A guide on real-time sentiment analysis for enterprise support teams

Leveraging real-time sentiment analysis for enterprise-level support teams can help businesses meet the growing demand of today’s software customers. By surfacing negative experiences in customer support interactions, sentiment analysis allows service reps to identify and prevent technical escalations before they impact customer retention.

Table of Contents

• Escalation Workflow Challenges
• The Benefits of Real-Time Sentiment Analysis
• How to Build Real-Time Sentiment Analysis Workflows
• How to Configure Real-Time Alerts in HubSpot Service Hub
• Comparison of Sentiment Analysis Tools
• Tips for Making the Most of Sentiment Analysis
• Frequently Asked Questions

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Escalation Workflow Challenges

When customer requests are continuously rolling in, and often from different channels, I find it hard to know which customers truly require urgent help and need to be bumped up in the service queue.

It’s common to address customer requests in the order they came in, and most of the time, this method may work well for us. But what about when it's busy season, or when there’s a sudden increase in the volume of requests? In times like these, processing requests in a first-come, first-serve order runs the risk of overlooking urgent or time-sensitive requests.

When you work in customer success like I do, you naturally want to help all of your customers immediately. I’ve found that being able to easily identify which support requests are time-sensitive helps me better organize my response strategy (and my inbox!)

Implementing real-time sentiment analysis has helped me organize and address urgent requests, flag at-risk customers, and better manage my daily workload. Let’s dig into some of the benefits of using real-time sentiment analysis.

The Benefits of Real-Time Sentiment Analysis

Leveraging real-time sentiment analysis in customer service enables service teams to move from reactive to proactive support. The result is faster response times and a better customer experience. Both the customer and the support agent win.

Based on my experience as a customer support professional, I’ve put together a list of a few key areas that real-time sentiment analysis can help with.

Faster Resolution Time

Faster resolution time creates a better customer experience. When a team can detect sentiment in real time, they can prioritize urgent or negative sentiment tickets so they’re higher in the queue. From there, reps can either spend extra time on resolution or loop in the right people who can fix the issue at hand.

Without real-time sentiment analysis, teams often take a first-come, first-served approach to service. This leaves urgent requests too far down the queue. Customers who need urgent help have a worse experience, damaging brand loyalty.

Additionally, service reps can keep your high-priority customers happier. By automatically flagging negative sentiment on high ARR accounts, service agents can move them to the front of the line and make sure they get a quicker resolution time.

Sentiment analysis helps surface if a customer is confused about something (like a knowledge base article) or if they’re experiencing unexpected behavior. This gives reps clearer insight into the issue and helps them quickly work on asking clarifying questions that help bring about a resolution.

Minimized Churn Risk

By proactively detecting frustration or anger, service teams can intervene before a situation escalates. Reps can also flag unhappy customers as a potential churn risk. From there, leaders can find a proactive save strategy that can help mitigate churn and improve customer retention.

For example, if a customer calls in asking for help with a feature and expressing negative sentiment about the product, the account team will be notified. From there, the account manager can help the customer better adopt that feature. Without this real-time insight, the account team may not know about the customer’s frustration, leading to churn at renewal time.

In my past roles, I’ve seen many benefits from using real-time sentiment analysis. For example, I used call recording software that picked up on customer sentiment. It would alert me if any of my accounts expressed frustration.

When I was notified that a customer expressed negative sentiment in a commercial conversation with my sales team, I was able to reach out to the customer. From there, I could work to understand how I could support them.

Without this technology, I wouldn’t have known the customer was frustrated until my next interaction with them, and we could have lost the renewal contract.

Pro tip: If you’re including sentiment in your customer health score, you can integrate the sentiment from your support tool so that it automatically updates that score for your account teams.

I also suggest creating an automation that alerts account teams once that score updates to reflect support ticket-related sentiment.

Automated Escalation Paths

You can use real-time sentiment to automate processes and workflows that enhance the customer experience. For example, you could create an escalation path that automatically triggers when it recognizes both negative sentiment and high ARR.

