Predictive analytics means using data analysis, statistical modeling, and machine learning technology to ‘predict’ likely outcomes. This prediction requires the construction of predictive models. In your business, such models can identify and analyze patterns and trends in historical (and real-time transactional) data to identify risks and opportunities.
Determining which model suits your business needs is key to getting the most out of a predictive analytics solution. For example, a retailer wants to reduce customer churn. They might not benefit from the same predictive analytics models used by a hospital that predicts the volume of patients admitted to the emergency room in the next ten days.
In this article, we’ll explore five popular predictive analytics models you can choose from to increase profitability. You can then get it implemented in your data analytics tools.
Top 5 Predictive Analytics Models
This is considered to be the simplest type of predictive analytics models. It classifies data into categories based on what it learns from historical data.
Classification models can easily answer yes or no questions and provide broad analysis to take decisive action. Some of the questions this model can answer are:
- Retailer: “Will this customer complete the checkout process or abandon the cart?”
- Loan provider: “Will this applicant be a defaulter in the future?”
- Online banking provider: “Is this a genuine transaction?”
The classification model can be applied to many different industries with ease.
It’s one of the most widely used predictive analytics models and deals in metric value prediction. It estimates the numeric value for new data based on learnings from historical data.
Some of the scenarios where this model can be applied include:
- A SaaS company can predict how many customers they are likely to convert within a given week.
- A call center can estimate how many support calls they will have to answer per hour.
- A shoe store can calculate the inventory they should store in order to meet demand during a particular sales period.
The forecast model also takes into account multiple input parameters. If a restaurant owner wants to understand the number of customers she is likely to receive in the following week, the model will consider factors such as:
- Is there an event close by?
- What is the weather forecast?
- Are there any illnesses going around?
Figuring out the implementation of this model looks complicated. You can always take the help of a good predictive analytics consulting service to understand such models better.
The clustering model sorts data with similar attributes into separate, nested smart groups. For instance, an e-commerce shoe company wants to implement targeted marketing campaigns for their customers. They can use the clustering model to quickly separate customers into similar groups and design strategies for each group.
Other use cases for this technique include grouping loan applicants into “smart buckets” based on loan attributes and benchmarking SaaS customer data into groups to identify global patterns of use.
Time Series Model
The time series model consists of a sequence of data points, captured using time as the input parameter. It uses data from the previous year to develop numerical metrics and predicts data for the next three to six weeks using that metric.
Use cases for this model include the number of daily calls received in the past four months, sales for the past 30 quarters, or the number of patients who showed up at a given hospital in the past 12 weeks. It understands the way a singular metric is developing over time accurately. It also considers the seasons of the year or events that could impact the metric.
The time series model can better model exponential growth and align with a company’s trend. It can also provide forecasts for multiple projects or multiple regions at the same time. EZlytix, a leading data management consulting company, uses such complex models in its 150+ customized KPI reports.
The outliers model revolves around anomalous data entries within a dataset. It can spot anomalous figures either by itself or in conjunction with other numbers and categories.
- A spike in support calls could indicate a product failure that might lead to a recall.
- Anomalous data within transactions, or insurance claims, help identify fraud.
- Find unusual information in your NetOps logs and notice signs of impending unplanned downtime.
The outlier model is very smart and has numerous applications in the retail and finance industries.
Get a Predictive Analytics Solution for Your Business Today
Predictive analytics is no longer a matter of choice anymore for businesses. A traditional analytics solution cannot serve your needs in the long term. Before picking any model, make a list of what type of predictive questions you want to answer for your business. Sometimes, you might have to implement more than one model in your analytics system. Expert analytics consultants and managed analytics solutions can help you do the same. Get started today and boost your profitability.