What is Predictive Decision Making?
Predictive Decision Making is the integration of predictive analytics and decision modeling. Thus, this integrated tool ensures that in the business ecosystem prediction are optimized and decisions are well predicted. The prediction brings valuable insights to accelerate the management processes and transform the operations of financial services, healthcare, retail, insurance, and other such industries that are seeking accurate predictions for decision making. Thus, the combined approach of predictive analytics and decision models are all set to revolutionize the way decision–support system functions today.
Use Cases of Predictive Decision Making
The increasing data explosion has created the need for simpler, faster, and, cost-effective solutions to solve complex data-related issues and discover new opportunities for businesses. By gaining expertise in data modeling and analytics the corporate is able to develop its competitive potential. As a result, some of the common use cases of Predictive Decision Making are:
| Use Case | Description |
| Detecting and Analyzing Fraud | With cyber security becoming the biggest concern of the financial and technological industry today has created the need for high-performance behavioral analytics. As a result, the predictive decision-making tool helps to improve the process of detecting fraudulent patterns and preventing criminal behavior. It identifies the anomalies that indicate fraud, persistent threats, and vulnerabilities, based on which the future decisions are formulated. |
| Optimizing the Marketing Campaigns | The predictive analytics model has always helped companies in determining customer response and promoting the available cross-selling opportunities. As a result, the companies are able to attract and retain their customer base and expand their reach through well-researched marketing strategies. |
| Improved Operations | Generally, large retail companies encounter several operational challenges in forecasting inventory and managing resources. Thus, predictive analytics are put in place for allowing organizations to ease the complexity involved in logistics and function more efficiently. |
| Reduce Risk | Credit scores are the primary benchmark that defines the consumer’s purchasing pattern. This is an apt use case of predictive analytics which helps the financer make a decision about the creditworthiness of the person. Other risk-related uses of predictive decision-making are processing insurance claims, performing verifications, and activations. |
Metric Used for Predictive Decision Making
The KPIs are the reflection of the company’s strategic goals and objectives. As a result, drives the business behavior, organizational culture, and decision–making in the company.
| Metric | Description |
| Robust Insights | Measuring the business performance and impacts of decisions manually is not just time-consuming but also complex. The automated and robust insights about the business trends help to frame refined decisions and take the calculative decisions on the same. |
| Quality Data | To develop robust insight into the business processes the companies rely heavily on quality data. As this is where the journey of organizational decision-making begins. The unreliable data sources can lead to poor management and thus impacting the overall performance of the company. |
| Accountability | The decision made on the basis of quality data helps to build a robust organizational structure. Each decision is backed by a data-driven structure and develops better accountability in the company. |
Examples of Predictive Decision Making
The application of predictive decision-making analytics extends to almost all industry types. As a result, the big industry players have adopted this combination of predictive analytics and decision modeling which helped them to operate more efficiently.
| Example | Description |
| General Electric (GE) | The appliance giant has used predictive analytics in gathering data from sensors that are installed in gas turbines and jet engines. The data derived from these sensors helped the company to increase operational efficiency where it experienced a 1.5% boost in its productivity level. |
| Starbucks | This famous coffee company brought predictive analytics to use for taking business decisions. These decisions include identifying the store location by considering metrics like location traffic, demographics, customer behavior, etc. Thus, this refined data helped the coffee giant in predicting potential success rates and experience revenue growth. |
| Ralph Lauren | The company uses the predictive analytics tool for strengthening product design and development decisions. Furthermore, the tool uses machine learning to minimize markdowns and take advantage of top-selling products, and enhance the gross margins of the company. |
Tools Used for Predictive Decision Making
Some of the popular software based on the concept of predictive analytics and decision modeling are mentioned here. These software or tools have revolutionized the decision-making structure for companies.
| Tool | Description |
| IBM Watson Studio | It is a leading predictive analytics tool that simplifies the idea of predictive decision analytics for data scientists and improves decision-making for business users. Moreover, the platform includes various features to enhance and execute responsible and explainable predictive models. |
| SAS | The company is always known for its innovation and provides suitable tools for statisticians and data scientists to modernize data structures. Using the machine learning algorithms and workflows SAS allows companies to take advantage of augmented workflows, data stacks, and simplified deployment of the same. Furthermore, it ensures maintaining a strong relationship with the leading cloud providers and simplifies the implementation of predictive analytics across various workflows. |
| H2O Driverless AI | H2O is the recent player in the predictive analytics ecosystem and simplifies AI developments through its open source and custom recipes. In addition, its automated and augmented capabilities provide features of engineering, model selection, and semantic analysis. The common models used by H20Deiveless AI are – Casual graphics, Shapley, Decision tree surrogate, and LIME methods. |
| Microsoft Azure Machine Learning | This tool complements the core capabilities of predictive analytics and includes supporting tools like Azure Data Catalog, Azure HD Insight, Azure Data Factory, etc. As a result, supports the process of application development and RPA tooling to deploy predictive analytics capabilities and streamline the business workflows. |
| Rapid miner Studio | It is a comprehensive set of predictive analytics which includes tools built around core data and text mining strengths. These core capabilities of the tool allow companies to extract data from diversified sources and incorporate them into building predictive modeling workflows. Their advanced features allow sharing of predictive models across various organizational setups and strengthen organizational governance. |
Scope of Predictive Decision Making
According to the 2013 reports of Mckinsey Global Institute Report, 50% to 60% potential value of predictive analytics is used to capture location-based data industry, 30% to 40% in the retail and healthcare industries while the proportion drops to 20% – 30% in the manufacturing sector. As a result, shows that several industries still stand deprived of predictive analytics benefits. Ideally, manufacturing companies can use predictive models for inventory and resource management, optimizing production, and distribution channels. On the other hand, industries like health insurance use this to discover fraudulent claims, and risk factors related to patients and build effective intervention policies.
Alongside, the government and public sector organizations use predictive analytics to work on cyber security concerns. While the retail industry makes use of this tool to boost the marketing and promotional strategies of the company. Furthermore, the oil and gas industry minimizes the safety risks and predicts the requirement for equipment maintenance. The banking sector finds its solution in the concept of predictive analytics which detects the credit risk and maximizes the up-sell and cross-sells opportunities. Thus, predictive analytics has a long way to go and benefits all industry types in some or the other way.
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