Terms like “artificial intelligence” or “deep learning” are sprinkled into articles and conversations without explaining their meaning, making it difficult to truly understand the value that the newest technologies can bring to businesses. The same is true of Machine Learning (ML), with numerous articles discussing whether organizations should implement this technology and how it could be beneficial.
This interest is also reflected in market data. The worldwide machine learning market was worth USD 6.9 billion in 2018 and is expected to grow to 96.7 billion by 2025. Suffice to say though, businesses and stakeholders should understand machine learning and how it can be used in business operations before deciding on its implementation.
This article aims to break down the meaning of the buzzword and discuss how to use machine learning for business processes, what machine learning is used for in business, and how it's beneficial.
What is machine learning, really?
Machine learning has gained a lot of traction in recent years due to its use across a wide variety of industries. From credit card fraud detection to targeted advertising on social media, ML has been successfully used for tasks that were once done manually by humans but can now be automated through algorithms that draw on large databases of information.
Machine learning is the application of artificial intelligence (AI) and Data Science used to extract patterns from data without prior knowledge of what the patterns mean. It's a technique where algorithms learn from data and make predictions without being explicitly programmed. Machine Learning imitates human learning, and becomes more accurate as more data is gathered and analyzed.
Machine learning for business – common myths
Here are some common myths about machine learning that may be claimed while discussing the buzzword:
AI and ML will replace humans. This is a common myth. Machine learning is meant to help us, not replace us. It frees us up to concentrate on more important tasks like strategy or creativity.
Machines learn with experience. While this isn’t untrue, it doesn’t tell the whole story. Machine learning primarily needs data to function. Machines learn from this data and develop algorithms to tackle real-world issues.
AI and ML are the same. These words are similar, but not identical. Machine Learning is an AI subset. If you are interested in diving a little deeper into what machine learning is and how it differs from AI, we've got you covered with this article.
How is machine learning used by businesses today?
To understand what machine learning is used for in business and how it works, it’s important to know how most machine learning algorithms operate. There are four key categories:
The program identifies a statistical connection between two actions, which gives a probability depending on how often those actions occur. For example, a customer who purchases a food product from a particular category (X) is likely to also purchase from category Y. As a result, we may recommend category Y to consumers who buy from category X since there is a 50% chance that they will be interested in it.
In order to generate predictions, machine learning systems must first fit a model to some previously collected data. For example, let’s assume we want to categorize consumers via sentiment, i.e., either happy, neutral, or dissatisfied. We can compile all of the information we have on clients and create a rule to determine if they fit into one of the categories. Next, based on their previous learnings, the algorithm will classify new clients as either those who are satisfied with our services or those who aren’t. If you’d like to learn more about data classification, be sure to give our dedicated article a read.
Supervised and Unsupervised Learning
ML uses a mix of supervised and unsupervised learning. Let’s break down what this means.
Supervised learning utilizes a training set to educate models by using data that is already labeled or tagged with the correct answer. The algorithms can be trained to correctly categorize data or predict outcomes. As a result, supervised learning lets businesses tackle real-world issues at scale, like separating spam from your email. Unsupervised learning, respectively, evaluates and clusters unlabeled data, finding information on its own. These algorithms automatically uncover hidden patterns or data groupings. Compared to supervised learning, unsupervised learning algorithms can handle more complicated problems. In addition, its capacity to compare and contrast data makes it excellent for exploratory data analysis. Unsupervised learning allows companies to examine data in an exploratory manner, enabling them to discover patterns faster than via human observation.
Supervised learning gathers data from a prior experience or creates a data output from that event. It assists in optimizing performance requirements based on previous experience and solving a variety of real-world computing issues. On the other hand, unsupervised learning finds all kinds of previously undiscovered patterns in data and assists in the discovery of characteristics that are helpful for classification.
Through a mix of supervised and unsupervised learning techniques, a business may classify consumers based on data that is currently available versus data that has yet to be discovered.
Reinforcement learning trains computer learning models to make choices by placing the AI in a game-like situation. The computer solves issues through trial and error. The computer is either rewarded or punished to perform what the programmer desires. After a series of random trials, the computer must determine how to achieve the task best to maximize the reward. Reinforcement learning is currently the best way to inspire a machine's creativity.
What are some specific uses of machine learning in business?
Here are three use cases of artificial intelligence and machine learning being used for business:
Help with decision making
Machine learning can help companies convert their data into value-adding insights. Humans cannot evaluate information and run many potential scenarios at the size and speed required to take the best course of action. As summed up by Lean Manufacturing Research LLC's creator and chief analyst, Dan Miklovic, machine learning "doesn't replace people, but rather helps people do things better".
