What is Predictive Analytics? An Introductory Guide

Jarmany | 01 July 2022

Predictive analytics has many proven benefits. From helping to optimise pricing to predicting the amount of demand for a particular product, this important tool has a track record of helping businesses gain a competitive edge.

The amount of data available to companies has exploded in recent years. Driven by the wide-scale digitisation of everyday processes, it’s now possible to gather information on an unprecedented scale — and this is a trend that looks set to continue.  

According to one survey:

  • The volume of data created, captured, and consumed worldwide in 2021 was approximately 79 zettabytes
  • By 2025, this number is predicted to grow to 181 zettabytes – a 129% increase1

Given the volumes of data they now have access to, businesses are searching for ways to leverage it in order to obtain a competitive advantage with well-informed, rapid decision-making that can ensure consistent and high-quality outcomes. That’s exactly what predictive analytics can deliver when implemented correctly. 

This article will offer an introduction to predictive analytics, examine how it works, and outline some of the benefits it can bring to your business.

Defining predictive analytics

Predictive analytics is the process driven by propensity modelling that looks to transform historical and current data into future insights. It uses a combination of artificial intelligence (AI) and machine learning (ML) techniques, which are able to identify patterns and trends in data.

When done right, predictive analytics offers a reliable, transparent, and accurate way to predict future events, trends, and outcomes — offering a competitive advantage to businesses that leverage it effectively. 

How does predictive analytics work?

Predictive analytics is a complex process that requires a good deal of planning and attention. Before you can think about results, it’s important to take a step back and understand what business goals you are looking to achieve. For example, your goal might be to:

  • Avoid future skills gaps
  • Understand how customer behaviour might change over time
  • Ensure the correct staffing levels to meet customer demand
  • Maximise revenue through optimal pricing
  • Drive operational efficiencies to ensure resources are distributed to the right place at the right time 

Whatever your goals may be, you’ll need to make sure you have the right data to model accurate predictions. Predictive analytics requires large data sets to generate clear, actionable insights. The cleaner, more accurate, and more complete the data, the more reliable those predictions will be. 

Once you have the right data, it is then fed into neural networks or statistical models designed to spot trends and predict or identify outcomes. For example, a predictive maintenance model assesses the chances of essential equipment breaking down, allowing you to foresee when replacements will be needed. 

Some other real-life examples of this process in action include: 

  • Airlines use predictive analytics to decide how many tickets to sell at a specific price for a flight
  • Hotels predict the future number of guests they can expect on any given night in order to adjust prices, maximise occupancy, and increase revenue
  • Marketers are able to determine cross-selling opportunities based on customer responses and purchases
  • Banks use it to determine a person’s credit score, affecting their eligibility for credit cards and loans
  • Insurance companies use it to understand the chances of a claim being made, allowing them to adjust premiums when pricing a policy

Predictive analytics best practices

Knowing how predictive analytics works and the outcomes you want to achieve is one thing, but it’s not enough to get the desired results. That requires skills, knowledge, and the implementation of processes that help drive success.

So, let’s take a look at some best practices that businesses can apply to maximise the effectiveness of predictive analytics, resulting in accurate and reliable outcomes that contribute toward a competitive advantage.

Ensure that your data sets are large and valid  

Predictive analytics relies on historical data to predict future outcomes. Over time, the machine-learning algorithms and models used to generate predictions fine-tune themselves, becoming increasingly more accurate. But the original data that you start with is all-important.

If you use data sets that are small, incomplete, or even invalid, the quality and accuracy of those predictions will be severely compromised. Furthermore, trusting predictions based on poor or incomplete data can have severe consequences for businesses, as decisions based on inaccurate data are likely to have adverse outcomes.

Before you embark on your predictive analytics journey, make sure that your data sets are large enough to work with. You can’t make accurate predictions based on a week’s worth of data. You should also ensure that the data sets you use are complete, clean, reliable, and valid. 

Identify and draw from the best data sources

Not all data is equal. In many cases, you’ll have data spread across multiple different sources, including a business's various platforms and systems. As a result, some datasets will inevitably be better quality or more appropriate than others.

