How Is Predictive Analytics Used in Business?

In today’s competitive environment, predictive analysis helps you take your business a whole step further. As the name suggests, it helps you predict your customer’s next move or potential disasters before they can even hit you. 

Whether you are a tech startup looking to optimise growth or aiming to make your business more cost-efficient, predictive analytics can give you the edge to make informed decisions in a much smarter and faster way.

So, keep on reading to know more about predictive analytics and how you can leverage it in different real business scenarios.

What is predictive analytics?

The name gives it away. Predictive analytics takes historical data that we give it as input and applies statistical algorithms and machine learning techniques to that data to identify the likelihood of future outcomes.

Traditional reports will tell you what happened. Predictive analytics, on the other hand, will predict what will happen. And the reason why it is a game changer is because by knowing what will happen, we can plan for what we should be doing about it.

So instead of reacting to an event (perhaps a business opportunity or a threat) once it has already occurred, you can now anticipate them beforehand, and make decisions based on that.

For example:

• A baker can predict how many customers buy bread on weekdays, and order the raw materials based on past customer behaviour. This will help him avoid losses (if fewer people showed up) or an opportunity to gain more profit (if more people showed up) in the case if he relied solely on his gut instinct.
• A ride-hailing app can predict which areas of the city will have the highest demand for rides during rush hour or a major event, based on historical data and real-time traffic patterns. Once they have identified the major areas, they can proactively position drivers in those locations.

How predictive analytics works

Like any other ‘intelligent technological systems,’ predictive analytics also works based on a structured process by combining several essential steps, which we have discussed below:

Data Collection

The core foundation of any technological system is built on data. We might call them intelligent systems, but they cannot think for themselves. The way they work is by recognising patterns from the data that we give to them.

Predictive analytics also follows the same principle. But where does all this data come from? Sources like CRM systems, sales platforms, finance tools, or IoT sensors store huge amounts of data that are backed up on servers. This data could be anything – from a spreadsheet tracking sales of bread in a bakery store to an e-commerce platform tracking customer behaviour.  

Feature Engineering

Once the raw data is collected, cleaned and structured by the system, the next step is to extract information that will be actually useful for prediction. This process is called feature engineering.

It involves selecting, transforming, or even creating new variables (features) that will help the model understand the relationships between different factors within the processed data. These features are key attributes or signals that might influence future outcomes.

For example, instead of just using a customer’s raw transaction history, the predictive analysis system might calculate their average purchase frequency or the time between visits.

Model Selection and Training

Now that the important variables have been identified, the system then selects a model (a mathematical learning structure) that will test and train on the data.  

There are many types of models, each with its pros and cons. For example, decision trees, neural networks, and regression models all work in different ways. The predictive analysis system will choose a model that will optimise the process. However, we can also define a specific model for the system to work with.

We have already fed data into the system. The training starts when the model recognises the patterns and relationships between different features. Essentially, the model learns to map inputs (like customer behaviour) to outputs (like the likelihood of making a purchase).

Validation and Testing

After we have trained the model, it now needs to be tested to ensure that it performs well, not just on the data that it has already processed and worked on before, but on new, unseen data too. This is where the fourth step, i.e. validation and testing, comes in.

The historical data is split into separate parts so the model can be evaluated objectively. If the model performs well on the test data, it suggests that it has learned genuine patterns rather than just memorising the training data. This step is critical to avoid what is called overfitting, where the model works great in practice tests but fails in real-world situations.

Deployment and Monitoring

The model has been trained and tested. It is now ready to make predictions for your business. You can integrate it into existing systems – like a sales platform, recommendation engine or logistics dashboard – so it can start delivering real-time insights.

However, it does not just stop here. You will have to monitor the model continuously to ensure that it stays accurate as new data is fed into the system. It is an iterative process of training and testing on data, and making improvements in the system to optimise the process. 

Because customers’ needs, behaviour patterns and even market conditions may change over time. And so, we need to make sure that not just our businesses, but also our predictive analysis models, are adapting with changing times.

Real-world use cases of predictive analytics

Let’s take a closer look at how businesses are applying predictive analytics in real-world scenarios.

Marketing

1. Customer Retention and Behaviour Prediction

One of the biggest challenges that businesses face is customer churn (losing customers faster than gaining new ones). Predictive analytics can tackle this problem by identifying which customers are at risk of leaving and why. 

