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 boost.ai, 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.
Related Article: What Customer-Centric Predictive Analytics Looks Like
Sentiment Analysis Understands Emotion
AI-driven sentiment analysis enables brands to obtain actionable insights which facilitate a better understanding of the emotions that customers feel when they encounter pain points or friction along the customer journey — as well as how they feel when they have positive, emotionally satisfying experiences.
Julien Salinas, founder and CTO at NLP Cloud, told CMSWire that AI is often used to perform sentiment analysis to automatically detect whether an incoming customer support request is urgent or not. "If the detected sentiment is negative, the ticket is more likely to be addressed quickly by the support team."
Sentiment analysis can automatically detect emotions and opinions by classifying customer text as positive, negative or neutral through the use of AI, natural language processing (NLP) and ML.
Pieter Buteneers, director of engineering in ML and AI at Sinch, said that NLP enables applications to understand, write and speak languages in a manner that is similar to humans.
"It also facilitates a deeper understanding of customer sentiment,” he explained. “When NLP is incorporated into chatbots and voice bots it permits them to have seemingly human-like language proficiency and adjust their tones during conversations.”
When used in conjunction with chatbots, NLP can facilitate human-like conversations based on sentiment. “So if a customer is upset, for example, the bot can adjust its tone to diffuse the situation while moving along the conversation,” said Buteneers. “This would be an intuitive shift for a human, but bots that aren’t equipped with NLP sentiment analysis could miss the subtle cues of human sentiment in the conversation, and risk damaging the customer relationship."
Buteneers added that breakthroughs in NLP are making an enormous difference in how AI understands input from humans. “For example, NLP can be used to perform textual sentiment analysis, which can decipher the polarity of sentiments in text."
Similar to sentiment analysis, AI is also useful for detecting intent. Salinas said that it’s sometimes difficult to have a quick grasp on a user request, especially when the user’s message is very long. “In that case, AI can automatically extract the main idea from the message so the support agent can act more quickly.”
The Challenges of Using AI and ML
While AI and ML have continued to evolve, and brands have found many ways to use these technologies to improve the customer service experience, the challenges of AI and ML can still be daunting.
Kaye explained that AI models need good data to deliver accurate results, so brands must also focus on quality and governance.
“In-memory analytics databases will become the driver of creation, storage and loading features in ML training tools given their analysis capabilities, and ability to scale and deliver optimal time to insight,” said Kaye. He added that these tools will benefit from closer integration with the company’s data stores, which will enable them to run more effectively on larger data volumes to guarantee greater system scalability.
Iliya Rybchin, partner at Elixirr Consulting, told CMSWire that thanks to ML and the vast amount of data bots are collecting, they are getting better and will continue to improve. The challenge is that they will improve in proportion to the data they receive.
“Therefore, if an under-represented minority with a unique dialect is not utilizing a particular service as much as other consumers, the ML will start to ‘discount’ the aspects of that dialect as outliers vs. common language,“ said Rybchin.
He explained that the issue is not caused by the technology or programming, but rather, it is the result of the consumer-facing product that is not providing equal access to the bot. ”The solution is more about bringing more consumers to the product vs. changing how the product is built or designed."
Final Thoughts: AI and ML Support Overall Customer Experience
AI and ML have been incorporated into the latest generations of CDP and CRM platforms, and conversational AI-driven bots are assisting service agents and enhancing and improving the customer service experience. Predictive analytics and sentiment analysis, meanwhile, are enabling brands to obtain actionable insights that guide the subsequent interactions between a customer and a brand.