If one of your high-ARR accounts starts asking for refunds or cancellations, you could trigger a workflow that automatically escalates the request and loops in a manager.

Pro tip: I’ve been on a team that did this specifically for conversations with negative sentiment and a renewal date within the next 6 months. When this escalation workflow was triggered, it also sent an email to the account team so they could be kept in the loop.

As an account team member, this helped me to intervene on behalf of my customer and also made sure there was no surprise frustration in my next interaction with the customer.

Improved Agent Performance

Understanding customer sentiment helps agents better respond and relate to customers. By easily surfacing emotions like frustration or confusion, agents can avoid asking repeat questions or creating unnecessary back-and-forth.

Real-time sentiment notifications can also help agents make better decisions in real time. For example, an agent may recognize that they’re talking to a customer who’s come back about an issue and pick up negative sentiment. The rep can then automatically loop in Tier 2 or Tier 3 support or even route to a manager.

Additionally, it can help your reps be more empathetic, which leads to a better experience all around. When a rep spots negative sentiment, they can start the conversation by letting the customer know that they understand their frustration and are committed to helping them find a resolution. This builds trust and helps put the customer at ease.

Real-time sentiment also helps support teams with coaching conversations. If constructive sentiment about the rep interaction surfaces, managers can also use this to train reps. A customer might say something like the last rep they talked to “spoke too fast,” or “wasn’t listening to me. Then, the leader can work with reps on their communication skills.

Feedback Loop Creation

Real-time sentiment creates an excellent feedback loop that service teams can use to improve products, processes, or interactions. Customer support tools can recognize if similar negative (or even positive) feedback happens around a certain product, feature, or even with a specific rep. This allows teams to quickly adapt and drive improvements to whatever is causing the influx of negative sentiment.

For example, if customers repeatedly express frustration with a newly rolled-out feature, service leaders can work across the organization to address the friction points. The success team may need to work with the product team to translate sentiment about recent UI updates. Or perhaps the customer education team needs to create more training on a new feature.

Service leaders can also track things like competitor mentions or sentiment that indicates customers may be shopping around.

As I always say, customer feedback should be a consideration in every major business decision.

How to Build Real-Time Sentiment Analysis Workflows

Define sentiment indicators and thresholds.

Before diving into sentiment analysis, customer service reps need to know what negative actually means. Teams need to understand what negative sentiment looks like in their industry and what their specific company should be concerned about.

For instance, an enterprise software support conversation means something is broken. That differs significantly from general customer service interactions. Words like “broken,” “failing,” or “urgent” can indicate everything from actual emotional distress to a simple broken hyperlink.

To do this, customer service reps should work through the following steps:

• First, analyze historical escalation data. Reps can use this information to identify linguistic patterns that preceded major customer issues. Look for phrases, words, and communication patterns that correlate with churn or escalation. From there, identify which customer service interactions ended well and which were negative.
• Next, consider account-specific factors when defining thresholds. Customer service reps won’t be able to give every customer service touchpoint the same level of attention. A mildly negative sentiment from a high-value enterprise customer might need immediate attention. Meanwhile, the same attitude from a smaller account might only trigger monitoring.
• Finally, document your sentiment indicators clearly and make sure all team members understand what actually triggers alerts. Try to include obvious signs of unhappiness indicators, like explicit complaints or demands for management. Then, take note of subtle warning signs, like shorter responses, longer resolution times, and decreased engagement in solution discussions.

Pro tip for HubSpot users: HubSpot Sentiment AI allows for dynamic threshold setting based on account characteristics and relationship history.

Integrate ticket, chat, and call data sources.

After defining what negative interactions look like, customer service teams need to build a system that flags dissatisfaction early. That means integrating data across customer communication channels into one system. From there, service teams can perform comprehensive sentiment analysis and spot unhappy customers.

