Failure to adapt to the future has scuppered many a business. Blackberry never saw the touchscreen coming, Blockbuster didn’t plan on streaming’s success and Myspace were more short-sighted than Facebook.

Planning for the next corner is difficult in business but it’s not impossible. The data of today can reveal the secrets of tomorrow, so it’s worth analysing the world around you. This is where big data comes in to play.

Predictive analytics involves statistical analysis and algorithms of datasets in order to calculate how likely an outcome in the future is based on clues we have now.

The rise of digital

The use of social media, ecommerce and mobile technology has seen a surge in the amount of data being collected these days. So much data can be overwhelming though. The amount of Big Data is increasing exponentially: it’s not easy to analyse all of it, however, good data management and study can help you to make informed decisions.

Data is often categorised into three different kinds. There’s structured data, which is already stored in a database, ordered and able to extract from at any time. Unstructured data is not as easy to easy to derive value from and can contain text files, videos and images altogether. Semi-structured data is a mix of the two; it’s possible for data to be defined but not structured.

These challenges mean that many businesses struggle to properly harness predictive analytics. Predictions that are made from data are more likely to be informed guesses on how the future may play out.

Incorporating predictive analytics for leads

Big data analysis can be invaluable to businesses. It can reduce costs, improve your decision making and lead to exponential growth. It’s no wonder that so many companies want to harness predictive analytics.

Predictive analytics are used for a number of reasons. They can help map customer journeys for a start and recognise how customers respond to marketing initiatives, giving a business a better understanding of how customers purchase and how to convert prospective customers into paying customers. This is where big data is so important: assigning predictive analytics to customer data can help a business to spot the right patterns for how to offer customers a better experience.

Predictive analytics leads to data-driven lead scoring with the target customer in mind. It’s possible to score leads based on data that hints at behaviour and demographic, making it easier to find the customers who are more likely to resonate with your business.

Of course, not every lead needs nurturing in the same way. Predictive analytics can help with this too, offering customised approaches depending on the customer involved.

Improving distribution and value

Predictive analytics can also help you to improve the product you’re creating itself.

It does this by analysing the types of content that resonate most with customers of different backgrounds before distributing content that different demographics would enjoy. With a dataset that explains what content works well and what doesn’t, it’s easier to be more accurate than ever.

Predictive analytics can also identify new trends and opportunities for growth, providing insight into industries and giving businesses a chance to tailor and tweak products. The better the data analysis, the more prepared the company will be for the future.