Using Predictive Analytics to Improve Decision Making
If you are in the tech industry, there’s no doubt you've heard about machine learning and predictive analytics. You’ve likely seen your newsfeed flooded with articles on these technologies, explaining how companies are using them to build innovative products and streamline operations. Even cloud and SaaS providers have machine learning platforms and integrated solutions that make building new features much easier than in the past. It's a rapidly growing area of information technology—but are you doing anything with either of these capabilities?
If you aren't using them because you don't understand what they can do—or you can't think of a scenario where they could be useful—watch this 3-minute video about how AgileThought’s data science team helped a client use predictive analytics to lower their churn rate and ultimately boost revenue and sales activities.
In this scenario, it wasn't a year-long project. It didn't take hundreds of thousands or millions of dollars. It was a four-week effort to identify the data, scrub it, and build and test a version-one model to predict one very specific metric (customer churn within the next three months). Now, with that metric, specific actions can be taken to improve the potential outcome—and the outcome can be easily measured. And from there, a feedback loop will be created to drive additional learning by the model. This is a beautifully simple example of how a small investment in data science can have real impact in the short and long term.
Machine learning and predictive analytics empower you to peek into the future, understand potential outcomes, then take action to improve those outcomes. Whether it's revenue prediction for a customer or location, predicting customer churn, reducing employee turnover, improving manufacturing lead time, or forecasting inventory levels, the possibilities are truly endless. If this sounds compelling to you, you’re in the right place—at AgileThought, we have a methodology to help you attack these kinds of highly targeted problems to create and deploy models that can predict the future. Check it out at https://agilethought.com/products/predictive-analytics-discovery-machine-learning/
General Partner at TampaBay.Ventures
5 年Super interesting! The fact is that most organizations already have tools that are generating the type of data that can be used for really interesting insights. Most business leaders think that a data-driven decision strategy involves huge amounts of data and complex systems to be effective when in fact, the simplest forms of data science can yield the largest results in a shorter span of time. The Valpak case is a perfect example of that.