An Intro to AI and ML to Improve Customer Experience
Artificial Intelligence (AI) and Machine Learning (ML) are trending buzzwords and enablers for many interesting use cases across a number of industries. When it comes to improving the customer experience (CX), how exactly does an AI solution help solve business problems that intrinsically require a human touch?
It helps to understand what type of data AI and ML are best applied to and why it’s able to easily navigate through complexities that humans sometimes struggle with.
What do AI and ML do best?
As humans, our brains quickly get overwhelmed with having to process large volumes of data or making multiple decisions at once. By contrast, AI is best suited for processing large volumes of data and navigating through a space where a very large number of decisions are possible. In the 1990’s IBM’s Deep Blue demonstrated its ability to make decisions by successfully beating the world’s best chess players. More recently in 2016, AlphaGo beat the highest ranking Go players in the world—a feat that was once thought impossible because of the creative and intuitive aspect of the game.
AI and ML may still lack a level of human intuition, but it overcomes this with its ability to navigate an infinite amount of possibilities to make decisions. It doesn’t need intuition when it can use deep learning to sort through millions of possible solutions, track different variations and thousands of unique combinations, and generate answers from that data set. AI and ML are a broad field of algorithms and can also be used for exploration. In other words, by starting out with no data and making small changes, AI and ML can learn from those changes and identify which will deliver better outcomes.
AI + data + complexity = improve CX
It’s these exact characteristics that make AI especially well-suited to improving CX and coping with the many factors that influence it. Change just one idea and the number of possible variations and resulting combinations that generate unique experiences grows exponentially. In trying to test how effective that one idea is to improving CX, one suddenly finds that there are thousands of possibilities to consider.
This is where traditional testing methods end up having to cull variables or ideas because they don’t have the ability or capacity to effectively test more than a few ideas at a time. But AI does and it can. It helps solve the problem of complexity in CX by being able to navigate multiple ideas to improve a single webpage as well as any touchpoints in a sales funnel and across different devices or channels. The exact complexity that makes improving CX challenging is what AI thrives on. It can target achieving specific outcomes related to company Key Performance Indicators (KPIs) such as conversion rates, or cart abandonment rates, and experiment with hundreds of ideas in thousands of combinations until it finds the top performers that make the most impact.
Traditional testing requires human input at almost every step in the process, from figuring out which ideas can improve CX to selecting the testing sample, and analyzing the results. It takes time and testing capabilities are limited. As more data, more traffic, or more possibilities are added, humans aren’t able to keep up with the constant change and many possible variables. This explains why good ideas are seldom enough to improve CX. It’s constantly evolving, which means that experimentation efforts need to be able to build on previous learnings and continuously improve to serve up the unique and personalized experiences that connect with visitors and customers.
The difference that AI makes to optimization and experimentation is that it is able to get more accurate with growing volumes of data. It is able to extract additional value in the form of personalizing experiences that traditional, human-driven, experimentation programs are leaving on the table. AI and ML algorithms are set up to model the perfect process and deliver the best experience to website and mobile apps visitors and customers every time.
Optimization at scale is an incredibly complex process, especially in medium to large companies where there are so many inputs impacting CX. AI and ML are perfect for getting CX right despite the massive volumes of data and the complexity of managing all the processes. It enables companies to continuously deliver better digital experiences and keep pace with change in a way that impacts KPIs and improves CX simultaneously.