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Title:Forecasting the primary demand for a beer brand using time series analysis
Authors:ID Bratina, Danjel (Author)
ID Faganel, Armand (Author)
Files:.pdf Organizacija_2008_Bratina,_Faganel_Forecasting_the_Primary_Demand_for_a_Beer_Brand_Using_Time_Series_Analysis.pdf (395,07 KB)
MD5: C06D2C94B81C1A5122ACDEBF00F0B326
PID: 20.500.12556/dkum/be59ae3e-8d47-4fca-8c79-6e93b757d4fa
 
URL http://www.degruyter.com/view/j/orga.2008.41.issue-3/v10051-008-0013-7/v10051-008-0013-7.xml
 
Language:Slovenian
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FOV - Faculty of Organizational Sciences in Kranj
Abstract:Market research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.
Keywords:market research, time series forecasting, beer demand
Publication status:Published
Publication version:Version of Record
Year of publishing:2008
Number of pages:str. 116-123
Numbering:Letn. 41, št. 3
PID:20.500.12556/DKUM-69116 New window
ISSN:1318-5454
UDC:339.13
ISSN on article:1318-5454
COBISS.SI-ID:2635223 New window
DOI:10.2478/v10051-008-0013-7 New window
NUK URN:URN:SI:UM:DK:EYAJPSPR
Publication date in DKUM:30.11.2017
Views:1235
Downloads:381
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
BRATINA, Danjel and FAGANEL, Armand, 2008, Forecasting the primary demand for a beer brand using time series analysis. Organizacija : revija za management, informatiko in kadre [online]. 2008. Vol. 41, no. 3, p. 116–123. [Accessed 31 March 2025]. DOI 10.2478/v10051-008-0013-7. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=69116
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Record is a part of a journal

Title:Organizacija : revija za management, informatiko in kadre
Shortened title:Organizacija
Publisher:Moderna organizacija
ISSN:1318-5454
COBISS.SI-ID:610909 New window

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:30.11.2017

Secondary language

Language:English
Title:Model povpraševanja po blagovni znamki piva z uporabo analize časovnih vrst
Abstract:Trženjski raziskovalci pogosto operirajo s podatki, ki so ekvidistančno porazdeljeni v času. Teorija časovnih vrst je primerno orodje za analizo tovrstnih podatkov. Tipično se uporablja za napovedovanje, ugotavljanje vzročnosti pojavov, v trženju pa je največkrat uporabljena pri analizah učinkov diskretnih dogodkov skozi čas. Članek prikaže osnovne koncepte regresijske analize časovnih vrst in predstavi njihovo aplikacijo v trženjskem raziskovanju. S pomočjo analize časovnih vrst postavimo model napovedovanja povpraševanja po znani Slovenski blagovni znamki piva z uporabo POS podatkov z dveh večjih slovenskih hipermarketov. Naša analiza je prva, ki kot podatke zajame dnevno prodajo blagovne znamke (dosedanje so uporabljale širše časovne intervale – tedenska, mesečna ali celo letna prodaja). Slabost dnevne prodaje naj bi predstavljal visok nivo šuma v podatkih. Čeprav vsebujejo podatki veliko nepojasnjene variance, v prispevku pokažemo, da z uporabo časovnih vrst pojasnimo precej več variance kot z uporabo klasične multiple regresije. Analiza časovnih vrst nam tudi pokaže, da so učinki cenovnih akcij, kot enega izmed dejavnikov prodaje, kratkoročni, ter tako potrdi druge raziskave iz področja analiz učinkovitosti cenovnih akcij.
Keywords:trženjsko raziskovanje, analiza časovnih vrst, pivo, povpraševanje


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  1. Organizacija

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