Companies can use historical price data and other data sets to learn how certain conditions impact goods and services' demand. Insights from machine learning algorithms help businesses dynamically price their products depending on many factors, helping them maximize revenue. This pricing method is most often seen in the transportation sector, such as surge pricing at Uber or sky-high airline ticket costs during school holiday weeks.
Chatbots are a popular type of automation. They have bridged the gap between people and machines by allowing us to interact with machines that can then perform activities based on specific requests. The earliest chatbots were programmed to obey predefined rules that told them what to do depending on keywords.
Machine learning and natural language processing, a subset of machine learning, allow chatbots to be more productive and engaging. Alexa, Google Assistant, and Siri are all notable instances of modern chatbots. These new chatbots react better to consumers' requests and interact more like actual people.
Tips for applying machine learning to business problems
Let’s now look at some of the best practices when it comes to applying machine learning for business decisions.
- Define a large problem instead of focusing on a minor one
Businesses should avoid using machine learning just for its novelty. This results in teams lacking motivation or dedicated resources to achieve tangible results. Instead, start with an issue that matters a lot and has a good chance of getting handled. Then, narrow down that issue by thinking about what business information is highly desired but not currently accessible.
- Come up with the context; data on its own won’t suffice
While ML algorithms are good at finding correlations, they don't grasp the context of the data. So, choosing the data to feed your algorithm can be complex. Here are three ways the “context” may hinder the development of ML solutions.
Predicting the lifetime value of an eCommerce customer
Let's suppose that many of the customers with the greatest lifetime value were reached through a phone outreach campaign that lasted for over two years, but failed to break even, despite generating new sales. If a phone follow-up program like this isn't going to be a component of future eCommerce sales development, then this data is irrelevant for the algorithm.
Medical recovery time estimation
A computer may use data to decide therapy for first- or second-degree burns. As a result, the computer may anticipate that many second-degree burn patients will only require as much time as first-degree burn victims. Because the context wasn't in the data, the computer assumed second-degree burns heal as quickly as first-degree.
A recommendation engine for an eCommerce store over-recommends a product. However, these promotional purchases were sold more based on the “deal” and the cheap price, and less based on the real alignment with the consumer.
- Be ready for continuous adjustments
Choosing algorithms, data, cleaning data, and testing in a live setting takes considerable thought and testing. Unique and complicated commercial use cases need custom machine learning solutions. Iteration and modification are required even for very typical use cases. A business that embarks on an ML project without sufficient resources may never produce a meaningful outcome.
What's next for machine learning?
We have massive quantities of data and a greater understanding of how algorithms operate, and machine learning is accessible to everyone, not just engineers. Machine learning solutions will continue to integrate changes into fundamental company operations and become increasingly common in our everyday lives.
In fact, Gartner predicts that, by 2022, 70% of consumer contacts will include new technologies such as machine learning and chatbots. Here are a few more things we will likely see in the future of machine learning:
Quantum computing is likely to improve machine learning. Quantum computing enables simultaneous multi-state processes, allowing for quicker data handling. Google's quantum processor completed a job in 200 seconds in 2019 that would have taken a supercomputer 10,000 years to accomplish. Quantum machine learning may enhance data processing and provide deeper insights. Companies can outperform more conventional machine learning techniques with these performance gains.
There is no commercially viable quantum computer yet. But a few major internet firms are investing in the technology, so it’s safe to say that quantum machine learning is coming.
Automated machine learning streamlines the application of machine learning algorithms to real-world activities. With AutoML, anybody may use sophisticated machine learning models and methods without prior knowledge, indicating its potential to transform technology. AutoML can automate the following phases of machine learning model development:
- Enhance data quality, convert unstructured data into structured data, and more.
- Automate feature engineering using machine learning techniques to generate more flexible features.
- Extraction of characteristics from various datasets to enhance outcomes and decrease data processing size.
- Select just relevant characteristics for processing.
- Automatic algorithm and hyperparameter selection.
- Deploy a model based on the framework and monitor its health through dashboards.
Making data-driven choices is essential to conducting successful company operations. However, with buzzwords, such as "machine learning" constantly flying around in industry-related content, it can be hard to be informed about the newest analytical techniques that can assist in such choices.
Machine learning, a subset of AI, is the study of computer algorithms that learn from experience and data. Machine learning techniques help companies capitalize on important opportunities to gain deeper insights into data.