The Client
A fast-growing e-commerce company operating in a D2C model in the European market, with several hundred thousand monthly users and multichannel sales (own online store and marketplaces). The organization had an internal development team but lacked dedicated AI/ML capabilities.
The challenge
The company aimed to increase conversion rates and average basket value through personalized offers and improved demand forecasting. Transactional and behavioral data were collected but not used in an advanced way.
Key challenges included:
- no dedicated person responsible for building and maintaining ML models,
- product recommendations based only on simple rule-based mechanisms,
- inaccurate demand forecasts leading to overstock or stock shortages,
- limited resources to conduct a lengthy recruitment process.
The solution
Under an outsourcing model (B2B, staff augmentation), we provided one Senior Machine Learning Engineer with experience in e-commerce.
The scope of responsibilities included:
- analyzing and preparing transactional and behavioral data,
- building a product recommendation model (recommendation engine),
- implementing a demand forecasting model at the SKU level,
- integrating models with the existing e-commerce platform and monitoring their performance,
- collaborating with the marketing team on A/B testing.
The specialist worked as part of the product team, reporting directly to the Head of Technology.
The conclusions
- 9% increase in conversion rate within four months.
- 7% increase in average order value (AOV) through personalization.
- 15% reduction in excess inventory levels.
- Rapid launch of AI capabilities without the need to build a full data team.
The client achieved measurable AI-driven impact on revenue and implemented a scalable solution that can grow alongside the business.
