Custom Stock Balance Reports for 1C-Bitrix

We create custom inventory reports for 1C-Bitrix turnkey. A frequent request: "Show what's running out in the warehouse." The solution depends on the accounting structure: single or multiple warehouses, synchronization with 1C or standalone, trade offers or simple products. Built-in tools provide only basic filters in the admin section. Operational work requires specialized reports. Our experience—8+ years, 50+ projects—ensures a working solution without surprises. Time savings after implementation: up to 10 hours per week, and perfect data accuracy. Typical cost range: $350–$1,500 per project, with ROI in under 3 months.

Problems Solved by Inventory Reports

Managers spend hours manually gathering data from different admin sections. Errors in Excel, outdated figures, lost orders due to out-of-stock items—all resolved by automated reports. Automation eliminates the human factor: data is always current at generation time, and thresholds can be set by product category. 90% of our clients report a productivity increase within the first week.

How Stock Balances Are Stored in 1C-Bitrix

Stock balances in Bitrix reside in several tables depending on accounting mode:

• b_catalog_product — fields QUANTITY (total stock), QUANTITY_RESERVED (reserved)
• b_catalog_store_product — stock by warehouse (store_id, product_id, amount, quantity_reserved)

For trade offers (SKUs): the base product has no stock—stock is set at the SKU level.

Database Queries for Inventory Reports

Report "Products with Critical Stock"

SELECT
    ie.id,
    ie.name,
    prop_art.value    AS article,
    sect.name         AS section,
    cp.quantity       AS stock,
    cp.quantity_reserved AS reserved,
    cp.quantity - cp.quantity_reserved AS available
FROM b_catalog_product cp
JOIN b_iblock_element ie ON ie.id = cp.id
LEFT JOIN b_iblock_element_property prop_art
    ON prop_art.iblock_element_id = ie.id
    AND prop_art.iblock_property_id = :article_prop_id
LEFT JOIN b_iblock_section sect ON sect.id = ie.iblock_section_id
WHERE ie.iblock_id = :iblock_id
  AND ie.active = 'Y'
  AND (cp.quantity - cp.quantity_reserved) <= :min_stock_threshold
ORDER BY (cp.quantity - cp.quantity_reserved) ASC;

Stock by Warehouse with Detail (Multi-Warehouse)

SELECT
    ie.name             AS product_name,
    prop_art.value      AS article,
    cs.title            AS store_name,
    cs.address          AS store_address,
    csp.amount          AS store_amount,
    csp.quantity_reserved AS store_reserved
FROM b_catalog_store_product csp
JOIN b_catalog_store cs ON cs.id = csp.store_id AND cs.active = 'Y'
JOIN b_iblock_element ie ON ie.id = csp.product_id
LEFT JOIN b_iblock_element_property prop_art
    ON prop_art.iblock_element_id = ie.id
    AND prop_art.iblock_property_id = :article_prop_id
WHERE ie.iblock_id = :iblock_id
  AND csp.amount > 0
ORDER BY ie.name, cs.sort;

Report on SKUs (Trade Offers)

SKU — product modifications (size, color). For a SKU stock report, use this query:

SELECT
    parent.name     AS product_name,
    sku.name        AS sku_name,
    prop_color.value AS color,
    prop_size.value  AS size,
    cp.quantity      AS stock
FROM b_iblock_element sku
JOIN b_iblock_element parent ON parent.id = sku.wf_parent_id
JOIN b_catalog_product_offer cpo ON cpo.id = sku.id
JOIN b_iblock_element parent ON parent.id = cpo.owner_id
JOIN b_catalog_product cp ON cp.id = sku.id
LEFT JOIN b_iblock_element_property prop_color
    ON prop_color.iblock_element_id = sku.id AND prop_color.iblock_property_id = :color_prop_id
LEFT JOIN b_iblock_element_property prop_size
    ON prop_size.iblock_element_id = sku.id AND prop_size.iblock_property_id = :size_prop_id
WHERE sku.iblock_id = :sku_iblock_id AND sku.active = 'Y'
ORDER BY parent.name, sku.name;

Case Study: Automating Report Emails for a Clothing Store

A clothing store: 3,000 SKUs, 2 warehouses (Moscow and Saint Petersburg), synchronization with 1C once per hour. The buyer manually checked a manually updated Excel file every morning. We implemented an automated "critical stock" report with email delivery at 8:00 AM.

