Predictive Success Blog

I You may be asking yourself what is predictive analytics? It is the area of the advanced analytics that is most commonly used when making predictions about potential future plans at a business.

The predictive analytics tools that are included when analyzing existing data and making decisions about upcoming plans are statistics, data mining, machine learning, models, as well as artificial intelligence.

Running a predictive analysis will incorporate information technology, management, and best business practices, to pull data from several data mining, predictive modeling, as well as analytical techniques in order to make smart predictions. Uncovering any sequences from past data can be used to reveal areas that may lead to potential trouble or positive opportunities down the line.

The predictive analytical models that indicate the ranking value of the correlation between a variety of factors, helps organizations assess risks to certain areas. Correct application of the results discovered by the predictive analysis can allow for beneficial outcomes of big data.

What is Predictive Analytics Used For?

Using statistics, data mining, as well as a text analysis, opens the window to new predictive data for those looking for correlations and patterns in structured and unstructured information. The information that can be included to the final analytical report are methodical data, such as gender, age, sales, salary, relationship status. Looking at textural information that can be found in call centre files, social media posts, or other areas when free-flowing text can be extracted with the overall tone and feeling is what is know as unstructured data. The use of unstructured data can be referred to when creating the analytical method.

Using predictive analytics at your workplace makes it easier to adjust for the future, and address potential issues sooner because of the proof that the data presents. Prescriptive analytics however, does even more, by making a list of actions that can be taken to increase the positive outcomes of the predictive analysis, as well as decision-making plans for a variety of obstacles.

Understanding the Steps for a Predictive Analysis

Step 1: Decide what you need: Knowing what you need for the outcome, how much time it will take, organizational goals, realistic output, and what the data requirements are.

Step 2: Collecting the information: Running a predictive analysis will require data mining which can be gathered from various sources. This collection will allow businesses the scope to see the client interactions.

Step 3: Running the data analysis: The data analysis is the method of reviewing, tidying up, transforming, and presenting the information determines concluding results.

Step 4: The statistical analysis: Looking at the information that the statistical analysis provides can be useful in proving the hypothesis or other presumptions. These can be compared against common statistical processes.

Step 5: The predictive model: Using the predictive model allows for the detailed creation of models that can precisely predict the future. Depending on the desired outcome, there are alternative available choices for multi model assessments.

Step 6: The predictive model deployment: When looking to use the outcome of the analytical report to enhance the choices made by management, the wisest assessment is run against the predictive model deployment.

Step 7: Making sure to monitor the models: Managing and monitoring models to compares against model’s performance, makes sure that they are matching what is expected of the outcome.

The Prescriptive Analytics Tool

The use of the prescriptive analytics tool allows for smart automation for complicated choices and to know the pros and cons so that it determines predictions. These predictions are used to make new adjustments to suggestions based on changes that best support the predictions.

Where to Use Predictive Analytics

1. Customer relationship manager (CRM): Using predictive analytics tools to hit CRM targets in areas like sales growth, client services, and marketing tactics. Applying the customer relationship management analysis to the lifespan of the client to the organization, from firstly gaining their business, relationship development, retaining the client, and re-sell the product back to them.
2. Medical Care: Applying predictive analytics to medical care can select which patients are more likely to develop certain lifelong health issues like asthma or diabetes. The support system for the medical decision makers uses the predictive analysis reports to make urgent medical decisions made by medical professionals.
3. Analytics from Collections: Applying predictive analytics to make the most out of the data from collections by pinpointing which collection agencies produce the best results, how they contact the client, best legal solutions that can be taken to achieve the most recovered funds, as well as ways to decrease the expenses of the collection agency.
4. Cross Selling: Using the predictive analytics tools to pull data from client shopping and spending behaviors, which can allow for increased cross selling or up-selling to existing customers from companies that offer a range of merchandise and services.
5. Detecting Fraudulent Behavior: Applying predictive analytics to reveal risky credit applications, in-person and digital fraudulent purchases, character theft, and inaccurate insurance requests.
6. Managing Risks: The predictive analysis tool can determine which future investments are going to be most successful, and which portfolios are most risky.
7. Targeted Marketing Practices: The predictive analysis can be useful to determine the best selling conjunction of products, what to use in marketing, where to advertise, as well as best times to market to the target clientele.
8. Underwriting Practices: Using predictive analytics to assist with underwriting, so that predictions on the risks of sickness, evasive behavior, and bankruptcy can be properly assessed. Applying predictive analytics can help direct the system of gaining clients by determining potential liabilities of a particular client with the use of information from the early applying process.

Sectors that Utilize Predictive Analytics

There are many industrial sectors that find themselves using predictive analytics to their advantage. These sectors include financial institutions, advertising, travel, medical, telecommunications, business services, utilities, drug manufacturers, gas and oil, and many other service-based industries.

Further Details About the Predictive Analysis System

To know further details about the predictive analytics tool and how its flow works, please see the descriptions for each section that sums up: information gathering (data mining), analyzing the information, analyzing the statistics, predictive models, and deploying the predictive model.

Answering the Question: What Is Predictive Analytics?

The area of the advanced analytics that is most commonly used when making predictions about potential future plans at a business is that of predictive analytics. The predictive analytics tools that are included when analyzing existing data and making decisions about upcoming plans are statistics, data mining, machine learning, models, as well as artificial intelligence.

Answering the Question: What Is Prescriptive Analytics?

The use of the prescriptive analytics tool allows for smart automation for complicated choices and to know the pros and cons so that it determines predictions. These predictions are used to make new adjustments to suggestions based on changes that best support the predictions.