18-20 March 2015
Cologne University of Applied Sciences, Germany
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A 7: Consumer Research
Online Consumer Search Behaviour: An International, Cross-Sector Analysis
1University of Münster, Germany; 2University of Manchester, United Kingdom
Relevance & Research Question: Online Consumer Search Behaviour, Consideration Set.
The Internet has revolutionised access to information and transformed consumer search and buying behaviour. (Hu et al. 2014, Dinner et al. 2014). The consideration set is an important construct in marketing to measure search. Contrary to economic theory (Stigler, 1961), existing Internet studies report relatively small online consideration sets between 2 and 3 (Johnson et al. 2004, Meyer & Stobbe 2010, Holland & Mandry 2013). Our research questions are: 1) Why are online consideration sets relatively small? 2) How do search strategies evolve? 3) What are the differences in search behaviour for constellations of consumers defined by: a) Product and market sectors b) Demographic variables, c) Differences in media use, i.e. computer versus mobile devices, d) Nationality and culture.
Methods & Data: Clickstream Analysis, Online Panel Data.
Clickstream analysis provides granular data on a very large scale, and can therefore be used to develop research experiments and insights into actual consumer behaviour compared with surveys, which rely on recalled behaviour and statements of future intent. International panel data from ComScore is used to investigate differences in consumer behaviour across six market sectors: banking, insurance, grocery, telecommunications, automotive and airline.
Results: International results, small online consideration sets
Initial results from the US, UK and German markets show that the online consideration sets are relatively small. This is contrary to economic and marketing theory, which predicted more extensive search patterns because of lower search costs. Online search also appears to be more narrow than similar pre-Internet studies.
Added Value: Empirical contribution, theory development, commercial data sets
The research builds on the few previous empirical uses of online panel data, and develops an ambitious international, cross-sector empirical database. Most research in this area, including recent survey based research, is typically a snapshot of activity in a single sector within one country, using sample sizes of between 30 and a few hundred. This research is based on longitudinal data samples with sample sizes of millions of users.
Modeling Online Hotel Choice: Conjoint analysis as a multivariate alternative to A/B-testing
SKIM, Netherlands, The
Relevance & Research Question: How can researchers best investigate what drives online purchase decisions? Currently, A/B-tests of isolated features on two otherwise identical versions of a website are the gold standard for testing online purchase environments. Despite its simplicity, A/B-testing is tedious since only a single feature can be changed at a time and possible interactions between features are not accounted for.
In this study, we examine whether conjoint analysis can be used as a multivariate alternative to A/B-testing. Choice-based conjoint is a powerful method to analyze purchase decisions as it allows for optimization across various attributes and levels. While in most conjoint applications attributes and levels pertain to the product itself, the method can also be applied to optimizing the user interface (where elements are placed, how they are designed and executed, the use of rankings, reviews, and promotions). We apply choice-based conjoint to test which booking site features impact purchase decisions for hotel accommodation.
Methods & Data: We conducted an online survey with N=1492 respondents, asking them to choose hotels in a choice-based conjoint experiment as if on a booking site. We manipulated user interface features such as position of items, ranking options, reviews, and promotions using an orthogonal experimental design. To mimic the purchasing process and click-through in a live A/B-test, we assume that each respondent goes through one choice task only. We estimate the impact of feature changes on respondent preferences for hotels and assess model fit using out-of-sample prediction.
Results: Robust model estimation is possible at the aggregate level taking into account a single choice task for each respondent. Using the model estimates we arrive at robust predictions of hotel choices and click-through rates. The results give valid insights into which site features impact online purchase decisions.
Added Value: The study illustrates how conjoint analysis can be used as a multivariate alternative to A/B-testing of online shopping sites. Results of preference estimation are useful for optimizing the design and execution of online shopping environments and for playing what-if-games with a multitude of variables rather than just single isolated features.
The role of market research online communities in qualitative market research: Increased situational validity for customer journey measurement concerning low-interest products
1MSR Consulting Group GmbH, Germany; 2Liveloop GmbH, Germany
Relevance & Research Question: It is well known that prototypicality and situational validity are key success factors for any qualitative research. Qualitative online research offers new opportunities for gaining deep insights. Especially for low-interest products and target groups with little experience, market research online communities (MROCs) offer a better approach to understanding the customer journey. In our presentation, we will show that exercises during a MROC will – with the same or a lower investment - lead to a deeper understanding than face-to-face approaches.
Methods and Data: For our presentation, we will draw on data from an extended MROC vs. data gathered through face-to-face interviews and focus groups. The aim of the research was to find out how young people approach financial services. We will compare the findings of both approaches and show potentials and limitations for each method.
Results: Our results show that MROCs work especially well for low-interest products. Without prior experience in buying processes for a product, different tasks are used to track the information process over time. This makes the research process for each individual much more naturalistic as information gathered will influence the usage of sources to solve the different tasks.
Added Value: We will not only argue that MROCs are (for some target groups and research questions) advantageous with regard to the research validity but also in an economical sense: With MROCs researchers can answer research questions for costs that are often lower than for more traditional qualitative research designs.
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