Machine learning reduces friction at every stage of your business, whether you come up with new product ideas or deliver products to your clients. Increases business efficiency, improves customer relationships, and drives sales.
We explore what machine learning is, share four important emerging business uses for machine learning, and show you how to integrate exciting technologies into your company.
What is machine learning?
Machine learning is a way for computers to learn to perform new functions without human programming. Instead of following an existing command prompt through installed software, Machine Learning allows computers to analyze large datasets, identify patterns, apply new information to resolve issues and predict results.
Simply put, machine learning can teach oneself through examples, just like how humans learn by looking and practicing.
Machine learning vs. artificial intelligence
They are closely related, but artificial intelligence and machine learning are not the same thing. The goal of artificial intelligence is to achieve the desired outcomes. When AI fails, it looks at where it is missing and changes the way it solves the problem to see if the new approach is better.
The role of machine learning is much more limited. It queries a large dataset to find a pattern it can interpret. It does not learn from its own mistakes. Instead, machine learning relies on human input to change the way we approach problems.
“Instead of hard coding rules, machine learning uses algorithms and statistical methods to analyze data, improve performance and adapt it to new inputs,” says Dexter Nelson, founder, lead engineer and programmer of TechDex Development and Solutions. “You can give it data, but you need to teach it how to think about it. You have to give it a structure to learn. It's what information is good and what information is bad. So it's not this unsupervised learning experience where you know you provide it and get it all together.”
How can machine learning help your business grow?
Whether you reduce the use of transport fuels or direct the right people to call or email, here are how machine learning can help businesses achieve competitiveness.
1. No waste manufacturing
Machine learning apps save businesses money by streamlining inventory management and making production more efficient.
They are good at breakdowns of potential equipment before they occur. Machine learning apps can predict failures with 92% accuracy thanks to sensors mounted on the device. This helps businesses plan preventive maintenance schedules for individual items on the machine. A decrease in downtime equals increased capacity and increased revenue.
Image regression technology is one way of machine learning to lead to smarter manufacturing, allowing manufacturers to distinguish between defective or non-conforming products. They do that by comparing images of newly created products with ideal images. Quality control engineers can also program the technology to consider certain types of defects. According to McKinsey, the checks are done at high speed, increasing the fault detection rate by 90%.
Machine learning is also useful for supply chain management. Machine learning apps accurately predict how many customers will purchase a particular type of product and when they will purchase it. That information will help factories move into a more efficient, just-in-time production process. This will increase production capacity by up to 20% and reduce material waste by 4%, as reported in production tomorrow. It also minimizes excess inventory.
2. More efficient logistics
Machine learning tools reduce the high costs of delivering products to end users. For example, there are two complex factors that make air cargo expensive. First, regulators, cargo flight operators, airports and freight carriers work independently. Secondly, many sectors operate in time, making future planning difficult. Machine learning provides a better organization for all stakeholders by prioritizing the order of transportation by urgency, the type of goods being transported, and the time of travel to the airport. As a result, airlines have a lower reserve capacity. The same applies to exporters' freight charges.
Thanks to technology, ships are now carrying more cargo and lowering their rates. It also helps ship owners, ports and clients to more accurately predict the arrival times of container ships. Machine learning also reduces carbon footprint by optimizing routes and accurately calculating the amount of fuel needed for your journey. For example, simply add water or jaw for the captain to help you respond to changes in ocean conditions. We saved 250,000 tons of carbon dioxide transport. That amounts to $90 million in gas.
Many major road carriers and courier companies implement the best fleet management services and tracking systems to maximize vehicle capacity and save fuel costs. This has resulted in a significant drop in the price of individual delivery, especially for multi-drop drivers.
Logistics companies can better plan preventive maintenance schedules thanks to machine learning sensors connected to each vehicle or vessel. This means reducing repair costs and fewer days of action.
The ability to forecast demand provides accurate benefits. AI-powered retail forecasting tools allow retailers such as Amazon to create predictive delivery protocols that determine the number of specific products each fulfillment center should receive. Brick and mortar retailers with their own online e-commerce stores use the same model to ensure that they don't run out of stock at their respective physical branch locations or online. This increases store revenue and prevents customers from making the store unhappy.
