Find out how Machine Learning can predict churn, segment users into easily targetable groups, and personalize your marketing interactions
Gone are the days when computers needed a highly complicated set of instructions to process even the simplest of tasks. Over the last few years we have witnessed impressive advances in computational power and it is now possible for computers to emulate and even outperform humans at specific tasks. Machine learning isn’t restricted to the upper echelons of academia, and is actually a relatively simple concept to understand when explained thoroughly. This article will break down machine learning as a concept, and explain how you can utilise machine learning to increase the effectivity of your marketing efforts.
What is Machine Learning?
Machine learning involves computer algorithms which build ‘machine learning models’ out of large amounts of data. These algorithms then identify patterns in the data which can be utilised in various fields. For example, machine learning would be able to analyse the data from purchases made in a large supermarket chain. You would be able to distinguish which items are bought together frequently, and adjust the supermarket layout accordingly to increase sales. This explains why you often see snacks at the sides of the alcohol aisle in supermarkets.
However, this isn’t the only way machine learning can be utilised. The pattern recognition which stems from the machine learning algorithms is not limited to product data. Machine learning algorithms also work for areas in the field of technology, such as facial recognition. On Facebook, when you tag one of your friends in a photo this labels the data for the machine learning algorithms and provides it with the parameters for success. Facebook’s machine learning algorithms will prompt you to tag them the next time you upload a photo of them, as it recognises the patterns in the data.
Machine learning is most effective when you have a volume of data which simply cannot be processed by humans effectively. If an app has thousands, or even millions of users, it is difficult to understand their behavior. A large volume of users equates to a large volume of data. It is often not possible for humans to effectively gauge the patterns in said data. Machine learning algorithms are able to automate this process, by analysing the data and establishing patterns for you.
How is Machine Learning different from other kinds of programming?
Machine learning is different from other kinds of programming in one important way. Normally, it is necessary to provide exact instructions on how to solve a given problem when working with a computer. In reference to user behavior, this would mean specifying what every click on a website or in an app means. However, when utilising machine learning algorithms, we instead instruct the computer to analyse historic data which allows it to solve problems for us. It is critical to distinguish that by doing so, we are not instructing the computer how to solve the problem at hand.
When discussing machine learning algorithms, it is essential to note that there are multiple methods which can be utilised. Each method differs in their core principles and can be used for different tasks. Two of the most prominent methods of machine learning are known as ‘supervised learning’ and ‘unsupervised learning’.
What is Supervised Learning?
This method of machine learning involves utilising machine learning algorithms which are trained using ‘labelled examples’. ‘Labelled examples’ in this case could involve any sort of data, however, the given data must have been marked prior with a class, tag, etc. For example, a label which may be used is a churned user or an engaged user. Following on from this, the machine learning algorithm will then receive the relevant data set which you are looking to analyse and then categorise each input as either ‘X’ or ‘Y’ (in this case it would be a churned user or an engaged user). The algorithm will then allow the machine to learn by comparing its own outputs with the given correct outputs and adjusting the model appropriately. When this process is completed more and more the algorithms will produce much more precise results. The supervised learning model is commonly used to utilise past data and predict future events. At goedle.io, we utilise this machine learning method to effectively predict when a user is likely to churn from your product and allow you to take targeted action to try and prevent them churning before it is too late.
What is Unsupervised Learning?
Unlike supervised learning, this method of machine learning does not involve labelling the data which is input into the computer algorithms at all. Instead, the bank of data is simply input into the computer and the machine learning algorithms are then tasked with interpreting what exactly it is being shown. Over time the computer will recognise structure and patterns in the data without any human input – hence the name unsupervised learning. This division of machine learning is highly effective in segmenting users together based upon similar attributes. With goedle.io’s machine learning algorithms, we can segment your users together based upon their attributes so that they are then easily targeted with your personalised marketing campaigns.
Is Machine Learning a new concept?
Machine Learning has been around since the 1950’s, albeit a much more simplistic iteration. It has only recently rose into prominence in the technological landscape due to a combination of different factors, one of which being the vast improvements we have seen in computational power. As computational power increases so does the threshold for the amount of data which can be successfully analysed, making machine learning much more effective and precise.
In addition to this, we have a much larger volume of data available due to the prominence of the internet in our daily lives. Data is at the forefront of machine learning. You may have impressive machine learning algorithms but if you do not stream solid, relevant data into the algorithms then your results will be lacking in quality and precision. Compared with 20 years ago, data was not nearly as easily accessible, meaning that machine learning was not nearly as effective.
How is Machine Learning used in marketing?
Machine learning can be used to assist marketing in a variety of different ways. Marketers know that retention is king, and they can utilise machine learning to increase their product’s retention rates. At goedle.io we employ machine learning algorithms to predict when your users are at a high risk of churning. This can prove to be a valuable asset to your company, as even a 5% increase in your retention can raise your profits by 95%! We utilise predictive analytics to analyse the behaviour of your user base which allows us to detect users which users are likely to churn in the near future. Our machine learning algorithms will allow us to reach these users before it is too late, and retain them as a valuable customer for your company.
One of the biggest issues marketers have is targeting the correct users with their marketing campaigns. Not only is it hard to target the correct users, it is also essential that your marketing campaigns are personalised so your message resonates with the recipients. Our segmentation software automates this process for you. The machine learning algorithms which goedle.io use will segment your users into easily targetable groups for you. Not only do we provide your marketers with the users which need to be targeted, we also provide them with actionable insights from their previous marketing campaigns and helpful tips to increase the effectivity of future campaigns. We help you to build your perfect engagement campaigns, making it easy and simple for your marketers.
As mentioned earlier, a wealth of relevant data is essential for successful machine learning projects, as more data equates to more intelligent predictions and concrete pattern recognition. Traditionally, the scale of analytics which is now possible with the inclusion of machine learning was exclusively available to larger enterprises. This was because they had the money and manpower to utilise their user data properly. Couple that with the fact that storage is becoming increasingly cheaper and the resurgence of machine learning becomes increasingly evident. It appears that the time is now right for us to fully utilise the potential which machine learning presents.