A guide to predictive analytics in 2024

Future planning is a critical part of business success. You’ll need to identify opportunities, mitigate risks, and make informed decisions if you want to thrive in an evolving landscape.

But forecasting is challenging without reliable predictions. In 2023, many businesses are relying on predictive analytics to gain a better understanding of the future. This is helping stakeholders lean into big data, rather than guesswork, to make better decisions - especially when it comes to user experience (UX) - and future-proof their businesses. 

With this guide, you’ll learn how to harness the power of predictive analysis to better your business.

Predictive analytics is the practice of using past and current data alongside statistical analysis to make forecasts about the future. It’s a part of advanced analytics and involves data mining and machine learning techniques to gain key insights.

Businesses use predictive analytics to help forecast future trends, inform better decision-making, and discover both risks and opportunities.

Typically, predictive analytics is seen as a more reliable way to make decisions. Rather than relying on assumptions made by a team, gut feeling, or manual-based predictions, predictive analytics provides a much more accurate basis from which to work.

It’s worth bearing in mind, though, that predictive analytics are not foolproof. The pandemic, among other world events, has taught us that no one, machine or otherwise, can completely predict the future.

Predictive analytics is used across many companies and industries to gain insights for several uses, including:

Forecasting

Accurately predicting future sales and engagement is critical for businesses across the board. Gaining an understanding of future trends can boost a business case for investment, help indicate where priorities should lie in the company, and impact heavily on the bottom line.

Supply chain

Forecasting is especially critical in the retail sector and manufacturing supply chain to ensure that customers can access goods when they need them, warehousing is sufficient, and shelves can be filled efficiently. It also helps to reduce waste – especially with perishable goods. 

Marketing

Gaining a deep understanding of how customers are reacting to past and current campaigns can help inform future marketing success. Data on demographics for the business can mean more informed marketing campaigns that are focused on an accurate target market.

Fraud detection

Through the visibility of transactions and usual patterns, businesses can more clearly see whether something, such as the case of fraud, looks unusual.

Credit scoring

For financial companies, quickly understanding a customer's credit history can be challenging. But predictive analytics can quickly build a picture of a person’s credit history to predict their borrowing risk.

Underwriting

In the same way, insurance companies can use predictive analytics to decipher the likely risk of a customer requiring insurance payouts in the future. 

Human resources

Predictive analytics can help improve clunky processes, better forecast workforce needs, and analyze employee data to increase employee success. The process can be useful for employee engagement, reducing employee turnover, and increasing employee success. Analytics can also be used to boost employee diversity and inclusion. 

A few main predictive analysis techniques are used to turn data into relevant insights. These techniques are also known as models. 

To make predictions based on a dataset, a mathematical model is required. This helps make sense of the data in a way that’s useful for analysis. In simple terms, the mathematical model replicates a scenario to help show the most essential points and give an idea of how the future is likely to play out. 

The predictive analysis models use data and statistics to organize the key points and provide reliable information. 

Some common techniques used in predictive analysis include: 

Decision trees

This technique can help to explain why people make certain decisions. The model looks similar to a tree with branches and leaves, which represent choices and decisions, respectively. 

Regression

One of the most common techniques in statistical analysis is regression. This is useful for seeing patterns in large data sets. There are many types of regression models, including linear regression, ridge regression, lasso regression, and non-linear regression. 

Time series

This model technique is based on the relationship with data and time – such as daily, weekly, or monthly – to build a picture of how a dataset relates to a time period. 

Clustering

Cluster models aggregate data that’s similar to split large sets of data into logical groups and categories. 

Neural networks

In an attempt to imitate the human brain, neural networks deal with complex data and pattern recognition, all backed by AI. 

Performing predictive analysis is a complex process. The precise steps you complete will depend on the technique you decide to use, your specific goals, and the overall business strategy. 

There are some best practice steps you can follow to ensure the insights you gather are useful and relevant: 

1. Set a goal 

At the outset, it’s essential to set a clear goal. This goal should relate back to your business strategy too. 

Are you trying to better understand your customers? Decipher demographics? Complete supply chain forecasting? Or increase employee engagement? 

Figuring out exactly what you’d like to know will help you hone in on the right data and the right method as you move through the steps. 

2. Choose a technique or model 

Once you have a clearly defined goal, it’s then important to decide which predictive data analysis model will help you accurately answer the question (or goal). 

Before deciding, ask, will this model best answer our question/goal/intention? 

A statistical analyst or researcher with in-depth knowledge will likely be the best person to answer this question. 

3. Collect data 

A key aspect of getting accurate answers and insights is collecting the right data. The more data you can collect, the more reliable your results will be. 

Data collection may come in the form of data mining, customer data, and web data. It may be found in databases, spreadsheets, and web archives. 

