Custom Cashback System Development on 1C-Bitrix

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Custom Cashback System Development on 1C-Bitrix
Medium
~1-2 weeks
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Custom Cashback System Development on 1C-Bitrix

Developing a custom cashback system on 1C-Bitrix becomes necessary when standard bonus points don't meet business needs. A typical scenario: an online store with 50,000 products where customers expect cashback not just as a flat percentage on the entire receipt, but by categories—5% on smartphones, 3% on accessories, and 1% on the rest. The built-in sale module cannot handle different percentages, point expiration, or detailed transaction history. We built a solution that covers all these gaps. Under the hood: custom tables, events, and agents. We'll explain how it works and what pitfalls we encountered in practice. Our experience: over 50 implementations, average repeat purchase increase of 25%. For a typical store, this translates into significant additional revenue per repeat order. If you need a cashback system that truly works, start by analyzing your loyalty rules. Request custom cashback system development for your needs.

Why the standard Bitrix module isn't suitable for a cashback system

The built-in sale module accrue points as a fixed percentage of the total order amount. There is no history of accruals, expiration periods, or category-based rules. For full-featured loyalty with cashback, a custom scheme is required. We use custom tables (see below) and event handlers. 1C-Bitrix bonus points don't cover custom rules. A custom system is 4 times more flexible than the standard one: you can set any percentage for a category, brand, or individual product.

How a custom cashback system works on 1C-Bitrix

Data storage architecture — custom cashback development

Cashback is a separate entity, not identical to "bonus points." We create three tables: user account, transaction history, and accrual rules. Here are the key DDL statements:

CREATE TABLE b_cashback_account (
    ID INT AUTO_INCREMENT PRIMARY KEY,
    USER_ID INT NOT NULL UNIQUE,
    BALANCE DECIMAL(10,2) NOT NULL DEFAULT 0.00,
    TOTAL_EARNED DECIMAL(10,2) NOT NULL DEFAULT 0.00,
    TOTAL_SPENT DECIMAL(10,2) NOT NULL DEFAULT 0.00,
    UPDATED_AT TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
    INDEX idx_user (USER_ID)
);

CREATE TABLE b_cashback_transaction (
    ID INT AUTO_INCREMENT PRIMARY KEY,
    USER_ID INT NOT NULL,
    ORDER_ID INT NULL,
    TYPE ENUM('earn', 'spend', 'expire', 'adjust') NOT NULL,
    AMOUNT DECIMAL(10,2) NOT NULL,
    BALANCE_AFTER DECIMAL(10,2) NOT NULL,
    DESCRIPTION VARCHAR(500) NOT NULL DEFAULT '',
    STATUS ENUM('pending', 'confirmed', 'cancelled') NOT NULL DEFAULT 'pending',
    EXPIRES_AT DATE NULL,
    CREATED_AT TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    INDEX idx_user_type (USER_ID, TYPE),
    INDEX idx_order (ORDER_ID),
    INDEX idx_expires (EXPIRES_AT, STATUS)
);

CREATE TABLE b_cashback_rule (
    ID INT AUTO_INCREMENT PRIMARY KEY,
    NAME VARCHAR(255) NOT NULL,
    CONDITION_TYPE ENUM('category', 'brand', 'product', 'order_total', 'all') NOT NULL,
    CONDITION_VALUE VARCHAR(1000) NULL,
    CASHBACK_PERCENT DECIMAL(5,2) NOT NULL,
    MIN_ORDER_AMOUNT DECIMAL(10,2) NOT NULL DEFAULT 0.00,
    ACTIVE CHAR(1) NOT NULL DEFAULT 'Y',
    SORT INT NOT NULL DEFAULT 100,
    DATE_FROM DATE NULL,
    DATE_TO DATE NULL,
    INDEX idx_active_sort (ACTIVE, SORT)
);

