Companies don’t always understand the advantages of Machine Learning, which in turn prevents them from starting to implement it. In fact, the lack of information and understanding is the key issue. We explained the advantages of Machine Learning in our previous blog article. Integrating Machine Learning into your company is a complicated task as it requires a hands-on approach and getting used to working with innovative technology. Innovation carries risks, it always does, but we are more than convinced that Machine Learning is here to stay.

This article is a guide that will lead you through the process and help you understand how you can start to implement Machine Learning in your company. These are the key principles.

1. Start with something simple

Sometimes companies, even larger ones, aren’t even trying to predict the number of customers that are going to unsubscribe from their services (also known as churn rate). They usually focus their efforts on getting more customers to sign up, without paying attention to those who leave. Companies don’t realize that they have sufficient data to be able to predict the number of leavers. The paradigm shift is very valuable: the economic cost of maintaining a customer is much lower than the cost of acquiring a new one. The prediction of casualties is a good way to start. The objective of these initial projects is to have quick-wins that help the business to understand the possibilities that are open to them.

This also helps companies identify the technological areas where Machine Learning can be helpful and how they can integrate it into their systems with ease. 

four people having a meeting in a conference room.

2. Start with Supervised Machine Learning

Supervised Machine Learning allows predictions in a simple way using historical data. The word "supervised" has nothing to do with a human "reviewing" the predictive algorithm, it merely refers to one of the possible Machine Learning techniques. With Supervised Machine Learning you can:

  • Predict demand (how much products or services will be purchased during a particular time frame say a week)

  • Predict customer escape (how many customers are going to opt to buy products/services from your competitors)

  • Detect fraud (which purchases or transactions are fraudulent)

  • Predict cancellations (of hotel reservations, medical appointments, etc)

  • Prevent delinquencies (predict if a customer is not going to pay)

The main advantages of Supervised Machine Learning are that it is easier to understand, answers specific questions (such as those in the previous paragraph) and has powerful methods to evaluate the quality of the algorithms before putting them into production. There is no doubt: it is the perfect technique to start if you’re looking to incorporate Machine Learning.

3. Don't start with Big Data

Working with Big Data is very expensive, and many companies don’t have adequate infrastructure to store a huge amount of information when they decide to enter the world of Big Data. This processing can take hours due to its high volume. But to use Machine Learning doesn’t necessarily mean to have such a huge amount of information at your disposal. Based on experience, companies currently have more than enough data to generate high-quality predictive algorithms.  It is more important to have good data than to have a lot of data.

A man and woman collaborating on a laptop in a professional office setting.

There are companies that have data storage problems. Big Data is a trend now (although it is becoming out-dated) and they collect more and more data - the more the better; but then "We'll see what we do with it" follows and this is where the problem arises. This approach is completely wrong! Companies usually have enough data to start working on interesting and exciting projects that can add value to the business, without having to store additional large quantities of data.

4. Use Machine Learning in the cloud

If you’re looking at Machine learning from a  ‘geeky’, or rather a technological point of view, it is even better with programming in Python or R. Well, we can’t agree with that, at least not entirely. And we have a few strong reasons for that. First, it is necessary to have highly specialized professionals focused on programming and algorithm tasks. Secondly, the algorithms programmed in these languages ​​are complicated, it is difficult to put them into production and they are hardly reusable, and finally, cloud platforms decrease costs dramatically.

Cloud platforms with API-based systems facilitate the reuse of algorithms by residing in one place, with access to their functionalities, not their code.

Large companies such as Facebook, Amazon or Uber are already implementing Machine Learning systems in the cloud internally and this serves them as an additional infrastructure in the company. Machine Learning systems are being incorporated as engines accessible by any employee, in the same way, that, years ago, companies had a database service available to any department (or programmer). 

5. And above all, start now

Your competition may already be taking advantage of Machine Learning. Yes, now is the time to get started. The advantages in areas such as tourism, retail, banking or insurance are indisputable and there are plenty of other possibilities that we are not yet aware of. The particular industry is irrelevant here, you can benefit from Machine Learning regardless of what sort of date you’re dealing with. We already know that it is not necessary to gather copious amounts to create high-value applications and improve your business.

data team meeting in a conference room

The emergence of cloud platforms and their increased availability decreased costs dramatically. The things that large companies like Apple, Amazon, and Facebook do, such as predicting how many customers they’re going to lose next month or if the demand for products and services is going to increase or decrease, are already available to other companies as well. Are you ready to start now? Contact us!