By analysing customer behaviour patterns, purchase history, and engagement data, you can design loyalty programs, personalised offers, or re-engagement campaigns to retain those customers who are likely to churn.

For example: A startup with a subscription model (like SaaS or streaming companies) can use predictive analytics to flag customers who are likely to cancel their service soon. And before they consider doing so, the company can step in and offer personalised customer service outreach and special offers.

2. Behavioral Targeting 

Why blast everyone with the same message when you can predict who is most likely to convert?

Predictive lead scoring models can use your historical sales data, demographic profiles and online activity of your customers to provide insight as to which segment of your audience you should prioritise. 

On the whole, this can help you allocate your marketing budgets more effectively by targeting high-value customers and choosing the right timing for campaigns for maximum impact.

3. Personalised Recommendations

Ever wondered why Netflix seems to suggest exactly those shows or movies that you would likely watch next? Or how Amazon suggests products you are most likely to buy? That is predictive analytics at work. 

By analysing your past actions, preferences, and even time spent on a specific genre of shows, predictive algorithms can suggest what you are most likely to enjoy or purchase next.

Making personalised recommendations with a predictive analytics model not only improves the user experience but also boosts conversion rates by increasing the likelihood of cross-selling and upselling. This can especially be a competitive advantage for small-scale startups and e-commerce websites. 

Finance

1. Fraud Detection and Prevention

Financial institutions deal with thousands of transactions per second, which puts them at a greater risk of fraudulent activities, and even detecting it may be a complex and time-consuming task. 

That is where predictive analytics can help. It can detect unusual behaviour and fraudulent transactions before they are even completed.

The model can be trained and tested to recognise patterns of legitimate vs. suspicious behaviour. For example, unusual spending patterns, geographic mismatches, or rapid multiple transactions can trigger real-time alerts by the model. As a result, fraud prevention becomes faster, more accurate, and more effective.

2. Credit Risk Assessment and Underwriting

For digital lenders or BNPL startups, it is difficult to always rely on traditional credit scoring. In this case, a predictive model can be used to assess a borrower’s creditworthiness. It will use alternative data sources such as mobile phone bills, transactions and smartphone usage patterns to evaluate their risk of default. 

For a startup or SME, this can help them make informed decisions as to who to lend to and reduce default risk. This makes financial products more accessible while keeping risk manageable, which would ultimately protect both the lenders and borrowers. 

3. Financial Forecasting and Budget Adjustments

Beyond risk management, predictive analytics plays a role in financial planning as well. The model makes revenue projections based on historical cash inflows and outflows and macroeconomic trends. You don’t just see where the business is – you get a glimpse of where it is going.

Supply Chain

1. Demand Forecasting and Inventory Management

Stocking too much (overstocking) ties up capital and results in losses. Stocking too little (understocking) leads to a loss of sales. By analysing seasonal trends, past sales data, and market demand, predictive models forecast how much stock will be needed and when.

Remember the example of the baker we gave in the beginning. With predictive analytics, he can maintain his stock of bread at optimal inventory, without solely relying on his gut instincts. 

2. Supply Chain Management

Global supply chains are interlinked with each of the countries, and thus are quite fragile. A single disruption in the supply chain can cause a problem for the whole product. While this was a huge cause of worry for the parties involved before, with predictive analytics, it has become much simpler and easier.

Because predictive models can anticipate these disruptions – like supplier issues, weather conditions, or geopolitical risks – companies can adapt and respond accordingly. It also allows companies to optimise logistics and transportation by predicting the best routes and ideal delivery times.

This can be especially useful for SMEs or global e-commerce websites that rely on just-in-time delivery models or international suppliers. 

Human Resources

HR teams can use predictive analytics to forecast future hiring needs, identify high-potential employees, and reduce turnover. 

For example, it can help determine which employees are most likely to leave based on engagement levels, work patterns, and performance data. 

Taking a step back, it can also identify potential causes for employee disengagement. This way, HR can intervene beforehand and help boost the employee’s productivity so as to retain talent.  

