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Predictive Analytics: What It Is and How It Works

You may have heard the term ‘predictive analytics’ being often used when talking about digital marketing or data science. So, what does it mean, how will your business benefit from it, and how does it work exactly?

Read on for everything you need to know.

  What Is Predictive Analytics?

Put simply, it is a way of analyzing historical data in order to predict future events. Typically, companies will utilize historical data to build a mathematical model. Given data and the model, the computer can make a prediction of the future. These trends can refer to the immediate future, just seconds, minutes, hours or days ahead, or can look much further into the future. This model can then predict what will happen next, or suggest steps a business can take in order to best meet its goals.

Predictive analytics is a subset of data analytics, and within data analytics, there is also descriptive analytics, which is to leverage historical data to better understand what has happened in a business, meaning paint the picture of the past.

Predictive analytics uses a wide range of technologies like big data, data mining, statistical modeling and machine learning to crunch data in order to uncover trends and predict possible future events, so your business can plan accordingly. These predictions are rooted in data, giving them a lot more credence.

It is often used in today’s predictive marketing, which is a marketing technique that leverages data analytics to determine how likely a business’ marketing strategies and activities will succeed.

  The Benefits of Predictive Analytics

So why would a business use predictive analytics?

Predictive analytics lets companies use data to find patterns (for example, by uncovering relationships between various behavior factors) to predict the future. They can then exploit these patterns to either capitalize on an opportunity that will arise from a certain set of conditions, or to minimize risk.

This can apply across a whole range of businesses and a wide array of use cases. Retailers can use predictive analytics to predict demand for certain products, and hence tailor their supply chain requirements accordingly. It lets airlines forecast how popular certain flights will be, and so they can set the right prices to reflect the demand. Restaurants can also leverage it to anticipate how many diners they can expect on a particular night.

In the case of digital marketing, predictive analytics allow businesses to optimize their marketing campaigns in order to send out personalized messaging in the right timing. By doing so, they can generate new customer responses or purchases, and cross-sell to customers, helping them attract, retain and refine their customer base.

  Predictive Analytics in Action

Chances are many consumers have already experienced predictive analytics in their online activities, for instance through Amazon’s ‘frequently bought together’ feature – this recommends other items someone might like to purchase based on their purchase history and what they are currently buying. Buy a coffee machine, and it might recommend some coffee pods to go in it, for example.

Or a customer has received a marketing email promoting replacement heads for her electric toothbrush three months after she last bought them. Here, using predictive analytics to help you anticipate that your customer will need to order the replacement every three months, and so you can deliver an email reminder or an offer for loyal customers to address her need and keep her hooked.

This is the power of predictive analytics – it predicts consumer behavior to help you tailor your marketing messages accordingly, and better allocate your marketing spend.

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