5 Powerful Ways to Use AI Sentiment Analysis

It goes without saying that customers are the lifeblood of a business. Not only do they help organisations remain operational by buying their goods and services, but they also play a pivotal role in shaping public perception of the business through their opinions (both positive and negative).

Businesses have long understood the importance of gathering these opinions to guide future decision-making. Eighty-four per cent of customer support leaders say they rely heavily on customer data and analytics to meet their organisational goals. However, it has traditionally been a labour-intensive endeavour, with businesses often unable to effectively act on what their customers are telling them.

That’s where AI sentiment analysis comes in.

In this article, we take a look at how AI is revolutionising how businesses gather and use customer sentiment to their advantage, as well as the types of sentiment analysis available. We’ll also explore how businesses use AI sentiment analysis and highlight the benefits and challenges related to its use.

Sentiment analysis is a natural language process (NLP) strategy that examines how customers feel about a business’s products or services. It’s achieved by scanning large amounts of textual evidence from various sources (social media platforms, review sites, etc.) and categorising the relevant evidence into positive, negative, or neutral sentiments.

What follows is an example of what would be considered a negative customer sentiment. A customer relations manager would find this evidence, immediately tag the one-star rating and the criticisms, and categorise them as negative sentiments.

A lot of businesses focus heavily on the numbers side of things to determine success (costs, revenue, profits, etc). However, sentiment analysis can often be an early indicator of positive or negative numbers. If positive sentiment about a product launch is high, for example, it suggests that the numbers will be good and the strategy could be used again.

Artificial intelligence (AI) is transforming virtually every aspect of business operations, and sentiment analysis is no different. The core concept remains the same, but AI automation is taking the process of customer sentiment analysis to new heights.

Not so long ago, customer relationship managers would spend their days sifting through various communication channels to find coverage of their business. They would have to read each article, review, or post manually before categorising it. It took a lot of time and effort and often yielded limited insights.

Now, AI can scan thousands of text pieces across the entire internet in seconds. It can also detect the sentiment within these pieces and accurately categorise them. The power and scale of AI have allowed businesses to expand their sentiment analysis exponentially, giving them the ability to understand the narrative surrounding their business better than ever before.

The great thing about AI sentiment analysis is that the process largely remains the same regardless of which industry or sector your business happens to be in. It will generally work like this:

1. Data collection: It all starts with raw data found within text sources. AI sentiment analysis tools will scan the internet for mentions of the business and collect them for interpretation.
2. Natural language processing: These text sources will then be processed by AI, extracting aspects such as tone, keywords and context from the material.
3. Machine learning (ML): Once enough data has passed through the process, AI models will be able to automatically recognise sentiment patterns based on the content and context of the sources.
4. Classification: With this new knowledge in place, AI machines can then tag the sentiment as either positive, negative, or neutral.
5. Insight and action: From there, it will be down to marketers and customer relationship managers to glean insights on current trends and adjust strategies and processes accordingly.

The global text analytics market size was valued at USD $5.86 billion in 2020 and is projected to reach USD $29.42 billion by 2030. The numbers illustrate just how critical this information is for businesses to ensure success.

Sentiment analysis can reveal a surprising amount about how a business is performing that numbers alone can’t uncover. With this form of analysis, businesses have a better understanding of how their business impacts their customers (both positively and negatively) on a more emotional level.

Aside from revealing customer insights on how the business is performing, businesses will use sentiment analysis for other reasons, including:

• Competitor benchmarking: Often, businesses will not only analyse their own customer sentiment; they’ll also explore how people feel about their competitors, with the hopes of uncovering reasons for their success.
• Proactive problem-solving: If a product launch hasn’t gone well, for example, businesses can examine customer sentiment to understand why it failed. This will provide valuable guidance for future events.
• Direct communication: Customer sentiment often provides a direct link between a business and disgruntled customers, who will often appreciate seeing evidence of steps being taken to address their concerns.

At its most basic level, sentiment analysis is about detecting and extracting feelings and opinions from text-based data. However, many businesses delve much deeper into what their customers are thinking (and why) by using several different types of sentiment analysis, each with its own unique objective.

With this type of analysis, businesses can pinpoint emotions and opinions around specific aspects or features of a product or service.

In the above example, this user has mentioned the blower, the bag, and the power button. It’s clear they approve of the blower portion (positive sentiment) but have issues with the bag and the stiffness of the power button (negative sentiment). This type of analysis can be more useful for product developers.

This is still related to customer satisfaction overall and feelings about a product or service, but it segments the categorisation a little further into things like very positive or negative, or mostly positive or negative. This can help businesses prioritise issues or complaints more effectively.

Many businesses will use intent analysis to identify the reasons why a customer is posting a comment. Some common examples of intent include making a complaint about a product or service, requesting support, or indicating purchasing intent.

This ties in closely with the work marketing teams will do to provide customers with targeted advertising and personalised experiences.

This is a form of advanced sentiment analysis, and it’s primarily used in industries that have very specific terminology and jargon.

For example, the term ‘bearish’ suggests negative sentiment within the finance and investment sectors, whereas it might not necessarily mean that in a more general sense. Machine learning models will need to be specifically trained to recognise the context behind these types of terms.

We’ve established that using AI for sentiment analysis has been transformational for business, largely because of the scale and speed at which it can operate. With this improved functionality, businesses are able to use sentiment analysis in new and exciting ways. But the core goal – to extract customer emotions and use them to make data-informed decisions – remains the same.

These are five of the most important uses of AI-powered sentiment analysis:

1. Real-time crisis prevention
2. Predictive market trend analytics
3. Personalising customer experience in real time
4. Employee engagement and burnout prediction
5. Multimodal sentiment fusion

Let’s take a look at each of these in more detail.

