Making Better Decisions: How Can Predictive Analytics Improve a Business?

According to a Zion Market research report, the global predictive analytics market is projected to incrementally grow to about $26.3 billion by 2026, at a compound annual growth rate (CAGR) of approximately 21%.

That being said, for any modern corporate organisation in today’s knowledge-based economy, data is the cornerstone of innovation. However, this data only becomes meaningful when dissected and processed to allow decision-makers to assess their investment and business risk. This is where predictive analytics plays a key role.

This article seeks to explain what predictive analytics is, how it helps businesses and why it’s so essential for businesses to exploit this data analysis paradigm.  

What is predictive analytics?

Predictive analytics is a data analytics technique that involves programmatically leveraging confirmed relationships between explanatory and criterion variables derived from historical data, statistical modelling, deep learning, and machine learning to predict future events. 

Data AnalyticsIt is deployed in many fields, including healthcare, finance, and marketing, to detect trends and adapt policies or strategies accordingly.  

Steps involved in predictive analytics

Predictive analytics starts with identifying project deliverables, scope, and business objectives. Subsequently, relevant raw data is collated from different sources. 

The next step is data preparation. It involves inspecting, cleaning, organising and transforming the raw data into a consistent format before statistical analysis is performed to discover important information. 

Subsequently, statistical analysis is performed to validate the derived hypotheses, and data is tested using standard statistical models.

In this phase, predictive modelling tools are employed to build or generate accurate predictive models that are deployed to get results, reports and actionable output. 

In practice, the prediction model will learn from historical data and current data to predict the future. 

Lastly, the predictive model is frequently monitored to ensure it continuously outputs correct predictions.

Predictive analytics tools

It’s not enough to just have data. You need to know how to use that data effectively. You need a predictive analytics tool/platform that can help you make sense of the data so you can do more with it—and do better things with it.

Predictive analytics tools enable businesses to produce actionable information. they also help to make more intelligent decisions and predict future events by analysing the volume, veracity, speed, and value of large amounts of data. 

data analytics

Photo by Lukas

In practice, predictive analytics tools use algorithms that analyse data from different sources (such as social media) in order to find patterns or trends in one’s business data. These patterns can then be used for predictive modelling purposes—to predict what will happen next based on past trends and events.

Some of the most popular predictive analytics tools in the market used for everything from customer service to sales and marketing campaigns are:

Why is predictive analytics important to businesses?

Data is the lifeblood of a business. Without it, companies cannot fully grow or innovate.

Data is also incredibly important because it allows businesses to make better decisions than ever before—decisions that can have a real impact on their bottom line and even the world at large!

Read: Why is data driven analytics of interest to companies?

Predictive analytics leverages data to help businesses make better decisions. It does so by predicting customer behaviour, customer satisfaction and other factors that may affect a business.

In the past, predicting customer behaviour was difficult because it required large amounts of data on each individual customer and their interactions with the company. However, now that more companies have access to massive amounts of data through social media platforms like Facebook or Twitter.  

They can exploit predictive analytics to make more informed decisions about how they should tailor their products and services based on what customers are posting online.

Overall, predictive analytics enables businesses to be future-orientated, proactive, and forecast outputs and user behaviours based on data instead of assumptions. Additionally, it suggests actionable instructions that can be integrated into business applications to benefit users from its predictions. 

Applications of predictive analytics in business

1. Sales forecasting

Sales forecasting involves the analysis of prior sales, seasonality, and market-moving events to derive a realistic prediction of the demand for a service or product. 

Predictive analytics can facilitate sales forecasting by enabling businesses to predict customer behaviour by predicting what people will want next. 

This information can then be used to create new products that are more tailored to their needs (e.g., personalised offers).

2. Creating better marketing strategies

Predictive analytics has many uses beyond just helping organisations run more smoothly. It’s also useful for evaluating new marketing ideas before committing resources toward them fully. 

This is because there’s no way any human can possibly know everything about every possible marketing scenario beforehand. 

Predictive analytics can help determine customers’ purchase response and publicise cross-sell opportunities to attract, retain and increase valuable customers. 

3. Inventory management and demand forecasting

Overstocking inventory can be very costly to a business. On the other side of the coin, stock-outs may also have an adverse impact on both revenue and customer sentiment. So, predictive analytics can be exploited to adjust stock accordingly based on demand to avoid over or understocking scenarios. 

inventory management

Photo by Tiger Lily

4. Risk management and fraud detection

Predictive analytics can help identify problems before they occur so that fixes can be implemented before anything goes wrong. This is especially useful in operations management to optimise every moment of time and money spent on business activities.

