This paper looks at AI-powered voice surveys as an alternative survey format for commercial research. Specifically, the paper is looking at whether this type of alternative format can be more effective at capturing a broader more inclusive spectrum of respondents, including disempowered members of society. To explore the effectiveness of the voice survey format, this research was conducted in an experimental two-by-two design, with the traditional online survey as the comparison. This paper documents the challenges of utilizing an AI-powered voice survey for research and determines what some of the benefits are of this alternative survey format.
Stop! Is your methodology biased? Ad measurement provides 'Accountability' (to prove that ads work), however, we argue that measurement should produce 'Incrementality' (help businesses grow with ads). To measure true ad effectiveness - incrementality, we have to move away from the long-accepted methodology: pre vs. post-campaign or non-exposed vs. exposed. For this presentation we will redefine ad measurement and demonstrate how we measure it at Google with examples.
Stop! Is your methodology biased? Ad measurement provides 'Accountability' (to prove that ads work), however, we argue that measurement should produce 'Incrementality' (help businesses grow with ads). To measure true ad effectiveness - incrementality, we have to move away from the long-accepted methodology: pre vs. post-campaign or non-exposed vs. exposed. For this presentation we will redefine ad measurement and demonstrate how we measure it at Google with examples.
Using a quasi-experimental design in which data collection methods and recruitment techniques as influencing factors were varied Ipsos Germany conducted a comparative investigation for Langnese/Unilever to assess the validity of online-panel surveys. Against the background of both the debate on methodological standards of online research and the popularity of online panels, empirical findings are required for an objective and thorough assessment of the possibilities of using this instrument. The results of this survey are therefore relevant for the further development of online research based not only on the panel approach.
At the Copenhagen Business School we have been running a research project concerned with sponsoring since the late eighties. In the present paper some conclusions and some late findings from this programme are presented.
This chapter is divided into two distinct parts: experimental design and models. The researcher must consider the question of experimental design and models before proceeding to the choice of sampling techniques, sample size or questionnaire design, hence the placing of these two subjects early on in this section.
All designing a controlled experiment should begin with the design of the ideal experiment. If the ideal experiment cannot be executed because of financial, factual, moral, or legal obstacles, we should make a systematic effort to save the controlled character of the experiment by redesigning its objectionable features. As a rule, the redesigned experiment will be less powerful than the ideal one, and we must then decide whether we want to live with that loss or move to quasi-experimental designs. The review in this paper of redesign strategies that have saved controlled experiments in the past should help us to be prepared and inventive the next time our ideal experimental design runs into a roadblock.
In this paper a study will be described which attempts to investigate certain behavioral dimensions of decision making. The study consists in part of an experimental design employing a problem- solving and decision-making marketing task within the framework of a concept termed cognitive style. Out of several current research efforts both in Europe and the U.S.A. a strong relationship between cognitive style and characteristics of problem-solving behavior have been found. If the findings appear to have direct relevance to management practice, they should provide useful insights into marketing education.
Determining the number of lines per range requires assortment decisions on the number of items and facings displayed in the stores. The constraint of limited shelf space and demand and cost interactions among the various products of the assortment must be considered. In many cases, retailers try to solve the problem by trial and error. An alternative is to conduct controlled experiments using experimental designs. They allow definite statements that have a known and controllable probability of being correct and consequently provide a reliable basis for decision-making. The testing procedure is illustrated by two case studies using latin square designs to answer the fundamental questions of adding/deleting a product and changing shelf space allocation.
For in-store designs one has to define a standard of measurement. In most cases it is no use to design something with the use of only one store. It is preferable to use a reasonable number of stores. Great attention should be paid to the unsolvable problem of comparability between stores. It is preferable to use the more simple research designs instead of the sophisticated ones, because if something goes wrong, and in most cases it does, one need not throw the full experiment away. One should try to design an experiment that takes exogenous factors into account like seasonal fluctuations, trends, etc. It pays to devote great attention to the human factor involved by motivating people who participate, but not too strongly. Rigid field control is an absolute necessity in order to reach honest evaluation.
Covariance analysis is a combination of regression analysis and the analysis of variance. From the latter's standpoint it can be used with any of the above experimental designs to increase the precision of the experiment by removing one or more uncontrolled variables from the error term. From a regression standpoint it can be used to compare several regression lines to test whether they differ significantly from each other and to identify the sources of variance. This paper will illustrate the use of covariance analysis, primarily from a regression standpoint using an example taken from industrial marketing. At a later stage it will be suggested that covariance analysis can be used in the analysis of variance context to examine the competitiveness of price-setting policies by companies,