Whether it is the director of the CDC using modelling and analytics to respond to disease outbreaks in real-time before they spiral out of control, or the Coach of a sports team using player performance measures to make talent recruitment decisions, the use of data analytics is prevalent across businesses and organisations of ALL kinds. Even subconsciously, informal social group leaders use behavioural pattern analytics to shape their actions toward other group members and dictate which alliances they choose to foster.
In many ways, when used strategically, analytics can be the secret weapon to maximise efficiency, growth, and competitiveness for any organisation. One key comes through an understanding of the real difference between Predictive and Reactive Analytics. Both rely on data analysis (and the more data sets the better) however the two methods should be seen very much as separate processes. Reactive Analytics is retrospective, based on the questions of what is happening within a business and to what extent. These observations can even be extended to the question of why certain events or patterns are occurring, which of course is crucial to successful business functioning.
Predictive analytics approaches things from a different angle, however. The question becomes what can happen and how. Predictive analytics is about compiling as much data and information as possible and applying the observations to predict results before a particular business process is even executed (saving valuable time and resources along the way).
A straightforward modern example of predictive analytics in business is Netflix, which according to Statista has more than 220 million subscribers. The streaming company uses AI-powered algorithms based on a user’s watch history, search history, demographics, ratings, and preferences to predict what they are likely to watch next. These predictions have a proven accuracy of 80%. Netflix then uses these predictive analytics algorithms in its recommendation engine.
The true trick-up-the-sleeve that takes this method of analysis from a classic “predict the future” risk to a calculated method of decision making comes with the implementation of modern techniques such as the use of AI and Machine Learning. Using these methods for internal data compiling creates a “sky is the limit” effect. Generative AI enhances predictive analytics by combining observed algorithms with both internal and external learned data to deliver priceless forecasts of business trends. The more an AI based program learns about a business, the more accurate and usable the output of analytics gets.
When it comes to predictive analytics in the accounts receivable sector of your business, the bottom line focus is on keeping money flowing into your business. This brings a more intentional element to the concept. Yes, it’s based in prediction, but the implications aren’t just theoretical, it’s about streamlining the literal life blood of your organization. The more accurate the predictions, the more resources you free up to focus on growth as opposed to damage control. Payment Dynamics provides consulting on how to implement our REPAI software to create actionable outcomes based on these concepts.
Understanding and capitalising on the combination of Generative AI and Predictive Analytics can empower businesses to stay competitive and streamlined while making informed choices through any level of growth and change.