For marketers and customer journey leaders, artificial intelligence is a powerful tool for improving the customer experience. With the growth of customer data and the continual decrease in computing costs, artificial intelligence has become more and more accessible to businesses in many industries. This is a good thing for consumers when business leaders use AI to build smarter, more responsive journeys. But artificial intelligence isn’t as simple as flipping a switch. That’s why we wrote our AI White paper, which describes how AI and Machine Learning techniques can be used in the realm of customer journey orchestration. In that document, we detail the power of predictive models, rule-based machine learning, and adaptive learning models. In this blog, we break down three of the four ways you should use machine learning in your business. If you’re interested in a more in-depth primer on this topic and learning the fourth application, we highly recommend you consult our full AI White Paper.
Problems for which there exist no human experts.
Despite being the creators of artificial intelligence and all human machines, humans can sometimes be bad at understanding our own creation. That’s one powerful reason to employ AI. Where a person might not be able to detect tiny changes in manufactured parts, for example, an AI-powered with automated sensors could perform this task at scale. In a more customer-facing case – there is no human expert who can seamlessly identify small changes in customer behavior in real-time across an entire population.
Just like the result of tiny manufacturing flaws, a human can see the result of a failure in the customer experience, but not necessarily the tiny errors that can cause them. There are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system that can listen to every interaction a customer might have with your business. Thankfully this is not necessary. A machine learning system can study recorded data and learn prediction rules that can inform a human journey manager when something is about to go wrong.
Problems where human experts exist, but where they are unable to explain their expertise.
Humans are often unconsciously good at things that we do every day, like determining if someone intends to tell a joke, or if they are angry. But even though we may be good at this, only a few experts can explain, for example, the chemical and cognitive processes behind the behaviors we observe. With AI we are able to take this expert knowledge and apply it to large swaths of data.
For example, with speech recognition, handwriting recognition, and natural language processing, human experts can break down the parts of these systems, but not necessarily explain step-by-step the exact way that we understand them. When you read this blog, no human expert can explain the exact nerve pattern without using complex machinery to read your brain. What they can do is provide machines with examples of the inputs and correct outputs for these tasks. This means that even though they cannot “understand” reading, a machine learning algorithm can learn to map the input of letters to the output of meaning.
Dynamic problems where phenomena are changing rapidly.
People use machines for many reasons, but primarily to make their lives easier or to automate things that they would otherwise have to do by hand. This is why marketers use email service providers instead of manually sending emails one-by-one. Often though, the situation changes too rapidly to build simple one-size-fits-all automation. For example, if you would like to advertise clothing items on an online store that are appropriate to the customer’s current weather experience, writing a single automation rule that always invites them to try on rain jackets and to add umbrellas to their cart would not accomplish this. Other examples are stock market trading or exchange rates.
For all of these examples, behaviors change frequently, so that even if a programmer could construct a good predictive computer program for one set of circumstances, it would need to be rewritten frequently. A learning program can relieve this burden by constantly modifying and tuning a set of learned prediction rules. By adapting to new data, the system can be much stronger than a more simple tool.
Artificial Intelligence and Kitewheel
Customer Journeys are complex systems that impact every element of your customer experience and that reach all parts of your business. To achieve success in the customer journey space, you need to know how to work smart, in real-time, and across every channel. Artificial intelligence, both native within the Kitewheel Hub and integrated through our built-in adaptors can make this process faster and easier. If you’re interested in learning how you can apply Artificial Intelligence and Machine Learning at your own business, we highly recommend our AI White Paper. It’s chock full of information on the continually growing field of artificial intelligence and machine learning.