No matter what industry your business is a part of, machine learning offers huge benefits that will greatly increase the profitability of your business. These benefits include a broader range of data resources, quicker and more accurate data analysis and decision-making, and greater operational efficiency.
More specifically, in Refinement, we utilize a machine learning system that stretches across all our companies. What this means is that our machine learning collates and comprehensively analyzes a wide range of data from our diverse pool of businesses. This gives each Refinement business access to a deeper well of data and, therefore, more meaningful data analyses upon which to base their marketing strategies.
By utilizing machine learning in a way that includes the wealth of data each of our businesses has to offer, we are able to draw more accurate conclusions about buyer and market behavior that lead to greater profitability for all companies involved.
What Is Machine Learning?
Machine learning is a type of artificial intelligence in which computers use data and algorithms to imitate the way humans learn. This is a tool especially helpful in the world of marketing, in which it is crucial to be able to continually analyze what consumers are looking for.
Before machine learning, humans had to collect and analyze data all on their own, or they at least had to program computers to do so every single time they needed them to. Both of these strategies are tedious and time-consuming. More recently, machine learning has created a new opportunity to gather data and analyze trends automatically through computers, which heightens the efficiency and accuracy of data analysis exponentially.
Here in Refinement, we are able to use machine learning to collect data that customers give our companies and find patterns in their interactions with our websites. This, when coupled with predictive analytics, provides us with a solid understanding of what our customers are interested in. Once we have this information and know how to more effectively market to our customers, we will be able to have increased success in our advertising efforts, leading to greater customer satisfaction and more business for us.
Machine Learning Models
Machine learning models are expressions of algorithms that are trained to identify certain types of patterns in collected data. You provide a machine learning model with an algorithm that it uses to analyze a certain set of data. Once you have trained your model, you can use predictive analytics based on the trends the computer has gathered from the data.
Machine learning models are either unsupervised (finding patterns in input data without references to outputs) or supervised (mapping inputs to outputs based on example pairs of inputs and outputs).
The three main types of unsupervised learning models are clustering, dimensionality reduction, and principal component analysis (PCA).
There are many types of supervised models: linear regression, decision tree, random forest, neural network, logistic regression, support vector machine, and Naive Bayes.
Difference Between AI and Machine Learning
In the simplest terms, the difference between artificial intelligence (AI) and machine learning is that machine learning is a subcategory of AI. So really, when we look at the difference between these two capabilities, we are more specifically looking at the ways they relate to each other.
AI is the ability of a computer system to simulate certain human cognitive functions, such as learning and problem-solving. This can be used in a variety of areas, including predictive analytics, speech recognition, and recommendation engines.
Machine learning is another one of the many applications of AI. With machine learning, machines learn from data without being specifically programmed to do so. Computers use mathematical models to learn and analyze data without direct instruction.
How Can I Use Machine Learning for My Business?
One way you can use machine learning for your business is by joining Refinement. Refinement businesses have access to greater machine learning capabilities than if they were to go at it alone because our machines pull data from all different kinds of businesses.
The data our machines collect include an identifier (information unique to each individual—typically their email address), general personal data (such as the individual’s name, age, and other demographic descriptors), and specific data (information that directly pertains to what our businesses are ultimately looking for from an individual—such as heart health data for a business selling heart health products). All of this information helps our machines learn which customers are most likely to buy what your company is selling.
So, customers provide any Refinement company with information about themselves, and Refinement machine learning pulls that data into a comprehensive database. As our machines collate data from all Refinement companies, they have an ever-growing database from which they can accurately discern the optimal individuals for your business to target in your marketing strategies.
Trend Analysis
Trend analysis is primarily used in the financial branch of business, but is also essential to market research and machine learning. Market trend analysis is a kind of trend analysis that focuses on detecting trends in past and current market and consumer behaviors.
Market trend analysis is especially important in systems like Refinement, where we let data drive our decisions. As machines detect trends in data, our businesses will be equipped with the information we need to employ the most profitable marketing strategies.
