Uporaba podatkovne analitike pri napovedovanju klicev glede na oglaševalske kampanje : diplomsko delo

Kurnik, Marcel (author), Škulj, Damjan (mentor)

Uporaba podatkovne analitike je postala vse bolj razširjena v vseh panogah zaradi pojava masovnih podatkov in zahtev, ki jih narekuje trg. Napovedna analitika je vse bolj priljubljena v sklopu podatkovne analitike, saj se vse več organizacij zaveda pomembnosti napovedovanja prihodnjih vrednosti. Napovedovanje je pomembno tudi v klicnem centru zaradi optimizacije dela zaposlenih. Pri takšni vsebini napovedne analitike podjetja uporabljajo različne metodologije napovednih modelov, med njimi tudi regresijsko analizo. Na povečanje števila klicev lahko vpliva veliko dejavnikov, eden izmed najpogostejših pa so oglaševalske kampanje. V svojem diplomskem delu sem z linearno regresijsko analizo napovedal število klicev glede na oglaševalske kampanje, kjer sem ugotovil, da je linearna regresija primerna metoda za napovedovanje števila klicev. Rezultati so pokazali, da gre za pozitivno povezanost med številom klicev in oglaševalskimi kampanjami, ki je statistično značilna v mesečnem in tedenskem obdobju. Dobljene rezultate sem s pomočjo Erlangovega kalkulatorja uporabil za prikaz optimizacije urnika v klicnem centru.

• Language: Slovenian
• Keywords: podatkovna analitika, napovedovanje, linearna regresijska analiza, oglaševalske kampanje, optimizacija urnika
• Work type: Bachelor thesis/paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FDV - Faculty of Social Sciences
• Place of publishing: Ljubljana
• Publisher: [M. Kurnik]
• Year: 2020
• Number of pages: 51 str.
• PID: 20.500.12556/RUL-121303
• UDC: 659.1:303(043.2)
• COBISS.SI-ID: 37062147
• Publication date in RUL: 03.10.2020

Secondary language

• Language: English
• Title: Use of data analytics to predict calls based on advertising campaigns

Abstract

The use of data analytics has become more widespread in all industries due to the emergence of massive data and market driven demands. Predictive analytics is increasingly popular with data analytics as more and more organizations are becoming aware of the importance of predicting future values. Forecasting is also important in the call center to optimize workload. With such content, companies use predictive analytics and various methodologies of predictive models, including regression analysis. Many factors can influence the increase in calls, and one of the most common are advertising campaigns. In my thesis, I predicted the number of calls based on advertising campaigns with linear regression analysis. I have found that linear regression is a suitable method for predicting the number of calls. The results showed that there is a positive correlation between the number of calls and advertising campaigns, which is statistically significant in the monthly and weekly period. I used the obtained results with the help of Erlang's calculator to show the optimization of the schedule in the call center.

• Keywords: data analytics, forecasting, linear regression analysis, advertising campaigns, schedule optimization