Here are some tips on how to flag negative interactions across your support system:

• Use AI tools to analyze text-based interactions across tickets, chat, and email. Service teams can also integrate AI with their call recording systems so they can easily process voice conversations for emotional indicators. This is all important data to track and have ready access to.
• Next, service teams should integrate their primary support channels, like ticketing systems and live chat platforms.
• The next step is to make this a live process. Configure real-time data feeds so that message sentiment analysis occurs immediately as customer conversations develop. It’s important to stay on top of this process in real time, as delayed analysis will cancel out the effectiveness of proactive intervention.
• Verify that the sentiment scores update in real time, that historical data imports correctly, and that all communication channels feed into a unified dashboard.

Pro tip for HubSpot users: Make sure that HubSpot's AI, Breeze, has access to full conversation histories, not just individual messages.

Monitor performance and refine models.

After teams integrate sentiment analysis into their support systems, teams need to make iterative changes to improve their workflows. That includes reviewing key metrics and keeping AI systems up-to-date with evolving information.

Step 1: Review escalation and churn metrics weekly.

• First, establish weekly sentiment analysis performance reviews. With reviews in place, service teams can track metrics like alert accuracy, intervention success rate, and false positive rate.
• Then, compare churn rates between accounts that received sentiment-driven interventions versus those that didn't. This comparison will demonstrate the business impact of proactive sentiment monitoring.
• Be sure to document the sentiment patterns and whether they led to successful interventions or escalations. This analysis helps service reps refine threshold settings and procedures over time.

Step 2: Retrain AI with industry-specific technical terms.

• Each industry has its own lingo. Each sentiment analysis system should include phrases relevant to that business’ industry.
• From there, service teams must review AI system flags and toss out any incorrect indicators. For instance, technical language like “system crash,” “service failure,” or “critical error” typically describes operational states rather than customer emotional distress. That data can be misunderstood during early analysis.
• Collect feedback from support agents about the accuracy of sentiment analysis systems. Agents can flag situations where sentiment scores didn't match their perception of customers’ emotional states. This feedback becomes training data for model improvement.
• Finally, feed successful customer interactions into AI systems. AI will then know what positive resolutions look like, so it can better assess successful technical support interactions in the future.

How to Configure Real-Time Alerts in HubSpot Service Hub

Step 1: Create a sentiment property in HubSpot.

• In HubSpot’s Service Hub, access the Properties configuration and establish a custom field titled “Real-Time Sentiment Score.”
• Next, set this as a numerical property with values ranging from -100 (extremely negative) to +100 (extremely positive). Make sure to set the default value at 0 (neutral) for all new contacts and support tickets.
• Add additional sentiment properties for trend analysis. That can include trends over a time period or total sentiment history. These properties will track sentiment changes over time, enabling predictive churn analysis.
• Finally, make sure that sentiment properties are visible on contact records, ticket records, and company records. This will allow support agents and account managers to quickly assess customer emotional state during interactions.

Step 2: Set up event triggers for sentiment spikes.

• Next, establish workflow triggers that activate based on sentiment score fluctuations. For example, a service rep can configure immediate alerts when sentiment falls below -30 for high-value accounts and below -50 for standard accounts. Change up these threshold levels as they best suit your business model.
• Service reps can also set up secondary triggers for trend analysis. For instance, if an account‘s 7-day sentiment trend shows consistent decline, a rep can trigger proactive outreach workflows even if current sentiment hasn’t reached critical thresholds.
• Service teams should also configure escalation triggers that activate when sentiment scores remain negative for extended periods. This way, a customer maintaining -40 sentiment for more than 48 hours would trigger different intervention protocols than a much more temporary spike.

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Step 3: Build a workflow to notify support leads.

• Next, develop notification workflows that deliver comprehensive context beyond simple alerts. Include the customer's account value, recent interaction history, contract renewal timeline, and specific conversation segments that prompted the alert.
• Creating role-based notification rules is also a good idea. For instance, support managers should receive immediate alerts for high-value account sentiment spikes, while team leads handle standard account alerts.
• Use escalation timers so that unaddressed sentiment alerts automatically escalate to senior management. This will prevent anything from falling between the cracks.
• Lastly, create mobile notifications for critical sentiment alerts. Even if they’re away from their desks, support leaders will be aware of any revenue-threatening sentiment changes, and they can then ensure teams respond quickly.