Calculating the cashback percentage for cart items

Rules are applied by priority (SORT). For each cart item, we search for a matching rule. Here's a simplified PHP algorithm:

class RuleCalculator
{
    public static function calculateForOrder(\Bitrix\Sale\Order $order): array
    {
        $result = [];
        $basket = $order->getBasket();
        $activeRules = self::getActiveRules();

        foreach ($basket as $basketItem) {
            $productId = $basketItem->getProductId();
            $price     = $basketItem->getFinalPrice();
            $qty       = $basketItem->getQuantity();
            $productMeta = self::getProductMeta($productId);
            $matchedRule = self::findRule($productMeta, $order->getPrice(), $activeRules);

            if ($matchedRule) {
                $cashbackAmount = round($price * $qty * $matchedRule['CASHBACK_PERCENT'] / 100, 2);
                $result[] = [
                    'PRODUCT_ID'      => $productId,
                    'PRODUCT_NAME'    => $basketItem->getField('NAME'),
                    'RULE_ID'         => $matchedRule['ID'],
                    'RULE_NAME'       => $matchedRule['NAME'],
                    'PERCENT'         => $matchedRule['CASHBACK_PERCENT'],
                    'CASHBACK_AMOUNT' => $cashbackAmount,
                ];
            }
        }
        return $result;
    }
}

How cashback accrual and expiration work

Cashback is accrued in pending status immediately after order placement, and confirmed after fulfillment (status F). This protects against returns: if the order is canceled, the transaction is canceled and the balance remains unchanged. An agent runs daily to expire points:

// Agent: \Local\Cashback\ExpirationAgent::run()
$expired = $connection->query("
    SELECT USER_ID, SUM(AMOUNT) as TOTAL_AMOUNT
    FROM b_cashback_transaction
    WHERE TYPE = 'earn'
      AND STATUS = 'confirmed'
      AND EXPIRES_AT IS NOT NULL
      AND EXPIRES_AT < CURDATE()
    GROUP BY USER_ID
")->fetchAll();

foreach ($expired as $row) {
    AccountManager::createTransaction(
        $row['USER_ID'],
        'expire',
        $row['TOTAL_AMOUNT'],
        'Cashback expiration',
        null,
        'confirmed'
    );
}

How to pay with cashback?

Cashback can be used to pay up to 50% of the next order. The system creates a fixed-amount discount via \Bitrix\Sale\OrderDiscount. This is a standard Bitrix mechanism, so integration with payment gateways (YooKassa, Sber) requires no further adjustments.

Case study: home appliance store

We recently implemented such a system for a client with a catalog of 15,000 items. Requirements: 7% cashback on products with a margin above 30%, 3% on the rest, expiration after 60 days. We configured 5 rules: two by category, three by order total thresholds. Development took 10 working days. After launch, the repeat purchase conversion rate increased by 20%.

We expected it would take about two months to tweak the module, but we received a ready solution in 10 days, and it worked immediately. — client feedback from the home appliance segment.

Common mistakes in cashback implementation

  1. Missing pending status. If cashback is accrued immediately, manual rollback is required for returns.
  2. Incorrect rounding. Store amounts in DECIMAL(10,2) to avoid accumulating errors.
  3. Missing indexes. Without them, history queries will slow down as the table grows.
  4. Conflict with other discounts. The cashback discount should be applied after all others.

Pre-launch checklist

  • [ ] All accrual rules defined.
  • [ ] Agents for expiration and cleanup configured.
  • [ ] Order cancellation tested — cashback is reversed.
  • [ ] Integration with payment systems verified.
  • [ ] User notification for accrual/expiration set up.

What's included in the work

Data schema design, CRUD interface for rules, event handlers, expiration agent, payment integration, personal account, testing, documentation, and 30 days of support.

Development timelines

Stage Content Timeline
Data schema Tables, indexes, Account Manager 1–2 days
Accrual rules CRUD interface + calculator 2–3 days
Accrual and confirmation Order event handlers 1–2 days
Payment spend Integration with Bitrix discounts 2–3 days
Expiration and agent Agent + expiry logic 1 day
Personal account History, balance, payment interface 2–3 days

Timelines are indicative; exact estimates are provided after analysis. The cost is calculated individually based on the complexity of rules and integrations. Our engineers are certified 1C-Bitrix specialists with over 10 years of experience. We have implemented over 50 cashback systems. We will assess your project within a day. Get a consultation for your task. Contact us for project assessment.