In many cases, predictive analytics models have to be dynamic, meaning they take account of new data in real-time, adjusting their models and predictions accordingly. By identifying the most suitable data streams, you can work to guarantee the best predictive outcomes. 

Present predictions clearly

High-quality data visualisation is an essential component of effective predictive analytics. That’s because the predictions and insights generated need to be communicated in a way that is:

  • Clear
  • Easy to understand
  • Actionable

This is essential for all stakeholders — whether it’s the decision-maker whose job is to turn insights into action or the executive who wants a clear understanding of ROI. Presenting the analytics results in a straightforward and visually pleasing way helps ensure stakeholder buy-in and facilitates seamless decision-making processes.

The benefits of predictive analytics for business

In the words of Angela Ahrendts, the former senior vice president of retail at Apple, “consumer data will be the biggest differentiator in the next two or three years. Whoever unlocks the reams of data and uses it strategically will win.”2

So, the message is clear — businesses looking to stand out from the competition and drive long-term success need to find ways of utilising their data effectively. As we have outlined above, one of the best ways of doing just that is with predictive analytics.

But what are the actual benefits of predictive analytics? And how do these translate into successful outcomes for businesses? Let’s take a closer look. 

Improve decision-making

In a world of constant and rapid change, human experience and intuition are less reliable than ever as the basis for decision-making. While they have a role to play, decision-making needs to be informed by other factors, including data. 

Predictive analytics leverages massive data sets to predict trends and events before they happen, taking into account the multitude of competing factors that influence outcomes. The resulting insights allow you to make smarter, better-informed decisions about the future. 

Predict demand

Without a reliable way to predict demand for a particular product or service, it can be difficult to know how to organise and manage supply chains. Too little of a product and you’ll lose customers and revenue. Too much and you’ll waste time and resources. 

Predictive analytics takes into account current and historical trends in customer behaviour to allow you to meet supply with demand in the most cost-effective and efficient way.

Optimise pricing

Pricing goods and services aren’t as simple as it seems. You need to maximise revenue while remaining competitive. Set prices too high and customers will look elsewhere. Set them too low and you’ll cut into your profit margin. 

Predictive analytics allows you to set prices at the optimum level at any given moment, helping you to squeeze the maximum amount of revenue from your offering.

Improve customer retention

Customers are the lifeblood of any business. But in an ever-changing world, how do you keep them happy and loyal to your brand? Predictive analytics ensures that your customers stick around for longer by predicting:

  • Churn before it happens, allowing you to re-engage at-risk customers
  • Customer lifetime value, allowing you to focus your efforts where it matters most
  • What customers are likely to buy next, allowing you to target them with marketing campaigns and cross-selling opportunities

Reduce risk

Predictive analytics provides you with powerful insights into the likelihood of future events and how trends and behaviour may change. This allows you to prepare for the future, mitigate risk, and adapt to change seamlessly. 

Gain a competitive advantage

Gaining an accurate picture of the future is a game-changer. Predictive analytics helps you to see around corners, allowing you to increase efficiency and productivity. Put simply, businesses that leverage the power of predictive analysis stand to gain a competitive advantage over those that don’t.

Getting started with predictive analytics

Predictive analytics is a serious business. To be able to trust your data-driven and data-informed decisions, you need to be able to trust the insights that guide them. But such insights are only as accurate or relevant as the data used to generate them. 

Remember, the consequences of getting predictive analytics wrong are significant. Using poor data or carrying out poor analytics processes can result in poor decision-making and negative outcomes that set your business back considerably.

While it is possible to tackle predictive analytics in-house, it requires time, money, and data science expertise to get it right. In many cases, you’ll need to hire data analysts or upskill your current staff in the field of data analysis, diverting resources away from critical business objectives.

This is why it pays to partner with third-party providers like Jarmany. As one of the UK’s leading data analytics companies, we help our clients get the most out of their data, allowing them to enjoy all the benefits of predictive analytics without hassle or risk. We use cutting-edge analytics tools to deliver forecasting solutions that provide operational insights for some of the biggest companies in the world. If you'd like to find out how we could do the same for your organisation, get in touch with our team today.



  1. Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025
  2. Four forms of insights that are must-haves for your brand

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