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How machine learning is changing the way businesses think about customer behavior

Before today’s renewed interest in AI/ML, many people thought it was just a fad. Now, AI/ML is changing how we do things—including how customers interact with products and services online.

How machine learning is changing the way businesses think about customer behavior
[Source photo: Pavel Danilyuk/Pexels]

The machine learning revolution is here, and it’s changing the way we do—and conceive of—business. From generative AI chatbots like ChatGPT to Netflix’s automated personalized movie recommendations, machine learning (ML) is blazing across multiple industries with lightning speed. Particularly, advancements in ML-powered technologies are helping to solve one of the biggest challenges businesses face today: delivering a flawless customer experience.

More businesses are leveraging ML to improve customer experience. Owing to the increasing adoption of technological advancements, research suggests the ML market size will balloon from $21.17 billion in 2022 to $209.91 billion by 2029, at a compound annual growth rate of 38.8%. To attract and retain consumers, providing quality customer experience is important. In fact, 85% of consumers say the experience a company provides is as important as its product or service. Little surprise, then, that more organizations are now relying on ML to gain deeper insights into their customers’ motivations and preferences.

ML analysis can take many different forms. Intelligage, for example, claims it “utilizes emotionally intelligent AI models to decipher interactions in Zoom and Gong conversations to capture unique buyer intent, extending customer platforms to read a fourth dimension of each person. Organizations can capture every customer interaction, understand what is being said, and deliver insights for an enhanced customer experience.” In addition, the platform also offers a “Digital Coach” designed to train the team on how to use emotional intelligence while working with customers.

On the other side of the equation, Israel-based OrCam harnesses the power of artificial vision to develop pioneering artificial intelligence solutions to empower people who are blind or visually impaired and persons with reading challenges to participate in everyday consumer activities. Incorporating proprietary knowledge from various AI domains like computer vision, ML, automatic speech recognition (ASR), natural language processing (NLP), and natural language understanding (NLU), the company provides its customers with handheld and wearable devices.

OrCam’s products for low-vision users, OrCam MyEye and OrCam Read, are offered in more than 50 countries and in 25-plus languages—representing a significant geographic reach. Also, the recently launched OrCam Learn is currently available in English and has its market in the U.S. and the U.K.

“The growth of big data and the availability of advanced AI technologies and the advancement of emotion detection technology are driving this trend,” says Amir Liberman, CEO of voice analysis and AI decisions company Emotion Logic. “We see this happening strongly in Japan, where customer service is considered sacred and our users are leveraging the emotional insights and styles of their customers to improve customer experience, boost satisfaction, and drive sales.”


Consumers make decisions in real time and are quick to jump ship at the slightest delay or inconvenience. Surveys and personality tests can help yield generalized information, but in order to get a leg up, today’s businesses need real-time data on customer behavior, analysis on how that behavior affects decision-making. Such data can provide a road map to address consumer needs and improve bottom line.

For context, marketing and sales platform HubSpot reported in 2018 that 90% of customers sought an “immediate” response to a customer service question, and 60% defined “immediate” as 10 minutes or less. Not much has changed today, as 52% of customers stopped buying from a company during the pandemic because of the long wait time, according to a survey by Freshworks.

For Thomas Been, chief marketing officer of AI data company DataStax, real-time data is all about the outcome and the ability to act in the time that you have when you have a user behind their phone or when you have to decide on routing an asset or a fraudulent transaction that needs to be stopped. “It’s about allowing the applications to understand the context of whatever situation and act in that moment. Data that are instantly available led to this access. The impact of real time is this ability to drive a certain outcome or prevent a certain outcome in the moment,” he adds.

DataStax, built on the open-source NoSQL database Apache Cassandra, lets any enterprise mobilize real-time data and quickly build smart, high-growth applications at unlimited scale, on any cloud. Its database supports startups and about 90% of Fortune 500 organizations in various sectors, like gaming, SaaS, and banking. Headquartered in Santa Clara, California, the company currently serves 800-plus customers in more than 50 countries, including Apple, Netflix, and Uber.

As opposed to a batch-processing system that runs data at a scheduled time and leaves room for outdated information, real-time data processing is immediate, ensuring information is up to date and not delayed. According to Been, DataStax’s customers in financial services measure real time in milliseconds, while other customers measure in minutes, but the end goal is about acting quickly in a window of opportunity while interacting with a customer.

“Nuances matter, and there is so much data that goes unnoticed which could have an impact on the outcomes that drive customer behavior,” says Intelligage cofounder and CEO Bryan Plaster. For example, he says, consider a rep’s opinion of a happy customer marked “green and 90% healthy.” What does that actually mean? Going one level deeper with data, their sentiment is at 76% and the last interaction with them showed more articulation, higher subjectivity, and polarity with word choice that resulted in an attitude at 81%. This is how ML helps to find patterns in data and enable businesses to know what makes their customers happy and keep them so.


Many of the companies leading the ML revolution aim to solve problems that have for decades plagued the customer experience journey.

For instance, following new advanced capabilities in AI-powered voice analysis, more consumers are now using voice search to get fast information. With voice analytics and advanced emotional detection capabilities, businesses can better understand the concerns and genuine emotions of every customer. When you think about it, Emotion Logic’s Liberman says, the potential is enormous in both traditional customer interaction settings as well as in new metaverse environments.

“It’s crucial to ensure the emotional data captured accurately reflects the inner feelings of the customers rather than just their expressed emotions through specific keywords or loud expressions. Systems based on nonrelevant data are likely to result in a waste of time and money and will probably have lower success rates compared to systems that incorporate genuine emotion detection and personality assessment,” he adds.

This new frontier of so-called Emotional Intelligence-as-a-Service has the potential to significantly impact customer behavior by providing valuable insights into their preferences and motivations. By personalizing the customer experience based on this real-time emotional intelligence, organizations can improve customer satisfaction and assist customers in achieving their goals more effectively.‌ In addition, shopping experiences will be even more convenient for consumers, especially in new metaverse worlds.

“In these new virtual environments, just imagine the impact of a virtual agent in a new Web3 or metaverse world that has its own unique personality and style and can truly understand yours,” Liberman says.

So, what’s the future of AI/ML in the dynamic and ever-changing customer experience journey? Leaders like Uber, Netflix, Zoom, Gong, and others are increasingly using real-time data to power their ML and AI initiatives, according to DataStax CMO Been. But going forward, he adds, there will be even more efforts to make ML models produce predictions and recommendations quickly that make customer experience more seamless.

For his part, Intelligage’s Plaster notes that “with conversations now digitally captured from Zoom, online chats, and more because of the COVID-19 pandemic, sales rep productivity is changing fast as an automatic notetaker can quickly summarize a meeting and add it to your CRM.” He adds that generative AI and emotional intelligence play a big role in winning or losing as a business.

“We are on the brink of a real-time AI breakthrough and that’s going to define or redefine the future of applications,” Been says. “Some analysts predict that 70% to 90% of applications are going to be powered by AI/ML, as a new generation of applications that are real-time, AI-powered, and cloud-native drive a merger between the world of data science and application development. AI is going to provide an extra layer of value, enabling businesses to create more personalized interactions for their customers. It’s a win-win relationship. Customers are happier, consume more, and the company grows.”

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