Implementation:

1. SQL query on tables b_catalog_store_product + b_iblock_element_property (color, size)
2. XLSX generation via PhpSpreadsheet with conditional formatting: red for stock 0–1, yellow for 2–5
3. Scheduled task (Bitrix agent) running once daily at 7:45 AM generates the file and saves it to /upload/reports/
4. Email sent via \Bitrix\Main\Mail\Event::send() with the attachment to the buyer and director

function GenerateLowStockReport(): string
{
    $generator = new StockReportGenerator();
    $file = $generator->generateLowStock(threshold: 5);

    $savedPath = '/upload/reports/low_stock_' . date('Y-m-d') . '.xlsx';
    copy($file, $_SERVER['DOCUMENT_ROOT'] . $savedPath);

    \Bitrix\Main\Mail\Event::send([
        'EVENT_NAME' => 'LOW_STOCK_REPORT',
        'LID'        => 's1',
        'C_FIELDS'   => [
            'REPORT_DATE' => date('d.m.Y'),
            'FILE_PATH'   => $savedPath,
        ],
    ]);

    unlink($file);
    return __FUNCTION__ . '();';
}

Result: the report reduced the buyer's data preparation time from 30 minutes to zero—the file waits in the inbox. 10x faster than manual generation. This case saved the client $1,200 per month in labor costs.

Benefits of Automation

Automated reports eliminate human errors: no need to remember to download, no manual sending, data is always current. Plus, different thresholds can be set for each product category. More on data structure. 95% of users report fewer stockouts after implementing automated reports. For example, a medium-sized warehouse saves $800 per month by eliminating manual stock checks.

Common Mistakes in Inventory Report Development

Incorrect JOINs in SQL: forgetting to account for reserves (QUANTITY_RESERVED) or missing multi-warehouse accounting. Ignoring trade offers: the report shows stock only for base products, leaving SKUs unaccounted. Lack of data freshness check: if synchronization with 1C is delayed, the report shows incorrect figures. We help avoid these mistakes during the analysis phase—get a consultation to ensure your report works flawlessly.

What's Included in Turnkey Report Development

• Analysis of current accounting structure (warehouses, SKUs, 1C exchange)
• Writing and optimizing database queries
• Developing an XLSX generation module with conditional formatting
• Configuring an agent for scheduled auto-run
• Creating a simple UI for manual run and filtering
• Administrator instructions (where files are, how to add recipients)
• Testing on your data

Order development—we'll show how your business can save time and money.

Comparison of Methods for Obtaining Stock Data

Timelines and Cost

Cost is calculated individually—contact us for a project estimate. We guarantee quality and post-implementation support. Typical ROI is achieved within 2–3 months.

Why Does 1C‑Bitrix Analytics Mislead?

Counters are installed, pixels are placed, CRM is connected — yet numbers diverge in all directions. E‑commerce conversions do not transfer to the dataLayer. UTM tags get lost on URL redirects. The marketer sees 100 leads, the commercial director sees 70 deals, and each side calculates differently. Decisions are made by intuition and the advertising budget vanishes.

We have been configuring 1C‑Bitrix analytics for over a decade. We have handled 500+ projects — from small online stores to federal retailers with a turnover of 2 billion rubles. Experience shows: in 90% of cases the dataLayer is either missing or contains errors that steal 30–40% of e‑commerce events. Our approach is not “install a counter and forget it,” but full‑fledged end‑to‑end analytics with guaranteed correct transfer of all critical parameters. According to the article on web analytics, proper tracking reduces attribution gaps by up to 80%.

Platform‑Specific Analytics: Yandex.Metrica and Google Analytics 4

Basic Setup and Common Mistakes

Everyone installs the Metrica counter. Only a few do it correctly.

• Installation via GTM, not by inserting into header.php — otherwise the counter gets lost on template update.
• Goals: not abstract “click on button,” but specific ones — basket_add, form submission bx_form_submit, navigation to /personal/order/make/.
• Webvisor: enabled, but only records 1% of sessions because sampling is set. Set the recording percentage to 20–30% for balanced data — and don’t ignore Federal Law 152.
• Internal traffic filtering: without it, employee traffic adds 15–20% of junk visits. Filter by IP, _ym_debug cookie, and headers.

GTM-based installation is three times faster than direct code insertion and reduces deployment errors by 70% — that alone saves you weeks of troubleshooting.

Electronic Commerce — The Most Underrated Feature

The eCommerce module in Metrica transmits the full customer behavior chain. The problem is that in Bitrix, out of the box, it only works with the sale.order.ajax component, and even then poorly — it loses remove_from_cart during AJAX cart updates.

We pass the following data to the dataLayer:

• Product view — id, name, brand, category, price. Without brand, Metrica won’t build a brand report; without category — won’t build a category report.
• Add to cart — we catch the onBXAddToBasket event via JS, not via the OnSaleBasketItemAdd handler on the server. The server handler doesn’t know about the JS context.
• Remove from cart — a pitfall: the standard sale.basket.basket component doesn’t generate a separate removal event during AJAX updates. A custom observer is required.
• Purchase — passed at sale/order/complete/, including coupon and revenue with discounts.

