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CASE STUDY 02 · Customer Relationship Management

Customer Relationship Management System

-35%: Drop in Average Handling Time (AHT) for support tickets within 90 days.

+22%: Improvement in overall Customer Satisfaction (CSAT) scores.

+25%: Increase in telesales/subscription conversion rates driven by direct agent proxy ordering.

95%: Internal app adoption rate achieved within the first month of deployment.

Customer relationship management console shown in a high-contrast editorial style

01 / 03 · Customer relationship management console shown in a high-contrast editorial style

02

The Challenge

Consolidating offline retail store transactions, online e-commerce activities, and complex pharmaceutical prescription verification pipelines into a single screen.

Support agents lacked unified tooling, preventing them from resolving account friction instantly, triggering subscription refills, or processing manual phone-in orders for less tech-savvy patients.

03

Problem Statement

Siloed Omnichannel Identity: Disjointed customer data across retail pharmacy cash registers and the web platform causes severe agent delays when verifying customer purchase history.

Friction in Support-Fulfillment Routing: Executives lack direct backend permissions to place orders or fix broken subscription flows mid-call, requiring slow manual handoffs to separate fulfillment teams.

Complex Subscription Blockades: High administrative friction in modifying recurring dosages, pausing refill dates, or applying updated prescription documents during a live complaint call.

04

Competitor Analysis

Competitors Assessed: Generic e-commerce support tools.

Gaps & opportunity

  • Strategic Gaps Identified: Standard out-of-the-box support tools only track chat logs and tickets; they completely lack deep integration into active retail pharmacy inventory and prescription validation layers. This forces agents to switch apps to place orders, creating an operational opportunity to build a "Service-to-Sales" terminal customized for healthcare compliance.

05

User Research

  • MethodologyTime-and-motion floor shadowing of 15 support agents, ticket classification audits, and analysis of 200 customer escalation recordings.
  • Insights DiscoveredAgents spent over 40% of their call handling time explaining to elderly or remote customers how to fix checkout/subscription bugs on their phone app. Over 30% of frustrated callers openly requested the agent to "just place the medicine order for me," a workflow the legacy support system could not execute securely.

Research was conducted before any design work; findings shaped the problem definition and strategy.

06

Discovery

Through internal operational audits, we discovered that support inquiries weren't just transactional complaints, but high-intent purchasing moments. We realized the core system requirement wasn't just a communication log, but a robust "Proxy Order Engine" embedded directly into the CRM interface, allowing customer executives to securely attach valid prescriptions, set up recurring intervals, and process orders instantly on behalf of the customer.

01

Contextual interviews

Sat with customer support executives during live calls to understand how elderly and remote customers described their ordering struggles in real time.

02

Journey mapping

Mapped the caller journey from complaint to resolution, revealing the exact moment a support conversation turned into a missed purchase opportunity.

03

Analytics review

Reviewed call categorization data and handle-time metrics to size how many inquiries were actually order-intent moments the system couldn't capture.

07

Strategy

  1. Problem

    Support agents suffer from system fatigue, toggling between separate ticketing tools and billing systems while trying to manually handle customer orders or subscription configurations.

  2. Insight

    Over 30% of incoming pharmacy calls are high-intent buying moments where patients want agents to actively place orders or manage chronic refills for them.

  3. Opportunity

    Convert a costly support cost-center into an active omnichannel revenue generator by empowering agents with direct ordering capabilities.

  4. Strategy

    Build an integrated, secure fulfillment and proxy-ordering workflow layered directly inside the agent's core customer data window.

  5. Solution

    Deploy a unified CRM terminal containing a "Proxy Checkout & Subscription Control" tab to instantly schedule deliveries on behalf of patients.

  6. Roadmap Prioritization

    MVP built the multi-channel aggregation infrastructure and timeline UI. Version 1.5 deployed automated macro-actions and proxy ordering capabilities. Version 2.0 integrated automated supervisor routing rules based on customer loyalty tiers.

08

Design Process

We mapped the service layout blueprint for support calls and designed a clean, tri-pane terminal dashboard layout. The layout was run through rigorous simulation stress tests with real agents to shave down interface response times.

01

Wireframes

Tri-pane dashboard sketches aligned the permanent customer profile, unified message stream, and contextual action cards into a single agent view.

02

Exploration

Service blueprint layouts were evaluated against live support call flows to identify handoff points and reduce cognitive load on agents.

03

Prototypes

Interactive prototypes linked customer records to live store inventories, billing, and proxy subscription placement for one-click resolution.

04

Testing

Simulation stress tests with real support agents measured response times and task success, driving refinements to the tri-pane layout.

05

Final Design

A clean terminal dashboard that keeps customer context, prescription history, and actionable next steps permanently visible to agents.

Only the iterations that changed the direction of the work are shown — not every exploration.

09

Final Experience

Final experience visuals for Customer Relationship Management System

10

Personas

Persona 01

Portrait of Priya Sharma, a research persona

Priya Sharma

The Customer Service Manager · Customer Support Operations Manager

Oversees the support team, tracks performance metrics, and ensures rapid resolution of highly escalated issues.

Goals

  • Improve overall customer satisfaction metrics, optimize agent productivity, and maintain strict service level standards.

Frustrations

  • Balancing team workloads during peak hours, handling complex customer escalations, and aggregating messy data from multiple sources.
  • Age: 39
  • Married
  • Bengaluru, Karnataka
  • Archetype: The Process Optimizer

Persona 02

Portrait of Amit Patel, a research persona

Amit Patel

The Customer Service Executive · Front-Line Support Executive

Front-line support agent interacting directly with frustrated customers to resolve daily inquiries, place proxy orders, and troubleshoot account problems.

Goals

  • Deliver rapid, highly accurate answers, achieve excellent first-contact resolution rates, and seamlessly place recurring orders on behalf of elderly patients.

Frustrations

  • Toggling between disconnected internal tools and suffering from frustrating delays when trying to fetch live customer data.
  • Age: 24
  • Single
  • Ahmedabad, Gujarat
  • Archetype: The Empathetic Troubleshooter

11

Impact

-35%

Drop in Average Handling Time (AHT) within 90 days

+22%

Improvement in overall Customer Satisfaction (CSAT) scores

+25%

Increase in telesales/subscription conversion rates

95%

Internal app adoption rate within the first month