What Is Predictive Modeling?

In this modern world it is hard to imagine visiting a website that doesn’t automatically personalize what you see or predicting what product you will want to buy? It seems like the whole world wide web already knows who we are. Well, this is what predictive modeling enables us to do!

What is Predictive Modeling?

Predictive Modeling refers to a set of methods that are used to calculate the probabilities of various outcomes. Typically using a combination of statistics and logic. Each predictive model is designed to come with multiple assumptions which are based on different variables/parameters. These variables can be weighted based on how likely they are to influence future outputs of a data set. With these techniques you can forecast, predict outcomes, and figure out what tweet to show you next.

Predictive Modeling Life Cycle

The life cycle of predictive modeling goes through five major steps:

1. Firstly, you need to collect and collate data from different sources. In the case of firms, this data is about customers (such as past purchases, genders, interests, etc.).
2. The next step is to clean this data and structure it for easier analysis. This is usually the longest step as it requires parsing each column and making sure it is as expected(especially if it is the first time you are working with the dataset).
3. With the data cleaned the data scientists or analysts can start to analyze the data using various techniques to help reject or accept their hypothesis. They might first start with some more exploratory techniques. Often a data scientist will create charts and graphs to analyze the data set. This helps gain an understanding of the data and see what questions can be answered.
4. Once the data teams have a better understanding of the actual data, then they can use different predictive modeling techniques along with business understanding in order to forecast different business insights. These insights can later be used in website layout and marketing tactics to increase efficiency.
5. Just because a model is developed doesn’t mean the process is over. Often times predictive models need to be updated (either automatically or manual). This means the data scientists will have to track the effectiveness of the model

Application of Predictive Modeling:

Much of the field of predictive modeling comes from weather forecasting and meteorology, where a significant portion of the field is based on predictions. In the modern era it is providing accurate insights which help companies maintain a competitive advantage since they have forecasts for future outcomes beforehand. It is essential for the simple fact that it allows you to get accurate predictions of future outcomes or unknown variables. These accurate predictions have a huge impact on both academia and businesses for different purposes. Some of the main applications include:

1. Bayesian Spam Filters: In order to predict the probability of a message being spam or not, these filters enable companies to identify fraudulent activities and spam messages.
2. Marketing: It also has great significance in marketing, and advertising field since the precious data of surfers determine the kind of products they can be interested in.
3. Customer Relationship Management: Here, predictive Modeling is simply used to target the correct customers — who are likely to make a purchase

Aspects of Predictive Modeling

Used mainly to attract, retain and grow their profitable customers, companies can utilize predictive modeling to confirm their mark in the future of industries. In order to understand this concept, you can consider three main aspects of predictive Modeling:

1. Data: This refers to the sample data that we collect in order to describe our problem along with the relation between the inputs and their outputs.
2. Learning Model: This aspect discusses about the algorithm chosen to use on the sample data and create a model which can be used repeatedly to train data at a later stage.
3. Predictions: This aspect covers the usage of the learned model on new data for testing when we are not aware of the outcomes.

Pros and Cons of Predictive Modeling

Since this field is entirely based on predictions and forecasts, it has multiple pros and cons. While you can predict the future outcome of a certain variable, you may also face organization barriers in accessing required data. Let’s discuss further:

Pros:

Predictive Modeling reduces the costs required by businesses to forecast their decision outcomes and utilize competitive intelligence. Apart from this, this e also helps in:

1. Churn Analysis: The attrition rate of a customer in a company is known as customer churn analysis. With predictive analysis, you can acquire this to implement effective retention strategies and keep customers associated with your brand. Forecasting External Factors: Companies can predict the future external factors and their impact on the firm’s current services so as to develop relevant strategies.
2. Modeling Credits: Predictive Modeling also helps organizations in assessing the risk and value of their portfolio credits.
3. Workforce Planning: You can analyze, forecast and plan workforce by assessing gaps and determining fresh talents to get the right skills placed at the right time in your organizations using accurate datasets.

Cons:

Predictive Modeling is great! But it comes with a number of challenges. Some of these challenges are as follows:

1. A huge challenge is acquiring the correct data to use in developing an algorithm. Data Scientists take approximately 80% of their time on this stage.
2. It is not just a mathematical problem. The firms must also prepare for organizational and technical barriers that might prevent them from implementing the predictive models.
3. In order to achieve consistently successful outcomes, companies need to get sufficient data sample size. Often, professionals don’t have a sufficient amount of data to construct their models which obviously influences the results.
4. Often, the problem is ensuring that predictive modeling projects address the real challenges of business. Sometimes the models developed might not align well with the business needs or might actually impact strategy.
5. Lastly, the systems storing this useful data are often not connected to the centralized data warehouses, which makes it difficult for data scientists to access data time to time, simultaneously.

Career in Predictive Modeling

Owing to the technological advances and explosion of data, this field is poised for high growth in the coming decades. While many companies are aware of the need to apply predictive modeling techniques in to their businesses, there is a shortage of available candidates with suitable skillsets. So what are some of the best jobs available?

1. Data Scientist
2. Statistician
3. Data Analyst
4. Business Intelligence Developer

Some of the skills you need to master, in order to ace this field, include: Machine learning, SQL Programming, Python programming, Stata programming, R programming and Matlab.

Future of Predictive Modeling

Like everything else, the future of predictive modeling is also tied closely with businesses and changes in technology. With the constant race for better tech, computing powers continue to expand, and so does our data collection. In this scenario, predictive modeling is expected to become much more sophisticated in multiple fields. With faster computers, predictive modeling specialists will adapt to newly available technology.

This will open up new opportunities. Not only for predictive modelers, but also for 3rd parties to come in and fill the technical gap.

Want to read more posts about programming and big data?

The Software Engineering Study Guide
Statistics Review For Data Scientists
Learning Data Science: Our Top 25 Data Science Courses
The Best And Only Python Tutorial You Will Ever Need To Watch
4 Must Have Skills For Data Scientists
What Is A Data Scientist
Analyzing Healthcare Data With BigQuery