In short, trend analysis yields the following benefits:
- Provides insights on performance
- Offers data to drive decisions and boost long-term strategies
- Helps your business better understand and cater to customers
- Provides insights into the overall market scenario and your industry as a whole
- Helps organize and summarize long-term data for easy reference
No Subjective Analysis
With any kind of market trend analysis, it is important that there be no subjective analysis. When the feelings, opinions, points of view, or interpretation of an individual begin to affect findings, results become less trustworthy.
Therefore, we want our information to be as objective—fact-based, measurable, and observable—as possible. One benefit of machine learning is that it largely eliminates the need for human involvement in the data analysis process. This means our data analyses will be a more reliable foundation on which to base our marketing strategies.
Handling Multi-Dimensional Correlated Data
Because machine learning is data-driven, your machine learning model will only be as helpful as the data your machines are able to collect. Multi-dimensional data is one model of data collection that many machine learning models use, and multi-dimensional visualization is one of the most helpful tools for analyzing multi-dimensional data.
When you are dealing with multiple dimensions of data, correlations are key. You want your machines to uncover the relationships between the different dimensions of your data so your machines can filter out the noise of too many unrelated features. This helps your data to be more easily understood by your machines and thereby more easily applied to your marketing strategies. Patterns are everything in handling multi-dimensional correlated data and fully utilizing machine learning.
Automatic, Continuous Improvement
Automatic, continuous improvement (or continual learning) is the ability of a machine learning model to continually and automatically learn from data. The goal of continual learning is to use incoming data to automatically retrain the machine learning model—this way, the model’s accuracy and performance stays consistent over time as data continually changes.
Ideally, as our machines continually collect and analyze data from Refinement companies, they will keep up with the ever-changing patterns of the market. They will be able to learn from buyer’s habits and continually improve the accuracy of their analyses as they factor fresh data into the equation.
A Note on Predictive Analytics
Contrary to what many think, predictive analytics is different from machine learning. Predictive analytics uses machine learning, along with data mining, algorithms, and statistics to forecast the probabilities of future outcomes by analyzing past and current data. However, even though machine learning and predictive analytics are separate things, it is smart to use them in conjunction with each other to get the most out of the data you collect.
Predictive analytics is used in many different industries, but more and more businesses are utilizing it in the marketing sector to anticipate what the market will look like in the future, including what customers will be looking for. When you have this information, you can proactively optimize your marketing strategies, helping you stay relevant and alluring to buyers—no more falling behind the times with predictive analytics.
This is the way we with Refinement plan to use predictive analytics alongside machine learning. So, when you join Refinement, you will not only gain access to our machine learning, but you will also get to use Refinement’s predictive analytics to boost your marketing strategies and your overall profitability.
Below, we will delve deeper into the differences between predictive analytics and machine learning and explore the ways they can be used together to maximize the profitability of your business.
Machine Learning Is Most Powerful with Predictive Analytics
Although machine learning is a significant asset on its own, it is most effective when used hand-in-hand with predictive analytics. Where machine learning adds value to the data you collect from consumers, predictive analytics increases that value all the more.
Why? Because data is useless on its own and only carries as much value as the tools you have to analyze it. And while machine learning can be helpful on its own to analyze past and current market behaviors, your data gains a whole new layer of usefulness when you can look to the future with predictive analytics. With predictive analytics, you will be all the more able to utilize your data to the utmost.
Machine Learning Looks Back, Predictive Analytics Look Forward
As mentioned above, one difference between predictive analytics and machine learning is that predictive analytics looks toward the future while machine learning looks at the past and present. That difference, though, is the reason it is most beneficial to use predictive analytics and machine learning together.
With Refinement, we use machine learning to sift through historical and current data to identify patterns in the market and buyer behavior. Once our machines have detected these trends, predictive analytics can be employed to help our businesses understand the likelihood of future market outcomes, which helps us stay on top of the marketing game.
We Will Cover Predictive Analytics in This Article
So far in this article, we have covered the basics of predictive analytics as they relate to machine learning. However, there is much more to discuss when looking specifically at predictive analytics, what they do, how they can benefit your company, and how we will be using them in Refinement.
In order to cover these bases of predictive analytics, we have written another separate article for you to check out. So click here to see how Refinement can enhance your business with predictive analytics!
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