Machine learning for business
The purpose of machine learning is to discover patterns in your data and then make predictions based on those often, complex patterns to answer business questions, and help solve problems.
Machine learning helps analyse your data and identify trends.
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 To Implement Machine Learning
Working with you to understand your desired goals, we can build small training sets of data that can then be applied to all of your data automatically. So 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 thought!
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 by taking a look at our blog: “How to get your data to talk to you.” Or read on to learn more about the technical side.
More About Machine Learning
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis. Microsoft offers Azure Machine Learning
Machine learning models fall into two broad categories:
- Supervised learning
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, for example, the probability that an e-mail is spam or a credit-card transaction is fraudulent.
- Unsupervised learning
In unsupervised learning, the computer isn’t trained, but is presented with a set of data and challenged to find relationships in it.