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How Machine Learning Is Boosting Business Growth
Machine learning helps businesses improve productivity and profitability by finding valuable insights buried in your company’s databases.
Table of Contents
Machine learning reduces friction at every stage of a business, whether you’re coming up with new product ideas or getting the goods delivered to the client. It increases business efficiency, improves customer relationships and boosts sales.
We’ll examine what machine learning is, share four key emerging business uses for machine learning and show you how to integrate the exciting technology 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 existing command prompts through installed software, machine learning allows computers to analyze large data sets, identify patterns and apply the new information to solve problems and forecast outcomes.
In simple terms, machine learning lets computers teach themselves through examples, much like the way humans learn by seeing and practicing.
Machine learning vs. artificial intelligence
Although they’re closely related, artificial intelligence and machine learning aren’t the same thing. The goal of artificial intelligence is to deliver a desired outcome. When AI fails, it examines where it fell short and alters the way it solves a problem to see if a new approach would work better.
The role of machine learning is much more limited: It queries large datasets to find patterns it can interpret. It doesn’t learn from its own mistakes; instead, machine learning relies on human input to change the way it approaches a problem.
“Instead of hard-coded rules, machine learning uses algorithms and statistical methods to analyze data, improve performance and adapt to new inputs,” said Dexter Nelson, founder and lead engineer and programmer at TechDex Development and Solutions. “You can give it data, but you have to teach it how to think. You have to give it the structure in which to learn. It has to know what information is good and what information is bad. So it’s not this unsupervised learning experience where you give it data and know it will figure it all out.”
How does machine learning help businesses grow?
Whether it’s by reducing shipping fuel use or directing calls and emails to the right person, here’s how machine learning helps companies achieve a competitive edge.
1. Leaner manufacturing
Machine learning apps save businesses money by streamlining inventory management and making production more efficient.
They’re good at spotting potential equipment breakdowns before they happen. Machine learning apps can predict failure with a 92 percent accuracy rate, thanks to sensors attached to the equipment. That helps companies plan preventive maintenance schedules for individual items of machinery. Less downtime equals greater production capacity and higher revenues.
Image regression technology is one of the ways machine learning is leading to smarter manufacturing, allowing manufacturers to distinguish products that are faulty or nonconforming. They do that by comparing an image of a newly made product with an ideal image. Quality-control engineers can also program the tech to look out for specific types of defects. The checking is done at high speed and increases fault-detection rates by 90 percent, according to McKinsey.
Machine learning also helps with supply-chain management. Machine learning apps accurately predict how many customers will buy a particular type of product and when they’ll want to buy it. That information helps factories move to a more efficient just-in-time production process, which increases production capacity by up to 20 percent and reduces material waste by 4 percent, as reported by Manufacturing Tomorrow. That also minimizes excess inventory.
2. More efficient logistics
Machine learning tools are bringing down the high costs of getting products to end users. There are two complicating factors, for example, that make air freight expensive. First, regulators, cargo flight operators, airports and freight forwarders work independently. Second, many sectors operate just in time, which makes planning for the future difficult. Machine learning provides better organization for all parties by prioritizing order of carriage by urgency, the type of goods being transported and travel time to the airport. As a result, airline spare capacity is lower — as are freight fees for exporters.
Thanks to the tech, ships now carry more cargo for reduced rates. It also helps ship owners, ports and clients more accurately predict container ship arrival times. Machine learning also reduces carbon dioxide emissions by optimizing routes and calculating exactly how much fuel is needed for a journey. The Just Add Water, or JAWS, app for captains, for example, helps them respond to changing sea conditions. It has saved 250,000 tons of shipping carbon dioxide emissions — the equivalent of $90 million in gas.
Many leading road haulers and courier firms are implementing the best fleet management services and tracking systems to maximize vehicle capacity and save money on fuel costs. That has led to a big drop in the price of each individual delivery, especially for multidrop drivers.
Logistics companies are also better able to plan preventive maintenance schedules thanks to machine learning sensors attached to each vehicle or vessel. That means lower repair costs and fewer days out of action.
The ability to forecast demand precisely offers further benefits. AI-powered retail prediction tools allow retailers such as Amazon to create anticipatory shipping protocols that determine how many of a particular product each fulfillment center should receive. Brick-and-mortar retailers with their own online e-commerce stores use the same model to ensure they don’t run out of stock at each physical branch location or online. That increases store revenue and prevents a customer from leaving the shop unhappy.
