KPI Behavioral Analysis

Data Science and Artificial Intelligence

Key Information

Behavioral Profiling Framework for KPI Targeting using Machine Learning and Artificial Intelligence

  • Implementing behavioral profiling framework with targeted KPI analysis¬† used for profiling the underlying hidden behavior of selected business process
  • Delivering actionable and measurable results with specific KPI targeting
  • Discover hidden behavior in your business process that is used to drive revenue, reduce expenses and optimize risk exposure
  • Communication: On-site, Skype
  • Location Croatia
  • Server Infrastructure NY (USA), LD (UK), AMS (NETH)
  • e-mail

About Behavioral Framework

Social analytics in behavioral predictabillity

Behavioral Profiling Value

Targeted Propensity Behavioral Profiling Framework


Business Integration Flow

CRISP-DM integration of data science and business intelligence

Concept Examples

Applied Analysis of Behavioral Framework
Retail Store KPI Profiling
Retail Store KPI Profiling
IoT Demand Response Profiling
IoT Demand Response Profiling
QoE Quality of Experience Profiling
QoE Quality of Experience Profiling
B2B Lead Follow-up Assessment
B2B Lead Follow-up Assessment
Online Loyalty Marketing Targeting
Online Loyalty Marketing Targeting
HR Departure Risk Profiling
HR Departure Risk Profiling
Artificial Intelligence, Consumer, Profiling


Get in Touch

Get in Touch for Data Science Analysis and PoC Work

Here to provide more information, answer any questions you may have or find a solution for your needs. If you have working data you can get an appraisal of current database possibilities regarding data science.

Please contact using email or contact form.

Zagreb, Croatia

Hypothetical Performance Disclaimer and Risk Disclosure

Past performance is not necessarily indicative of future results. Hypothetical performance results have many inherent limitations, some of which are described below. No representation is being made that any business solution related to data science and artificial intelligence will or is likely to achieve profits or losses similar to those shown in advance; in fact, there are frequently differences between hypothetical performance results and the actual results subsequently achieved by any particular model. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical analysis does not involve different risk types, and no hypothetical backtest can completely account for the impact of financial risk of real modeling implementation. For example, the ability to withstand adverse exposure or to adhere to a particular model output in spite of realized monetary targets are material points which can also adversely affect actual business results. There are numerous other factors related to the business in general or to the implementation of any specific data science solution. These risks include but are not limited to: operational risk, modeling risk, financial risk, it risk, liquidity risk,  strategic decision business risk etc. It is the sole responsibility of the counterparty to assess and accept those risk for themselves and any potential shareholders they represent.