Predictive analytics is a powerful analytical method which uses historical data to predict what will happen next. It’s all about spotting trends and patterns in data and using this to make informed predictions. However, it’s not just about predicting what will happen next. Predictive analytics becomes most useful when you can then act on that insight to change the pattern going forwards.
Predictive analytics in practice
One way of implementing predictive analytics is through machine learning models. Predictive modelling is a way of using algorithms to spot those trends and patterns in your data, and automatically make those predictions.
Mapping data in this way allows you to discover different correlations and understand how different factors can affect that trajectory. You can use this insight to encourage a positive outcome or to mitigate negative scenarios.
Here are a few examples of what you might use predictive analytics for:
Demand forecasting
You might use predictive analytics to spot peaks in demand and understand what is causing those peaks. In this scenario, predictions can help you manage inventory requirements, ensuring you have enough stock so you don’t miss out on sales.
Customer lifetime value
Customer lifetime value (CLV) modelling is another example of predictive analytics. It enables you to identify which customers are likely to spend more with you so you can nurture them in a way to maximise this potential. For example, you may launch a loyalty programme for customers with high lifetime value to keep them engaged.
Propensity modelling
Propensity modelling can help you understand how likely a customer is to do something. By identifying behaviours that, from past experiences, have led to a particular action, you can intervene and influence an outcome. For example, you may have noticed contacts that unsubscribe from your communications typically ignore 5 emails in a row before unsubscribing. Based on this, you could identify which people are at the highest risk of unsubscribing and communicate with them in a different way.
Predictive analytics is even used in some circumstances to detect and prevent fraud. It can be used to identify out-of-the-ordinary occurrences or behaviours so you can intervene before any damage occurs.
Where do I start with predictive analytics?
As a general guide, there are three questions you should ask yourself to get started with predictive analytics:
- What business questions am I trying to answer?
- Do I have the data to support this?
- Once I have my prediction, can I do anything about it?
For example, if you’re looking to increase subscription reactivations, a predictive model could help identify the people in your database who are most likely to reactivate. Once you’ve got your target segment, you can decide how best to communicate with them.
It’s also key to remember that effective predictive analytics relies on accurate, up-to-date data to make accurate projections. So, before you start to use your data for decision-making, you should carry out a data quality review (DQR).
Finally, consider which mapping system or programme you’re going to use to help you. Many of our clients use Apteco FastStats for statistical modelling as they find it invaluable in helping them to understand and visualise their data. But, there are other ways to build models depending on where your data is sitting at the moment. You may need a model built in another tool to integrate with your existing systems.
If you need help getting started on your predictive analytics journey, get in touch with our team of experts here.
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