It uses data from the past to predict future developments. A mathematical model is developed that contains the most important trends. Predictive analytics are used in the following areas, among others:
Through predictive analytics, economic correlations are intended to facilitate management’s decisions for the future. Predictive analytics is based on business analytics, discussed in the next section.
The predictive analytics technique uses data from data mining for its predictive models. Assessments and empirical values from the past also flow into the analysis. The information used is extracted with the help of ETL processes. Predictive patterns are then calculated from this. ETL processes are data flow processes in a data warehouse or similar system. In an ETL process, data is pulled from various sources, converted, and imported into a target system. However, the predictive analysis also uses other methods, such as machine learning, elements from game theory, or various simulation methods.
Variables from past events and their effects, as well as other dependencies from data sets, which impact future events, flow into the data of the prediction models. How meaningful these predictions depend on the quality of the assumptions made. Classification is of crucial importance for predictive analytics. Type involves different grouping of objects with their respective characteristics into classes. Through variety, it is possible to create a classification system applied in various fields, such as science and information technology. In the context of predictive analysis, classification causes data to be extracted according to predefined rules. Subsequently, search words are defined for the document search. With the help of semantic analysis, the document search can be carried out even more precisely.
It is possible, for example, with predictive analysis to fine-tune marketing campaigns. In this way, website visitors’ behavior can be optimized in terms of click-through rate and conversion rate. The click-through rate (CTR) indicates the percentage of how many visitors clicked on an advertisement displayed to them. On the other hand, the conversion rate suggests in percent how many visitors to a website complete an action, for example, a purchase process in an online shop, as opposed to visitors who abandon this process.
Predictive analytics has become abundantly crucial in recent years as the technologies that support it have made great strides. This is especially true in the two areas of Big Data and Machine Learning. For large data sets from which analyses and conclusions for future events are to be drawn, the method of predictive analysis is best suited.
What questions are answered by predictive analysis?
- Questions about what happened: What happened when?
- Questions about the quantity of an event
- Questions about the frequency of an event
- Questions about the causes of an event
To answer these questions, various tools are used, such as reporting (KPIs and metrics), automated monitoring (an alarm is triggered when a specific value is exceeded or undershot), dashboards, ad hoc queries, and online analytical processing (OLAP).
In online analytical processing, hypotheses are made, and information is requested to confirm or reject the previous assumption.
How can business analytics be used?
Business analytics relies on statistical analyses of company data to look at future events. In doing so, business analytics answers the following questions:
- Reasons of events
- Effects of events
- Interactions of events
- Consequences of events
Scenarios are also run through, and alternatives are shown to how one can act differently. Which parameters must be adjusted to achieve this or that result or event?
Wich analysis tools does business analytics use?
To improve the planning process, Business Analytics uses various analytical tools:
A/B testing: different variants with different variables are tested to check decisions.
Statistical or quantitative analysis is used to answer why a particular event did or did not occur.
New patterns and correlations in data are discovered through data mining.
Future events are predicted through predictive analytics. Predictive analytics is thus a sub-discipline of business analytics.
What are the procedures in predictive analysis?
Defining the project: The project’s outcomes need to be determined, how much effort will be required, business objectives, and what data will be used.
Collecting the data: Data mining provides data from different sources for predictive analysis. This provides an overview of customer interactions.
Analysis: Analysing the data allows review, cleaning, and modeling. This filters out the valuable information that is then needed for further processing.
Statistical analysis: It is possible to support or confirm the assumptions and hypotheses made previously. In addition, this is verified by standard models.
Modeling: Predictive modeling is used to create predictive models for future events.
Predictive deployment: Predictive model provisioning is used to ensure that analysis results can be incorporated into the decision-making process daily.
Monitoring the models: The models are managed and monitored to check whether the expected results were ultimately achieved.
What types of models are there?
Different models can be used in the predictive analysis:
- Predictive models:These models use mathematics and computer science methods so that results or events can be predicted. Changing the model inputs indicates an outcome of a future state. The model is fed with training data sets and tested in an ongoing process. Then it is checked how accurate the predictions are. For this purpose, different machine learning approaches are used to determine the most suitable model. The training samples are the available sample units whose properties are known. Pieces that contain known properties but unknown performance are referred to as training samples.
- Descriptive models:Descriptive models provide data to group customers and prospects into different classes. Predictive models always refer to the prediction of single customer behavior. On the other hand, explanatory models always refer to several different relationships between customers and products. Customers are classified in descriptive models according to their preferences and life stage.
- Decision models: These models show the relationship of the totality of elements of a decision. These include known data (including data from predictive models) and forecast decision results. As a result, the outcomes from these decisions are predicted with different variables. With the help of these findings, effects can now be optimized. Decision models are used when a decision logic or business rules are to be developed to lead to the desired response in any customer or circumstance. Because real-world decision problems are complex, the model usually needs to be simplified. One way to streamline the model is not to include all possible variations of the data in the model