Have you ever watched a recommended video on YouTube? Have you ever noticed the effectiveness of the automatic corrector on your smartphone? If so, you have benefited from one or more machine learning applications. For several years now, companies have been exploring the development possibilities that Machine Learning can offer. In this article, I’ll list
Have you ever watched a recommended video on YouTube? Have you ever noticed the effectiveness of the automatic corrector on your smartphone? If so, you have benefited from one or more machine learning applications.
For several years now, companies have been exploring the development possibilities that Machine Learning can offer. In this article, I’ll list the competitive advantages that machine learning can bring you.
What is Machine Learning?
Before seeing all the benefits that machine learning can bring you, let’s start by defining it.
In practice, machine learning is all about understanding data and statistics. Simply put, it is a process where computer algorithms find patterns in the data and then predict likely outcomes.
This is, for example, the case when you receive an email; your email provider will scan for words in the subject line, links, etc. Following this analysis, it will classify the email as either spam or legitimate email.
In some cases, your email provider’s algorithms will be wrong, but what makes machine learning really useful is that the algorithm can “learn” and adapt its results based on new information. This means that when spammers change tactics, the machine quickly detects new models and correctly identifies questionable messages as SPAM again.
How Businesses Use Machine Learning
Email monitoring is just one small example of many. Machine learning is everywhere.
Machine Learning: Risk of Fraud
- When you use Google Translate, an algorithm translates this text into another workable text.
- PayPal uses different machine learning models to identify and predict cases of fraud.
- Facebook uses it to analyze photos and detect faces, then suggest that users tag people the algorithm finds in the image.
However, machine learning goes far beyond the examples we just mentioned.
It can be used to predict transportation traffic, illnesses, financial asset prices, hardware failures, etc.
The challenges of machine learning
All the uses of Machine Learning are very exciting and interesting. However, implementing machine learning in any organization poses challenges.
- The first is to understand the problem and determine what type of algorithm to use to solve the problem. For example, a classification algorithm can be used to classify a restaurant customer as being more likely to take a full menu or just a dish, but it cannot be used to predict the impact of price increases on sales.
- The second being the risk of data “over-learning”, which involves training the system to understand a set of data to the point where it loses all ability to generalize, learn, and make predictions based on new data. data.
Should Your Business Adopt Machine Learning?
When properly implemented, Machine Learning can help you solve huge problems in your business. Also, it can help you predict the behavior of customers and prospects in order to develop your business.
So if you can use machine learning to analyze data and make predictions that will help your business grow, why not do it?
To create a good machine learning system, you need:
- An understanding of Machine Learning.
- Knowledge of the different algorithms available and the types of problems they can solve.
- Data (from different sources; internal and external)
And don’t forget to make sure your business is following the big tech trends.
Here are 10 machine learning apps aimed at solving business problems and realizing real benefits:
1. Real-time chatbot agents
The first form of automation, chatbots are the symbol of human-machine interaction. They allow people to converse with machines capable of acting on requests or needs expressed vocally by humans. In the first generation, chatbots followed scripting rules that told them what to do based on keywords.
2. Decision Support
Decision support is another area where machine learning can be used to transform the plethora of data companies have into actionable insights and added value. In this case, algorithms trained on the historical data and other relevant datasets can analyze the information and evaluate the different scenarios, at a scale and at a speed unthinkable for humans, in order to recommend the best course to follow.
3. Recommendation engines for customers
Machine learning is at the heart of recommendation engines aimed at improving the shopping experience for customers and providing them with a personalized interface. In this case, the algorithms process the data points for an individual customer, their purchase history for example, but also other data sets (current store stock of the brand, demographic trends, purchase history of other customers) to determine which products and services to recommend to each.
4. Modeling customer attrition
Companies are also relying on artificial intelligence and machine learning to study the warning signs of attrition risk and attempt to address them. With ML, businesses can take hold of an age-old problem: loss of business.
5. Dynamic pricing
Companies can explore their historical pricing data, along with a host of other variables, to understand how certain dynamic factors (time of day, weather, and season) affect demand for goods and services. Machine learning algorithms exploit this data and combine the results with other market and consumer data, to help companies dynamically price their goods accordingly. A strategy that ends up paying off for businesses.
6. Market and customer segmentation study
Machine learning doesn’t just help businesses set their prices, it also helps them deliver the right products and services to the right places at the right time, including inventory planning and customer segmentation.
7. Fraud detection
8. Classification and image recognition
Companies are turning to machine learning, deep learning, and neural networks (a set of algorithms that facilitate the recognition of patterns) to best interpret images. This Machine Learning technology has many applications: Facebook which seeks to tag the photos published on its site, security teams in a hurry to identify faulty behavior in real-time and self-driving cars having to have a perfect view of the road, for example.
9. Operational efficiency
Although some machine learning applications are very specialized, many companies are implementing this technology to manage routine business processes, such as financial transactions and software development.
10. Information extraction
Combined with natural language processing, machine learning can automatically extract pieces of information from documents, even if the information sought is in an unstructured or semi-structured format.