The project aimed to analyze customer behavior in an online store to segment them by purchasing habits, tailoring the store to meet segment needs, and boosting satisfaction and sales through faster product discovery.
Objectives
The project’s goal was to create a behavioral analysis mechanism to segment online store customers based on their shopping behaviors, with the aim of personalizing the shopping experience to increase satisfaction and sales.
Challenges
Analyzing large volumes of data from various countries, particularly Poland, with a much higher number of customers and events compared to the Baltic states.
Adapting the store interface to enhance usability for different customer segments.
Solutions
Implemented a customer segmentation system based on purchasing behavior and browsing habits.
Used behavioral analysis methods and machine learning algorithms to group customers, enabling personalized offers and interface adjustments for different segments.
Benefits
Increased Sales: Personalization led to a significant rise in sales and customer satisfaction.
Improved User Experience: Customers found products faster, boosting spending and reducing ineffective store visits.
Operational Efficiency: Enhanced store performance through better alignment with customer needs.
Key results
15% Sales Growth: Personalization and segmentation boosted sales.
20% Lower Bounce Rates: Better product matching improved user experience.
25% Higher Retention: Targeted offers increased repeat customers.
Scalable: Successfully piloted with 4F, adaptable to other platforms.
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