Predictive analytics marketingHumans may not like to admit it but most of us are quite predictable. Unsurprisingly, technology has now found a way to predict this behaviour by using statistics and machine learning. These predictions can be made on both large and small scales.

Insights such as knowing the best time to spend on ads, or when your content will be most effective in reaching new potential customers can all be gathered by making use of predictive analytics. These insights allow for an increase in profits, time to be saved and losses to be prevented.

Google Analytics is a great example of a place to get started with predictive analytics. Free and easy to use it allows you to get to grips with your company’s data and then use this to tailor marketing campaigns.

Being successful with predictive analytics requires three steps:

  • Extract – Whether your data is coming from Facebook insights, Google Analytics, or another area is the first thing to do is to extract the data.
  • Refine – Usually carried out by data scientists, the raw data need cleansing to make sure it is something that you can use.
  • Utilise – Once you have this cleansed data, you can act upon it and begin making a difference to your business.


Despite all the ways in which predictive analytics can help, there are still questions on how accurate they actually are. This is quite a difficult question to answer, as the accuracy of predictions relies on the underlying data itself and the algorithms used with it.

Bad data is one of the main things that predictive analytics can’t help you with. If the data is bad to begin with, the predictions are more likely to be off and could end up doing your company more harm than good. If bad data is a constant with your company, steering clear of predictive analytics may be the best option.

If something has never happened before it can also be difficult to predict what happens next. When there is no data from the past to draw from, forecasting the future becomes much harder. An example of this can be seen with Malaysia’s recent political election. Here the government that had always been in power was voted out, and in came a new party. With no past data to draw on it was near enough impossible to determine what would happen next.

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