Uploading that data into one simple-to-use platform is essential to manage the data seamlessly. Advanced predictive analysis software is usually needed to do this. Manual processes are not only too slow, but they also leave more room for mistakes and inaccuracies. 

4. Build and train a predictive model

Forecasting can be a complex process. If using neural networks – which involve AI – it’s necessary to not only build but train a predictive analysis model to become increasingly accurate.

To build out a model, you’ll need to give AI ground truths to base it on. Ground truths can relate back to previously collected data that represent solid patterns and facts. In a customer churn software example, that may include building the model to train itself on:

• The ground truth of what our customers were doing this time in years prior

• The ground truth of churn rates from prior years

• The ground truth of common causes of churn and their outcomes

Once the training is complete, you’ll need to trial the model you’ve created against data to see whether it performs well. You’ll make iterations before testing again to make incremental improvements. 

The training process can take time, but it will ensure that the predictions are reliable and beneficial for the company. 

5. Perform the analysis

Once you’ve created the most helpful model, it’s then necessary to perform an analysis of the dataset you’ve chosen. 

This process should be performed within predictive analysis software like Qualtrics or with assistance from a data analyst or researcher. 

For some companies, it may be necessary to outsource the process if the qualified people are not part of the current team. It’s essential to ensure that predictive analysis is accurate so that the insights gathered are as reliable as possible. 

There are many real-world examples where predictive data analysis helps stakeholders to better their offerings, make better decisions, and future-proof their businesses. These examples include healthcare, finance, manufacturing, marketing, and more. 

Healthcare

There are many use cases of predictive analytics in action in healthcare. One example was a study conducted by UnityPoint Health utilizing the process to reduce readmissions. Readmission in hospitals is expensive. Medicare, for example, spends roughly $26 billion annually on the issue. 

To reduce readmissions, survey data from readmitted patients was aggregated into a predictive model. The model predicted when patients were likely to start reexperiencing symptoms. That meant the healthcare provider could set aside time to speak to patients as expected. This reduced their need to seek in-person care. 

Within 18 months of using the tool, readmissions were reduced by 40%. 

Finance

Predictive analytics can also play a significant role in many finance-related tasks. Predicting future cash flow, for example, is a common use. 

Cash flow plays a critical role in whether a business can survive or not. So accurate forecasting, in this case, is essential. 

In one example, PwC was able to help a financial institution to forecast its cash flow through the use of a predictive model. The use of this model helped the organization increase its typical forecast period from 3 to 12 months. 

Manufacturing

Predictive analytics is also common in manufacturing. It can help to predict future growth that impacts the entire supply chain. 

One common use in manufacturing is predicting malfunctions that can heavily impact the process. This can ensure businesses are able to plan for pauses or shutdowns in manufacturing to maintain or replace machines. Analytics can also ensure that the lines are better and more efficiently utilized to keep up with demand. 

In one example, analytics revealed that tool failure was occurring as equipment amperage (electrical current) increased. Researchers proved an 80% correlation between spindle load and amperage. By monitoring this through model patterns, they could predict likely tool failure and also reduce the amperage range to reduce the risk. 

Marketing

Another crucial role of predictive analytics is in marketing. By looking at the past performance of advertisements, for example, it’s easier to make accurate predictions about future performance. 

Typically, a marketing team would manually look at this information to draw conclusions. This can take time and lead to mistakes. Predictive analytics not only fastens this process but is also significantly more accurate. 

In one example, using data models helped a digital company increase potential leads. The learning-based model could sort leads into interested, slightly interested, and not interested. This meant the marketing team could follow the most interested leads and increase lead conversion by 38%. 

In 2023, companies that don’t lean into the power of predictive analytics may get left behind. Being able to accurately plan for the future, identify opportunities, and mitigate risks is essential for boosting business success and staying competitive in a continually changing landscape. 

To make as reliable predictions as possible, using predictive analytics helps stakeholders use big data, rather than guesswork, to make better decisions and future-proof their businesses.

It’s a process that not only helps businesses but can better the offering for customers too.

There are several predictive models. The ten most common include: 

1. Classification model

2. Forecast model

3. Clustering model

4. Outliers model

5. Time series model

6. Decision tree

7. Neural network

8. General linear model

9. Gradient boosting model

10. Prophet model

But the three most common predictive models are the decision tree, linear regression models, and the gradient boosting model.

Regression analysis is one of the predictive models. It’s used to analyze the relationship between a dependent and an independent variable. This model is typically used for finding causal effects between variables, forecasting, and time series modeling. 

Linear regression is another predictive model—it falls under the subset of regression models. It’s most commonly related to forecasting. It analyzes linear relationships between dependent and independent variables. 

Linear regression could be used, for example, to analyze how the past performance of an advertising campaign boosted sales to draw conclusions about a future campaign. 

Things such as marketing success, the stock market, and sales may all be predicted using this type of model.