What is predictive analytics?
Predictive analytics is a branch of data analytics aimed at making predictions about future trends, behaviors, outcomes.
Modern predictive analytics solutions are powered by machine learning models. To put it simply, models are the templates that allow users to turn historical and current data into actionable, human-readable insights.
In the real world, predictive analytics is used everywhere you look:
- Retailers use predictive models to forecast inventory requirements, manage shipping schedules and configure store layouts to maximize sales.
- Airlines use predictive analytics to forecast demand and set ticket prices.
- Ecommerce giants use it to assess risk and detect activities out of the ordinary, ranging from credit card fraud to cyberattacks.
Predictive Analytics and B2B SaaS
In the world of B2B SaaS, companies use predictive models to gain competitive edge, by forecasting:
- Customer Lifetime Value – to identify customers likely to spend more on products and services.
- Customer Segmentation – to group customers based on similar characteristics and thereby improve personalization.
- Customer Retention – to spot and address customers likely to churn.
The above are just a few examples of common applications, as data-driven organizations typically leverage predictive analytics to tackle dozens and sometimes hundreds of business challenges.
From a technical standpoint, predictive analytics solutions are based on a set of techniques.
For example, decision trees, which rely on a schematic, tree-shaped diagram to determine a course of action or to show a statistical probability.
The branching method can also be used to show every possible outcome of a particular decision and how one choice may lead to the next.
Regression techniques are often used in finance-oriented models to forecast asset values and help users understand the relationships between variables, such as commodities and stock prices.
On the cutting edge of predictive analytics, techniques are neural networks, which are algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind works.
Getting Started with Predictive Analytics
In the past, using predictive analytics was a privilege saved only for analysts and data scientists.
However, new advancements in no-code software make it possible for non-technical business-oriented managers to realize value in a self-service fashion.
In fact, modern predictive analytics solutions can even be leveraged by relatively small teams, as long as they have enough data at hand, and as long as they are willing to put in the time to set up the system.
Beginning with a limited-scale pilot project in a critical business area is the best-practice for controlling costs and minimizing time to value.
Once a predictive analytics model is put into action, it generally requires little upkeep as it serves as an insight-generation engine that can automatically grind out actionable insights that can make a massive business impact.