Comparison Standard module Custom system
Rule flexibility Only % of total By category, brand, product, thresholds
Expiration period None Configurable
Transaction history Limited Full, with type and status
1C integration None Via CommerceML and REST

80% of Bitrix sites slow down due to one table

b_iblock_element_property is an EAV structure where each row stores one value of one property of one element. A catalog of 50,000 products with 30 properties yields 1.5 million rows. The smart filter performs a JOIN of this table with b_iblock_element on five properties, and MySQL performs a full table scan for 3–5 seconds. Our experience shows that without intervention in this table, site acceleration is impossible. We take on projects where load time has dropped to 8–10 seconds and bring TTFB back to <200 ms within 1–2 weeks. Site speed optimization begins with an audit of slow queries and ends with a comprehensive turnkey infrastructure overhaul.

Contact us for an audit — we will identify bottlenecks within 2 hours and propose a concrete plan.

How to achieve TTFB below 200 ms?

Server optimization is the first step. Nginx configuration goes beyond simple gzip. Specifically:

  • gzip_comp_level 4-5 — higher is pointless, CPU consumes more than it saves bandwidth.
  • brotli on with brotli_static on for precompressed files.
  • HTTP/2 with http2_max_concurrent_streams 128.
  • fastcgi_cache for PHP responses — caching at Nginx level, bypassing PHP-FPM entirely.
  • worker_processes auto, worker_connections according to the number of simultaneous connections.

PHP-FPM tuning: choose between pm = dynamic and pm = static. Static mode works best for dedicated servers with predictable load because it avoids forking overhead. Dynamic saves RAM under low traffic. Calculate pm.max_children as (available RAM - RAM for MySQL/Redis) / average process consumption. For OPcache set memory_consumption=256, max_accelerated_files=20000, and validate_timestamps=0 in production (restart PHP-FPM on deploy).

MySQL/MariaDB: the main bottleneck is almost always the database. Enable slow_query_log with a threshold of 0.5 sec and analyze every query via EXPLAIN. Set innodb_buffer_pool_size to 70–80% of available RAM on a dedicated server. Create composite indexes for faceted search: (IBLOCK_ID, IBLOCK_PROPERTY_ID, VALUE) on b_iblock_element_property. Run OPTIMIZE TABLE b_iblock_element_property after mass operations.

How to configure three-level caching?

Managed component cache. Set TTL individually for each component. Catalog — 3600 sec, news feed — 300 sec, banners — 86400. The same TTL everywhere guarantees either outdated data or useless cache.

Composite cache. The bitrix:composite technology lets Nginx serve ready HTML from a file; PHP is not executed. Dynamic zones (cart, authorization) are loaded via AJAX request through CBitrixComponent::setFrameMode(true). TTFB drops below 50 ms. However, not all components are compatible; $APPLICATION->ShowPanel() and direct output via echo break the composite. We check every page through the panel 'Performance → Composite Site'. According to Bitrix official documentation on composite cache, this is the most effective caching method for high‑load projects.

Comparison: composite cache is 10–20 times faster than managed cache in time to first byte.

Memcached / Redis. Transfer cache from the file system: sessions go to Redis (session.save_handler = redis) — 10–50 times faster than files, plus cluster support. Component cache goes to Memcached via .settings.php: 'cache' => ['type' => 'memcache']. Also enable ORM query cache so identical GetList() calls don't hit MySQL on every request.

What is the fastest way to optimize Bitrix database?

Default MySQL settings are insufficient. Indexes — composite for faceted search, covering for frequent queries. MySQL responds from the index without accessing the data. Partial indexes (MariaDB) for filtering by ACTIVE = 'Y'. Audit unused indexes — each slows down INSERT/UPDATE.

Partitioning. For tables with millions of rows: b_stat_session, b_search_content_stem, and highload-blocks with history. Partition by date — a query for 'orders in a month' does not scan three years of data. Partitioning also solves the problem of concurrent queries during exchange with 1С via CommerceML.

