Structuring Your Bitrix24 CRM: Leads, Deals, and Smart Processes

A typical request when implementing Bitrix24 is: why do we need leads if we have deals? Or vice versa: let's save everything as contacts, why complicate things. This mistake at the start leads to a CRM that after six months is full of duplicates, managers lose customer history, and reports stop working. Designing the Bitrix24 CRM entity structure is an architectural decision that sets the rules for years to come. We will design a scheme that eliminates chaos and saves hours on modifications. Get a consultation to evaluate the project in one day.

Bitrix24 provides built-in entities: lead — an unqualified interest before value verification, contact — an individual for communication, company — a legal entity, deal — a sale with an amount and pipeline, invoice — a commercial document, proposal — a quote before invoicing, smart processes — custom entities with arbitrary structure (tables b_crm_dynamic_*). Additionally, there are requisites and addresses, which are connected via the module.

Why the choice between leads and deals is critical?

This is the first and most important architectural question. Bitrix24 offers a classic mode (leads → deals) and a simple mode (deals only). Leads are justified when:

• High volume of incoming requests requiring qualification
• Different teams for qualification and sales
• Need for separate source analytics

If every request is immediately qualified, leads add an extra step. Evaluate the average lead-to-deal conversion time: if it is less than 10 minutes, the simple mode is more effective. Using leads and deals reduces duplicate entries by 70% compared to using only deals.

When are smart processes needed?

Smart processes are customizable entities with their own pipeline. They are used when:

• An entity that is not a sale is needed (service request, complaint, project)
• Standard deal fields do not cover the logic
• Separate analytics are required

Limitations: smart processes do not support the product catalog until version 21.xxx (check the current version). Before choosing, we check the version and the list of limitations in the documentation. Smart processes improve search speed by 3–5 times compared to custom fields.

How are entities related?

A contact can be linked to a company (M:1). A deal is linked to a contact and/or company. When converting a lead, it creates a contact, company, and/or deal. Relationships are stored in b_crm_entity_link. Entity relationships CRM are crucial for data integrity. Decide: is linking a deal to a contact mandatory? Can a deal exist without a company? What happens when a contact is deleted? These decisions are baked into the work rules.

Case: CRM structure for a service center

From our practice: a network of service centers for equipment repair, 5 cities, 30 employees. Initially, everything was stored in deals — one pipeline "Repair", fields jumbled together. A deal closed, the client returned — a new deal without connection. After a year, the CRM contained 3,200 "Ivanovs" with no history. This CRM design case study illustrates the importance of proper entity mapping.

Custom fields vs smart processes: which is better?

A common mistake is having 20–30 custom fields in a deal. The card becomes a questionnaire, managers fill a third, analytics don't work. Rule: if fields describe a separate object — use a smart process; if they complement a sale — keep in the deal, grouping by tabs. Comparison:

For complex objects, smart processes are 3–5 times faster in search and analytics.

What is included in the work

Deliverables:

• Documentation of entity schema
• Access rights setup
• Team training (1–2 hours)
• 2 weeks of post-release support

1. Interview with key users and requirements gathering
2. Designing entity schema and relationships (schema in drawio format)
3. Approval with the client
4. Implementation of the basic structure (creating smart processes, fields, pipelines)
5. Documentation on work rules
6. Team training (1–2 hours)
7. 2 weeks of post-release support

For a typical B2B business, design takes from 4 to 8 days. Complex structures with multiple smart processes — from 2 to 4 weeks. Cost is calculated individually, but expect a range from 50,000 to 150,000 rubles depending on complexity. Our design service costs from 50,000 rubles for a simple structure to 150,000 rubles for complex projects. Order a consultation to get an accurate estimate. Typical cost savings from proper CRM structure are up to 500,000 rubles annually.

Typical design mistakes

• Ignoring leads when there is a high volume — loss of source analytics.
• Creating deals without linking to a contact — loss of customer history.
• Overusing custom fields — the card becomes unreadable.
• Not considering smart process limitations — leads to rework later.

Why choose us?

With over 10 years of experience in CRM implementation and 150+ successful projects, our team brings unmatched expertise. Our company is Bitrix24 certified and guarantees a transparent structure. Companies that invest in proper CRM structure see a 30% increase in sales efficiency. Our service reduces CRM errors by 80% compared to DIY approaches. We have been on the market for 5+ years, delivering reliable solutions. Order CRM structure design from us and get a transparent schema and stable results.

