Analysis of the efficiency of social media for the marketing campaign in a company

Valeriia Malakhovskaia

Master's thesis

ANALYSIS OF THE EFFICIENCY OF SOCIAL MEDIA FOR MARKETING CAMPAIGNS IN A COMPANY

Abstract:

The master's thesis provides a theoretical context for understanding the suitability of social media for marketing, approaches to measuring efficiency, and the main advertising features. The thesis aims to create a decision tree that helps marketers choose social media advertisements based on industry, target audience, objective, and content type. Thus, the thesis is divided into three parts. The first one is theoretical and built on an overview of social media platforms, explaining why social networks are suitable channels for marketing nowadays. The thesis also analyzes discreet social media metrics for measuring marketing efficiency, describes the main advertising features, and proposes a holistic framework for the study. The methodology part describes the approach for creating a decision tree through an interactive online quiz where a person gets unique suggestions for social media advertisement. It is a valuable tool for making marketing decisions and increasing marketing efficiency. In the practical part, the thesis analyzes the specific marketing campaigns and their performance against set internal and external benchmarks based on the recommendations from the thesis’s decision tree. The practical part is done based on the actual data of a company. In the concluding parts, the thesis summarizes the outcomes. It provides an approach for deciding whether a single campaign is efficient and the dependencies between the metrics analyzed. The thesis concludes that digital campaigns add to the marketing efficiency and validity of the thesis’s decision tree for social media advertising decisions. It sets out further areas of exploration that a company may undertake in optimizing digital campaigns on social media.

Abstract:

The master's thesis provides a theoretical context for understanding the suitability of social media for marketing, approaches to measuring efficiency, and the main advertising features. The thesis aims to create a decision tree that helps marketers choose social media advertisements based on industry, target audience, objective, and content type. Thus, the thesis is divided into three parts. The first one is theoretical and built on an overview of social media platforms, explaining why social networks are suitable channels for marketing nowadays. The thesis also analyzes discreet social media metrics for measuring marketing efficiency, describes the main advertising features, and proposes a holistic framework for the study. The methodology part describes the approach for creating a decision tree through an interactive online quiz where a person gets unique suggestions for social media advertisement. It is a valuable tool for making marketing decisions and increasing marketing efficiency. In the practical part, the thesis analyzes the specific marketing campaigns and their performance against set internal and external benchmarks based on the recommendations from the thesis’s decision tree. The practical part is done based on the actual data of a company. In the concluding parts, the thesis summarizes the outcomes. It provides an approach for deciding whether a single campaign is efficient and the dependencies between the metrics analyzed. The thesis concludes that digital campaigns add to the marketing efficiency and validity of the thesis’s decision tree for social media advertising decisions. It sets out further areas of exploration that a company may undertake in optimizing digital campaigns on social media.

Keywords

digital marketing, social media, decision tree, campaign, Instagram

Keywords

digital marketing, social media, decision tree, Instagram, campaign

Language used: English

Date on which the thesis was submitted / produced: 28. 6. 2023

Thesis defence

• Date of defence: 29. 1. 2024
• Supervisor: Antonín Pavlíček
• Reader: Tomáš Sigmund

Vysoká škola ekonomická v Praze

Master programme:
Information Systems Management