Businesses today are amassing data like never before. But to what end? All the cost and effort only pay off if you use it to make decisions. Predictive analytics is the practice of analyzing current and historical facts to make predictions about future events. For companies, this can be looking at historical sales lead data to predict which future leads will most likely convert. Or it can be detecting patterns in manufacturing machine failures to predict which machines are most likely to fail next, and so on. The smartest businesses use predictive analytics to feed their decision-making process.
A lot of companies have been crunching numbers for a long time. But now, with natural language processing (NLP), they can weave in so much more to their data analysis. Now, AI can analyze sales notes and transcripts alongside sales history and marketing data. Now, notes on patient symptoms can be analyzed alongside diagnostic test results. And now, you can weigh the sentiment or tone of a customer service interaction alongside more readily available numerical measures like resolution time or contact history. Data crunching just got a lot more holistic.
In our customers’ experience so far at Akkio, this is where they’ve seen the juice. It’s combining the notes, the sentiment, the descriptions, the addresses, the dates, and the like with the numbers -- and creating a predictive model of critical outcomes. Our platform lets a user view the fields of their data that have the most predictive power. Frequently, users tell us the areas that rise to the top are ones they always suspected play a significant role, but they could never really quantify that role in a model before. With ML, we’re making predictive models with a better and better “gut feel” for the data.
You can create predictive analytics with surprisingly good “intuition” that a sales team can use to predict a customer’s spending pattern, or health care providers can use to predict which patients are most at risk of not taking their medication, or HR departments can use to predict what employees might be unsatisfied in their role. But almost no one is doing it because it’s just too hard and too expensive. You need data scientists and developers who know their way around ML. You need an ops team that can deploy and operationalize the services they create. You need to beat Google/Facebook/Apple at hiring those people. And then your project needs to rise to the top of the work stack of that team.
At Akkio, we’re trying to make the power of AI-driven predictive analytics available to you in a way that’s so quick and easy to use that it’s not a big deal. Give it a 10-minute try, see what you think, and start imagining what you can predict.