Predictive analytics uses machine learning and advanced statistical modeling to analyze customer data, find patterns, and predict future behavior such as churn or a likelihood to convert. It allows data analysts to take advantage of huge data sets that might otherwise be wasted.
Enterprise businesses gather huge amounts of customer data. But in many cases, they aren’t taking advantage of it all because they are only gleaning insights from the tiny percentage their data scientists are able to manually dig through.
Predictive analytics is designed to help these businesses tap into this untapped potential. Hidden in that data are patterns that reveal meaningful customer insights that — when used effectively — can have a very real impact on the bottom line. Predictive capabilities draw on machine learning and advanced statistical models to dig automatically through enormous amounts of data, searching for those patterns that most analysts simply don’t have time to look for.
At the end of the day, predictive analytics tools help data analysts and marketers alike get the most out of the data they gather by helping them find the insights the really matter.
With the predictive capabilities in Adobe Analytics, you can use your data better. We invest heavily in the technologies that make predictive analytics possible, a fact which contributes to our Leader status in the Gartner Magic Quadrant for Digital Marketing Analytics and multiple Forrester Waves.
We allow you to take really glean insights from huge data sets by helping surface important changes. Our predictive tools also do a lot of the heavy lifting of analysis, helping you get from data to insight faster.
Alexander Gaertner, Head of Digital Analytics, DER Touristik
Travel customers expect a highly personalised experience from start to finish. To meet that expectation, DER Touristik is taking control of their data to create better user experiences — and generate greater returns.
Statistical modeling automatically finds unexpected trends in your data by analyzing metrics and determining a lower bound, upper bound, and expected range of values. When an unexpected spike or drop occurs, the system alerts you in the report.
Discovers hidden patterns within your data to explain statistical anomalies and identify correlations behind unexpected customer actions, out-of-bound values, and sudden spikes or dips for metrics across audience segments.
We make it easy to create and manage alerts based on data anomalies and “stacked” alerts that capture multiple metrics in a single alert.
