CUSTOMER SERVICE

Sentiment Analysis

Prioritizing customer emotions to improve support

COMPANY

Freshworks

ROLE

Product designer

EXPERTISE

UX/UI Design

Timeline

3 months

The Problem

1 in 3 escalations happened before support knew the customer was upset

Most support teams focused on meeting SLAs (Service Level Agreement) and relied on reactive CSATs to gauge how customers felt. But by the time feedback arrived, the damage was often already done. These metrics failed to capture emotional shifts as they happened, leaving no early signs for the support to act on. As a result, frustration quietly built beneath the surface, leading to preventable escalations and churn.

The Outcome

85% of agents actively used sentiment analysis to prioritize tickets in real time

67.8%

Feature Adoption Rate

67.8%

Feature Adoption Rate

85.5%

Agent Engagement

100%

Active Usage by Adopters

65

New customers onboarded

The Strategy

Customer calls revealed 3 critical gaps: sentiment blind spots, workflow disconnects, and feedback delays

📞 Conducted 8 customer interviews

🧑🏻‍💼 Spoke to Admins, Supervisors, and CX Analysts

✉️ Focused on ticket-based workflows

Key pain points highlighted by customers:

Supervisors lacked real-time visibility into sentiment shifts
75% said they couldn’t track sentiment shifts during a ticket, making it hard to step in before escalations.
One-time scores weren’t enough, they needed sentiment trends and patterns
75% wanted to track sentiment trends over time and identify recurring issues by agent and topic.

Admins needed sentiment-driven workflows
63% wanted to automate actions like prioritization and escalation based on sentiment.

CSAT surveys were skipped or came too late
63% said customers often didn’t respond, and when they did, feedback arrived too late to act meaningfully.

No real-time alerts for sentiment drops
50% wanted admins to be automatically notified when a ticket’s sentiment turned negative.

Agents needed sentiment as an additional prioritization factor
38% said sentiment should be used alongside critical parameters like SLA and ticket priority to help agents triage tickets more effectively.

Takeaway:

Sentiment solution needed to drive proactive action, enabling supervisors to intervene early, admins to automate workflows, and agents to prioritize effectively, beyond just reporting trends.

Competitive analysis showed most tools treated sentiment as a passive metric, giving Freshdesk a chance to make it real-time and action-oriented.

What we found to draw inspiration from the competitors:

Gaps identified as opportunities for Freshdesk:

Gaps identified as opportunities for Freshdesk:

Prioritise. Prototype. Progress

While our path from discovery to delivery focused on solving critical problems, we first prioritised quick wins that delivered immediate value

From sticky notes to screens. Here’s how we turned ideas into interactions.

The solution we delivered in our first release

Agent capabilities

Supervisor capabilities

Admin capability

Our first release on Sentiment Analysis gained traction fast. But real-world use revealed a subtler challenge: visibility alone wasn’t enough; teams needed sentiment they could act on with clarity and control.

The challenges uncovered post adoption

Admin challenges: A one-size-fits-all sentiment model didn’t work

• V1 used a fixed sentiment scale, but businesses defined sentiment differently.
• There was also no way to track how sentiment changed during a conversation.

Agent challenge: Negative overload, no clear prioritization

Most tickets showed up as “Negative,” making prioritization unclear and leaving agents demotivated.