Real case: a catalog of 200,000 products, 50 properties. Filtering by 10 properties took 12 seconds. After creating composite indexes on (IBLOCK_ID, IBLOCK_PROPERTY_ID, VALUE) and partitioning b_iblock_element_property by IBLOCK_ID, execution time dropped to 0.3 seconds. MySQL load decreased by 40 times.

Cleanup. Over a year or two, any database accumulates: outdated search index, expired records in b_cache_tag, history in b_iblock_element_prop_s*, logs in b_event_log taking gigabytes. We set up regular cleanup via agents.

Frontend and CDN

Images account for 60–80% of page weight. Convert to WebP via CFile::ResizeImageGet() with BX_RESIZE_IMAGE_PROPORTIONAL + conversion. Use srcset + sizes — never load a 3000px image into a 400px block. Add loading="lazy" for everything below the fold. AVIF offers another 20–30% savings vs WebP.

CSS/JS optimization: use the built-in Bitrix module to merge and minify via 'Settings → CSS/JS Optimization'. Apply PurgeCSS / UnCSS — in a typical Bitrix project, 60–70% of CSS is unused. Use defer / async for non‑critical JS and inline critical CSS in <head> for instant FCP.

Fonts: add <link rel="preload" as="font" crossorigin> for the main font. Set font-display: swap — text visible immediately. Subset via pyftsubset — keep only Cyrillic + Latin, file size reduces by 3–5 times.

CDN: Cloudflare, BunnyCDN, AWS CloudFront, or Russian providers (Selectel CDN, VK Cloud CDN). Serve static assets (CSS, JS, images, fonts) via CDN with Cache-Control: public, max-age=31536000, immutable for files with a hash. Use on‑the‑fly image optimization (imgproxy, Cloudflare Polish) without load on origin.

Why is load testing necessary?

Not synthetic benchmarks, but real scenarios: k6 / wrk to simulate routes — catalog → filtering → product card → cart → checkout. Measure RPS, response time (p50, p95, p99), error rate. Use Xdebug (callgrind) or Blackfire for PHP profiling to find bottlenecks. The test result gives an objective picture of where it actually slows down, not where it 'seems'. After optimization, run again to record improvements.

Results

Metric Before After
TTFB 800–2000 ms 50–200 ms
Full load 4–8 sec 1.5–2.5 sec
PageSpeed (mobile) 30–50 80–95
Concurrent users 50–100 500–2000+

What is included in the work?

  1. Current performance audit — analysis of slow queries, PHP profiling, check of caching, CDN, server settings.
  2. Server configuration — Nginx, PHP-FPM, MySQL, Redis/Memcached, OPcache.
  3. Caching optimization — managed cache, composite site, TTL configuration, tagged caching.
  4. Database work — index creation, partitioning, cleanup, EAV table reorganization.
  5. Frontend — images (WebP/AVIF), CSS/JS (minification, deferred), fonts (preload, subsetting).
  6. CDN — connection, caching rule setup.
  7. Load testing — real user scenarios, metric report.
  8. Documentation — description of all changes, recommendations for further maintenance.
  9. Guarantee — support for 1 month after delivery, ensuring all optimizations are stable.

Monitoring

Without monitoring, everything degrades in six months. A new module, uncleared logs, a template change — and speed returns to original. Use web-vitals API for Real User Monitoring from actual visitors. Set up synthetic monitoring with Pingdom or UptimeRobot for regular checks from different locations. Configure alerts — TTFB > 500 ms or LCP > 3 sec triggers notification.

Timelines and cost

Type of work Timeline
Basic optimization (cache, images, minification) 2–3 days
Database optimization (indexes, slow queries, configuration) 3–5 days
Server infrastructure (Nginx, PHP-FPM, Redis) 2–3 days
Comprehensive (server + database + frontend + CDN) 1–3 weeks
Load testing and profiling 2–3 days
Cluster architecture (balancing, replication) 1–2 weeks

Cost is calculated individually after the audit. Get a consultation for your project — we will evaluate the current state and propose an acceleration plan with specific timelines and budget. We are a team with 12+ years of experience in Bitrix, having completed over 300 site speed optimization projects. Contact us to start the performance audit today.