How AI and Machine Learning Tools Shape Customer Experiences

Many of today’s marketing processes are powered by AI and machine learning. Discover how these technologies are shaping the future of customer experience.

By using artificial intelligence (AI) and machine learning (ML) along with analytics, brands are in a much better position to elevate customer service experiences at every touchpoint and create positive emotional connections.

This article will look at the ways that AI and ML are used by brands to improve customer service and support.

AI and Machine Learning Dramatically Enhance CRMs and CDPs

AI improves the customer service journey in several ways, including tracking conversations in real-time, providing feedback to service agents and using intelligence to monitor language, speech patterns and psychographic profiles to predict future customer needs.

This functionality can also drastically enhance the effectiveness of customer relationship management (CRM) and customer data platforms (CDP).

CRM platforms, including C2CRM, Salesforce Einstein and Zoho, have integrated AI into their software to provide real-time decisioning, predictive analysis and conversational assistants, all of which help brands more fully understand and engage their customers.

CDPs, such as Amperity, BlueConic, Adobe’s Real-Time CDP and ActionIQ, have also integrated AI into more traditional capabilities to unify customer data and provide real-time functionality and decisoning. This technology enables brands to gain a deeper understanding of what their customers want, how they feel and what they are most likely to do next.

Related Article: What’s Next for Artificial Intelligence in Customer Experience?

AI-Driven Customer Service Bots to the Rescue

Artificial intelligence and machine learning are now used for gathering and analyzing social, historical and behavioral data, which allows brands to gain a much more complete understanding of their customers.

Because AI continuously learns and improves from the data it analyzes, it can anticipate customer behavior. As such, AI- and ML-driven chatbots can provide customers with a more personalized, informed conversation that can easily answer their questions — and if not, immediately route them to a live customer service agent.

Bill Schwaab, VP of sales, North America for, told CMSWire that ML is used in combination with AI and a number of other deep learning models to support today’s virtual customer service agents.

“ML on its own may not be sufficient to gain a total understanding of customer requests, but it’s useful in classifying basic user intent,” said Schwaab, who believes that the brightest applications of these technologies in customer service find the balance between AI and human intervention.

“Virtual agents are becoming the first line in customer experience in addition to human agents,” he explained. Because these virtual agents can resolve service queries quickly and are available outside of normal service hours, human agents can focus on more complex or valuable customer interactions. “Round-the-clock availability provides brands with additional time to capture customer input and inform better decision-making.”

Swapnil Jain, CEO and co-founder of Observe.AI, said that today’s customer service agents no longer have to spend as much time on simpler, transactional interactions, as digital and self-serve options have reduced the volume of those tasks.

“Instead, agents must excel at higher-value, complex behaviors that meaningfully impact CX and revenue,” said Jain, adding that brands are harnessing AI and ML to up-level agent skills, which include empathy and active listening. This, in turn, “drives the behavioral changes needed to improve CX performance at speed and scale.”

Because customer conversations contain a goldmine of insights for improving agent performance, “AI-powered conversation intelligence can help brands with everything from service and support to sales and retention,” said Jain. “Using advanced interaction analytics, brands can benefit from pinpointing positive and negative CX drivers, advanced tonality-based sentiment and intent analysis and evidence-based agent coaching.”

Predictive Analytics Produce Actionable Insights

Predictive analytics is the process of using statistics, data mining and modeling to make predictions.

AI can analyze large amounts of data in a very short time, and along with predictive analytics, it can produce real-time, actionable insights that can guide interactions between a customer and a brand. This practice is also referred to as predictive engagement and uses AI to inform a brand when and how to interact with each customer.

Don Kaye, CCO of Exasol, spoke with CMSWire about the ways brands are using predictive analytics as part of their data strategies that link to their overall business objectives.

“We’ve seen first-hand how businesses use predictive analytics to better inform their organizations’ decision-making processes to drive powerful customer experiences that result in brand loyalty and earn consumer trust,” said Kaye.

As an example, he told CMSWire that banks use “supervised learning” or regression and classification to calculate the risks of loan defaults or IT departments to detect spam.

“With retailers, we’ve seen them seeking the benefits of ‘deep learning’ or reinforcement learning, which enables a new level of end-to-end automation, where models become more adaptable and use larger data volumes for increased accuracy,” he said.

According to Kaye, businesses with advanced analytics also tend to have agile, open data architectures that promote open access to data, also known as data democratization.

Kaye is a big advocate for AI and ML and believes that the technologies will continue to grow and become routine across all verticals, with the democratization of analytics enabling data professionals to focus on more complex scenarios and making customer experience personalization the norm.

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