The importance of predictive analytics in business intelligence

predictive analytics in business intelligence

Ever since the dot.com revolution, businesses have been exposed to more data than ever before. Today, modern businesses collect more data in a month than they did in the entirety of the 2000s.

That said, having access to vast amounts of data will become meaningless if a business doesn’t utilise it to create meaningful insights and make decisions that enhance its business functions. The business intelligence function helps businesses do just that.

Business intelligence spans technologies, processes, procedures that collect, integrate, analyse, and present data generated by businesses and their customers. Unlike traditional data analytics tools, BI tools present insights in a more coherent manner. 

With the right BI tools, businesses can leverage detailed insights that can help them make decisions regarding sales, marketing, product development, customer service and more.

Although traditional BI tools are positioned to provide insights on historical interactions or current happenings, with the integration of predictive analytics—which leverages historical data, statistical algorithms, and machine learning—BI solutions can give businesses a peek into the future. They do this by identifying opportunities and allowing businesses to be agile and proactive toward future developments. 

In this post, let’s explore the role of predictive analytics in business intelligence, and how businesses can leverage it to optimise their operations.

Predictive analytics improves service delivery

Optimising service delivery is one of the primary applications of predictive analytics in business intelligence. Businesses can create a better customer experience by studying past behaviours and preferences, and customising their service offerings to better suit the particular needs of each customer.

eCommerce websites like Amazon and eBay recommend products customers are likely to buy based on their past purchases and current searching behaviour. Netflix uses a similar approach to recommend new movies and TV shows to their subscribers based on their watchlist.

In short, businesses can improve their customer experience by leveraging predictive analytics.

It helps better police fraudulent activities

As long as businesses have existed, fraud has coexisted. This has led to significant financial losses to many businesses across the globe. A recent study revealed that, in 2019, fraud cost the global economy over $5 trillion—a figure that is expected to grow with the boom in digital interactions.

That said, not all industries are affected equally by business fraud—some industries are inherently more at risk than others. The insurance industry, for example, loses $80 billion annually, and in the UK, banks lost $620 million due to fraud in 2019.

Businesses need a robust solution to fight fraud, and business intelligence combined with predictive analytics could be that solution.

Unlike traditional fraud prevention methods, which rely on reactive measures to limit the damage caused by fraudulent practices, predictive analytics in business intelligence helps businesses identify potential fraud and proactively police their service delivery channels to prevent these transactions.

Predictive analytics in business intelligence helps optimise marketing efforts

Today, businesses have a wealth of information about their customers’ purchasing behaviour and preferences. With this information, predictive analytics can deduce the probability of a customer buying a product, which can, in turn, help businesses focus their marketing efforts on customers with a higher likelihood of purchasing their products.

Take ads on YouTube, for example. If a user’s watch history suggests that they are interested in learning about digital security, they will see at least one advertisement that markets VPN services the next time they are on YouTube. This is enabled by predictive analytics algorithms identifying the user as a potential customer for VPN services.

Predictive analytics can also help businesses keep the news cycle going during off-season months. Smartphone manufacturers, for example, identify months in which phone sales can slump due to loss of press, and release minor refreshes, new colours, or software updates to the existing models to keep the headlines talking about their products. Think of Apple releasing new colours halfway through the lifecycle of their latest iPhone model.

Leverage predictive analytics in business intelligence to boost your business

The cutthroat nature of the modern business landscape requires businesses to be on their toes to stay ahead of the competition. With business intelligence tools powered by predictive analytics, you can always stay one step ahead of your rivals.

Kaylene Dixon

Kaylene Dixon

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