Machine learning for business
The purpose of machine learning is to discover patterns in your data and then make predictions based on often complex patterns to answer business questions, detect and analyse trends and help solve problems.
Machine learning is effectively a method of data analysis that works by automating the process of building data models.
Machine Intelligence And Artificial Intelligence
Machine learning in business and other fields such as healthcare and governmental departments is not simply another term for AI (Artificial Intelligence). AI is the broad term given for machines emulating human abilities while machine learning is a particular branch of AI where machines are trained to learn; another description is the term ‘machine intelligence’.
The objective of machine learning is to make use of the ever increasing amounts of data being collected and to manipulate and analyse it without heavy human input.
Machine intelligence enables complex and larger data to be processed, analysed and the desired results to be achieved such as determining customer trends, detecting fraud or whatever the primary objective is.
Machine learning in business is therefore an important commercial benefit.
It’s highly likely that machine learning can be put to use in your business.
Our data science team use specialised machine learning techniques to predict the answers to our clients’ problems. Using a combination of state-of-the-art extreme-gradient-boosting machine and generalized linear modelling learning algorithms, our clients receive accurate predictions to their business questions.
How Machine Learning Is Used
The objective of machine learning varies depending on what field it’s deployed in. Some examples include:
Financial services – data can be analysed and machine intelligence can help spot investment trends so helping investors plan their trading and for institutions to prevent fraud.
Governmental – machine intelligence can help identify ways for cost savings to be made and increases in efficiency achieved.
Health – data can be analysed to identify trends and improve diagnosis; the increase in wearable tech and sensors produce considerable data about patients that machine intelligence can make use of.
Retail – the objective of machine learning is usually to help retailers understand their customers better and personalise their interactions; websites recommending purchases based on the customer’s buying history or pages listed is a classic case of machine learning in action.
How To Implement Machine Learning
Working with you to understand your desired objective of machine learning, we can build small training sets of data that can then be applied to all of your data automatically.
For example, by understanding the characteristics and behaviour of your best and worst clients, we can use these as a training set that can be applied against larger data sets. The result will be to sort prospective clients into those with a high probability of being a ‘best’ profitable client and those who may need further nurturing through relationship building.
Machine learning can also be used to:
- Compete Intelligently
- Enhance Customer Service
- Improve Lead Nurturing
- Manage your Sales Funnel
- Detect Fraudulent Activity
- Outsmart the opposition
- Predict Journey times
- Predict how long jobs may take
- Score your prospects and customers
- Understand the geography of your market
You can find out more about the concept of machine learning in business by taking a look at our blog: “How to get your data to talk to you” or read on below to learn more about the technical side.
More About Machine Learning
As mentioned earlier, machine learning is a subset of AI that provides computers with the ability to learn without being specifically programmed. Machine learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis; a key offering in this area is Microsoft’s Azure Machine Learning
Machine learning models fall into two broad categories:
In supervised learning, the model is “trained” with a large volume of data, and algorithms are then used to predict an outcome from future inputs. Most supervised learning models use:
- Regression algorithms to compute an outcome from a continuous set of possible outcomes, for example, your score on a test, or
- Classification algorithms to compute the probability of an outcome from a finite set of possible outcomes; the objective of machine learning here might be to detect fraudulent credit card transactions or flag up spam e-mail.
In unsupervised learning, the computer isn’t trained initially in terms of being told the ‘right’ answer but is presented with a set of data and challenged to find relationships in it.
Talk to us today to see how machine learning can help in your business.