What is the Predictive Model?

A Predictive Model is called a method of data analysis and statistics to define hypotheses or deduce future results or events. Modeling generates predictions with a degree of probability according to the variables analyzed.

Some most applied techniques of Predictive Modeling are:

• Decision trees
• Linear and logistic regression
• Neural networks
• Bayesian analysis
• Time series and datamining
• Support Vector Machines
• K-nearest neighbors
• Ensemble models
• Gradient boosting
• Incremental response
• Memory-based reasoning
• Partial least squares regression

What is the Predictive Model for?

The Predictive Model It serves to discover opportunities and prevent adverse situations.

In the world of marketing, for example, allows you to predict consumer behavior and examine the level of influence that can be achieved with certain actions in the market.

Therefore, a Predictive Model serves to intuit what can happen, through the use of techniques that combine mathematics and artificial intelligence, from specific variables.

Some practical apps and benefits of using the Predictive Model, they are:

• Detect signs of dissatisfaction to prevent turnover and design retention strategies.
• Identify which are the customer segments with high value, to maximize the life cycle.
• Identify customer segments with capacity and potential to increase purchases.
• Planning of campaigns aimed at the segments that register certain patterns, such as tastes, preferences, purchasing habits, activity in social networks, etc.
• Define compensation and customer loyalty systems, according to historical purchasing behavior.
• Identify the seasons of low sales, to design strategies that minimize the situation.
• Identify the aspects that affect the abandonment of shopping carts, to design remarekting strategies that lead to the purchase.

Predictive Model Examples

Companies like Ebay, Amazon and Netflix use Predictive Models to design your marketing strategies. Some of the models with greater application at the digital marketing level are:

• Cluster: Use algorithms to segment customers based on variables such as age, gender, geographic location, and shopping habits, among others.
• Propensity: Used to predict how likely a customer is to bond with the brand, buy or unsubscribe.
• Collaborative filtering: It is based on person-to-person recommendation, as well as direct and cross-selling.

If you are looking for help to start using predictive analytics in your business or project, contact us. We will be happy to help you design a tailored strategy for you.