Step 4: Assign follow-up tasks automatically.

• The best support teams go beyond resolving problems. They follow up with account holders to make sure they’re satisfied and feel supported. In Service Hub, enable automatic follow-up tasks so specific reps are in charge of checking in.
• Configuring automatic task assignment based on sentiment severity and account characteristics can help streamline your processes.
• Set up task templates that provide specific guidance for sentiment-driven interventions. Tasks should include conversation context, account history, and suggested intervention approaches.
• After initial intervention, there should be follow-up tasks to verify that sentiment improves following intervention efforts.

Comparison of Sentiment Analysis Tools

*HubSpot AI pricing varies by features and usage through their Breeze platform

Recommendations:

• Choose HubSpot if you want fast setup, native CRM alignment, and a growing set of AI features with low admin overhead.
• Choose Salesforce Service Cloud if you're already embedded in the Salesforce ecosystem and need scalable, partially customizable analytics.
• Choose Custom Solutions if you need highly specialized models, multi-language NLP, or deep technical flexibility, and have the dev resources to support them.

Tips for Making the Most of Sentiment Analysis

Sentiment analysis can help service teams avoid churn and surface feedback that pushes the product forward. In order to unlock these benefits, teams should consider:

• Weighing sentiment score by account value.
• Tracking executive stakeholder sentiment.
• Acting on sentiment insights.
• And leveraging AI.

Here’s how.

Weigh sentiment by account value.

Enterprise customers tend to have larger, more complex organizations and require more 1-1 support to get certain tools off the ground. This means that service reps may hear from customers more often, especially at the beginning of the relationship.

Weighting sentiment by account value helps ensure that high-paying customers’ negative sentiment or urgent requests make it to the front of the line. This gives high-value accounts a premium experience and helps keep them happy, which in turn helps reduce churn.

You can also apply this to lower-ARR customers who are important to you in other ways. For example, I’ve had large, household-name Enterprise customers that didn’t pay much for our product but were important to retain because of their logo. In this case, you can either do targeted weights or consider weighting by company size in addition to ARR.

Service reps could also weigh sentiment by other factors, such as renewal date or churn probability. If one customer segment is projecting more churn than others, leaders could weight by customer segment and renewal date to get the highest-risk accounts to the top of the support queue.

Monitor executive stakeholder sentiment.

For enterprise companies, the software renewal conversation does not happen unless the C-suite is on board. Because executive stakeholder involvement is critical to the relationship, service teams should monitor and track sentiment from executive champions as well as end-users.

Chances are, the C-suite cares less about the technical details and more about the overall experience and the team’s level of responsiveness. By weighting their sentiment differently, service leaders can create workflows and automations that fast-track them to the right people.

Create a plan to tag executive stakeholders’ sentiment separately and create an automation that shares the sentiment with the corresponding account teams. Additionally, if their sentiment turns negative during an interaction, have a plan in place for automatic escalation that loops in a manager or executive sponsor immediately.

Pro tip: By pulling in persona-level data, reps can quickly adapt their conversation style when they know they’re speaking to an executive stakeholder. This also helps them prioritize the ticket and move more quickly than they might normally.

Create a plan to take action on insights.

Sentiment analysis not only benefits reps in real-time, but it can also be a treasure trove of insights that help improve the entire organization.

Routinely analyze the sentiment that the team receives and consider sharing it with other parts of the organization. That may include product, account management, customer experience, marketing, or any other teams that the feedback may apply to.

Service leaders should also apply the findings to the support team, including updating content where necessary, improving support flows, and coaching reps.

Don’t let the benefits of sentiment analysis stop at the customer interaction. Make sure to have a plan in place to collate the analysis and action on it company-wide.

