Building a Unified Customer & Agent Performance Analytics Platform for an AgriTech Company

Background

The AgriTech company aims to target its top two priorities of understanding customers and improving agent productivity by consolidating data from multiple systems. Currently, the organization relies on several disparate platforms to manage marketing, customer engagement, transactions, and agent-customer interactions. These fragmented datasets make it difficult to derive unified insights into customer behavior, campaign effectiveness, and agent performance.

The goal of this initiative is to design and implement a centralized data architecture that integrates these diverse sources into a unified analytical model. This will enable the creation of a single view of the customer (Customer 360) and a comprehensive agent performance tracking system.

Business Objectives

1. Customer 360° View:
   Build a unified customer profile integrating data from marketing campaigns, app interactions, calls, and transactions.
2. Agent Performance Analytics:
   Track agent effectiveness across different call and conversion metrics.
3. Marketing ROI Measurement:
   Calculate customer acquisition cost (CAC), campaign conversion rates, and channel efficiency.
4. Operational Efficiency:
   Enable data-driven decision-making through automated ETL pipelines and unified reporting dashboards.

Data Sources and Integration

Proposed Data Architecture

1. Medallion Architecture

The solution will adopt a multi-layered Medallion Architecture approach, structured into three progressive tiers — Bronze (Raw Data Layer), Silver (Cleansed & Enriched Layer), and Gold (Curated Business Layer) — to ensure data quality, consistency, and optimized analytics delivery.

1. Layer Definitions

• Bronze Layer (Raw Ingestion Layer)

• Silver Layer (Cleansed & Conformed Layer)

Performs cleaning, standardization, and joins to prepare analytical datasets.

• Gold Layer (Curated Business Layer)
  Hosts curated unified views and KPI-ready data marts.
  
  Two key subject areas:
  1. Customer 360 Unified View
  2. Agent Performance & Efficiency View

3. Architecture

Unified Data Models (Gold Layer)

1. Customer 360 Unified View

KPIs:

• Active customers (weekly/monthly)
• Conversion rate (leads → paying customers)
• Customer retention rate
• Average revenue per customer (ARPU)
• CAC (Customer Acquisition Cost)

1. Agent Performance View

Derived KPIs:

• Agent Efficiency Index = (Conversions ÷ Total Calls) × 100
• Customer Engagement Index = (App Activity Score × Call Response Rate)
• Top Performing Agents = Agents with the highest conversions and lowest idle time

Outcomes and Benefits

• Holistic Customer Understanding: Unified customer profiles enable targeted engagement and churn prediction.
• Improved Agent Productivity: Transparent visibility into agent KPIs drives accountability.
• Marketing Optimization: Accurate CAC and ROI metrics support smarter budget allocation.
• Data Quality & Trust: Automated data pipelines ensure timely, accurate, and governed data delivery.

Conclusion

This AgriTech data platform unifies all customer and agent data streams into a single governed ecosystem. The design supports continuous scaling, accurate marketing attribution, and deep operational insights — enabling the company to optimize customer acquisition, improve agent efficiency, and ultimately accelerate rural digitization and growth.