Predictive Model in the Real World
Predictive modeling works from the result stage backward. For example, if you intended to sell software, that would be the result. You would then work back to identify the specific reasons for that successful purchase. This could be as simple as recognizing that budget existed, to something a lot more complicated, like understanding whether or not a decision-maker needed to be present at a product demonstration.
Predictive modeling is very different from forecasting. With forecasting, companies make predictions on broader trends that will impact their business in the future. Predictive modeling and analysis are a lot more granular in focus.
Predictive Model in the World of AI
There are many different types of predictive models, with each one serving a specific function.
Possibly the most common model, forecast models, make predictions by learning from historical data. An example of a forecast model is an analysis of the volume of calls a call center will receive on any given day. Based on past trends and time of day analysis, call centers are able to staff appropriately based on an analysis of historical trends.
Classification models look for ways in which data can be categorized. These models are used in a variety of different industries as a way of sourcing information from previous patterns of data.
An outlier model looks for discrepancies or outliers in a set of data. Outlier models are useful in detecting fraud and can also be used by companies looking for defects that could impact systems. By understanding what the data should be within an operation, an outlier model can quickly flag any divergences helping organizations save time and money.
Time Series Model
If the information of interest is data that is changing over time, then a time series model is the best way of understanding how a variable has changed. This type of model is better than other more conventional methods as it allows users to track a variable across multiple different projects or locations simultaneously.
Clustering models group data into specific categories based on common factors and attributes. Clustering models are useful when trying to segment information into smaller subsets for further analysis.
Predictive models have many business uses already, and their scope will only grow in the years ahead. However, despite its usefulness, it is not fool-proof. Predictive models need specific business conditions to be aligned to be useful, and they also need massive pools of well-categorized data to be effective. Another critical point to note is that while a model might provide relevant and valuable information in one case, that does not automatically translate to another.