Train AI on industry-specific technical terms.

If a software product is industry-specific, chances are the team’s AI model will need a bit of training to understand the jargon and technical terms. (Just like a new employee would, right?)

Industry specific terms often use severe language casually, with terms like default, loss, outage, exploit, etc. By taking the time to train the model on industry-specific terms and language, teams proactively reduce the chance of errors and improve the model’s output.

For example, if your company is in the healthcare industry, the model might misunderstand the term “critical” as negative sentiment when the customer is actually using it to describe a patient’s status.

In my role, my customers use the term “issues” frequently since it’s part of the auditing process, and it’s a feature built into our product. This can make it tricky for AI to correctly analyze customer support tickets for me, since the word “issue” could be a noun to describe a customer’s problem, or it could be referencing the feature or industry term.

To combat this, I have to train the tool I’m using with specific prompts and do a little data cleanup beforehand to reduce the chance of inaccurate analysis.

Frequently Asked Questions

How do you weigh sentiment by account value effectively?

Deploy a scoring matrix that combines sentiment severity with account value. This will generate priority rankings. For instance, a -40 sentiment score from a $500K account (priority score: 20,000) demands immediate attention, while identical sentiment from a $10K account (priority score: 400) enters standard monitoring protocols.

Pro tip for HubSpot Users: Configure HubSpot to automatically adjust alert priority using these weighted calculations.

What's the best approach for monitoring executive stakeholder sentiment?

Create separate sentiment tracking for C-level contacts and key decision-makers within customer accounts. Since these leaders communicate differently from other end users, they will need closer monitoring.

Set lower negative sentiment thresholds (-20 instead of -40) for these contacts. Then, make sure that any negative executive sentiment triggers immediate account management involvement.

How can you handle false positives in technical conversations?

Train sentiment analysis models to recognize technical language patterns that might seem negative but aren't emotionally charged.

Words like “critical,” “failure,” or “broken” often describe system states rather than user emotions. Service systems should be able to tell the difference. Service teams can implement context analysis to help AI understand the language in tickets and conversation flow, not just individual trigger words.

What's the process for training AI on industry-specific technical terms?

Start by creating a glossary of industry-specific terms and their emotional context. Technical terms like “latency,” “throughput,” or “integration failure” should be classified as neutral descriptors rather than negative sentiment indicators. Then, feed successful resolution conversations into the training data to help AI recognize positive technical outcomes.

Why do generic sentiment tools fail for B2B technical support?

Generic sentiment tools are trained on general consumer service interactions, which have different emotional patterns than B2B technical discussions. Enterprise customers often maintain a professional tone even when frustrated, using subtle language cues that generic tools miss.

B2B technical conversations also include specialized vocabulary that general-purpose AI interprets incorrectly. Specialization and training will really help you with accuracy here.

When should you loop in executive sponsors during sentiment alerts?

Escalate to executive sponsors when:

• Enterprise accounts show persistent negative sentiment for more than 72 hours.
• Multiple stakeholders within the same account report negative sentiment simultaneously.
• Sentiment alerts coincide with contract renewal periods.
• Technical issues affect multiple accounts from the same customer organization.

Solving Problems in Real Time

Real-time sentiment is quickly becoming a competitive advantage for companies that want to prioritize the customer experience. When today’s enterprise customers expect both technical expertise and human understanding, sentiment analysis enables enterprise support teams to deliver on both fronts.

By surfacing customer emotions in the moment, sentiment analysis enables smarter escalation decisions and helps proactively spot risk in accounts that can then be mitigated to avoid churn.

Stop losing enterprise customers to preventable escalations. Test HubSpot AI's real-time sentiment analysis for enterprise support teams.

The State of Customer Service Report

Unlock essential strategies for exceeding customer expectations and driving business growth in a competitive market.

• Exclusive insights from worldwide CRM leaders
• Analysis of modern customer behaviors
• Closer look at the AI opportunity in CRM
• Strategies for staying agile in 2024 and beyond

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