MASTER THESIS
The effect of household characteristics on multi-channel
advertising effectiveness
A hierarchical linear model using panel data for household spending on consumer electronics
By
Timo Mulder
Timo Mulder | S3013189 Master Thesis, August 2017 University of Groningen
Content
•
What & why?
•
Intro
•
Conceptual framework
•
Hypotheses
•
How?
•
Methodology
•
Discussion
•
Implications
•
Limitations & future research
Introduction
•
The problem of multichannel advertising
•
More firms use multichannel marketing communication
(Frambach, Roest and Krishnan, 2007)•
In 2015 the online digital ad spending in the U.S. was $59.8 billion
(eMarketer, 2016)•
Forecasts show an estimated U.S. digital ad spending of $113.2 billion in 2020, surpassing TV ad spending
(eMarketer, 2016)•
A strong need to manage the use of media campaigns to reach the desired communication objective
(Dijkstra, Buijtels and Van Raaij, 2005; Sethuraman, Tellis and Briesch, 2011)
•
Theory suggests:
•
Understand the role of each channel and the related consumer behavior in order to understand consumers’ choices.
(Neslin and Shankar, 2009; Gensler, Verhoef and Böhm, 2012)
•
Studies are needed to determine the effect of different channels in the purchase funnel across different products and service categories.
(Kannan, Reinartz and Verhoef, 2016)Introduction (2)
•
Research question:
•
What is the effect of certain household characteristics on the effectiveness of different advertising channels with regard to household spending on consumer
electronics?
•
Household characteristics:
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Household net income
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Household type (with or without children)
•
Advertising channels:
•
Television advertising
•
Print advertising
•
Display banner advertising
•
Firm-initiated channels (FICs)
•
A contact through a channel initiated by the firm. Aimed at the earlier stages of need recognition and information with the purpose to reach customers that
have not yet recognized a need for a certain service or product.
(Haan, Wiesel, Pauwels, 2016).•
TV and Print
•
To elicit cognitive responses in the pre-purchase stage
(Dijkstra, Buijtels and Van Raaij, 2005)•
TV for arousing emotions and Print for information request
(Tellis, Chandy and Thaivanich, 2000)•
In general mean elasticity equal
(Sethuraman, Tellis and Briesch, 2011)•
Banner advertising
•
Facilitating the actual purchase
(Dijkstra, Buijtels and Van Raaij, 2005)•
Offers a variety of targeting purposes
(Goldfarb, 2013)•
Consumer electronics
•
Utilitarian products on which the customer perceives high risk with regard to the purchase
(Kushwaha and Shanker, 2013)Related literature
Conceptual Framework
Offline advertising
(TV and Print)
Online advertising
(Banner)
Household with child
Household net income
Household spending
Figure 1: Conceptual framework
Hypotheses
•
H1
Households with a higher net income have a positive main effect on household spending
•
The higher the income, the more likely to adopt high-technology products
(Risselada et al., 2014)•
H2
An increase in household net income has a stronger positive moderating effect on banner advertising effectiveness on household
spending, than on TV and print advertising effectiveness
•
The higher the income, the more likely to engage in daily online activities
(Jansen, 2010)•
H3
Households with children have positive main effect on household spending
•
With children, the more likely to adopt high-technology products
(Brown, Venkatesh and Hoehle, 2014)•
H4
Households with children have a stronger positive moderating effect on banner advertising effectiveness on household spending, than
on TV and print advertising effectiveness
•
Younger aged people use more technological devices
(Rosseau and Rogers, 1998)Methodology
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Hierarchical Linear Model (Snijders and Bosker, 2012)
•
Panel data
•
Observations over time
•
Weekly observations are nested within households
•
Separate estimate of individual-level residual and group-level residual
•
Maximum Likelihood estimation
(Snijders and Bosker, 2012)•
Weighing factor
•
Model Fit
•
Likelihood ratio test
•
BIC & CAIC
(Whittaker and Furlow, 2009)•
Variance for fixed and random levels
(Snijders and Bosker, 2012)Data
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European retailer selling consumer electronics
•
Household data collected by GfK
•
Survey
•
Software
•
Weekly data (31 weeks)
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November 2010 – July 2011
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Retailer uses multiple offline and online advertising channels
Sample descriptives
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Sample
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9.934 households
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Majority living in western district (42%)
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50.5% of the households bought electronics
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18.7% bought at retailer considered
•
Variables
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Average spending of 785.38 euro at retailer
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36.7% of the households with children
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34% has a net income between 1500 and 2499 euro
Discussion
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(H1) Household net income has a positive main effect on household spending
•
Extra available budget for non-primary purchases
(Brown, Venkatesh and Hoehle, 2014)•
Lower perceived risk
(Dickerson and Gentry, 1983; Kushwaha and Shankar, 2013)•
(H3) Household with children have a positive main effect on household spending
•
Households with children positively correlated to adoption
(Brown, Venkatesh and Hoehle, 2014)•
Avoidance of high-technology products for older aged people
(Mead et al., 1999)•
(H2 & H4) No significant effect of HH-income and HH-type on advertising effectiveness
•
Limited contribution of FICs on a purchase
(Li & Kannan, 2014; De Haan, Wiesel and Pauwels, 2015)•
Traditional channels to initiate interest
( Dijkstra et al., 2005; Naik and Peter, 2009)Implications
•
This study supports research on multichannel advertising
•
Product category consumer electronics
•
Moderating role of household characteristics
•
Academic
•
Findings follow earlier research on the effectiveness of firm-initiated channels
(Xu et al., 2014)•
Attribution modeling
(Kannan, Reinartz and Verhoef, 2016)•
Managerial
•
Deliberate use of FICs for marketing communication to initiate an interest
•
Efficient use of display advertising Procter & Gamble and Unilever
(Van Venrooij and Van der Velden, 2017)•
Household specific targeting
Limitations & future research
•
Product category: consumer electronics
•
Future research: Focus on retailer selling multiple product categories
(Kanan, Reinartz, Verhoef, 2016)•
Limited number of advertising channels
•
Future research: Attribution modeling comparing FICs and CICs
(De Haan, Wiesel and Pauwels, 2015; Kannan, Reinartz and Verhoef, 2016)•
Customer journey
•
Future research: Use of path data
•
Weekly observations
•
Future research: Data collection at daily level
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Representativeness
•
Future research: More recent observations
Questions?
