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

Written by Vaibhav Soni | Oct 23, 2025 5:08:30 AM

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

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

Data Sources and Integration

Source	Description	Key Data Points
Callyzer & Knowlarity	Agent call data platforms	Call duration, call start/end time, agent ID, customer ID, call type (inbound/outbound), call recording URL
Google Ads & Meta Ads	Digital campaign data	Campaign ID, Ad group, Clicks, Impressions, CTR, Conversions, Spend, Customer leads
WebEngage	Customer engagement and app behavior	Customer app activity, session details, push notification engagement, funnel events
Zoho CRM	Customer transactions and lifecycle data	Transaction ID, Product type, Revenue, Date, Customer ID, Payment details
Offline Customer Master	Agent calling base	Customer name, contact details (Phone, Email Address)
Trackier	Affiliate and campaign performance tracking	Source, medium, campaign cost, attributed conversions, ROI metrics

Proposed Data Architecture

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.

Layer Definitions

Bronze Layer (Raw Ingestion Layer)

Stores raw data ingested from all sources in its native format.
Data is ingested via APIs, S3 dumps, and connector-based

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)

Customer 360 Unified View

Dimension / Metric	Description
Customer ID	Unique master ID mapped across all systems
Demographics	Name, contact, location, segment, acquisition source
Acquisition Channel	Google, Meta, Offline etc.
App Behavior	WebEngage engagement score, active sessions, notification engagement
Call History	Number of calls, average call duration, call response rate
Transactions	Total spend, frequency, last transaction date
Campaign Interaction	CTR, clicks, impressions, campaign cost
Agent Mapping	Assigned agent(s) with engagement status

KPIs:

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

Agent Performance View

Dimension / Metric	Definition
Agent ID	Identifier of the Agent mapped across all the systems
Total Calls	Total number of calls made by the agent
Avg. Call Duration	Average time per customer call
Call-to-Conversion Rate	% of calls leading to app registration or transaction
Active Calling Hours	Hours actively spent on customer calls
Customer Coverage %	% of assigned customers contacted
Conversion per Agent	Number of successful conversions

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.

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