Predictive Analytics: What It Is and Why It Matters

What is Predictive Analytics?

Predictive analytics involves using machine learning techniques and statistical modeling to analyze past and current data, to help predict future outcomes. It falls under the category of advanced analytics, and relies extensively on the patterns, observed trends and relationships present within data, to provide insights on possible future events as well as identify risks and opportunities.

There are several types of predictive modeling that can be used, including classification models that “categorize data based on historical data, describing relationships within a given dataset” as well as clustering models (creating segregated groups based on common features) and time series models (time-specific data inputs, such as daily or weekly). While each model can be deployed in different use cases, they all aim to support making data-driven decisions. 

By integrating with generative AI, predictive analytics can additionally help to create new data models that align with historical data, thus giving an even broader insight into current performance levels and anticipated future outcomes. 

Predictive Analytics in the Context of Innovation

When it comes to corporate innovation practices, predictive analytics models present various opportunities and benefits that can propel companies forward. Predictive analytics can be beneficial not just for innovators, but also for companies, when incorporated into their existing innovation processes or used in conjunction with innovation management systems. 

Some of the ways in which predictive analytics can be used in the innovation process include:

• Analyzing large data sets that human analytics simply could not match
• Predicting customer behaviors and preferences
• Visualizing and protoyping innovations before launching actual products or going to market
• Understanding the potential financial outcomes of innovations, ranging from profit margins to break-even points
• Highlighting what’s impacting revenue for existing innovations and products

In other words, predictive analytics modeling provides strategic foresight to give your organization a competitive edge even during the early stages of ideation. Essentially, introducing predictive analytics to the innovation process can help you analyze and scrutinize innovations based on real and relevant datasets to make informed decisions more efficiently and much earlier on in the process. This helps organizations to optimize costs and drive innovation in a much leaner way.

Introducing Predictive Analytics in the Innovation Process

When implemented in a structured and organized manner, predictive analytics models allow you to make informed decisions about ideas and innovations in your innovation funnel. At the same time, it is important to use predictive analytics responsibly and carefully, due to the limitations that come with it and it only being as reliable as the data it is based on.

Additionally, when it comes to certain types of innovation, particularly disruptive innovation, even some of the largest and most elaborate data sets cannot predict how an innovation will fare in the market.

When setting out to make predictive analytics part of an overall innovation strategy, having a  dedicated Idea Management System into which predictive analytics tools can be integrated, is recommended. While the idea management program helps to provide a structured framework to guide the ideas through the innovation process, predictive analytics help to enhance this process in various ways.

Here are six strategies to ensure your organization fully leverages the benefits of integrating predictive analytics into your innovation approach:

1. Know your project objectives as well as the desired deliverables before collecting ​data from multiple sources to create the most accurate picture for data-driven decisions.
2. Ensure that employees understand the importance of predictive analytics and incorporate them right from the earliest phases of idea development.
3. Make it a focal point of predictive analytics to identify problems that either kill ideas or encourage iteration to achieve better outcomes. 
4. Remember that innovation should be customer-centric and always relate predictive data back to potential client responses for contextuality.
5. Use predictive analytics to run tests of proposed incremental changes to existing products as well as analyze elements of new innovations.
6. Consistently utilize predictive analytics as an objective information source that removes emotional bias and directs you towards improved innovations.

Predictive analytics should be used to supplement human ingenuity rather than replace it. When utilized in a responsible manner, it can help to significantly speed up and streamline the innovation process in your company. From idea collection, to validation and potential implementation - predictive analytics models can revolutionize corporate innovation practices by providing a data-driven foundation for driving company growth and gaining a competitive edge.