Customer Explorer Analytics: 6-Weeks Implementation

Tredence Inc

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

Key challenges:

  1. Higher campaign build cycle due to manual processing of segments by sourcing data from different tools / systems
  2. Marketing associates apply their gut-intuition to filter through customers and does minimal rule-based business logic for campaign outreach
  3. Suboptimal customer targeted leading to slow growth and less customer satisfaction How do we address your challenges? Our solution will enable you to understand the customers and group them by:
  4. Developing a targeted segmentation list for Marketing Associates using two approaches: Rule-based which involves application of key business rules, and ML-based by deriving cohorts based on customer demographics, behavioral, and transactional data
  5. Editing, saving, and exporting customer segments to compare & benchmark the lists for various marketing campaign initiatives

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.

Weeks 4-5 (UI Functionalities) • Build UI capabilities to allow the end-user to create, edit, export the customer segmentation list using JavaScript-based web frameworks • Design UI workflows specific to each user personas – a) Marketing Associates to create, save, export the customer segments, b) Administrator to create / configure the business rules

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