Why use predictive analysis models for better decision-making

predictive analysis models

In an era where data has become the new oil, it is paramount to have the right techniques, models and tools for processing the 2.5 quintillion bytes of data produced regularly. Predictive data analytics is a technology that can anticipate future trends. It is an evolution of earlier data analytics models and works by predicting what will happen in the future by analysing historical data, discovering patterns and using that information to draw up predictions about the overall direction of the industry.

Predictive analysis models and findings are powered by machine learning and artificial intelligence. From customer service to social media to FinTech, predictive analysis models are playing a crucial role in driving what you see and when you see it. The technology offers incredible accuracy, making it a reliable tool for businesses across different industries, especially large corporations, working with terabytes of stored data.

We already see predictive data analytics in use – particularly, in service-based organisations. Those who browse the AliExpress product page will notice sections titled, “People who bought this, also bought…” to sell additional products, while platforms, like YouTube, recommend videos based on what users already viewed. Predictive analytics powers these features, and they are very useful because it allows platforms to upsell and cross-sell to customers, boosting sales and revenue. Due to the potential of predictive analytics models, organisations across different industries are investing in predictive analytics to cut costs and boost revenue.

How does predictive analytics actually help in better decision-making?

Smarter decisions powered by predictive analytics

The amalgamation of an increasingly complicated world, the vast proliferation of data and the pressing desire to stay at the forefront of competition have prompted organisations to focus on using analytics for driving strategic business decisions. Rather than “going with intuition” when maintaining inventory, pricing solutions or hiring talent, organisations are embracing analytics and systematic statistical reasoning to make decisions that improve efficiency, risk management and profits.

From personalising products and services to scaling digital platforms to match buyers and sellers, organisations are using predictive analysis models to enable faster and fact-based, decision-making. In fact, studies show that data-driven organisations that employ predictive analytics not only make better strategic decisions, but also enjoy higher operational efficiency, improved customer satisfaction, along with robust profit and revenue levels. Recent research also shows that data-centered organisations are twenty-three times more likely to acquire customers, six times as likely to retain those customers, and as a result, nineteen times as likely to be profitable.

Making the most out of consumer patterns to make the right decisions

In an increasingly customer-oriented era, organisations have amassed a wealth of consumer information and data. In order to remain competitive, it is imperative for organisations to use these consumer insights to shape their products, solutions and buying experiences. Research from Mckinsey suggests that organisations that are using predictive consumer behaviour insights strategically are outperforming their peers by eighty-five per cent in sales growth margins and by more than twenty-five per cent in gross margins. Hence, it is important for managers to consider the strategic importance of consumer information.

A telecom company, for instance, can use advanced and predictive analytical models to reduce customer churn and measure the effectiveness of marketing campaigns. Similarly, an online retailer can assess current market share online by seeking answers to questions such as the mix of new and returning visitors, bounce rate and average session duration. Such questions offer crucial insights into the type of content and channels that are likely to have the greatest impact on key consumer segments.

Predictive analysis models for better decision-making

In this volatile environment of data-driven disruption, business managers need to look through two lenses at the same time. Firstly, they have to identify high risk, rewarding opportunities like entering new markets and changing established business models. Secondly, they have to maintain their focus on incorporating analytics into their core business decision-making process. By embedding predictive analysis models into their core strategy, business managers can streamline internal business processes, identify unfolding consumer trends, monitor emerging risks, and build mechanisms for improvement. Driving analytical transformations will, thereby, enable companies to gain a competitive edge and stay at the forefront of digital disruption.

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