AI Sentiment Analysis in Customer Support

What is Support Sentiment Analysis?

As a customer support leader, you know how frustrating it can be to manually sort through thousands of support calls and conversations. 

If this is something you're struggling with, it means your sentiment analysis program is stuck in the past.  

As Sharad Khandelwal, CEO of SentiSum, said in a podcast interview, "brands are still tied up with practices from the 1990s." 

The “traditional” (a.k.a. manual) approach means support agents and leaders have to tag tickets based on their subjective analysis of the conversation. 

This is not only time-consuming, but also leads to biased insights. 

But what if you could tag and analyze thousands of support conversations and calls in seconds? 

That’s what an AI sentiment analysis tool like SentiSum can do. 

It analyzes every customer support conversation in seconds and tags each with insights like:

• Key reason for contact (e.g. Damaged Packaging)
• Sentiment (e.g. Negative)
• Intent (e.g. Wants a refund)
• Priority (e.g. Semi-urgent)

These insights are delivered in real-time too so you can quickly (and proactively) take action to reduce customer churn. 

"When customers decide to leave, it's often too late to make them stay,” Khandelwal wrote on LinkedIn. “The real insights come from looking at what customers say when they need help, especially in support conversations." 

You need an automated sentiment analysis tool like SentiSum if you want:

• Faster and more proactive issue resolution. When you analyze customer sentiment, you can identify issues early on and address problems before they escalate.
• Data-backed business decisions. Detailed insights into customer sentiment, key topics, and trends help your organization make better, data-driven decisions and prioritize efforts in the most impactful areas.
• A deeper understanding of your customers. Khandelwal explained, "The closer you are to your customers, the better you can serve them." 

In short, AI sentiment analysis tools can help you understand customers’ needs and feelings more deeply. 

This means you can move from a reactive to a proactive approach that drives customer satisfaction and loyalty.

How AI Transforms Your Conversations

AI sentiment analysis software can streamline and enhance how your team processes customer feedback. 

Here's a step-by-step look at how a sentiment analysis solution like ours can transform raw customer conversations into valuable, actionable insights.

(For the visual learners, here’s a 30-second YouTube video of a summary breakdown. We recommend watching it at 0.5x speed. ⬇)

Step 1: Collect and Integrate Data

SentiSum integrates seamlessly with your existing helpdesk platforms like:

• Zendesk
• Intercom
• Freshdesk
• Gorgias
• Dixa

You can explore the full integration list here. 

It then pulls data from all communication channels, including emails, chats, reviews, voice calls, NPS, and more.

The integration eliminates time-consuming manual data collection and gives you more time to focus on solving your customers’ problems.

Step 2: Automatic Labeling and Analysis

SentiSum uses machine learning-based natural language processing (NLP) to automatically tag and analyze text and call data.

For instance, if a meal kit delivery company has thousands of customer service logs mentioning "missing ingredients" or "box not delivered,” SentiSum will tag these issues quickly and accurately.

"AI can cut through the subjectivity of human opinion and handle complexity extremely well,” said SentiSum’s Chief Product Officer Kirsty Pinner.

📚 You might also like: Voice of Customer Analytics Guide 

Step 3: Identify Key Topics and Sentiment

Once the analysis and tagging process is done (usually takes seconds), SentiSum’s easy to use dashboard shows all the key insights, like:

• Trend of voice call volume over time 
• Main issues for contact (we call them “topics”)
• Top increases and decreases in sentiment and volume

If you click into each topic (e.g. “Request Refund”), you can see:

• All conversations tagged under the topic
• Increases and decreases over time
• AI chatbot called “Dig In” - which allows you to ask questions and get succinct, summarized answers.

Want to dig into how each support call or conversation went? No problem.

You can click into each call conversation and get:

• An AI summary of each call 
• Agent performance analysis
• Transcript of the call

📚 Read more: 6 Best Contact Center Analytics Software of 2024

Step 4: Insights are pushed to your help desk.