Negative sentiment can quickly snowball into a brand crisis. Not only does textual evidence of bad experiences being relayed through posts affect a business, but the knock-on, hidden impact on a business will also kick in, with 56% of consumers stating that they’ll quietly switch to a competitor rather than complaining.

That’s why sentiment analysis needs to be a continuous process, with AI and its automation processes geared towards achieving this. An autonomous AI agent can monitor X, Reddit, and news APIs in real time, tagging posts with sentiment scores and alerting teams when negativity strikes. The goal of sentiment analysis isn’t simply to react to crises; it’s to prevent them from developing in the first place.

At Salesforce, our Agentforce platform provides the perfect space to bring together all data on customer sentiment. Plus, our CRM Analytics software can track notable shifts in sentiment and send automated alerts to applicable teams before the situation can escalate.

Your product or service will often take on a life of its own once it’s out in the world, but that doesn’t mean you can’t intervene in the event of market shifts. AI can scan online reviews, customer feedback, forums and financial news to spot emerging positive or negative sentiment, then forecast where demand is heading.

Especially high positive sentiment will likely suggest a spike in future demand, which is valuable information you can pass on to your procurement teams and suppliers in anticipation of increased sales. Conversely, negative sentiment will inform marketers and product developers that a change in approach might be needed in the future.

Many businesses will also broaden the scope of their sentiment analysis to include brand monitoring in a more general sense. If a pattern begins to develop regarding a particular product that a competitor is selling, businesses can use this as the springboard for further investigation.

Salesforce can make this much easier. With our CRM Analytics software, businesses can effortlessly combine external sentiment with predictive forecasting to anticipate future demand levels.

AI chatbots are now standard on most business websites and customer support help lines. However, while they’re adept at recognising the language being fed into them and responding with solutions, they don’t always pick up on nuances such as tone and mood. Scripts can often be quite generic and stilted, which can further escalate an interaction with a frustrated customer.

Sentiment analysis tools can help counter this. They can analyse chat or call tone live, picking up on potentially inflammatory or encouraging words to decide upon the next action. It gives the chatbot a more human-centric feel, which customers will often appreciate.

Formula 1 needed such a solution. As the organisation experienced a surge in popularity, many of its service channels were overwhelmed with demand, and the fallout from poor service levels was becoming obvious. But with the help of Agentforce, they were able to pull together all customer data and sentiment into dedicated fan profiles, which could be instantly accessed during support calls and used to de-escalate situations quickly.

And when this combined data was integrated with our Marketing Cloud, sales teams were able to provide customers with highly tailored experiences related to their favourite teams, drivers, and events.

Many businesses use sentiment analysis for more than connecting with their customers. They’ll often use the same technology to analyse employee surveys and chat feedback and extract sentiment that could identify potential issues such as burnout and job dissatisfaction. HR and executives are then able to act before problems spread and become serious.

This can be especially helpful, as many employees don’t feel comfortable expressing their feelings about their job, or the business in general, publicly. It gives business leaders the information they need to remedy the situation and increase morale and productivity across the organisation.

Salesforce customers can access this information via our Tableau application. It provides business leaders with a complete picture of their sentiment data, with dashboards and filters for metrics showing precisely where sentiment is good and where intervention might be needed.

While text-based sentiment is often great at providing business leaders with clues about what’s going on, when used alone, it can sometimes miss important details. This is especially true when you’re looking to extract sentiment from voice and video calls.

Tone of voice and facial expressions are both key indicators of how a person is really feeling, and AI is now able to detect these aspects, as well. This data can play a pivotal role during sales and customer support calls.

Salesforce AI is leading the way in achieving this multimodal form of AI-driven sentiment analysis, starting with voice analysis. When you couple it with data supplied from customer relationship management (CRM) software, your agents will be fully equipped to handle each call and arrive at the most optimal resolution.

AI sentiment analysis is a developing technology. It has the potential to deliver significant benefits to businesses that use it. However, like all AI applications, until it’s had sufficient time for fine-tuning, there are likely to be challenges that businesses will experience along the way.

These challenges are tricky, but they’re not insurmountable. With the right time and investment, and by using the correct tools and platforms, businesses can overcome obstacles and pave the way for powerful AI sentiment analysis.

Getting started with AI sentiment analysis can seem daunting, particularly if you’re still getting comfortable with using AI software in your organisation.

It’s always good to start small when implementing technologies. Hold off applying mass sentiment analysis across all of your channels until you’re confident with the process.

We’ve broken down the process into the following steps.

First, you need to understand why you want to apply AI to your sentiment analysis. Are you looking to improve the customer experience with more personalisation? Do you want real-time insight into your upcoming product launches? Or do you want to keep a close eye on what customers think of your competitors?

Your goal will impact your choice of sentiment analysis, as well as the types of data sources you’ll need to target.

Next, you need to collect the raw sentiment data. The more data you collect, the better you’ll be able to train your AI sentiment analysis models in effectively extracting customer opinions. Sources will include social media sites and posts, review sites, customer surveys and questionnaires, competitor websites, and CRM systems.

Our customers use Data Cloud to store the vast amounts of data needed for successful AI sentiment analysis. It’s designed to harmonise information from various sources and communicate efficiently with external APIs. This information is then used to power Agentforce and enable automation and informed decision-making.

Once you’ve gathered the data, you need to select the tools you’re going to use to interpret and process it, which will then feed into your models. If you have access to skilled web developers, you can opt to build a custom model. This is particularly useful if you work in industries that might require fine-tuning to incorporate specialised terms.

Alternatively, you can opt for pre-built models or no-code tools.

Eventually, your models will be in a position to provide real-time visualisation of overall sentiments via dashboards. These are essential for instantly accessing the necessary information for insights into how customer sentiment is likely to affect the business in both the short and long term.