Creating cohorts of customers by profiling them based on demographics, behavioral, transactional data for effective marketing campaigns to drive growth and improve customer experience.
Objective: Develop a custom web application to create, save, and export customer segments on the fly using rule-based business logic and ML-based modeling by ingesting and processing the customer data attributes
Pilot outcome: A custom web app that allows end-users to create, edit, export customer segments using pre-built business rules and ML-based customer segmentation.
Implementation plan: The break-up of the implementation plan is as below:
Week 1 (Discovery) • Conducting Requirements gathering with key stakeholders, identifying critical data sources, business processes and the associated challenges • Understanding business rules and current state of classifying customers into different cohorts
Weeks 2-3 (Developing Customer Segmentation Module) • Build data pipelines for data ingestion into the centralized data store • Leveraging Machine Learning-based approach to classify similar characteristics of customers based on experimenting various models (champion vs challenger) • Repurposing existing business rules to create customer cohorts for various marketing campaign activities • Develop features to Create, Edit, Export customer segmentation lists on Azure components such as Azure Databricks, Data Lake Storage, SQL DB, Monitor.
Week 6 (Publishing Insights) • Comparing profiles of multiple customer segments across different categories such as Customer Demographics, Behavioral Patterns, Digital Footprint etc. • Publishing insights as reports for Customer Experience (CX) team and it will be used as a feedback input to further refine the ML Models