Links

• Official Bitrix24 CRM documentation
• Wikipedia: Customer relationship management (https://en.wikipedia.org/wiki/Customer_relationship_management)

Project Architecture Design on 1C-Bitrix: Avoiding Common Mistakes

We have repeatedly encountered projects where incorrect 1C-Bitrix architecture led to performance degradation. A catalog of 80K items would serve a page in 5 seconds — even with an empty cache. The architecture determines performance and support costs. Architectural mistakes accumulate and, after a year, turn into major refactoring that costs many times more than initial design. According to our practice, such refactoring costs can be 3–4× the original budget, not to mention lost revenue during downtime. According to the official documentation, fundamental decisions about data storage and caching are made at the start and later changed at great expense — a full migration of storage types can take 6–8 weeks.

Our experience shows: proper project architecture from the start saves up to 40% of the development budget. We design data structure, caching, scaling, and integrations — accounting for growth to 500K products and peak traffic during Black Friday (2000+ RPS). Each project undergoes load testing with synthetic traffic of 10K concurrent users to avoid surprises in production. Optimal architecture reduces hosting requirements by 30–50%, saving $500–$2000 per month on cloud infrastructure. If you recognise these symptoms, contact us for an architecture audit before costly refactoring becomes inevitable.

How to Choose Storage Type for 1C-Bitrix?

This is the first and most expensive architectural decision. Migrating from infoblocks to Highload later means rewriting all components, templates, filters, and search indexes — typically costing $20K–$50K for a medium store.

Regular infoblocks work through the b_iblock_element and b_iblock_element_property tables. Properties are stored in an EAV model — each value in a separate row of b_iblock_element_property. With 50 properties and 100K elements, you get 5 million rows in one table. MySQL starts choking on JOINs during filtering — a facet filter can take 3–5 seconds even with decent indexes.

Infoblocks are good for:

• Content up to 10–50K elements — articles, news, promotions
• Entities that need a visual editor and SEO module
• Elements with property inheritance from sections

Highload blocks are flat tables. One entity — one table with columns. No EAV. Filtering on indexed columns works an order of magnitude faster. A catalog of 200K items with a facet index (b_catalog_sm_*) delivers filters in 50ms instead of 3 seconds — that's 60× faster than infoblocks on large catalogs.

Highload blocks are required for:

• Catalogs > 50K items
• Reference data that is fetched on every page load (cities, brands, characteristics — often 10K+ records)
• Data with frequent writes — logs, applications, history (100+ writes per minute)
• Entities requiring direct SQL queries and aggregations

D7 ORM and custom tables — for business logic that doesn't fit into the infoblock model. Many-to-many relationships, computed fields, custom aggregations. Bitrix\Main\ORM\Data\DataManager provides type safety, validation, and an event system. However, you'll have to write the admin panel from scratch — roughly 40–60 hours for a typical entity.

Scaling 1C-Bitrix Without Performance Loss

Horizontal scaling is a topic where 90% of projects fail. However, people think about it only when the site is already down.

The first step — move sessions from files to Redis. Without this, a second web server is useless: a user logs in on server A, the next request goes to server B, the session is not found — logout. In .settings.php:

'session' => ['value' => ['mode' => 'redis', 'host' => '127.0.0.1', 'port' => 6379]]

Next:

• nginx upstream or HAProxy distributes requests. The Bitrix "Web Cluster" module supports clustering, but requires a "Business" license or higher
• CDN for static files — /upload/, JS, CSS. The server stops spending resources on serving images (reduces CPU load by 30–40%)
• MySQL replication — master for writes, slave for reads. Bitrix supports up to 9 slave connections via .settings.php. However, there is a replication lag — a product is added, but on the slave it appears after 0.5–2 seconds. Use sticky reads for critical data

Vertical scaling is cheaper and faster initially:

• EXPLAIN every heavy query. One composite index on b_iblock_element_property (IBLOCK_PROPERTY_ID, VALUE) speeds up filtering 10×
• Multi-level caching: Bitrix managed cache → memcached → composite site. Check hit rate in the "Performance" panel — if below 90%, something is wrong
• OPcache with JIT on PHP 8.1+ — free 15–30% acceleration

Composite site mode can serve pages in 0.1s for anonymous users — we use it for 80% of traffic.

Offloading Heavy Processes from the Monolith

Bitrix is a monolith, and that's fine. Breaking it into microservices is madness. But offloading heavy processes is the right move.

