How do these segments influence customer-initiated touchpoints and purchase decisions?
Delina Post
S2937034
University of Groningen
Faculty of Economics & Business
Master Thesis Marketing Intelligence
Every customer has their own unique customer journey, but most of these customers do have certain similar characteristics.
Channels as steppingstones for gathering information (Montaguti, Neslin and Valentini, 2016) and they can be used separately or simultaneously during the customer purchase journey.
Research Question
The purpose of this study is to examine whether geodemographic segments can be identified within the customer base of the travel agency and if these segments differ in the way they are influenced by customer-initiated touchpoints in making the decision to make a purchase.
Model aims to advance general marketing knowledge and to get case specific insights for the travel industry.
Research Question:
Purchase Decision
Frambach, Roest & Krishnan (2007) and Wolny &
Charoensuksai (2014); important relationship between the buying stage and channel usage intention.
Fennis & Stroebe (2016); intention behavior gap
Howard and Sheth buying behaviour model the five-stage consumer decision-making process the purchase decision hierarchy
Channel Choice & Customer-Initiated Touchpoints
Li and Kannan (2014); coming across certain touchpoints leads to conversion,whereas coming across other touchpoints will not lead to a conversion
Lemon and Verhoef (2016) -brand-owned
-partner-owned -customer-owned
-social/external/independent
Segmentation
Konus, Verhoef & Neslin, 2008; CBS, 2014; CBS, 2017
Conceptual Model
Hypothesis 1: When a customer encounters a customer-initiated touchpoint he or she is more likely to make a purchase.
Methodology
GFK data as “Good Data” due to availability, quality, variability and quantity
7647 respondents
2473 made a purchase 726.132 purchases in total
LCCA (McLust)
Latent class cluster analysis is a model-based approach that offers a variety of model
selection tools and probability-based
classification through a posterior probability of membership
Regression Analysis
Dependent variable is a count variable:“Ordinal variable that takes on non-negative, discrete values: 0, 1, 2, 3, 4, …; › Reflecting the number of occurrences of a specific event in a fixed period of time”
Poisson regression (maximum likelihood estimation) -the mean is not equal to the variance
-zero events cannot be observed -more zeros than expected
Findings Hypothesis 1
Hypothesis 1: When a customer encounters a customer-initiated touchpoint he or she is more likely to make a purchase
Discussion Hypothesis 1
This relationship explains that when a customer initiates contact with the
organization via one of the existing touchpoints, he or she is more likely to increase their total amount of purchases. Interacting with one of the touchpoints considered in the dataset should lead to a purchase.
Findings Hypothesis 2
Hypothesis 2: The influence of the type of customer-initiated touchpoint on purchase decisions differs across segments
Discussion Hypothesis 2
The influence of the type of touchpoint depends mostly on family composition and geographic location, contradicting the statement from Verhoef (2015) that geography becomes less relevant due to the increase of online channels. Nowadays, it is still relevant for companies to consider geography when creating a competitive strategy.
The influence of income due to socio-economic inequalities within districts, as argued by Sleutjes et al. (2018), cannot be supported with this research paper.
Conclusion
The way in which a customer initiates contact with a company determines if they will decide to make a purchase or to increase their total amount of purchases.
The residence of a customer, based on certain geodemographic characteristics, determines to a certain extend which customer-initiated touchpoint most often leads to the highest increase in purchases.
Companies can use these findings through keeping the geography of their customers in mind when creating their competitive strategy that does not fit everyone, but that also does not only fit one.
Limitation and Recommendation
The geographic information about the customers is based on only two variables.Neslin et al. (2006) support the relevance of research about touchpoints by showing proof that single-channel customers purchase less than
References
CBS. (2004). Grote regionale inkomensverschillen in de afgelopen halve eeuw. Retrieved from: https://www.cbs.nl/nl-nl/nieuws/2004/06/grote-regionale-inkomensverschillen-in-de-afgelopen-halve-eeuw
CBS. (2017). PBL/CBS Regionale bevolkings- en huishoudensprognose 2016–2040: analyse van regionale verschillen in vruchtbaarheid. Retrieved from: file:///C:/Users/delin/Downloads/regionale-bevolkings-en-huishoudensprognose.pdf
Fennis, B.M., & Stroebe, W. (2016). The psychology of advertising (2nd Ed.). New York, NY: Routledge.
Frambach, R., Roest, H., & Krishnan, T. (2007). The impact of consumer internet experience on channel preference and usage intentions across the different stages of the buying process. Journal of Interactive Marketing, 21(2), 26-41.
Konus, U., Verhoef, P., & Neslin, S. (2008). Multichannel shopper segments and their covariates. Journal of Retailing, 84(4), 398-413. Lemon, K., & Verhoef, P. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96.
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Sleutjes, B., De Valk, H., & Ooijevaar, J. (2018). The measurement of ethnic segregation in the netherlands: Differences between administrative and individualized neighbourhoods. European Journal of Population, 34(2), 195-224.
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