An Introduction to Machine Learning and Business

Machine learning is the primary tool that organisations use to leverage data to evaluate what people do.

The insights derived from machine learning can be used to predict how individuals behave under given sets of conditions.

Conclusions can then be made as to what action to take in relation to an individual’s behaviour.

In so doing machine learning can maximise the objectives of the organisation.

If you have a smart phone or credit card, if you buy stuff from the supermarket or play computer games, if you are employed or use healthcare, then machine learning will have a direct impact on your life.

Machine learning can be used to enhance all of our everyday lives via “artificially intelligent” machines and applications.


applications of machine learning
Applications of machine learning from AI-Initiative


Examples of such tools that benefit our lives are Apple’s Siri, Google Translate, Amazon’s Echo, Waze and Moovit. 

These are just three of the many tools that are being incorporated into all of our lives at an ever increasing rate. They appear in everyday tasks such as mortgage evaluations, credit checks, medical diagnoses and how we travel to and from the office.

It is important that we understand what machine learning is and the role that it plays in all of our lives.

It is precisely these tools that will decide whether we will be subject to a tax audit, a criminal record check, or whether we will receive a good retail offer or a bad one.

In so doing it will be possible to understand why an organisation treats us one way or another.

Main Uses for Machine Learning

Here is a short list of some of the main areas where machine learning is being used:

  • Research
  • Consumer behaviour analysis
  • Fraud prevention
  • Market projection / sales forecasting
  • Internet and IT security
  • Office automation

The table below shows how the tools derived from machine learning can be applied to specific industries that effect all of our lives:


how can data mining and machine learning help in business intelligence


Machine Learning and Business

Another important reason why we should understand machine learning is because it is now a mainstream business tool.

Any organisation that deals with makes decisions that influence people will be using tools derived from machine learning.

Machine learning can be used to solve business problems intelligently. In addition, it can improve company efficiency and address specific problems that businesses face at an early stage.

Machine learning and predictive analytics can be used to make predictions about customer behaviour.

From a marketing perspective these insights can be enormously beneficial when it comes to maximising conversion rates and developing future products.

Optimove, for example, is a powerful predictive modelling tool used in email marketing that applies machine learning to analyse customer behaviour and value.


optimove-benefits of customer lifetime value


With Optimove the marketer can understand and predict customer evolution. They will then have the power to improve customer retention by better engaging customers, influencing behaviour and increasing customer lifetime value.

What Is a Data Scientist?

Data scientists work with enormous amounts of data.

The data could be already organised or it could be completely unstructured.

Either way they use their skills as mathematicians, statisticians and programmers to clean, decipher and organise it.

They then apply knowledge of their industry and businesses that they work.  A healthy dose of skepticism of existing assumptions is also a useful plus.

When done correctly data science can uncover previously hidden solutions and potential challenges that businesses may face.

Below is an infographic form The Marketing Distillery, dealing the full range of skills that a data scientist should possess:


data scientist job skills

Data Scientists and Business Management

The data scientist must be able to engage with the business users to understand what problems they are facing.

There must be an understanding of the culture within a company. This would include how amenable it is to new ideas, and technology. Furthermore, the data scientist must align with the legal framework and codes of practice that the business adheres to.

No matter how effective a technological innovation may be if it doesn’t conform to business practices and objectives it is likely to be a waste of time and money.

This is because machine learning is a two way relationship. A data scientist must understand how an organisation operates. Accordingly the members of the organisations need to know a little of how machine learning works.

Without this agreement it is unlikely that an organisation will be able to apply the full benefits of machine learning to work to its advantage.

According to a 2016 study by CBS Interactive 42% of respondents said that their company lacked the knowledge and understanding of machine learning to be able to implement it effectively.

Take a look at the CBS Interactive infographic below for a few more interesting facts about Machine Learning:

machine learning and business

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