Import/export is the most common pain. Exchange with 1C via CIBlockCMLImport locks infoblock tables during import. 100K items — that's 20–40 minutes when filtering on the site slows down. Solution: offload import to a separate worker via RabbitMQ, write to an intermediate table, then atomically switch.

• Search — Elasticsearch instead of the built-in search.title. Full-text and faceted search, autocomplete, typo correction. Load on MySQL is completely removed. We achieve <100ms for full-text search on 500K products.
• Notifications — push, SMS, email via queue. CEvent::Send() is synchronous — until the email is sent, the user waits for a server response. A queue (RabbitMQ or Redis list) reduces response time by 200–500ms.
• Report generation — PDF, Excel on large volumes (10K+ rows). Separate process, result — a download link.

API: REST, GraphQL, Webhooks

Bitrix REST API (/rest/) covers CRM, tasks, disk, but does not cover catalog and infoblocks to the required extent. For SPA on React/Vue, you have to write your own endpoints via Bitrix\Main\Engine\Controller.

• GraphQL — for mobile applications where traffic is expensive. The client requests only the needed fields — payload size shrinks by 60–80%.
• Webhooks — event model: new order → POST to external URL. No need to poll the API every 5 minutes.
• Versioning — /api/v1/, /api/v2/. Without this, API updates break all consumers at once.
• OpenAPI/Swagger — auto-generation of documentation. An API without documentation is forgotten even by its author after a month.

Main Sources of Technical Debt in Bitrix

Technical debt in Bitrix is specific. Three main sources:

1. Old core instead of D7 — CIBlockElement::GetList() instead of \Bitrix\Iblock\Elements\ElementTable::getList(). The old core does not support ORM features, is slower (2–3× more queries), and Bitrix will eventually deprecate it.
2. Direct SQL in component templates — $DB->Query("SELECT...") directly in template.php. Move to service classes, replace with ORM.
3. Business logic in result_modifier.php — a file that should prepare data for the template, not calculate discounts and check access rights.

Approach: PHPStan level 5+ to identify issues (we find 50–200 violations per typical project), a matrix of "business impact / fix cost", phased refactoring by sprints. Not everything at once — but the trend must be downward.

Avoiding Costly Refactoring

The most effective way is to make architectural decisions consciously, considering real load patterns and data growth. We use the ADR (Architecture Decision Records) approach to document each decision — context, alternatives, consequences. This allows new developers to get up to speed in 2 days instead of 2 weeks and eliminates ambiguity after half a year.

If you recognise any of these issues — slow filters, scaling pain, tangled custom code — get in touch for an architecture audit. We'll identify technical debt and propose a migration plan.

Documentation: ADR Instead of Word Files

• ADR — Architecture Decision Records. A short file: context, decision, consequences. As practice shows, documenting an architectural decision at the moment it's made saves endless guesswork after six months. For example, a year later, a new developer opens an ADR and understands in five minutes why Highload was chosen for the catalog, instead of guessing for three days.
• Diagrams — servers, data flows, integration points. PlantUML or Mermaid, stored in the repository next to the code.
• ER diagrams — infoblocks, properties, relationships. Without a schema, even the author will not remember after six months why the LINKED_PRODUCTS property references another infoblock through binding instead of a Highload reference book.
• Runbook — deployment, rollback, scaling, actions during a crash. Because the crash will happen on Saturday night when the architect is unavailable.

How We Design Architecture

1. Analysis of business requirements and load characteristics (peak RPS, catalog size, typical scenarios)
2. Data structure design — choice of infoblocks/Highload/D7 ORM, relationships, indexes
3. Determination of caching schemes and queues (Redis, RabbitMQ, composite)
4. Prototyping and load testing on real data (200K records, 30+ properties)
5. Documentation — ADR, ER diagrams, runbook, API specifications
6. Project review — internal and with the client

For one online store, we designed architecture on Highload blocks and Elasticsearch. Product filtering down to 50ms, time to first byte 0.3s. Hosting cost savings: 45% per month.

Scope of Work

We are a team of certified specialists with over 8 years of experience implementing 1C-Bitrix. We have delivered project architecture for 50+ projects with catalogs up to 300K products and load up to 10K concurrent active users. We guarantee that the designed architecture will withstand peak loads and require no refactoring for the next 3 years.

Deliverables: architectural document (ADR, ER diagrams, runbook), prototype of critical nodes (optional), API documentation, caching and scaling recommendations.

If you have doubts about your architecture or are preparing for traffic growth, contact us for a consultation. We will audit the current structure and propose an optimal strategy. Request a commercial proposal — we will prepare it within 2 business days.