These insights shouldn’t just sit in your analysis tool, but also be pushed back to your helpdesk.

On SentiSum, you can create automation workflows using a custom AI model and push triage and prioritization rules back to your helpdesk platform. 

This helps your agents understand which issues to fix first based on sentiment and urgency (e.g. angry customers need to be attended to quickly).

Want to see what the platform looks like and how it can help you? Book a demo with us.

Case Studies: How Real Support teams Use Sentiment Analysis to Drive Change

So, you’ve got the insights. Now what?

Here are two examples of how real businesses are using these insights to achieve meaningful business results. 

How Hopin Saves Time & Drives Customer-Centric Product Development 

Before the events tech platform Hopin partnered with SentiSum, the team struggled with manual tagging in Zendesk and reporting of customer support tickets.

This limited their ability to communicate customer challenges effectively across departments. 

“There were issues under the surface that support could see … but we couldn't win that argument with the product teams,” explained Gareth Elsby, Hopin’s Customer Operations Manager.

Once Hopin integrated SentiSum with its Zendesk account, the team could categorize support tickets with a level of granularity and accuracy that wasn’t possible with manual tagging. 

This automated system saved time and provided more reliable data so everyone across the organization could deep-dive into customer issues easily.

“We … plugged it in and as soon as the dashboard was available we were like, ‘Wow.’ So many rabbit holes for us to dive down,” said Elsby. “The way that SentiSum provided the data for us … encouraged our curiosity.”

With SentiSum’s Views function, Hopin’s product managers could create curated dashboards for a “self-serve” approach to data insights.

These insights could now inform the product development cycle based on data-backed evidence about the issues causing the most frustration for customers. 

“Sending it upstream to the product team got rid of all of those … ad hoc requests that would come in and occupy your time,” added Elsby.

How British Airways Holidays Provides Proactive Customer Service 

Before partnering with SentiSum, British Airways Holidays faced significant challenges in analyzing customer feedback. 

The team had to manually label hundreds of customer reviews, a time-consuming process prone to biases and limited by sample size.

This method was inefficient and often led to an unclear picture of brand drivers over time.

With SentiSum’s AI-powered platform built on machine learning NLP, BA Holidays analyzed hundreds of thousands of reviews and quickly identified key topic and sentiment trends, which saved countless hours of manual tagging and analysis. 

"In less than 5 minutes, we [were] able to understand the drivers of our advocacy from over 100k reviews," said the Head of Customer Service.

SentiSum flagged relevant reviews with 95% accuracy, significantly higher than the 15% relevance rate achieved with keyword searches.

With the ability to track brand advocacy drivers accurately and efficiently, BA could now turn detailed customer insights into actionable strategies across the organization.

For instance, the team uses SentiSum insights to identify and address specific customer issues flagged in reviews. 

"[SentiSum allowed] us to quickly identify very recent feedback that indicates a customer experienced unexpected work taking place at a hotel that has impacted their stay."

Data like this means the team can proactively prevent negative customer experiences and improve overall service.

Key Things to Note When Buying AI

Thinking about buying an AI sentiment analysis tool?

Here’s a quick list of the features to look for:

🔲 Detailed and actionable insights. The AI should provide hierarchical, granular, and contextual insights to make informed decisions.

🔲 Seamless integration with your data. The software should effectively work on your unique data and business domain, not just generic training data.

🔲 Easy setup. Look for one-click integration and API support to minimize the need for extensive IT involvement.

🔲 Multichannel analysis capabilities. Verify that the tool can analyze interactions across all customer communication channels, including email, chat, surveys, reviews, social media, and voice calls.

🔲 User-friendly interface. The software should be intuitive and require minimal training, promoting company-wide adoption and use.

🔲 Customizable reporting. The AI tool should offer flexible reporting options to senior executives and operational team members.

Every organization is unique, so please contact SentiSum to discuss how to set up the right AI for your business.