3. Improve consumer outcomes
Sentiment Analysis uses the same technology that Google employs to understand the intent of a language when looking for information. For example, IBM's natural language understanding tools can detect feelings such as sadness, joy, fear, and anger in social media content, discussion forums, online reviews, and comments about companies and their products and services.
Comments from these types of “wild” users are more authentic than what you get from customer service personnel and clients exercising restraints in the hopes of gaining an advantage. Doing sentiment analysis gives you a real sense of where it works well and where it needs to be improved.
Sentiment analysis also lets you know what your customers think about their competitors and their products. It helps you see the areas you are ahead and the areas where your target audience feels you need to be better.
“If, for example, you start doing trend analysis instead of getting publicly available data and cutting down the web, comparing it to customer data and trying to create a product and sell customers, I can take what they want, create it and sell it directly,” Nelson said. “You can create the right time and the right place and put it in front of the right people.
Machine learning is also perfect for websites. We can provide recommendations as soon as a customer arrives on the site based on purchase history, demographics, and purchase history of other customers who have purchased the same product. You can also be more successful with social media marketing and create email newsletters for your business to drive revenue.
Software and app companies use AI and machine learning to detect potential customer terminations. If a customer is not using the critical features that other customers rely on, they can find it and reach out to help them understand the path around the app.
4. More effective decision making
Most companies don't know how much data they generate or use. The question of how to accept big data still exists for large companies.
Machine learning allows you to do the quick work of finding values in structured data, such as Excel files with descriptors in each value.
They are becoming more difficult to make unstructured and semi-structured data difficult. For example, according to ProjectPro, machine learning analysis of unstructured data from 233,000 claims over the past six years has resulted in the Canada Insurance Agency identifying $41 million ($10.2 million) of fraudulent claims. They now employ the same analysis for all their claims ahead, hoping to save $2 million a year.
“One of the biggest assets of machine learning is logic,” Nelson said.
“For example, you can use machine learning to analyze large datasets, but if you're looking for an application to use, you can write a script and assign tasks,” he said.
Such an example is similar to an If-Then statement in Excel, but has a reach that goes far beyond the boundaries of a spreadsheet.
“We can give AI what we call “conditions.” I give it a condition and say, 'If this condition is met, I want you to run this script,'” Nelson said. “It's not just looking at the data and giving recommendations because you can give instructions on how to manage things. You're actually working.”
It's not just C-Suite where big data is useful. Integrated with Customer Relationship Management (CRM) software, the machine learning app can now direct sales managers and reps to prioritize, thanks to tools that certify leads, predict transaction sizes, and even determine the time to close.
How to introduce machine learning to your company
Machine learning helps businesses increase sales and future plans. Before you decide whether it's right for you, call an independent data scientist to analyze what data you have and what data you can extract from it.
1. Build an internal IT team.
For businesses that anticipate ongoing machine learning tasks and the need for complex data management, it is worth considering building an internal IT team. In cities like New York City and Los Angeles, hiring internal experts to handle specific tasks costs around $50,000 to $60,000 a year.
While the upfront costs may seem high, they may be more cost-effective than relying on third-party software that keeps prices rising in the end. Additionally, having an in-house expert will allow you to better customize and optimize your system to suit your company's unique needs.
2. Experimenting consumer products
Before committing to a huge investment in machine learning, start with small off-the-shelf machine learning solutions. Search for “No Code Machine Learning Platform” and look for the range of plugin apps on sites like Makeml, Pycaret, RapidMiner.
Depending on your level of technical confidence, you may need a freelancer to help you use the no-code tool, but again, it is much cheaper than the development team.
3. Attend a local meeting
If you're unsure where to start, we recommend exploring local machine learning conferences in your area. This technology feels new, but there are many companies and individuals with years of experience sharing. The International Conference on Machine Learning (ICML) held its 42nd conference in Vancouver in June.
4. Participate in a live software demo
Technically, it's a sales pitch, but live software demos with companies with experience in machine learning can help shed light on the various applications that can be used for business needs. Most technology and SaaS companies are willing to schedule time for potential customers with product experts to showcase new services that can improve revenue.
Additional reports by Jeff Hale and Nacho de Marco.
