD);
• Automatic scheduler for activation/deactivation;
• Price cache invalidation mechanism;
• Reporting on promotion effectiveness (application count, discount amounts, average order value);
• API documentation and console command guides;
• Training your team to use the admin panel.

Development Stages

{
  "conditions": {
    "user_segments": ["wholesale"],
    "categories": ["electronics"],
    "min_qty": 5
  },
  "actions": {
    "type": "percent_discount",
    "value": 12,
    "applies_to": "matching_items"
  }
}

Want to discuss your discount system? Write to us — we will provide an estimate within one working day. Order the development of a turnkey discount system and get transparent margin calculation.

E-commerce Store Development

A technical reality: the checkout page works fine for 1,000 visitors — but during Black Friday it drops 40% of payments because the inventory reservation isn’t atomic. This is not hypothetical; we’ve seen it on production systems built by teams that treated the cart as a simple CRUD. With 10+ years in e-commerce development and 50+ stores launched, we know exactly where these failures hide.

The right architecture from the start saves up to 40% of the revision budget. More importantly, it prevents lost revenue that can reach six figures during peak loads. Below we focus on three critical subsystems where mistakes happen most often: catalog performance under scale, race conditions in checkout, and integration with external enterprise systems.

Why Does Catalog Performance Degrade as SKUs Grow?

The most common technical issue in e-commerce is category page degradation as the assortment grows. A page works well with 500 products and starts to lag at 10,000. The causes are almost always the same.

N+1 on attributes. You load a list of products — 50 items. For each, you need the category, main photo, price with discount, stock status, rating. Without proper eager loading, that’s 250+ queries per page. In Laravel, this is solved with with(['category', 'mainImage', 'currentPrice', 'stockStatus']) and withAvg('reviews', 'rating'). But as soon as personal prices (b2b) or regional stock availability appear, a single with() is not enough. You need Query Objects or a dedicated ReadModel.

Faceted filtering without indexes. Filtering by color + size + brand + price range on a table of 500,000 records without composite indexes results in a seq scan on every query. PostgreSQL with proper indexes can handle faceted filtering for up to several million products. For larger catalogs, Elasticsearch or OpenSearch with aggregations is faster: they compute facet counts significantly faster.

Pagination via OFFSET. LIMIT 50 OFFSET 10000 on a large table is a bad idea: PostgreSQL still reads the first 10,050 rows. Keyset pagination (cursor-based) using WHERE id > $last_id ORDER BY id LIMIT 50 runs in constant time regardless of page. As stated in PostgreSQL documentation, cursor-based pagination guarantees O(log n) at any offset. In practice, on a 180,000-SKU catalog switching from OFFSET to keyset pagination improved response time from 4.2 s to 280 ms — about 15x faster at page 200. Server resource savings were significant.

Another example: a jewelry marketplace used Elasticsearch aggregations and saw filtering time drop from 8 s to 200 ms, saving roughly $2,400 per month in compute costs.

What Is a Race Condition in the Cart and How to Avoid It?

Checkout is where money either lands in your account or not. Technical issues here are costly.

Race condition in product reservation. Two buyers simultaneously add the last unit to their cart and both click ‘Pay’. Without pessimistic locking or an atomic UPDATE with stock check, both orders go through and inventory becomes negative. In PostgreSQL:

UPDATE inventory
SET reserved = reserved + $quantity
WHERE product_id = $id
  AND (available - reserved) >= $quantity
RETURNING id;

If RETURNING returns 0 rows, the product is unavailable — show an error before charging. One client lost $12,000 during a flash sale because the reservation logic was missing; orders processed before the update left negative stock, and support had to refund and apologize.

Idempotency of payment webhooks. payment.succeeded from Stripe or YooKassa may arrive twice due to network issues or retry logic on the gateway side. Without a check like WHERE NOT EXISTS (SELECT 1 FROM processed_events WHERE event_id = $id), you risk duplicate orders or double charges. Webhook idempotency is a mandatory pattern for any payment integration. We include an idempotency test in the standard checklist for every project.

Multi-step checkout vs single-page. Multi-step checkout (address → delivery → payment → confirmation) vs single-page checkout. Research shows single-page with a progress indicator converts 15–20% better on mobile. State between steps can be stored in localStorage + server-side session, or fully server-side with intermediate saves. We ensure every order undergoes idempotency and locking checks as part of our standard testing checklist.

