Artificial Intelligence Solution for Optimizing Companies’ Social Media Campaigns (EMODI)

Project no.: PP59/2012

Project description:

Given the changes in customer behaviour resulting from information and communication (ICT), it is appropriate to seek a deeper and broader understanding of the characteristics of company messages, its links to consumer engagement behaviour on social media (SM), and business performance. In addition, optimizing SM for effective corporate campaigns is an integral part of a company’s daily routine. Despite the fact, that companies collect various data on customer behaviour on SM, traditional analytical methods do not allow for complex processing and forecasting of customer behaviour and improvement of company’s performance (e.g. sales). This can be achieved through Artificial Intelligence (AI) solutions that enable companies to manage the effectiveness of SM campaign results, which can ensure their competitiveness in the marketplace. Therefore, these studies are characterized by interdisciplinary access based on marketing and information technology theories. The aim of this project is to create a machine learning model for predictive solutions of optimization of a company’s SM campaigns.

Project funding:

KTU Research and Innovation Fund

Project results:

1. Theoretically grounded assumptions on the impact of diverse characteristics of the company’s messages on customer engagement behaviour on social media.
2. Theoretically grounded assumptions on the impact of customer engagement behaviour on social media on a company’s performance.
3. Developed conceptual framework of a machine learning model for prediction of customer engagement behaviour and company’s performance.
The main result of the project – developed prototype, which enables social media managers and coordinators to predict customer engagement behaviour based on various characteristics of brand posts and develop messages further.

Period of project implementation: 2020-04-14 - 2020-12-31

Ineta Žičkutė-Daugelavičienė

2020 - 2020

Academic Centre of Economics, Business and Management, School of Economics and Business