Predictive Analysis

Predictive Analytics

NextUser power IBM Watson and capture user’s characteristics, needs, and values to drive the next level of digital personalization.

  • By calculating customer lifetime value metric (CLV): Calculating probability that a customer will purchase based on their recency, frequency of visits and overall monetary value.
  • By analyzing cohorts of users, you can objectively measure site’s success-rate in user retention and growth/churn.
  • By further segmenting users into behavior-based user-groups, pinpointing precise sources of problems can be achieved.

Benefits of predictive analytics

  • Predict the next trend: knowing the next "hot item" is key to having the upper hand on your competition.
  • Determine which customers are more likely to perform a purchase, or identify how to increase a probability of a customer to purchase (incentivize visits etc.)
  • Get a better understanding of customer behavior and purchase likelihood.
  • Prepare for future demand with price optimization as well as more product sourcing and efficient stock management.
  • Increase user retention, upsell and cross-sell.

Use case

Qapa - Salary prediction based on user variables

Predictive analytics is a game changer in the world of online recruiting. By making this technology available to Qapa, a popular French online recruiting service, NextUser revolutionizes the job market.

Users of a particular service have different demographic attributes, which enables NextUser to determine a degree of association between all of them. Users that have similar statistical characteristics are grouped into clusters to predict salary ranges. Cluster analysis classifies users based on location, age, gender and other relevant user information.

The salary prediction can be used to personalize all type of communications to onboard and retain the user on the service.