17540917. MEASURING IMPACT OF EVENTS ON AFFINITY CLUSTER USING PROPENSITY DIMENSIONS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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MEASURING IMPACT OF EVENTS ON AFFINITY CLUSTER USING PROPENSITY DIMENSIONS

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Stan Kevin Daley of Espanola NM (US)

Sandipan Sengupta of Kolkata (IN)

Raman Harishankar of Blacklick OH (US)

Lucia Larise Stavarache of Columbus OH (US)

Charbak Chatterjee of Kolkata (IN)

Chinmohan Biswas of Kolkata (IN)

MEASURING IMPACT OF EVENTS ON AFFINITY CLUSTER USING PROPENSITY DIMENSIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17540917 titled 'MEASURING IMPACT OF EVENTS ON AFFINITY CLUSTER USING PROPENSITY DIMENSIONS

Simplified Explanation

The patent application describes a system and method for building propensity inclination dimensions of an affinity cluster, which is a group of individuals with similar interests or preferences. The system identifies next best priority signals based on influencing events to determine a next best priority and applies a predictive algorithm to derive an influencing index. This index is used to determine a tolerance level of the affinity cluster and the duration of time for this tolerance level. The system also determines a critical mass of the affinity cluster required to achieve an objective of an organization.

  • The system builds propensity inclination dimensions of an affinity cluster.
  • Next best priority signals are identified based on influencing events.
  • A predictive algorithm is applied to derive an influencing index.
  • The influencing index determines the tolerance level of the affinity cluster.
  • The system determines the duration of time for the tolerance level.
  • The critical mass of the affinity cluster required for achieving an objective is determined.

Potential Applications

  • Targeted marketing campaigns based on the interests and preferences of affinity clusters.
  • Optimizing resource allocation and decision-making based on the influencing index.
  • Identifying and engaging with key influencers within affinity clusters for brand promotion.

Problems Solved

  • Difficulty in understanding and targeting specific groups of individuals with similar interests.
  • Lack of a systematic approach to prioritize influencing events and determine next best priorities.
  • Inability to accurately predict the tolerance level and duration for an affinity cluster.

Benefits

  • Improved efficiency and effectiveness of marketing efforts by targeting specific affinity clusters.
  • Enhanced decision-making and resource allocation based on the influencing index.
  • Increased success in achieving organizational objectives by determining the critical mass required within an affinity cluster.


Original Abstract Submitted

A system and method including building propensity inclination dimensions of an affinity cluster, the propensity inclination dimensions maintained at a group profile level of the affinity cluster, identifying next best priority signals based on influencing events to determine a next best priority, applying a predictive algorithm to derive an influencing index, which is used to determine a tolerance level of the affinity cluster, deriving a duration of time of the tolerance level, and determining a critical mass of the affinity cluster required to achieve an objective of an organization.

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