Fantastic Machine Learning use cases: how to make ML work for your business
Jun 4, 2020
Just imagine, the first mention of Machine Learning (ML) appeared more than 70 years ago. Have you read “I, Robot” by Isaac Asimov? He does not use the term Machine Learning, but judging by how robots work with data and self-learn, that was it. Already back then, within the boundless imagination of the author, robots and ML algorithms were used for business.
In this article, we take a look at the reality of 2020 and find out what modern Machine Learning use cases in business are.
What Is Machine Learning
The essence of the Machine Learning technology is that this system learns directly in practice guided by the original knowledge embedded in it.
And it’s worthwhile to also define Artificial Intelligence, as these two concepts are usually used in the same context.
Artificial Intelligence is a more complex ML algorithm that uses deep learning and strives to maximally repeat the thinking mechanism of the human brain.
Yes, it’s hard enough to imagine how it works without analyzing Machine Learning use cases. Let's do it together.
Machine Learning use cases — 6 industries that will be reshaped by ML and AI
In fact, the application of Machine Learning is not limited to the following six industries. Machine Learning can be applied anywhere where there is at least a minimal set of data.
Modern companies have already realized this. That is why it is expected that the global Machine Learning market will grow to $20.83B in 2024 (compared to $ 1.58B in 2017).
So, here is how Machine Learning in business works making a profit, reducing costs, optimizing production, caring for the environment, and providing customers with the best service.
Moreover, Machine Learning can easily complete all these tasks for your business as well.
Let’s start with Machine Learning use cases that now are dominating in retail.
Virtual Fitting Rooms
Applications for virtual fitting rooms are already a reality. And this is the case when Machine Learning works side by side with Augmented Reality. The essence of the application is that you can upload your image and start trying on clothes and make a more thoughtful shopping list.
Moreover, advanced applications are able to give you tips based on knowledge about you, plus check the availability of the model in the nearest store. This is definitely a cool user experience.
Robots helping to choose clothes at physical points of sale are also a reality. And here is the latest example. Simbe Robotics created a robot that helps to get goods from shelves. The main goal is not only the convenience of users but their maximum distance from each other.
Data-driven shop windows
As they say, data knows better. In fact, this is another way how Machine Learning in business can work with data.
In this case, the Machine Learning algorithm analyzes the behavior of customers, the products they buy most often, and suggests ideas on how to improve the storefront so that users immediately find what they want and buy without too much thought.
Voice and visual search
Both of these technologies work through Machine Learning and Artificial Intelligence. Optimizing your site for voice and visual search is another way to boost retail sales.
By the way, AnswerThePublic AI application can help you find out what people are asking within your niche and optimize your site accordingly.
Demand prediction and price optimization
Because Machine Learning works with a huge amount of data, it can take into account subtle fluctuations in forecasting demand.
For the same reason, this technology can tell what price buyers are willing to pay for a particular product in a certain period of time. You would have to agree, these are valuable insights if your goal is to break even.
Face and mood recognition
Face recognition technology no longer seems surprising — just unlock your iPhone. But the technology of recognition of intentions and moods can give retail a lot of advantages.
At that moment, when you find out for what purpose a certain buyer has entered the store, you can make an even more irresistible and instant personalized offer.
One-third of all manufactured food products is wasted. Machine Learning can help develop more sophisticated food sales strategies based on seasonality, price, and demand, with one goal in mind: to make sure that all food is sold, not spoiled, and thrown into the trash.
Supply chain improvement
This Machine Learning in business application is a logical consequence of the previous paragraph. This technology allows you to develop smarter logistics, taking into account the factors of weather, traffic jams, and other emergencies. As a result, your products are more likely to get to the point of sale unspoiled.
It is also interesting to find out the marketing-related Machine Learning use cases.
For example, apps like Crayon or BrightEdge can help you explore the most relevant, interesting, and conversionable topics for your blog posts. Market Brew can give tips on how to optimize your content to get the best SEO results.
