<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis

Faculty of Management Koper, University of Primorska, Cankarjeva 5, 6000 Koper, Slovenia

Organizacija

{"article-title":"Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis"}

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...

Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis

Data publikacji: 12 gru 2008

Zakres stron: 116 - 124

DOI: https://doi.org/10.2478/v10051-008-0013-7

Słowa kluczowe
market research, time series forecasting, beer demand

This content is open access.

Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis

Data publikacji: 12 gru 2008

Zakres stron: 116 - 124

DOI: https://doi.org/10.2478/v10051-008-0013-7

Słowa kluczowemarket research, time series forecasting, beer demand

This content is open access.

Słowa kluczowe
market research, time series forecasting, beer demand