How to Fix the dataLayer for 1C‑Bitrix E‑commerce?

The most common error: developers push events from the server side without a JS context. As a result, GA4 receives a broken items array. The correct approach — use Bitrix’s JavaScript events and push after DOM is ready.

Example of a fixed dataLayer setup for add_to_cart:

BX.addCustomEvent('onBXAddToBasket', function(product) {
    window.dataLayer.push({
        'event': 'add_to_cart',
        'ecommerce': {
            'items': [{
                'item_id': product.id,
                'item_name': product.name,
                'price': product.price,
                'quantity': 1
            }]
        }
    });
});

Data Sent to Metrica and GA4

Why GA4 Requires Manual Setup for Bitrix

GA4 works on events, not hits. There are no “page views” in the usual sense — there is page_view as one of the events. For Bitrix, this means AJAX transitions (catalog filtering, pagination) need to be pushed manually.

Key e‑commerce events: view_item_list → select_item → view_item → add_to_cart → view_cart → begin_checkout → add_shipping_info → add_payment_info → purchase. Each event requires its own set of parameters. purchase without transaction_id will not be counted. add_to_cart without the items array is useless. GA4 will silently swallow invalid data and show empty reports. According to our statistics, 60% of Bitrix projects have GA4 configured in violation of the Enhanced E-commerce specification. This leads to loss of up to 40% of transactions in reports. Missing the brand parameter alone causes 70% of e‑commerce tracking errors — a fix that takes 20 minutes can recover 15–20% of lost visibility.

User Parameters That Really Matter

Don’t pass everything. Five parameters that give 80% of the value:

• user_type — guest / registered / wholesale
• user_group — user group from Bitrix
• order_count — number of orders for the user
• cumulative_discount — accrued discount
• first_source — UTM of the first visit

End‑to‑End Analytics and Dashboards

Metrica sees visits. CRM sees deals. Ad accounts see spend. But the link between them is broken. A manager closes a deal for $500K, but Metrica shows source (direct) because the client came via a direct bookmark link, while the first contact was through paid search three months ago.

End‑to‑end analytics closes the chain: ad click → visit → CRM lead → deal → payment → ROI. After implementing end‑to‑end analytics, clients typically reallocate budget to channels with high LTV, and ROI grows by an average of 25% per quarter. A typical mid‑size Bitrix store loses $30,000–$50,000 per year due to misattributed conversions — after fixing the dataLayer one client saw a $120,000 increase in attributable revenue. Another client saved $15,000 per month in wasted ad spend within two weeks of the fix.

How We Collect and Aggregate Data

1. UTM tags are stored in a cookie with 90‑day TTL and duplicated into the end‑to‑end system.
2. When a lead is created in Bitrix24, we write UTM into custom deal fields.
3. Call tracking replaces the number and links the call to the visit.
4. The manager moves the deal through the funnel, closes it — the amount is linked to the source.
5. The service aggregates expenses via ad account APIs.
6. ROI = (revenue — expenses) / expenses for each campaign.

Tools Comparison

Where Exactly Is the Hole in Your Funnel?

Typical Bitrix store funnel:

The checkout drop‑off is the most expensive. The user already wanted to buy, already added to cart, and then sale.order.ajax throws a 500 error due to an unconfigured delivery handler. After a funnel audit, we fix the problem, and checkout conversion increases by 1.5–2 times within a month.

Cohort Analysis and LTV Insights

We group by month of first purchase, look at retention after 30, 60, 90 days. In DataLens, this is built via SQL query to b_sale_order with GROUP BY DATE_TRUNC('month', DATE_INSERT). The main insight: which channel attracts high‑LTV customers. Context may give cheap first orders but zero repeat rate. SEO traffic converts worse but comes back. Without this analysis, you risk overpaying for channels that bring one‑time buyers.

What’s Included in Our Analytics Setup Service

• Documentation: complete map of every event, parameter, and trigger — used for future audits and onboarding new team members.
• Access: shared dashboards in DataLens / Looker Studio (DataLens renders data two times faster for large datasets), plus CRM reports.
• Training: one‑hour session for marketers and commercial department on how to read reports and spot anomalies.
• Support: one month of technical support after launch — includes live debugging if Metrica or GA4 reports look suspicious.

How to Start: Audit and Setup

Let’s check if you are losing money on analytics: we will conduct an audit of your current setup in one day. Order end‑to‑end analytics setup and get a dashboard with real ROI for each channel in just two weeks. Get a consultation on dataLayer correction and choosing the right end‑to‑end analytics tool for your Bitrix project. Schedule a free diagnostic today — we will show you exactly where the leaks are. Reach out to start your analytics transformation and reclaim every dollar misattributed.