In fact, predictive analytics-based decision-support systems can help estimate which operations are profitable, and which are not. Furthermore, it can also be employed to detect criminal behaviour and prevent frequent fraud occurrence via high-performing behavioural analytics that flags fraud actions, and zero-day cyber security vulnerabilities.

5. Customer experience and retention

Predictive analytics enables organisations to exploit their business data to focus on the right target audience and market segments they didn’t realise existed. This helps providing better service to different customer groups and enhance their overall experience of interacting with a brand. 

6. Human resources and talent management

Human resources (HR) can leverage predictive analytics to forecast future workforce needs and analyse employee data to identify elements that contribute to high turnover rates. It can also help HR departments to predict the career progression of employees whilst also designing diversity or inclusion initiatives.

Benefits of using predictive analytics

To understand customer behaviour

Businesses need to keep attracting new customers to avoid any loss in revenues. However, the cost of new customer acquisition is typically higher than retaining existing customers.

Predictive analytics can help prevent churn and improve retention of existing customers by identifying signs of discontentment and predicting which customer segments are most likely to leave. 

Consequently, businesses can analyse this information and take the necessary affirmative steps to enhance customer satisfaction.

Improved accuracy and efficiency of decision-making

Predictive analytics facilitates advanced decision-making by identifying patterns and trends to provide organisations with actionable insight that previously may have been unavailable. 

It can also be used in operations management to improve efficiency at work sites by streamlining processes and automating tasks, which may otherwise require manual effort. For example, handling inventory control or scheduling employees’ shifts around each other so they’re never left unreachable when needed most.

Better identification and management of business risks

It can be employed to construct accurate and reliable profiles of customers to aid with effective decision-making. For instance, to determine the creditworthiness of an individual and reduce the organisation’s risk. 

Enhanced customer experience and satisfaction

By leveraging predictive analytics, business entities can get an in-depth picture of who their customers really are and their needs. This insight can enable them to apply corrective actions in a timely manner to generate a considerable increase in revenue. 

Increased operational efficiency and cost savings

Predictive analytics also helps businesses save money by improving efficiency across departments within a company. 

For example, if a retail store has too many employees working at peak times during the day, this could result in fewer sales per hour than necessary. It is because there aren’t enough people available throughout the times when customers need something from them most urgently.  

Predictive analytics can help streamline operations and save time spent searching for employees who aren’t currently available either due to overworking or scheduling conflicts. 

This consequently helps make organisations more efficient whilst optimising performance and enhancing revenue by taking appropriate actions when needed.

Improved employee performance and retention

By leveraging predictive analytics, employers can analyse the causes of employee turnover in order to quickly deploy programs, policies and workplace conditions that prevent staff churn. 

Creating better pricing strategies

By leveraging historical pricing data, organisations can optimise their pricing decision-making to drive incremental sales and profits. 

In practice, predictive analytics can be exploited to expertly tailor promotions and effect margin enhancements based on the season of the year (for example, Christmas or Easter). 

As a consequence, this predictive pricing can help an organisation to exceed its profit margin targets.

Examples of predictive analytics in different industries

Retail and e-commerce

Retail companies can exploit predictive analytics to stock products people want the most by correlating historical sales data, purchase behaviour, and geographical reference data. 

Some retail companies also exploit this technology to drive predictive searches on their websites and power product recommendations for customers. 

retail sales pattern

Photo by Liza Summer

Financial services

Financial services often employ predictive analytics to assess candidates’ credit card spending, optimise risk management, detect and reduce fraud, and maximise sales opportunities. 

Various leading financial institutions also exploit predictive analytics to improve customer service and increase operational efficiency.


Some medical entities utilise predictive analytics to predict the possibility of illness durng a season, reduce hospital readmissions, identify high-risk patients, and reduce emergency room wait times. 

Some health managers also exploit predictive analytics to optimise healthcare inventory by reducing the probability of emergency pharmaceutical orders based on patients’ types and length of ward stays. 

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In some factories, predictive analytics is exploited to predict machinery failures to allow owners to take corrective actions to maintain or repair them before they break down. Thus, minimising the impact of potential failures and reducing the associated costs. 


As an interdisciplinary area of research, predictive analytics can help organisations to discover what is hidden in data and forecast the future to survive in this ultra-competitive business landscape. 

To stay at the forefront of competitiveness and improve decision-making, more companies should focus on leveraging predictive analytics to guide strategic business decisions. 

Contact Accord Training to learn how to effectively utilize predictive analytics training in your business operations.

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