Customers don’t always tell you the truth.
They may use words to soften negative feedback or exaggerate their feelings or the true impact of the issue. And manual, human analysis of customer feedback isn’t always accurate because there’s a huge chance of inconsistent misinterpretation of the data. Manual analysis can also be very time-consuming. That’s why forward-thinking businesses use AI to make the process faster and produce more accurate, consistent results.
Some AI-driven customer feedback analysis software, specifically Idiomatic, can help you streamline your sentiment analysis and get the actionable insights you need from customer feedback and data. Unlike other AI models, Idiomatic will create customized language models based on your specific business so you get an analysis that goes far deeper than generic surface-level keyword analysis. Idiomatic calls this “contextual AI”. With these deeper, niche-specific insights, you can make the changes your customers need, not necessarily what they “say” they want.
Keep reading to get our actionable insights and tips for using Idiomatic’s best-in-class AI for your customer feedback sentiment analysis.
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What is AI sentiment analysis?
AI sentiment analysis helps you interpret your customer’s tone of voice and understand what that’s communicating about their experience with your brand.
💡When people say ‘customer sentiment analysis’ they often mean ‘what is the customer trying to tell us?’. At Idiomatic, we mean, from the tone of voice of the customer, are they happy, neutral, unhappy, or do they have mixed emotions or sentiments?
In basic, human-led analysis, you can manually categorize each comment or piece of customer feedback as either a negative, positive, or neutral sentiment. But, you can’t guarantee accurate and consistent tagging. With the support of Idiomatic’s AI, you can determine sentiment faster, more consistently, and with greater depth and specific levels of sentiment beyond positive, negative, and neutral.
Most customer buying decisions are based wholly or partially on emotion. By understanding your customer sentiment score and using AI to correlate this to other customer data and buying decisions, you can get powerful insights to help your business sell more and nurture a loyal customer base.
👉 Learn more about sentiment analysis in our comprehensive guide.
The benefits of AI-driven sentiment analysis for businesses
Only the most sophisticated AI-driven sentiment analysis looks at the words used, the meaning and tone of the words, and their associated sentiment. By understanding the sentiment of your customers for different aspects of your business (such as product satisfaction, brand recognition, and customer service interactions), you can benefit from:
- Predictive analytics: The deeper insights help to correlate sentiment to customer behavior. It helps you predict buying trends, customer satisfaction, and loyalty.
- Increased speed and accuracy of analysis: Using AI to perform sentiment analysis, you can review large amounts of data quickly and have it analyze new data as it comes in, so you have a real-time understanding of sentiment changes. Also, with machine learning algorithms, your data tagging and categorization will be more consistent than manual, human-powered analysis, which is prone to errors.
- Increased customer brand satisfaction: By understanding what your customers genuinely mean rather than what they “say,” you can make the changes that they actually want. This can significantly increase customer satisfaction with your brand.
- Increased positive customer sentiments: You earn more happy customers by creating a better customer experience when you act on the real message of their feedback. When done well, you’ll notice an increase in positive customer sentiments.
- Improved customer support experiences: Transcripts from customer support calls and conversations can give you detailed, valuable feedback and insights. With AI analysis of these transcripts, you can quickly learn about customer pain points and their desired resolutions to improve support processes.
- Business growth and scalability: All of the above combined often correlate to overall business growth, including an increase in customer lifetime value, attraction of net-new customers, and subsequent increase in revenue.
👉 Learn more about Idiomatic’s AI-driven sentiment analysis software
How to maximize sentiment analysis with AI
Not all AI-powered sentiment analysis is the same. To ensure you’re using an AI model or algorithm that works for your unique business and niche, follow these tips:
Set clear objectives
Analytics can provide you with very detailed data and insights. If you’re not focused on your objectives or what you want to learn about your customers, it can bog you down with an overwhelming amount of information.
Instead, set clear objectives for what you want to learn. Then you can track the progress of these objectives through AI-generated reports. Idiomatic helps you create these custom reports to access anytime to see your goals progress and to share reports via email to team members as needed.
Integrate multiple data sources
You likely have many sources of customer feedback and data that can be used in your AI sentiment analysis. When you use AI to support your analysis efforts, you never need to worry about having too much data. Integrate all available customer data sources to get the deepest look at your customers.
The more data sources you can integrate, the deeper you can go into the specific reasons or issues behind overly negative or positive sentiments.
👉 Learn more about integrating Idiomatic with Zendesk for free customer sentiment analysis.
Use real-time analytics
Customer sentiment can change quickly, so being on top of analysis is critical to acting quickly before issues blow up. Ensure that your data sources have real-time (or as often as your APIs allow) bi-directional data integrations. This means new customer data is fed into your system and analyzed immediately.
Real-time analytics can help reduce customer churn by automatically escalating high-priority tickets and helping your team get ahead of spiking ticket volume when needed.
Customize your AI model
Ensure your chosen AI model doesn’t rely on off-the-shelf models, as they’re prone to common language errors. These errors come when the AI only analyzes keywords and phrases, not the intent or unique meaning of the words in your niche.
For example, the word “dispute” means something unique in financial businesses, but in most other businesses, it has a negative sentiment. If you use only keyword-level AI analysis, you may miss out on nuances like these in your niche.
💡 The Idiomatic AI is customized for each business to provide highly specific machine learning models that make sense in your industry or niche. This contextual machine learning uncovers the intent and meaning behind the words to provide human-quality insights at scale and is customizable to your business niche and needs.
Examples of success in AI sentiment analysis
A recent Idiomatic client used sentiment analysis to uncover the issues linked to the most negative sentiments. Most other companies without AI-powered analytics prioritize based on the customer issues they get the most frequently. This company used Idiomatic to take it a step further and prioritize the issues that were the most emotional or frustrating for their customers. Those are the issues they focused on automating first. As a result, they reduced customer frustration, not just minimized support calls.
With Idiomatic, you can click on any sentiment, tag, or category in your dashboard to see a real-time sentiment analysis for that topic. Learn how it’s done:
Can sentiment analysis be automated?
Yes, and the true power of sentiment analysis comes when you partner it with Idiomatics’s automated topic modeling/categorization. Then, you can really understand the full picture of what customers are telling you. Having all your tickets bucketed into “positive, negative, neutral” isn’t as helpful as knowing which specific types of cases (buckets) drive the negative sentiment and require further attention. If you can’t act based on what you are learning then you aren’t really driving change.
How can AI be used to monitor sentiment and enhance brand recognition?
When you pull in multiple data sources and integrations into your AI sentiment analysis software, you can monitor any issue or focus you want, including brand sentiment or recognition. You can integrate social media monitoring, chatbot data, and other voice-of-customer data sources. The AI can then be customized to tag instances of misinterpretation or confusion of your brand so you can identify the most common roadblocks to your brand recognition.
How Idiomatic’s AI makes sentiment analysis easy
Using a large language model approach doesn’t tell you why or what issues or events drive your customers’ emotions. That’s why Idiomatic developers will train a unique and custom model for every business we work with.
Idiomatic also separately analyzes what customers are saying through custom categorization and tagging of each voice-of-customer record. This unique combination of deep sentiment analysis and tagging helps you predict a customer’s future behavior and how what they’re saying may impact their behavior.
If you can’t act based on what you’re learning from your sentiment analysis, you’re not driving the change you need to improve the customer experience and grow your company.
👉Request a personalized demo of Idiomatic to see what it can tell you about your customers.
