The concept of artificial intelligence (AI) has been around for a while, but with the more recent rise of both machine learning (ML) and deep learning, it’s still a buzzword. These three are often used interchangeably, and thought of as the same thing. However, there is a significant difference between the three, which is valuable to know, especially when using the terms in conversation with people you want to impress!
What is AI?
Think Ex Machina or C-3PO. It’s computational systems that behave like a human, or imitates human behaviour.
What is Machine Learning?
According to Stanford University, “Machine learning is the science of getting computers to act without being explicitly programmed.” Machine learning is how machines would analyse data, not according to very specific pre-set models, but by trying different actions and remembering the outcome. The computer can recognise patterns in large sets of data and make accurate predictions.
Read more in our blog post What is machine Learning Learning? And Other FAQs…
What is Deep Learning?
Deep learning is a technique for implementing ML, or rather a form of ML. It’s inspired by the human brain and uses a layered structure of algorithms called an artificial neural network. It’s very effective in feature recognition.
How does ML or Deep Learning relate to AI?
In developing computation al systems like AI, the computer needs to learn. This is where AI comes in. ML and deep learning is used to automate decision-making, as well as actions taken. The more automation (thus the more ML), the closer we get to AI.
What are the business applications of ML?
Machine learning or deep learning is used for content or product recommendation by many companies, including Netflix and Amazon. Google has recently used machine learning for automated translations, and we’ve all heard about self-driving cars. Machine learning is also used by Google to optimise their search engines to show you the most relevant result for a search, and not just by keyword match.
But for those of us not working at the most innovative companies in Silicon Valley, ML still has value.
Machine learning can be used for customer lifetime value or churn modelling. You can pre-empt customer’s leaving your brand and, based on data analysis, evaluate whether it’s worth spending time, money and effort on retaining or re-activating them. Machine learning can also predict which customers would be worth the effort, but would require a special offer or discount. This can increase your marketing ROI and retention rates through improved targeting and optimised prioritisation.
It’s also useful for customer or lead segmentation: instead of trudging through a large list of prospects, you can identify which leads would be most likely to respond to your offer, but also have a low risk of defaulting. This allows you to spend your time, effort and money only on those who have a high probability of signing on. Your data could also tell you which customers to target with cross-sell or up-sell offers, and which would be most likely to take up your offer.
Read more about using machine learning and data analytics for lead acquisition strategies.
You can use machine learning to identify the right time to call sales prospects, and when they would not only be most reachable, but also most likely to take up your offer. The same can be done for debt collection: machine learning can identify when you are most likely to reach each of your debtors. You could also determine each debtor’s propensity to pay or to roll, and use this information to prioritise and segment calls.
That’s just the start: the sky is the limit with machine learning. With the right models and data, you could use machine learning’s powers for a range of things in your business. Find out more about Machine Learning as a Service if you are interested in using ML to improve your business without the infrastructure cost or investment.