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Should your e-commerce business by using predictive analytics?

Data analytics for ecommerce business is evolving rapidly. One of the latest buzzwords is predictive analytics. So, should you be using this as a tool to further your reach and revenue and what are the drawbacks to predictive analytics? We’ve got the basics to help you decide if it’s something you should be utilising in your business now.

What is predictive analytics?

First up, you need to get to grips with what predictive analytics is. It’s a branch of advanced analytics that aims to make predictions based on unknown future events. It encompasses many different data analytic techniques, from data mining through to artificial intelligence, in order to use past data to make predictions about the future. The process identifies patterns to perceive both opportunities and risks for businesses.

Among the main benefits of using predictive analysis are:

  • Act on data patterns – Data patterns give you an insight into the future by identifying a range of trends that you may have otherwise missed. With an idea of how a pattern will continue, you’re in a position to improve the stock you hold and market effectively.
  • Reduce risk – Predictive analysis also highlights potential risks, allowing you to take action to mitigate them as much as possible. Whether it’s a projected slump in sales or detecting fraud, it can ensure that your business is more robust.
  • Create effective customer profiles – The more you understand your customers, the better you’re able to create a loyal consumer base. Predictive analysis can give you a greater insight into behaviours and the reason behind them.
  • Give decision makers confidence – Predictive analysis places far more data at your fingertips, providing support for decision makers to back up the steps they take. The added confidence in their choices can deliver a significant boost to business.

While the benefits make predictive analysis a clear choice for businesses to take advantage of, it’s not always the right option. Firstly, to be effective, predictive analysis requires huge datasets to work from, allowing it to identify common patterns. Without large amounts of data to work with, predictive analysis results can be limited and are more likely to be inaccurate.

Even when performed correctly, it’s important to recognise the limitations of predictive analysis. Within any industry, there are a huge number of variables that can influence patterns, signalling the need to look beyond just predictive analysis. Consumer behaviour also changes over time and some datasets may be unable to capture this, focusing on how consumers have behaved in the past rather than how they behave now, it’s a particular challenge for industries that rapidly change.

If you have any questions about how best to deploy predictive analytics into your business, then get in touch.