More sales through AI-based product recommendations

The difference in approach

Why classic recommendation systems reach their limits

Traditional recommendation systems are often based on fixed rules, manual maintenance and historical user signals. As complexity increases, so does the effort involved - while relevance and dynamism decrease.

The burgdigital AI Recommendation Engine combines product information, usage situations and behavioral data in a common logic. As a result, recommendations continuously adapt to different product ranges, purchasing processes and usage contexts.

+20-40%
Conversion

Relevant recommendations increase conversion in a targeted manner

+10-30%
Shopping cart value

Dynamic recommendations increase the average order value

up to 30%
share of sales

Recommendations increase sales along the customer journey

For individual B2B logics

Product recommendations for complex B2B structures

In B2B, product recommendations arise from individual customer structures, roles and business logics. The burgdigital AI Recommendation Engine automatically takes these factors into account and integrates them into the respective company and authorization structure.

As a result, different users within a company receive precisely the products, content and product ranges that are relevant to their respective role, contractual relationship or purchasing task.

How can the AI Recommendation Engine be integrated into existing stores?

The burgdigital AI Recommendation Engine can be flexibly integrated into existing e-commerce and store systems - for example via APIs or existing data structures. Recommendations can be integrated at various points in the store, for example on product detail pages, in the shopping cart or in the search function.

What data is needed and when will the first results be available?

The AI Recommendation Engine uses existing product data and signals from the usage context to generate recommendations. In many cases, existing store and product data is already sufficient to provide initial recommendations. Initial results can often be seen after a short time in live operation.

Where can recommendations be used in the store?

Recommendations can be flexibly integrated at various points in the store - for example on product detail pages, in category views, in the shopping cart, in the search or within existing marketing and commerce processes.

How flexibly can recommendations be configured?

Yes, recommendations can be specifically adapted using product range logic, prioritization or company-specific requirements. This means that the AI Recommendation Engine remains flexible and can be individually adapted to existing business logic.

What distinguishes the AI Recommendation Engine from traditional recommendation systems?

In contrast to rule-based systems, the AI Recommendation Engine combines semantic product understanding with usage context and business logic. This results in recommendations that adapt flexibly to different product ranges, purchasing structures and usage situations.