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.
In this article, we’ll examine what machine learning is, share four key emerging business uses for machine learning, and show you how to integrate this exciting technology into your company.
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.
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:
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% accuracy rate, thanks to sensors attached to the equipment. This helps companies plan preventative maintenance schedules for individual items of machinery. Less downtime equals greater production capacity and higher revenues.
Image regression technology allows manufacturers to distinguish products that are faulty or nonconforming. They do this 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. This checking is done at high speed and increases fault detection rates by 90%, 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. This information helps factories move to a more efficient just-in-time production process, which increases production capacity by up to 20% and reduces material waste by 4%, as reported by Manufacturing Tomorrow. This also minimizes excess inventory.
Machine learning tools are bringing down the high costs of getting products to end users. For example, there are two complicating factors 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 this 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 CO2 emissions by optimizing routes and calculating exactly how much fuel is needed for a journey. For example, the Just Add Water, or JAWS, app for captains helps them respond to changing sea conditions. It has saved 250,000 tons of shipping CO2 emissions, the equivalent of $90 million in gas.
Many leading road haulers and courier firms use some of the best and most advanced GPS fleet tracking systems to maximize vehicle capacity and save money on fuel costs. This 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 preventative maintenance schedules thanks to machine learning sensors attached to each vehicle or vessel. This 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 like 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. This increases store revenue and prevents a customer from leaving the shop unhappy.
Sentiment analysis uses the same tech Google employs to understand linguistic intent when we’re searching for information. For example, IBM’s Natural Language Understanding tool 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.
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 this data on social media campaigns and email newsletters to drive revenues.
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 this and can reach out to help them understand their way around the app.
Most businesses don’t know how much data they generate or how to use it. The problem of what to do with big data still exists for small companies.
Machine learning can make quick work of finding value in structured data, like 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 ($10.18 million) of fraudulent claims, according to ProjectPro. They now employ the same analysis to all claims going forward and expect to make an annual savings of CA$200 million.
It’s not just the C-suite where big data can be useful. Machine learning apps linked to customer relationship management (CRM) systems can now tell sales managers and reps which deals to prioritize, thanks to tools that qualify leads, predict deal size and even the time to close.
Machine learning helps businesses increase sales and plan for the future. Before deciding whether or not it’s right for you, call in an independent data scientist to analyze the data you have and what could be extracted from it.
Alternatively, try smaller-scale, off-the-shelf machine learning solutions before committing to a significant investment in machine learning. Search for “no code machine learning platforms,” then look at the range of plug-in apps on sites like 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.
Additional reporting by Nacho De Marco.