Analytics & Insights
IT Cost Containment with Business Benefits
An effective data archiving strategy can save money while also enabling operational efficienciesRead More
Authors: Craig Kreps and Bryan Copeland
Most organizations are on the lookout for ways to operate more efficiently and improve their customer insights through data. While the phrase predictive analytics is popular to throw around in meetings, we have found few companies have a process to implement this popular concept. By leveraging data and effectively applying predictive analytics, companies can use statistical analysis to proactively make data-backed decisions that drive future business initiatives, reduce customer churn and improve customer value.
Framing the business problem is the first step for a successful project. We’ve all witnessed the deployment of automated models that fail to impress because they are not aligned with business goals. Stephen Hawking once said “A super intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we’re in trouble.” While trouble in this case would refer to a waste of staffing resources, time, and capital investment, project alignment to business goals is still mission critical. By framing the design of the model around a business’s goal, the result of the predictive model can effectively drive top and bottom line growth.
If predictive analytics is the goal, effectively sourcing and preparing your data should be the foundation. The internal and external data will be prepared, organized and cleansed in a way that will be able to feed your data model. The data foundation will determine the outcome of your analysis.
We know there are different levels of data maturity for implementation, so let’s explore what some of these phases could look like.
There are many different statistical options to choose from when performing predictive analytics and the best option will depend on the shape of your data. Once the model is built, it can be further refined by performing feature engineering. Finally, the output can be used to create business value and redefine what’s possible.
You can leverage predictive customer analytics to drive value in many areas of your business:
A successful predictive analytics campaign can have a lasting and sustainable impact on your business. It’s always fun to run with data, but make sure you understand your business problem first. Alignment is key. Especially when data is helping you solve the problem.