Data Science > Web Analytics > Predictive Analytics
Web analytics is the data science practiced to collect, measure, analyze, and report on web data to better understand and optimize people’s online user experience (UX) and which is comprised of website content quality and usability. The enhancement of predictive web analytics calculates statistical probabilities of future events online.
Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. These data science components analyze new and historical details, which help to predict future and otherwise unknown behavior and events.
PREDICTIVE ANALYTICS SOFTWARE
In the world of A/B/C testing all content personalization, predictive analytics tools are simplified with the following elements:
Predictive Segmentation: Significant audience segments are automatically found and produced, denoted by an increased likelihood to react in a foreseeable way to various events.
Predictive Targeting: Accurately forecasts the best possible experience for any audience segment or unique visitor. UX quality is measured by statistical probabilities of a business’ goals of a given event. This includes signups, completed first purchases, upsells, viral marketing (ie: social shares), etc.
Ecommerce and digital marketing managers can use these powerful techniques to improve workflows by system processes that automate routine manual tasks. This helps to diminish the time and effort required to complete the important related tasks.
Of course, both predictive segmentation and predictive targeting are required to truly gain from the great impact potential of predictive analytics.
Carrying out Predictive Segmentation without the important element of Predictive Targeting does produce valuable visitor segments, however it fails to deliver fully predictive insight about the best experiences available to deliver.
On the flip side, Predictive Targeting without Predictive Segmentation does have the ability to synchronize ideal UX with any and all audience segmentation, although segments must then be rolled out after they have been defined, and then they must be identified by hand (not programmatically), which makes them far less efficient and effective than fully implemented predictive analytics.