In addition, Machine Learning applications can help you search for the most relevant keywords, correctly translate your web pages, show lead generation forms at the right time, and personalize your content.
Personalized offers development
However, it is not enough to personalize the content. It is necessary to personalize your marketing offers as well.
Machine Learning algorithms can make the most accurate assumptions about what each user may like by simply analyzing his behavior on the site and supplementing this knowledge with information from social networks, for example.
This is a classic example of what Machine Learning is useful for. And by the way, the recommendation engine increased sales on Amazon by 35% . Do you still doubt whether your business needs this technology?
Predictive analytics is the strength of machine learning. Churn prediction is just one of Machine Learning use cases when it comes to predicting intentions.
In fact, the system simply analyzes the behavior of the user and looks for signals that can indicate that someone intends to cross over to competitors.
All that remains for you is to get ahead of this intention with the help of an improved and personalized offer.
When it comes to finances, Machine Learning in business applications are becoming even more serious. Let’s take a look.
Money laundering and terrorism financing prevention
2-5% of world GDP is laundered. Machine Learning systems can help you identify whether a particular client is involved in money laundering and terrorist financing, and thereby protect your business plus contribute to a global victory over these crimes.
Credit card fraud prevention
The biggest damage to the banking system is credit card fraud. Machine Learning provides dozens of ways to deal with this phenomenon — by analyzing behavior, detecting anomalies, and studying data in real-time.
ATM fraud prevention
The face recognition technology that we have already talked about can help you ensure greater security by instantly identifying the face of the cardholder at the ATM. That is, even if the card and password are stolen, it becomes impossible to cash out.
Loan and investing risks evaluation
By investing, we want to win and earn. Machine Learning can help evaluate the feasibility and profitability of an investment based on data cleared of emotions.
Yes, the thirst for profit can overshadow the mind, but not in the case of technology. This is also true for credit risk assessment. By analyzing real solvency and predicting client intentions, it is possible to assess whether a credit transaction will be profitable or risky.
Insurance intentions prediction
Similarly, Machine Learning technologies may help to find out the real intentions of the client when signing an insurance contract, as well as recognize possible fraud.
I bet you will be surprised by these Machine Learning use cases in healthcare!
Smartphone self-diagnosis apps are a powerful combination of Machine Learning, Artificial Intelligence, wearable devices, and instant data transfer technologies. And of course, this is a significant saving of time and energy for doctors and clinic owners.
The ability to catch anomalies can be useful for more careful monitoring of narcotics trafficking in a hospital.
For example, you may not be aware that your doctor, nurse, pharmacist, and the patient started a scheme to launder drugs for the purpose of further sale. The Machine Learning algorithm is able to track down data traces and make a verdict.
Hospitality and Catering Industry
What interesting applications can Machine Learning offer for this industry? Here are some of them.
What we said for the retail sector is also true for the hotel and restaurant business. By analyzing huge amounts of data, it is helpful to get insights about what tourists and visitors will want in the near future and to develop new strategies in response.
The restaurant and hotel business produces the largest amount of food waste. The all-inclusive system is not always as profitable as it seems. Machine Learning allows you to organize the work of the hotel according to the principles of smart consumption.
For example, Winnow Solutions has developed a smart wastebasket that analyzes the content and determines which foods are thrown away most often so that you can build a more sensible meal strategy in a restaurant or hotel restaurant.
Chatbots and travel assistants
These are already familiar tools for enhancing user experience, which can work both on the website of your hotel and restaurant, and become a pleasant addition to the service already received.
For example, the Eddy Travels chatbot will easily do all the travel planning work in your place, without leaving your favorite messenger.
In fact, this is not a complete list of Machine Learning use cases. Moreover, the next use case can become yours, bringing profit to your company.
Mentalstack is ready to develop an innovative ML & AI solution customized for your business needs — so don’t wait, contact us now!