sentiment-analysis-ai

Detecting Customer Sentiment With AI

Good marketers always listen to their customers. This can be done in many shapes and forms. The traditional way is to talk to them in person or over phone and collect direct or indirect feedback.

It can also be done using surveys and questionnaires. In the digital age, there are many more ways to listen to the voice of the customer, and with artificial intelligence, we can do things we never thought possible a decade ago.

Why is it important to listen to the customer?

Because by the time they leave, it is too late to rectify the situation. Customer churn is the final cry. Unhappy customers often show signs that they are unhappy long before they leave you. How can you detect this?

There are many ways, including measuring:

  • The number of website and blog returns and visitor behavior
  • The number of web shop visits and shopping behavior
  • Email opens and clicks
  • Open rates for push notifications
  • Social media engagement
  • Customer service interactions
  • Recommendations and reviews
  • Sentiment analysis of interactions
  • Conversion rates
  • The emotional state of your customers

While many such measurements are not based on AI per se, the accuracy of your reporting can be improved by using predictive marketing and machine learning, thus providing new ways to hear the customer’s voice.

You can then measure the difference in behavior for highly personalized content versus static mass-audience content. The personalized outreach is likely more efficient and can lead to deeper relationships with your customers.

With modern tools, you can get insights never before imaginable.

With modern tools, you can get insights never before imaginable.

Sentiment analysis is one of those areas. This is about detecting emotions in text to better understand how customers feel about your products, and their opinions and needs.

With sentiment analysis (also known as emotional AI, or opinion mining), you can measure if someone is positive, negative, or neutral towards your brand or product, and what emotional state they may be in.

In effect, you get to know how they feel when they write about your brand or products. This information is gathered by analyzing and understanding text in email, social media posts, product reviews, customer service tickets, chatbot conversations, and more using natural language processing and natural language understanding.

In short, you can see the polarity of their opinions, and how strong those opinions are.

You can see the polarity of their opinions, and how strong those opinions are.

A company in the text analytics space is Lexalytics. They have offered commercial sentiment analytics software since 2004 and their system processes billions of documents per day. Their solution analyzes and provides insights around a company’s text data from surveys, call logs, social media posts, message boards, and comments.

In addition to the more common cloud-based or on-premises solutions for text and sentiment analysis, they also offer an add-in for Microsoft Excel to easily analyze both structured and unstructured survey data, generate insights, and create reports and data visualizations.  

I spoke with Seth Redmore, CMO of Lexalytics. He argues there’s simply too much human communication out there to understand, analyze, and act on without AI assistance.

To address this, he says, “The Lexalytics Intelligence Platform helps businesses work better with text. This could be as part of a decision support system, or an analytics offering, or predictions. It could be looking backwards, or peering forwards.”

Redmore explained that “Text is language. To understand text is to understand meaning, in a way, it is to understand the nature of being human, by being able to untangle how we communicate.  We focus on conversational text—as examples, social media or customer feedback.”

We will see businesses harvesting more insights from text and sentiment analysis in the years to come. There are many vendors in the field, all with different strengths and weaknesses.

In general, they all use natural language processing algorithms to enable their magic. Depending what solution you chose, your experience may differ in terms of languages supported, or how advanced the language understanding is.

Some products might only support English, which may be a problem to companies in non-English speaking countries.

Talkwalker, a social media analytics company, launched an AI-based sentiment analysis tool in the autumn of 2017 that claims to detect customer sentiment with 90% accuracy, even understanding irony and sarcasm.

With machine learning, the tool understands the meaning of full sentences and determines the attitude in social media posts or other content. Their platform monitors and analyzes online conversations in real time across social networks, news websites, blogs, and forums—all in 187 languages.

The analytics engine analyzes the data across a wide variety of categories including social engagement level, sentiment, demographics, location, linked themes, and more.

It also features image analytics technology and sentiment analysis.Christophe Folshette, co-founder of Talkwalker, explains that, “All of Talkwalker’s AI technology is proprietary and is currently focused on image analytics capabilities, sentiment analysis and data classification. Talkwalker’s image analysis is able to detect not only brand logos, but also scenes and objects within images to give clients greater clarity on the context these images are used in.”

Folshette goes on to say, “We’ve rapidly increased our AI-based image applications, adding scenery, objects, gender and age detection. We’re seeing clients use this technology for a wide range of purposes from understanding the impact of sponsorship to analyzing Instagram data to create maps of customer tastes and preferences and direct business expansion.”

Companies with the right AI-powered tools certainly seem to have better insights to base their decisions upon, but that’s not the end of the possibilities. Sentiment analysis can be applied to audio as well.

Sentiment analysis can be applied to audio as well.

One example is the phone service company Nexmo, which provides real-time customer sentiment analysis of voice calls. A voice-based AI-bot monitors the conversation and estimates the emotions of calling customers.

The service agent can watch the estimated customer sentiment live as the call progresses, then react to change the course of the call as needed.

 Another machine learning solution that goes beyond words is Affectiva. They can analyze the emotional state of facial expressions in images, while Beyond Verbal does something similar thing voice recordings. These types of tools can help analyze how people feel based on images or audio recordings.

Given the vast amounts of footprints leads and customers leave in text, audio, and images, there is no shortage of data to harvest for emotional intelligence. As these systems develop and become ubiquitous, we as a society will need to think and act clearly on the legal and ethical aspects of these systems.