3. Better consumer outcomes
Sentiment analysis uses the same tech Google employs to understand linguistic intent when we’re searching for information. IBM’s Natural Language Understanding tool, for example, can detect emotions such as sadness, joy, fear and anger in social media content, discussion forums, online reviews and comments about a company and its products and services.
These types of “in the wild” user comments are more authentic than the ones you might get from a client who is exercising restraint with customer-service reps in hope of obtaining an advantage. With sentiment analysis, you can get a real sense of where you perform well and where you need to improve.
Sentiment analysis also allows you to find out what customers think of your competitors and their products. That helps you see the areas where you’re ahead and the ones where your target audience feels you need to do better.
“For example: If I take publicly available data and scrape the web and compare that to my customer data, and I start running trend analyses, instead of trying to create products and sell customers on it, I can take what they want, create it and sell it to them directly,” Nelson said. “You can create the right time and the right place and put it in front of the right people because you’re on top of it.”
Machine learning is great for websites too. It can offer recommendations as soon as customers land on your site based on their purchase history, their demographics and the purchase histories of other customers who have bought the same product. You can also use it to be more successful at social media marketing and create an email newsletter for your business to drive revenue.
Software and app companies use AI and machine learning to detect potential customer churn. If a customer isn’t using the key features other customers rely on, they spot it and can reach out to help them understand their way around the app.
4. More effective decision-making
Most businesses don’t know how much data they generate or how to use it. The problem of how to embrace big data still exists for small companies.
Machine learning can make quick work of finding value in structured data, such as in Excel files where each value has a descriptor.
They’re getting better at making sense of harder-to-analyze unstructured and semi-structured data too. For example, machine learning analysis of unstructured data from 233,000 claims in the previous six years helped the Insurance Bureau of Canada identify 41 million Canadian dollars ($10.2 million U.S.) of fraudulent claims, according to ProjectPro. They will now employ the same analysis to all claims going forward and expect to save CA$200 million annually.
“One of machine learning’s biggest assets is logic,” Nelson said.
“For example, I can use machine learning to analyze large sets of data, but if you’re looking for an application of how to use it, I can write some scripts and assign tasks,” he said.
Examples like this are similar to Excel’s if-then statements, but with a reach that extends far beyond the boundaries of a spreadsheet.
“I can give AI what we call ‘conditions.’ I give it a condition, and I say, ‘If this condition is met, I want you to execute this script,’” Nelson said. “I can give it instructions on how to manage things, so it’s not just looking at data and giving me recommendations — it’s actually doing work for me.”
It’s not just the C-suite where big data can be useful. Machine learning apps integrated with customer relationship management (CRM) software can now tell sales managers and reps which deals to prioritize, thanks to tools that qualify leads, predict deal size and determine even the time to close.
How to introduce machine learning into your company
Machine learning helps businesses increase sales and plan for the future. Before deciding whether it’s right for you, call in an independent data scientist to analyze the data you have and what could be extracted from it.
1. Build an internal IT team.
For businesses that anticipate a need for ongoing machine learning tasks or complex data management, it’s worth considering building an internal IT team. In cities like New York City or Los Angeles, hiring an internal expert who handles specific tasks could cost around $50,000 to $60,000 per year.
The upfront cost may seem high, but it can ultimately be more cost-effective than relying on third-party software that continues to increase in price. Plus, having an in-house expert allows you to better customize and optimize your systems to your company’s unique needs.
2. Experiment with consumer products
Start with smaller-scale, off-the-shelf machine learning solutions before committing to a significant investment in machine learning. Search for “no code machine learning platforms,” and then look at the range of plug-in apps on sites such as MakeML, PyCaret and RapidMiner.
Depending on your level of technical confidence, you may need a freelancer to help you use no-code tools, but, again, that’s far less expensive than a development team.
3. Attend local conferences
If you don’t know where to begin, it’s a good idea to explore local conferences on machine learning in your area. The technology feels new, but there are many companies and individuals with years of experience to share. The International Conference on Machine Learning (ICML) is hosting its 42nd conference in Vancouver in June.
4. Participate in live software demos
Although it’s technically a sales pitch, a live software demo with a company experienced in machine learning can help shed light on the variety of applications available for your business needs. Most technology and SaaS companies are happy to schedule time for potential customers with a product expert to walk you through new services that could improve your bottom line.
Additional reporting by Jeff Hale and Nacho De Marco.