Healthcare

Healthcare institutions and tech startups building patient management systems are using predictive analytics for numerous reasons. It is making waves in the healthcare sector, and for good reason. For instance:

• By analysing electronic health records and lifestyle data, predictive models can identify patients who are likely to develop diabetes or heart disease, which can help in early prevention. 
• Taking it even further, AI models can analyse lab results, genetic markers, and medical history to predict how chronic conditions (like diabetes) might evolve. This is a step ahead for those people concerned with early prevention. 
• Hospitals can use predictive models to assess which patients are likely to return within 30 days, so they can make preventative care planning beforehand.
• Clinics can forecast which patients are likely to miss appointments and automatically send reminders or rescheduling messages to them. 

Entertainment

From music to media streaming, predictive analytics is shaping the way we consume content by helping businesses create a better customer experience and a higher LTV (lifetime value). Predictive models can help by:

• Predicting what viewers will enjoy based on their watch history, interaction patterns, and even the time of day. The perfect example of this is streaming apps like Netflix and Amazon Prime. 
• Flagging users cancel subscriptions based on a drop in usage or skipped recommendations.
• Adjusting subscription offers or in-app purchases to maximise retention based on user behaviour, for instance, Spotify can offer personalised subscription plans to each user depending on what and how much music they listen to on their app.

Benefits of predictive analytics for businesses

As an SME or a startup, you might think that predictive analytics will not have much use for small-scale data. But the good news is that it scales. You can start off small – maybe use one model for customer retention or inventory planning insight – and expand from there, as your business grows. 

So, let’s take a look at how predictive analytics can add value to your company:

• Improved decision-making: With accurate forecasts and data-backed insights, you can make smarter, faster decisions.
• Cost efficiency: From reducing unnecessary inventory to optimising marketing spends, predictive analytics helps you allocate your resources more effectively.
• Increased revenue: Helps you in targeted marketing, making personalised recommendations, and optimising pricing strategies for higher conversions, retention and sales.
• Early detection: Detects fraud or anticipates supply chain disruptions/system failures early on and helps minimise costs in the long run.
• Customer satisfaction: Helps in understanding and anticipating customer needs so you can make their experiences better, leading to stronger loyalty and improved retention.
• Competitive edge: You can adapt faster to market shifts and customer needs, staying ahead of the curve instead of playing catch-up.

How GoodCore can help with predictive analytics solutions

Predictive analytics opens up new possibilities and insights in ways that could previously be based only on second-guessing. But there is more to it than just algorithms and coding. It requires the right mix of data strategy, domain understanding, and technical execution. 

For a startup or an SME, all of this might sound costly. But that is where GoodCore can help. We offer predictive analytics solutions tailored to your business and company goals. Here is how we help you deliver: 

• Custom-built models: Design machine learning models that can optimise your business objectives, goals and KPIs.
• Data integration & feature engineering: Lay the foundation for predictive analytics systems, from cleaning and structuring raw data to integrating it across your business.
• Planning a roadmap: Identify high-value areas where predictive analytics can influence measurable impact for your company.
• Cross-industry expertise: We have expertise in all areas, from retail and finance to healthcare. We align predictive tools tailored to your domain challenges.
• Dashboards & visualisation: Deliver real-time forecasts and insights through intuitive interfaces like dashboards for you to easily understand them.
• Assurance of data security: Our solutions comply with all industry-specific standards and best practices to ensure that your data remains fully secured.
• Continuous optimisation: Keep monitoring and refining models as new data flows in to ensure that the predictions are relevant and up-to-date with market trends.

FAQs

How long does it take to see results from predictive analytics?

It depends on the complexity of the problem and the quality of your data. For example, in cases like demand forecasting and consumer behaviour, you can see insights within a few weeks of the model development. And more advanced models may take 1-3 months to be fully implemented. 

How is predictive analytics different from forecasting?

Forecasting generally uses simpler, trend-based models to guess what might happen next. Predictive analytics uses advanced algorithms and more variables to dig deeper and predict what might happen next.  

What is the difference between predictive and prescriptive analytics?

Predictive analytics tells you what might happen. Prescriptive analytics goes one step further to suggest what actions you should take based on those predictions.

For SMEs or startups, what is the best use of predictive analytics in the beginning? 

Sales forecasting or customer churn (how many customers are leaving in a specified time-frame)  are great starting points, as both offer immediate ROI (Return On Investment) and are relatively easier to implement.