The system with the help of ML machine learning analyzes the user behavior (purchases, search, cart) product specifics (filler, substitute, perm alternative) and sets a recommendation for the customer
Based on data analysis, the best-personalized suggestions for specific users are generated in live mode, which can be offered in the online store, as well as by email or another form
The system makes it convenient for customers with personalized suggestions and simplifies the purchase process, saves customers time and energy, which increases their level of satisfaction
The system of recommendations consists of two layers – data and application levels.
At the data layer, a warehouse is created in which information from customer data sources is periodically stored/updated. In addition, analytical algorithms are periodically launched and the results are saved in special data warehouse tables.
Special rules are set at the Application layer, based on which the system determines exactly what type of recommendation should be given to the Customer.
In parallel with customer activity on the e-commerce platform, demand is created in the referral system. The recommendations engine generates a set of optimal suggestions according to pre-established rules and algorithms. After that, the suggestions created by the recommendation engine are returned to the customer in automatic mode.
In the process of setting up the system, the customer and the team of BDO Digital Data Lab are jointly involved, both technically and analytically. The process consists of the following main stages:
Irma has held various leadership positions, including as Lean Transformation Coordinator, IT Portfolio Manager, and Agile Coach. Lean Management was introduced in Georgia together with the consulting company Mckinsey & co and a local team, both in the front office and in the back office.