Guest Blogger

By Jeff Keyes, VP of Product at Plutora

Everyone uses and produces data – fact. Organisations are the biggest culprits, not only generating huge amounts of their own data, but also storing that of customers, and even anyone who interacts with them as little as one time. Regulations such as GDPR and the California Privacy Act are helping to ensure that businesses keep their data in check and look after it responsibly, but there is no stopping the sheer volume of data that is continually being created every second across the globe. This may seem daunting to many, and may even put off some businesses from collecting any data about their customers that is not absolutely essential.

However, all businesses are – or should be – striving towards exemplary customer experience in every interaction, and this is where all that data they’ve collected becomes useful. Predictive analytics helps companies to anticipate the future, meaning they can meet and ideally exceed customer expectations while preserving and improving the customer experience.

Getting to grips with predictive analytics

The practice of predictive analytics is how you can use data analysis to determine what may happen in the future. In doing so, organisations can discover better ways to serve customers and identify how many items to keep in inventory or detect fraud as it’s occurring, for example. Predictive analytics combines several data analysis techniques, including machine learning, data mining, statistics, and artificial intelligence to analyse data and develop an understanding of how past actions and behaviours can impact future outcomes.

In the business world, predictive analytics is rapidly gaining momentum, because when businesses execute predictive analytics effectively, they become more agile, more efficient, and – most importantly – more profitable.

Despite this, it is fair to say the current uptake of predictive analytics is still in its infancy, partly due to a few main obstacles. In this age of cloud computing, businesses collect and store more data than ever before. As a result, it becomes more difficult to create machine learning algorithms that can effectively answer questions and discover new opportunities.

The recent rise of predictive analytics may suggest that business analytics overall will be improved, but that’s not always the case. In reality, few companies are able to successfully understand all the data they’re collecting. This is largely because the technology required to interpret it is complex and resource intensive. Additionally, many organisations struggle with data silos. A company might have the perfect data set, but if it’s hidden away in some unknown repository it provides limited value to the business.

Regardless, some companies are still pushing forward with predictive analytics, and therefore it’s key to understand how it works.

Analytics for every business

For predictive analytics to share insights about what may come, it utilises a predictive model and an agreed outcome – such as a customer purchase – to calculate what is most likely to happen next. This model then inspires the development of a new model. For instance, one that might predict future customer purchase behaviours. Next, the model is used to predict future outcomes in relation to additional input variables (such as time of day or weather).

There are multiple types of techniques and models:

  • Decision Trees – A visual chart that demonstrates the likelihood of every potential outcome of a decision, generally resembling a tree shape.
  • Regression Algorithms – Can predict a numerical value, such as how many days will pass before a customer makes a repeat purchase, or how much money a customer will likely spend on a future purchase over a certain period of time.
  • Classification Algorithms – Can predict whether the subject in question is a member of a specific group or not, such as an immediate buyer looking for something specific or if they are just browsing.
  • Neural Networks – Also known as Artificial Neural Networks (ANN), these are highly advanced data processing techniques modelled after the human brain. ANNs power today’s most advanced technologies, including facial recognition and text-to-speech software.

Simple benefits for a big difference

Though the number of businesses that currently use predictive analytics is still small, the rapid increase in uptake is due to the benefits that they need to keep ahead of the competition.

  1. Cost savings

Businesses that can predict customer demand more accurately can reduce costs by having the right amount of inventory on hand. Furthermore, when a business understands who its ideal customer is, it can use that data to better target its marketing campaigns towards that ideal customer.

  1. Fraud detection

In an age of ever-increasing cybersecurity threats, predictive analytics helps businesses detect malicious activity across their web platforms. Businesses can quickly react to these threats and protect themselves before any damage is done. Through this, predictive analytics can save a considerable amount of money while keeping customer data security and brand integrity intact.

  1. Increased Efficiency

In the long term, predictive analytics can also be used to increase business efficiency. By analysing seasonal and historical data (alongside other influential factors), companies can discover when the busiest times of year are (e.g. an airline could predict peak times for ticket sales). A business that can predict what customers want can have their products ready the moment they want them, leading to higher satisfaction.

Predictive analytics has a wealth of real-life uses that most people may not consider. For example, navigation devices like a car’s GPS system, your smartphone’s navigation app, or your favourite airline’s in-flight computer system. When any of these devices offer a prediction of your arrival time, that’s predictive analytics in action. Or in the healthcare industry, where predictive analytics is already being used to improve health outcomes for patients.

The best businesses will always be those that are the most informed about what their customers need and when they need it, whether these are products or services. Predictive analytics is an exciting new technology that – though currently not widely used – will likely make a huge impact on many industries in years to come. With a range of detailed insights into their data, businesses will be able to make smarter decisions not just for now, but for the future as well.

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