Few innovations will create as dramatic an impact on industry as machine learning. It will change every facet of how we engage with customers, deliver services and direct our team members. Ultimately, machine learning will force us to reimagine every aspect of customer care.
Machine learning is a buzzword you hear, which is frequently
paired with its cousin, artificial intelligence (AI). Let’s take a moment to understand what machine learning really is and why it is game changing. Simply, machine learning is the ability for software to use data to identify patterns, make rules and then
make decisions without (or with very little) human intervention.
A remarkable example of machine learning is IBM’s Watson, which humbled some Jeopardy! champions in front of the world and Alex Trebek a few years ago. More recently, it has proven highly capable of aiding practitioners in improving patient medical diagnoses. This is after Watson was directed to read 50 years’ worth of medical journals!
Another example is SMS or text messaging services. Many have an auto-correct/fill feature that over time learns your typing habits and aids you in pushing through a message more efficiently. This means you might have a chance of keeping pace with your teenager’s thumb-muscle pulsing, red-lining texting abilities. (Okay, so it’s good, but not that good.) Two additional examples are Netflix’s movie recommendations engine and Amazon’s product marketing and communication system that learns over time how to talk to us and what to say.
Machine learning gives software the ability to learn more and faster than any human could, and what follows is an AI that can often make better decisions than a human. Here are five areas where machine learning will impact the customer care space. 1. Engagement (bots).
You may have heard of the Turing
Test. To pass it, a machine must trick a human into believing it’s a human and not a machine. Machines are getting close or in some cases, it might be argued, have already passed this test.
Bots are improving. They’re getting more capable at their functions, such as helping with simple requests like answering questions or collecting data. In the future, their capabilities will grow, as will our reliance on them. They will become an integral connection between the consumer and the brand. Soon, communicating online with a bot or with a human will become indistinguishable. This means greater efficiency and scalability for your contact or care centers.
In May, Google unveiled Duplex, which is an extremely realistic sounding voice assistant that is poised to help customers make simple voice calls such as setting
up meetings and making reservations. Duplex is revolutionary and makes it apparent that soon simple customer care demands will be completed by these digital assistants without human interaction.
In the near future, many simple interactions between customers and organizations may be completed through communication between two bots, one representing the customer and one the organization. However, human intervention will still be necessary in more complex requests and if there is some sort of complication. The impact of this on the customer care industry cannot be overstated. 2. Hyperpersonalization.
One of the great benefits of machine learning is the ability to ingest vast amounts of data to help it make decisions. It provides a tool that can consistently understand needs that customers may have to provide the level of convenience or support they desire.
For instance, software could recollect all previous interactions with a specific customer, every purchase the customer has made, and even scroll through social data to more effectively understand how best to engage that
A customer’s entire purchase history will automatically be recalled, helping to diagnose problems such as if a product has ever been serviced or may actually need to be serviced. Analytics from previous interactions can help assess how best to engage the customer (casual / friendly or straight to the point / all business). This all will
provide a quick and effortless diagnosis of a customer’s needs.
Faster resolutions, increased customer satisfaction and a more frictionless experience will all arise from using data and machine learning to anticipate needs, resolve issues with greater velocity and push customers toward the next stage in the journey. 3. Social listening and negative conversions.
Social listening tools have been widely adopted and have become a necessary part of customer care. In the future, machine learning platforms will automatically respond to customer questions and comments across social media, blogs, forums and the rest of the internet. These tools may also help you gain customers by reacting quickly to online comments about a desire to purchase a product or help you automatically convert a customer who was burned by
a competitor. 4. Cybersecurity and threat intelligence.
Machine learning will be used extensively in helping to keep organizations safe in the real world and in the digital world. Much like social listening, machine learning will be able to scour cyberspace to identify threats to organizations or any customers who could potentially pose a risk to the safety of an organization’s financial or physical assets, or team members.
One of the greatest challenges in cybersecurity is the vast number of threats that exist—too many for even the most skilled analyst to keep track of. Enter machine learning, which can comb through an infinite quantity of data to help pinpoint the most critical threats that exist for an organization. This allows human analysts to move with greater speed and focus.
Social engineering attacks, in which threat actors falsify an identity to gain information or favor from customer care or contact center operators, are becoming increasingly sophisticated. This is, in part, because of the vast amount of personal information available about customers that can be found on social media and elsewhere online or that has been leaked through cyber breaches.
Machine learning will help to strengthen authentication methods including voice recognition and behavioral biometrics such as keystroke, swipe and mouse action
tracking. This, coupled with operator cybersecurity training and testing, can lower the threat of social engineering attacks in contact or care centers. Hardening
your technology and strengthening your operators’ ability to recognize and thwart social engineering is the best way to prevent these extremely potent cyber attacks. 5. Hiring and training.
In April, Harvard Business Review posted the results of a study in which a machine learning algorithm was trained to help select a more effective board of directors using publicly available data. The findings were not all that surprising. People make hiring decisions based on biases. They often are prone to hire people inside their network, people who went to the same school as themselves, have similar degrees, or even people who have a similar appearance or background. Properly utilized, machine learning can help eliminate these biases. When looking to fill a position, you may need only to articulate the job requirements and the desired attributes in a candidate, and algorithms will be able to comb through hundreds if not
thousands of candidates to discover the most appropriate person for the job, not necessarily an executive’s fraternity brother or a friend’s nephew.
Learn More With Fellow SOCAP Members Learn how customer care professionals can find innovative ways to remain relevant and valuable in a future where consumer expectations continue to rise and digital channels are ever changing at “Re-imagine: SOCAP’s 2018 Customer Care Conference,” taking place Oct. 21-24 in Salt Lake City, Utah. We would love for you to join us and discover new ways you can think and act to meet the growing demands of the future. CRM
Scott Klososky has been on the forefront of technology and industry throughout his career. He is renowned globally for his ability to recognize and capitalize on future trends regarding the ways technology is shaping business and our world. He is currently invested in guiding leaders through the digital transformation. Scott is the founder and principle of technology consulting firm Future Point of View. Scott regularly advises C-suite executives inside some of the nation’s most recognizable organizations.