It’s one thing correctly storing, processing and managing your data. But how do you translate that to actions that align with your overall business strategy? One such way is through machine learning analytics models, often referred to as simply ‘modelling’.
What is a machine learning model?
Although it’s forgivable to assume that machine learning is the same as AI, they are actually completely different processes. By its definition, machine learning is the implementation of computer algorithms that improve automatically through experience and sustained usage.
Essentially, it’s a file that has been trained to recognise certain types of patterns and use this information to work out a result. You might sometimes come across it shortened to ML or MLOPS (machine learning operations).
How do machine learning models work in analytics?
Analytics, as you probably already know, is a way of interpreting and breaking down data in order to gain a deeper understanding of what it is telling us. If you are interested in learning more about different types of analytic methods, take a look at 4 Analytic Methods and When to Use Them.
Analytical modelling is the process of identifying patterns and trends in a sample data set and using this knowledge to build a ‘model’. This model, when applied to another set of data, can predict or identify an outcome. The machine learning element of modelling means that the algorithm used to determine a result is continuously improving and adapting based on learnings from the ever-growing dataset, becoming increasingly more accurate.
Building your machine learning model
Before the model can be deployed, you must first ‘train’ it on a sub-set of data. This is the primary step in the construction and deployment of a machine learning analytical model. Using historical data, spread across multiple sources, allows the model to learn the patterns, anomalies, and correlations to meet the required business objectives.
Once the model has been ‘trained’ you then need to validate it using another set of data. In fact, you might want to run multiple validation samples to check and double-check your model is accurate. Then, once you’re happy your model is ready, you then apply the model to your data universe. That is, the larger dataset you want to analyse.
Once applied to the larger dataset, the model can recognise any recurring trends and patterns. The model then outputs these insights in a way that is helpful to you (e.g gives the lead a score).
What are the benefits of implementing a machine learning analytical model?
Built through programs such as Apteco Faststats, or in programming languages R or Python, most organisations use models to predict customer behaviour. For example, if your model can help you identify who in your customer base is about to churn, you can do something about this. You could implement an anti-lapse strategy by offering vouchers to those people. Or reach out to them with a new type of communication. Whatever makes sense for your product and your customers.
Similarly, if your model can highlight a small group of people who are showing buying signals, you could nurture these customers to increase conversion rates. You might want to offer them a time-limited deal. Or, simply remind them of a particular service they might be interested in.
Machine learning can also sharpen your ability to forecast and predict demand. From products of interest to future purchases. Modelling can help you manage your inventory and give you an accurate forecast of the products you need in stock.
And, all this is automated, which is important because it takes away the risk of human error. It also means you can manage and analyse large sets of customer data, adding triggers depending on the outcome.
Putting machine learning analytics into practice
Wanting to find a more efficient way of reaching their audience, one of our charity clients recently required our assistance to build a number of machine learning models. With our help, and the implementation of Apteco, the charity was able to predict:
- who would be most likely to make a cash donation
- which products would be most appropriate for each supporter
- who would be the most likely to purchase from their shop
This meant the charity could reach out only to the customers who were most likely to convert for each product. They also crafted specifically relevant messages based on what messages were most appropriate for each person. This meant happier supporters, as they only received communications about things they were really interested in. And, a happier charity as they were able to save money by mailing fewer people, and generate more funds for their cause.
Get in touch
Models exist to solve a business problem. If you’ve got a business problem you’re trying to solve and you want to know how models could help you – come and talk to us.
Or, if you need assistance in optimising a model you already have, we can help.
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