AlgoPredict Service

Data Science and Artificial Intelligence

Key Information

Behavioral Profiling Framework using Machine Learning and Artificial Intelligence

  • Implementing behavioral profiling framework with top information value in profiling the underlying hidden behavior of analyzed 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

About AlgoPredict

Social analytics in behavioral predictabillity

Behavioral Profiling Value

Targeted Propensity Behavioral Profiling Framework

Tableau:

Business Integration

CRISP-DM integration of data science and business intelligence

Concept Examples

Applied Analysis in Behavioral 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

Contact

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
contact@algopredict.org
Consulting Hours Available

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.