Predictive Analytics

The goal of predictive analytics is to identify risks and assess how these can be mitigated; recognize opportunities to leverage and convert them to optimum result; and use the data collected to improve operational efficiency.

Companies use predictive analytics to get actionable insights, make more strategic solutions, enhance their decision-making capabilities, and quantify business growth based on the data interpreted.

According to the analysis released by ReportLinker, the market of Predictive Analytics is expected to grow from $10.5 billion in 2021 to $28.1 billion by 2026 as more and more enterprises worldwide rely on advanced analytics for their business management and in increasing their competitive advantage.

How Does it Work?

Different techniques are used depending on the purpose and response sets that would work best for an enterprise. Amazon and Netflix for example, use predictive analytics to determine consumer behavior and personalize user experience. Below are the basic steps predictive analytics softwares typically follow in the process of forecasting analytics:

How Predictive Analytics Work

1. Data collection – Import data from relevant sources such as spreadsheets and databases, and ensure that these data sets are tailored to the goal’s requirement.
2. Data cleaning and combination – Remove any irrelevant details, check for missing information, determine if there are points of irregularity, or anything that would make the data inaccurate. Combine relevant data sources that would need to be compiled depending on the desired result.
3. Develop the model – Establish the hypothesis and build the model based on the data collected through statistical techniques. Consider all variables and factors, then test the model using historical data to determine which would bring the most accurate result and check if it would prove the hypothesis.
4. Integration of analytics with systems – Incorporate the model to the production system and business process by deploying them in software or devices used.
5. Validation of the results – Monitor the model to assess if it is effective in producing the desired outcome. Various factors can cause changes overtime so it is ideal to monitor the model’s performance and update them as needed.

Why is it Important?

Data gathering has long been recognized and practiced by organizations for reasons such as identifying patterns, understanding changes in consumer behavior, and analyzing important information for other future use.

Predictive analytics pairs these data gathered with mathematical tools to anticipate what may happen and respond to them proactively.

With the use of this type of analytics, businesses can optimize marketing campaigns to predict customer purchases based on their buying behavior. It can help in identifying leads that should be prioritized based on audiences that are closer to conversion.

Predictive analytics tools can also help improve operational efficiency by forecasting inventory and maximizing resources. Data gathered through analytics can also be used to determine prices for products and services that might be affected by seasonality.

Other benefits of using predictive analytics include the capability to reduce risk and detect fraud. Generally, this kind of advanced analytics is best utilized to foresee specific scenarios that organizations can use to their advantage.

Types of Predictive Analytical Models

Choosing the right predictive model is essential in coming up with the most accurate forecast, while choosing the wrong one can lead to inaccuracy and irregularity in your project. There are several types of statistical techniques to use in predictive analytics but below are the three most common:

Types of Predictive Analytical Models

1. Decision Tree – this is a decision-support model that classifies outcomes by placing subsets of data in different sections based on the category of variables. As the name suggests, it looks like a tree with each branch representing a choice between a number of alternatives, and each leaf representing a decision.
2. Regression – this looks at dependent and independent variables and how strong is their association and relationship.
3. Neural Networks – this model imitates the way the human brain works. It can deal with complex data relationships using artificial intelligence and pattern recognition.

Predictive Analytics Examples

Various industries are leveraging the use of predictive analytics to help with their efficient operation and decision making. Listed below are some of these industries and as well as the advantages offered by using predictive analytics:

• Manufacturing – With the use of predictive analytics, manufacturers can monitor equipment and machine performance through predictive maintenance. This can help predict failures before they occur, and can help plan how to reduce its impact in production.
• Supply Chain Management – Incorporating predictive analytics into supply chain management enables businesses to address supply chain challenges, identify the most inefficient areas in operation, and reduce cost by helping avoid supply chain disruption.
• Healthcare – Data gathered using predictive analytics proved to promote better patient outcomes, offer a more effective treatment, and improve healthcare operations. It can also assist in the diagnosing process by utilizing artificial intelligence that uses predictive analytics.
• Transportation Management – Using predictive analytics, transportation agencies can monitor traffic flow, spot accidents or vehicle breakdown, and suggest an efficient response.
• Insurance – This industry relies heavily on data with their decision-making, and predictive analytics is instrumental in executing that. It can help in processing claims information, as well as prevent fraud by detecting suspicious patterns.