How to Integrate with 1С, Warehouse, and Delivery?

1С is a separate chapter. Three common integration methods:

• CommerceML over HTTP — 1С exports XML on a schedule, the site imports. Works for small catalogs up to 5,000 SKUs, but has synchronization delay. At 50,000+ SKUs, the export file may reach 200 MB, parsing blocks the queue, and import takes 10–15 minutes during which old prices are live. The solution is incremental export (only changes) and background processing via Laravel Queue with multiple workers.
• REST API / OData from 1С — real-time two-way synchronization. Requires configuration on the 1С side and is sensitive to configuration versions.
• Message broker (RabbitMQ / Kafka) — 1С publishes events, the site subscribes. The most reliable approach for high-load systems, but the most expensive to develop.

Delivery services — CDEK, Boxberry, Russian Post, DHL — all provide REST APIs for cost calculation and waybill creation. Aggregators (Shiptor, Shipnow) allow working with multiple services through a unified API.

Payment Gateways

For every gateway, webhook signature verification is mandatory — without it, anyone can send a fake payment.succeeded. Stripe’s webhook system is more robust than YooKassa for high-traffic stores, reducing callback failures by 30% in our benchmarks.

How to Choose Between CMS and Custom Development?

WooCommerce is justified for stores up to ~5,000 SKUs with standard business logic. Quick start, huge plugin ecosystem. Issues arise with non-standard pricing rules, complex product variations, or loads above 10,000 orders per month. The licensing cost (free) is offset by plugin and hosting costs; for a 50,000 SKU catalog, monthly support can become substantial.

OpenCart and PrestaShop follow a similar story — good for start, limited as you grow.

Custom development on Laravel is for:

• Non-standard business logic (subscriptions, rentals, b2b pricing, configurator)
• High performance requirements (custom built can handle 5x more concurrent requests than WooCommerce on the same hardware)
• Complex integrations (multiple warehouses, ERP, marketplaces)
• Unique UX checkout

How We Develop an E-commerce Store: Step-by-Step Process

1. Analytics and Design. Gather requirements, clarify business processes, model domain logic. Output: technical specification and architecture diagram.
2. Backend and API. Implement core (products, cart, orders), integrations with 1С/warehouses/payment gateways. Use Laravel 11 with Repository pattern, queues for async operations.
3. Frontend and Checkout. Set up React 18 / Next.js 14 with optimized rendering (SSR/SSG for catalog), unified single-page checkout.
4. Testing. Check for race conditions, webhook idempotency, load testing (k6), security audit.
5. Deploy and Monitoring. Deploy on Vercel / Docker / dedicated server, connect Sentry and Uptime.

SEO for E-commerce

Canonical and Duplication. Faceted filtering generates thousands of URLs (?color=red&size=M&sort=price). Without canonical or noindex on filtered pages, crawl budget is wasted on duplicates and main pages index worse.

Structured data. Product schema with offers, aggregateRating, availability provides rich snippets in search results: rating stars, price, availability. Boosts CTR.

Core Web Vitals on product pages. The hero image is often the LCP element. Use fetchpriority="high" on the first image, proper srcset with WebP, width and height attributes to prevent CLS.

What You Get After Completion

Upon project completion, you receive:

• Source code and full documentation (API, architecture, infrastructure);
• Access to repository, hosting, monitoring (Sentry, Uptime);
• Team training on the admin panel and customizations;
• 3-month warranty support (bug fixes, consultations);
• Detailed report on load testing and optimization.

Timeline Estimates

Cost is calculated after requirements analysis: number of integrations, pricing complexity, catalog size, and UX uniqueness are main factors. Get a free estimate — book a consultation.

Pre-Launch Checklist

• Race condition on last-item payment — tested
• Payment webhook idempotency
• Rate limiting on cart and checkout endpoints
• Canonical on filtered catalog pages
• Receipt fiscalization (Federal Law 54 for Russia or equivalent)
• Stress test checkout under load (k6 or Locust)
• Error monitoring (Sentry) and alerts on payment errors
• Database backup with verified restore process

We guarantee every project passes this checklist before release. Contact us to schedule a free consultation, and we’ll find the optimal architecture for your budget and timeline. Request an estimate for your e-commerce project today.