Reference list
Brown, S. A., Venkatesh, V., & Hoehle, H. (2015). Technology adoption decisions in the household: A seven‐model comparison. Journal of the Association for Information Science and Technology, 66(9), 1933-1949.
De Haan, E., Wiesel, T., & Pauwels, K. (2015). The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. International Journal of Research in Marketing, 33(3), 491-507.
Dickerson, M., & Gentry, J. W. (1983). Characteristics of adopters and non-adopters of home computers. Journal of Consumer research, 10(2), 225-235.
Dijkstra, M., Buijtels, H. E., & Van Raaij, W. F. (2005). Separate and joint effects of medium type on consumer responses: a comparison of television, print, and the Internet. Journal of Business Research, 58(3), 377-386.
eMarketer (2016, 13thof september). “U.S. Digital Ad Spending to Surpass TV this Year”. Accessed on July 15, 2017, from:
https://www.emarketer.com/Article/US-Digital-Ad-Spending-Surpass-TV-this-Year/1014469
Frambach, R. T., Roest, H. C., & Krishnan, T. V. (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.
Gensler, S., Verhoef, P. C., & Böhm, M. (2012). Understanding consumers’ multichannel choices across the different stages of the buying process. Marketing Letters, 23(4), 987-1003.
Goldfarb, A. (2014). What is different about online advertising?. Review of Industrial Organization, 44(2), 115-129.
Jansen, J. (2010, November 24th). Use of the internet in higher-income household. Assessed on April 14, 2017, from
http://www.pewinternet.org/2010/11/24/use-of-the-internet-in-higher-income-households/
Kannan, P. K., Reinartz, W., & Verhoef, P. C. (2016). The path to purchase and attribution modeling: Introduction to special section.
Kushwaha, T., & Shankar, V. (2013). Are multichannel customers really more valuable? The moderating role of product category characteristics. American Marketing Association. Journal of Marketing, 77, 67-85
Reference list (2)
Li, H. A., & Kannan, P. K. (2014, February). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. American Marketing Association.
Mead, S. E., Batsakes, P., Fisk, A. D., & Mykityshyn, A. (1999). Application of cognitive theory to training and design solutions for age-related computer use. International Journal of Behavioral Development, 23(3), 553-573.
Naik, P. A., & Peters, K. (2009). A hierarchical marketing communications model of online and offline media synergies. Journal of Interactive Marketing, 23(4), 288-299. Neslin, S. A., & Shankar, V. (2009). Key issues in multichannel customer management: current knowledge and future directions. Journal of interactive marketing, 23(1), 70-81. Risselada, H., Verhoef, P. C., & Bijmolt, T. H. (2014). Dynamic effects of social influence and direct marketing on the adoption of high-technology products. Journal of Marketing, 78(2), 52-68.
Rousseau, G. K., & Rogers, W. A. (1998). Computer usage patterns of university faculty members across the life span. Computers in Human Behavior, 14(3), 417-428.
Sethuraman, R., Tellis, G. J., & Briesch, R. A. (2011). How well does advertising work? Generalizations from meta-analysis of brand advertising elasticities. Journal of Marketing Research, 48(3), 457-471.
Snijders, T. A., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage.
Tellis, G. J., Chandy, R. K., & Thaivanich, P. (2000). Which ad works, when, where, and how often? Modeling the effects of direct television advertising. Journal of Marketing Research, 37(1), 32-46.
Van Venrooij, J., & Van der Velden, L. (2017). Hebben digitale advertenties hun beste tijd gehad? Assessed on August 10th, 2017, from:
https://www.volkskrant.nl/tech/hebben-digitale-advertenties-hun-beste-tijd-gehad-een-van-s-werelds-grootste-adverteerders-gelooft-er-niet-meer-in~a4509110/
Whittaker, T. A., & Furlow, C. F. (2009). The comparison of model selection criteria when selecting among competing hierarchical linear models. Journal of Modern Applied Statistical Methods, 8(1), 15.
Xu, L., Duan, J. A., & Whinston, A. (2014). Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Science, 60(6), 1392-1412.
