Evaluating artificial short message service campaigns through rule based multi-instance multi-label classification

• Marketers need new ways of generating campaigns artificially for their marketing activities. Many marketers assume proprietary systems are individualized enough. This article investigates an order of models used to measure how reliably a system can generate campaigns artificially while producing a campaign classification and generation models that are integrated into an intelligent marketing system. The order is between a Classification Model (CM) and a Generation Model (GM). The order also functions as an iterative model improvement process for developing the models by evaluating the models' accuracy distributions. The CM received a mean accuracy of 100%. The GM received 98.9% mean accuracy and a reproducibility score of 96.2%, implying the vast potential for increased resource savings, marketing precision, and less consumer annoyance. The conclusion is that the developed system can reliantly construct campaigns.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

Artificial intelligence

Intelligent marketing system

Iterative model improvement

Classification (of information)

Commerce

Machine learning

Springs (components)

Classification models

Iterative model

Marketing activities

Multi label classification

Proprietary systems

Reproducibilities

Resource savings

Short message services

Marketing

Information Systems

Publikations- och innehållstyp

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