Machine Scaling a company isn’t easy. You have to worry about marketing, advertising, finance, teams, and more. It seems like there’s always a never-ending amount of tasks piling up. Well, it doesn’t have to be that way. In fact, many entrepreneurs are embracing machine learning to automate tasks, save time, and work smarter.
Machine learning is a branch of artificial intelligence that works by training models and algorithms. This is done via leveraging historical and real-time data to create predictions, optimisations, and suggestions. Want to see how businesses can use machine learning to scale faster? Check out these five use cases.
Predictive analytics and forecasting
One form of machine learning is predictive analytics. Think of your traditional analytics solutions like Google Analytics or Google 360. These are what we called batched analytics. They take previous data, whether it’s from yesterday or an entire previous year, and present it to the user for interpretation.
That’s great. However, it’s outdated to an extent. Business moves fast, so you need the freshest data possible. Predictive analytics evaluates business operations and behavior to forecast marketing, advertising, and spending.
It allows a company to look into the future to determine if their efforts are going to have an ROI and worthwhile impact. If not, they can scrap those ideas and reallocate efforts elsewhere.
Think about how much time is spent testing and experimenting on the other hand. All of that is done automatically with predictive analytics platforms. You simply integrate it like any other tool, work as normal, and it provides accurate forecasts to streamline performance.
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Tighten up cybersecurity
Technology changes extremely fast. Exploits and vulnerabilities advance just as quickly because of this. Companies need to have peace of mind while they scale, otherwise they’ll be constantly looking in the rearview mirror.
Machine learning is one solution. It’s capable of being synced with cybersecurity to continually learn flaws and risks. These are presented to the user to fix while also helping marketers learn where problems tend to manifest. (User access, malware, etc.)
This is mostly achieved through a sub-set of machine learning called anomaly detection. This technology scans a business to find unusual activities, aka anomalies. They are graded one through three with one being minor and three being serious. Companies can then tackle issues as they are found versus constantly monitoring for them.
Discover the best marketing campaigns
Social media, SEO, PPC, blogging. The list goes on. Organisations invest in many different channels to acquire customers and increase brand equity. However, which generates the most conversions and revenue?
Most platforms will give you this information. But, you have to work for it first. This means investing the time and energy in planning, launching, and measuring campaigns. Wouldn’t it be nice to simply be told by software what to do?
It’s possible thanks to machine learning. Marketing security software analyses campaigns to find which ones are generating revenue and which ones are draining budget. Businesses can focus on high ROI activities while removing time-wasters.
Automate customer service with chatbots
Customer service takes a lot of time. You have to respond to requests, nurture relationships, fix problems, implement feedback, and more. It’s important, but wouldn’t you rather focus on launching that new product or strategy you’ve been worrying about? Most marketers do, too.
If you’ve ever visited a website and were greeted by a chatbot, you’ve interacted with machine learning without realising it. It’s one of the most accessible and easiest forms of AI to implement.
Chatbots are designed to interact with users, answer questions, provide information, and sell products. Moreover, they learn how customers behave to offer unique responses based on buyer personas.
There are many different chatbot apps, plugins, and platforms to use. They directly integrate with websites and you customize the message, profile, and funnel. Additionally, you can setup chatbots for free on networks like Facebook if you have a business page.
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Reduce fraud and financial risk
Companies involved in finance, transactions, and e-commerce understand that fraud is a large risk. Credit card fraud, identity theft, and similar crimes are at high levels. Becoming a victim to these crimes as a brand can result in lost revenue and customers.
Machine learning is capable of training models that analyze a company’s financial behavior to understand what is normal or risky behavior. This information is then used to determine if specific transactions are safe or unsafe to accept.
Stock and inventory optimisation
Brands that sell physical products, especially those working with external wholesalers, all have one major hurdle: inventory. Customers are continually buying and returning products, making stock levels flux every day. Keeping accurate inventory levels is critical for product fulfillment, accounting, and organization.
There are two existing approaches to this already. The first is having an assistant or similar individual manually update SKUs, stock levels, and manage inventory. The second is to use software that automates most of these tasks.
The next step up is inventory optimization with machine learning. This helps warehouses manage product levels in the most efficient way possible, ensuring that supply chains never become interrupted.
A great example of this is Amazon. They ship out millions of packages every day and use machine learning to keep their inventory accurate. Their system preemptively re-stocks popular products and predicts when items will be out of stock so customers never have delayed shipments.
There’s no shortage of technologies to adopt as a company in 2020. It can be overwhelming trying to decide on the best ones, too.
Machine learning—and artificial intelligence—are at the forefront of adoption right now. However, some are hesitant because it has a reputation for being complex and intimidating. Thankfully that’s no longer the case.