22 June 2016
How do Call Centers Leverage Artificial Intelligence?
The idea of an artificial intelligence (AI) system that can behave, think or act like a human has fascinated all since the first computer hit the market. Now, it’s alive and growing in all aspects of society, including the call center.
When developing AI, the best frame of reference was to mimic a human brain. So, in 1943, the foundation of AI neural networks, systems of interconnected “neurons” that interchange messages, was laid. AI organizations worked tirelessly toward a goal to create systems exhibiting intelligent behaviors by learning. This progress was slow, until the mid 2000s when Geoffrey Hinton invented fast learning algorithms based on the Boltzmann Machine. This generative stochastic artificial neural network became known as Restricted Boltzmann Machine (RBM).
RBM and deep learning made artificial intelligence (AI) more powerful than ever, and with dropping hardware prices, AI became an important player in fields like Natural Language Processing and Speech Recognition (tips) to mention a few. This opened a new door for B2C organizations as they evolved application design to better interact with customers.
Artificial intelligence in the call center has become even more important in the last five years, leveraging large data sets and predictive analytics for automated, personalized customer service and on-demand agent training based on reporting. However, with great power comes great responsibility.
Is AI a Magic Box or Paradox?
Companies often struggle to find the appropriate use for AI.
Some companies treat artificial intelligence (AI) as a magic box that can do anything, most likely influenced by Hollywood and the endless line of sci-fi movies, where robots and machines are smarter and more emotionally intelligent, than an actual human. Those companies expect AI to do all the work for them, including sensitive and critical operations like interaction with customers. Fully automated, front facing systems that intelligently interact with customers is their modus operandi.
On the other side of the extreme, there are companies that do not want to acquire AI help, thinking only of its impediments, like maintenance and resource costs associated with updating AI algorithms to fit new data structures Both groups are good examples of an erroneous AI perception.
Like with everything else in life, there is a nice middle ground, where AI can be utilized in a call center as a behind-the-scenes tool to improve customer experience. It can collect customer preferences and automate proactive outreach; it can judge when a customer or agent’s tone is out of norm and direct a supervisor to join the call, and it can streamline self-service optimization. Recognizing the limitations of current AI tools and how they should be leveraged is important.
Run AI Algorithms Against The Right Data Sets
In order to create good artificial intelligence (AI) models, an organization needs to have data sets that AI algorithms can run. This allows an organization to report on customer and employee behavior, using automation to create a more personalized customer experience and a more engaged employee base.
How big should your data set be to successfully run an AI algorithm against it?
The size of a data set does not matter as much as the quality of the data. However, there is a symbiotic relationship between the data and planning algorithm. Company data is only as valuable as the planning algorithm processing it. If the model is not solid, and we apply too big of an abstraction in our algorithm, even the best data sets will not benefit us to the fullest potential. Waiting and delaying analysis in order to collect more data is not optimal. Most of the time companies have enough data without realizing it. What they really need is a good solid model with the proper level of abstraction.
It is also important to stay focused and not lose track of the goal. Concentrating on specific processing tasks instead of a whole solution could result in more efficient processing of data sets. Such a breakdown of tasks makes the process of designing rational algorithms much more efficient and easier to manage.
AI for Predictive Analytics to improve Agent Performance & Customer Satisfaction
Where artificial intelligence really shines is in the analytical, non-consumer facing, time consuming operations of the call center. One use of artificial intelligence in the call center is for predictive analysis of an agent’s performance based on predictive voice analytics.
Predictive analysis of an agent’s performance is achieved by establishing patterns to capture good and poor agent performance in order to gauge an agent’s level of customer service. With automated, real-time monitoring of voice pitch, scripting, and key words on social, SMS or chat, call center operations can intercept negative customer interactions and provide a high level of service as incidents occur. Additionally, poor agent performance can be associated with low levels of enthusiasm and confidence, which can be improved upon by establishing training sessions tailored to the individual agent’s need. Such an approach would directly translate into increased customer satisfaction.
Further by creating personalized one to one experiences, where a system learns customer behavioral patterns and successfully avoid irrelevant customer engagements – a company can ensure customer satisfaction and sales acceleration. Providing proactive customer support by predicting what customers need or want will result in significant competitive advantage. This kind of analysis can automatically create new opportunities or cases, based on the type of algorithm used.
This concept could also be applied to customer CSATs, where predictive analysis is used to monitor customer satisfaction levels across voice, SMS, social and chat. This is very important because a customer left in a vulnerable emotional state for a prolonged period of time, is more willing to leave your organization behind for a competitor. Ignoring customer satisfaction levels leaves you open to a loss in customer loyalty and revenue.
AI for Customer Interactions
Artificial intelligence is a great tool, and it will continue to evolve with time. However, solely relying on AI to handle sensitive parts of business operations, like customer interactions, could spell disaster. With the advance of channels that create connectivity and openness, customers become powerful to brand stability and therefore must be treated as such. Companies can’t afford to make mistakes when interacting with them.
In order to avoid customer frustration due to an inability to resolve an issue efficiently in a customer’s channel of choice, automated processes should always exist in collaboration with human interaction. Customers are way too valuable to be handled by fully automated systems. Let’s face it, even some humans have a hard time interacting with other humans properly, sometimes due to a lack of emotional intelligence. Considering all those factors, it would be irrational to expect an artificial intelligence system to perform and handle a customer interaction at 100% satisfaction levels.
However, with continued advances in this field and with proper utilization, AI could help companies improve their relationship with customers and bring it to the next level. I am sure that as Millennials seek intelligent self-service and organizations seek automation, AI will continue to create a buzz in scientific circles in the years to come.
Stay tuned for my next blog on how artificial intelligence will be used to fill in data gaps and give real-time predictions of a customer’s intent, something that most organization’s data and analytics are unable to do.
Erni Mededovic is a Technical Architect at PTP. With over 16 years of experience in IT, he has helped multiple startups and leading global corporations improve customer experience. He has a great passion for technical innovation through the use of new technologies aimed to enhance customer experience.
In his free time, you can find him sailing the high seas. When not helping customers, he is helping boaters as an active member of the United States Coast Guard Aux.
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