Predictive Analytics

Predictive analytics requires several different models and algorithms depending on the use case. Most important part is to determine what predictive modelling technique is best suited to solve the use case and leveraging data to make insightful decisions.

What are the most common predictive analytics models and which predictive algorithms are most helpful to support them? here we are emphasizing on the most popular types of predictive models and algorithms that are being used to solve business problems today.

Predictive Analytics Models

Classification Model

The classification model is, in some ways, the simplest of the several types of predictive analytics models we’re going to cover. It puts data in categories based on what it learns from historical data.

Classification models are best to answer yes or no questions, providing broad analysis that’s helpful for guiding decisive action. These models can answer questions such as:

• For a retailer, “Is this customer about to churn?”
• For a loan provider, “Will this loan be approved?” or “Is this applicant likely to default?”
• For an online banking provider, “Is this a fraudulent transaction?”

The breadth of possibilities with the classification model—and the ease by which it can be retrained with new data—means it can be applied to many different industries.

Clustering Model

The clustering model sorts data into separate, nested smart groups based on similar attributes. If an ecommerce shoe company is looking to implement targeted marketing campaigns for their customers, they could go through the hundreds of thousands of records to create a tailored strategy for each individual. But is this the most efficient use of time? Probably not. Using the clustering model, they can quickly separate customers into similar groups based on common characteristics and devise strategies for each group at a larger scale.

Other use cases of this predictive modeling technique might include grouping loan applicants into “smart buckets” based on loan attributes, identifying areas in a city with a high volume of crime, and benchmarking SaaS customer data into groups to identify global patterns of use.

Forecast Model

One of the most widely used predictive model, the forecast model deals in metric value prediction, estimating numeric value for new data based on learnings from historical data.

This model can be applied wherever historical numerical data is available. Scenarios include:

• A SaaS company can estimate how many customers they are likely to convert within a given week.
• A call center can predict how many support calls they will receive per hour.
• A shoe store can calculate how much inventory they should keep on hand in order to meet demand during a particular sales period.

The forecast model also considers multiple input parameters. If a restaurant owner wants to predict the number of customers she is likely to receive in the following week, the model will take into account factors that could impact this, such as: Is there an event close by? What is the weather forecast? Is there an illness going around?

Outliers Model

The outliers model is oriented around anomalous data entries within a dataset. It can identify anomalous figures either by themselves or in conjunction with other numbers and categories.

• Recording a spike in support calls, which could indicate a product failure that might lead to a recall
• Finding anomalous data within transactions, or in insurance claims, to identify fraud
• Finding unusual information in your NetOps logs and noticing the signs of impending unplanned downtime

The outlier model is particularly useful for predictive analytics in retail and finance. For example, when identifying fraudulent transactions, the model can assess not only amount, but also location, time, purchase history and the nature of a purchase.

Time Series Model

The time series model comprises a sequence of data points captured, using time as the input parameter. It uses the last year of data to develop a numerical metric and predicts the next three to six weeks of data using that metric. Use cases for this model includes the number of daily calls received in the past three months, sales for the past 20 quarters, or the number of patients who showed up at a given hospital in the past six weeks. It is a potent means of understanding the way a singular metric is developing over time with a level of accuracy beyond simple averages. It also takes into account seasons of the year or events that could impact the metric.