Machine learning is a type of Artificial Intelligence (AI) wherein computers use historical and contextual data to observe and learn behavior in order to predict outcomes. Through the process of machine learning, data are collected and analyzed to recognize patterns and enable the prediction of future action.
Through machine learning, computers can auto-discover patterns and rules without being directly programmed to do so. Elements of machine learning include data collection, data classification, model building, training, and application deployment.
In the field of computer science, machine learning is meant to train computers how to identify patterns and behaviors. In other words, computers are trained to “learn” from algorithms and, over time, improve their performance accuracy without any human involvement.
Machine learning is a form of AI, and both rely heavily on rich streams of data.
AI learns behavior and is capable of mimicking human activity and even go beyond the abilities of a human. With AI, operators can program machines to trigger actions to occur without the involvement of humans. Such capabilities are enabled by machine learning.
With machine learning, computers and robots can learn algorithms, recognize patterns from data, and apply learnings to executive functions. In essence, machine learning is a form of artificial intelligence.
There are four main categories of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning.
In supervised learning, researchers essentially train the algorithm by labeling the variables they want to test as well as the inputs and desired outputs. Using this information, the computer has clear instructions on what to learn.
Unsupervised learning requires the computer to scan unlabeled inputs and start to build patterns and recognize structure on its own.
In semi-supervised learning, both supervised and unsupervised learning are involved. Testers would use both labeled and unlabeled data and allow the algorithm the freedom to explore the data without predetermined outcomes.
Finally, reinforcement learning is the process of teaching a machine to perform multiple steps with clearly defined rules. The researcher is highly involved in this process, prompting the machine with positive or negative reinforcement as the machine attempts to learn the algorithm.
Another type of machine learning is called Natural Language Processing, or NLP. NLP examines the connections between humans and computers through applications like speech recognition, natural language understanding, and natural language generation, each of which can be accomplished with machine learning.
Applying machine learning to your website conversion optimization efforts can be largely beneficial to improve conversion rates. Rather than relying on manual methods to capture or guess user behavior data, machine learning collects and analyzes website visitor data and can provide insight into ways to improve the user experience and provide a desired outcome.
From this data, marketing teams can understand both their existing customers and potential customers on a deeper level, tuning into their behaviors when visiting their brand’s website. Marketing teams also can use this information when building new web pages, strategizing on user experience, and deciding where to place calls-to-action (CTAs).
This information is especially helpful for ecommerce and B2B brands with high traffic volumes that simply produce too much data for human teams to process at-scale. Instead of employing manual tests to scan the data or analyze the traffic, teams can use machine learning and AI to reveal trends, gain insights about visitors, and enable activities like ad targeting. With the insights from machine learning, marketing teams can recommend content or products for users based on their actions on the brand’s website.
One objective of machine learning is to understand how data influences user activity and to leverage these learnings to improve web conversion. As the availability of data grows, machine learning can help predict more accurate outcomes. With machine learning, computers can mine data and help users build systems that achieve desired results. Real-time data collection and analyses can prove invaluable for high-traffic websites and companies that don’t have a dedicated data science team or other resources available to process high volumes of data.
Conversion Rate Optimization (CRO) solutions like Continuous Conversion™ from Intellimize, take the guesswork out of leveraging machine learning as it’s all automatic, leaving teams to focus on the optimization tasks that require human creativity. The power of machine learning is harnessed to create personalized and unique website experiences for every visitor (anonymous or known) with little manual input from the web development teams.
As machine learning and AI grow in tandem, their presence in the website optimization market grows more competitive each year. And with each passing year, the strength of such platforms also will continue to grow. As the technology becomes more advanced, machine learning and AI will continue to be more accessible, requiring less specialized training and fewer resources, a win for data teams and anyone wanting to reap the benefits of a more personalized web experience and more conversions.