CASE STUDY — FINANCIAL SERVICES
Product Recommender System
Provide your customers with instant product insights
Sector:
Financial Services, Retail, Consumer Goods
Focus:
Product Recommender System
BUSINESS CHALLENGE
Lack of advanced systems to predict customer needs
Lack of advanced systems to process large volumes of customer data made it challenging to identify behavior patterns, as traditional methods couldn’t accurately predict customer needs, limiting the effectiveness of personalized marketing and cross-selling opportunities.
SOLUTION IMPLEMENTED
A platform aggregating data and predicting product interest
Implemented a solution to aggregate and analyze customer data, visualize key behavior metrics through interactive dashboards, and apply AI models to predict customer product interests based on buying patterns.
TECHNOLOGY
Built to aggregate, visualize, and predict customer product interest
DATA PLATFORM
Azure Stack
Aggregates and cleans customer data, storing it in a database for efficient analysis of behavior patterns and purchase history.
SERVING
Power BI
Creates interactive dashboards that visualize key insights about customer behavior, enabling data-driven marketing strategies.
AI
Azure FOUNDRY
Builds predictive models that assess the likelihood of customers purchasing new products, analyzing behavior patterns and influential features.
Results & ROI
What changed once the model went live
TARGETED CAMPAIGNS
Selects the top clients most likely to respond positively to specific marketing campaigns, based on ML predictions.
ROI OPTIMIZATION
Focuses marketing resources on high-potential leads, reducing waste and increasing return on investment.
SALES & RETENTION
Improves sales and customer retention rates through tailored marketing efforts, strengthening overall market position.
