Identification of showrooming/webrooming behavior throughout the customer journey

1 | 05-07-2019

Identification of showrooming/webrooming

behavior throughout the customer journey

An exploratory study using site-centric data

Koen Molendijk

S3571823

First supervisor: Dr. Frank Beke

Introduction to the topic

› Showrooming and webrooming



68% of US internet-users showroom (Gensler, Neslin &

Verhoef, 2017)



73% of US customers showroom (Lemon & Verhoef, 2016)



88% of US customers webroom (Lemon & Verhoef, 2016)

› Customer journey



Online and offline channels used interchangeably (Skinner,

2010)

› Research questions



“Is it possible to identify showrooming/webrooming behavior

throughout the customer journey, using site-centric data?”



Sub-questions:

- “What browsing behavior corresponds to show- and webrooming

behavior?”

- “How should multichannel retailers respond to the observed

Showrooming/webrooming in the

customer journey

› Customer journey



AISDA  adapted version of AIDA.

- Attention  consumer first pays attention to product category - Interest  consumer becomes interested in product category - Search  consumer gathers information and compares products - Desire  consumer shows passion and has purchase intentions - Action  consumer makes purchase

› Showrooming

 ‘Searching for information and comparing products in the physical store

of a retailer, before buying the product online at the same retailer.’

› Webrooming

 ‘Searching for information and comparing products online on the

website of the retailer before buying the product in the physical store of the same retailer.’

› Contribution

 Linking show- and webrooming to customer journey  Identify show- and webrooming using site-centric data

 Firms often only possess site-centric data (Zheng, Fader, &

Conceptual framework

› Online purchasing segment

› Page Views & Session Duration

 Visit depth affects browsing behavior

(Bucklin & Sismeiro, 2003)

 PCP (Interest)  POP (Search)  PDP (Desire)

› Store Page

 Intention to visit a store

› Channel

 Display ads  Search ads

 Affect browsing behavior differently

Research design

› Type of data

Site-centric data

› Methodology

Data preparation

- Structure data on Session ID - Outliers/cleaning

- 355,220 observations remaining - Variable creation



Latent Class Cluster Analysis (LCCA)

- Latent Gold

- Low within-group variation, high between-group variation



Model selection

LCCA & MNL

› LCCA



No convergence

- Increase starting sets, iterations, sample size, different parameters,

tolerance level.

› Multinomial Logistic Regression



Segment creation

- Showroomers: Purchase, landing page = POP/PDP

- Webroomers: No purchase, landing page = homepage / category

page / product category page

- Online purchasing: Purchase, landing page ≠ landingpage

showrooming

- Other: base segment



Multicollinearity

- Rewrite number of pages viewed to relative number of pages viewed - Create interaction effects

MNL(II)

› MNL model fit & model selection



Model with relative page view variables and interaction effects

› Independence of Irrelevant alternatives



MNL assumes that all alternatives are independent



Hausman-McFadden (hmf) test

- 4 tests, leaving out 1 segment - IIA rejected

- Potential remedies:

- Use different reference level - 3 instead of 4 segments - Segments of similar sizes



Continue with Nested Logistic Regression

Nested Multinomial logistic regression

› Model fit & Model selection

› Model interpretation

Findings

› Showrooming

 Unlikely to visit pages early in journey, visit few PDP’s  Unlikely to visit storepage

 Likely access site via search ad (strong effect)  Online journey ‘Desire’ & ‘Action’ stage

› Webrooming

 Spend lot of time on homepages, category pages, PCP’s, POP’s & PDP’s  Unlikely to visit storepage (?)

 Inaccurate specification

 Online journey except for ‘Action’ stage

› Online purchasing

 Likely access site via search ad  Unlikely to visit storepage

› Segment comparison

Conclusion & Recommendations

› Conclusion

 “Is it possible to identify showrooming/webrooming behavior

throughout the customer journey, using site-centric data?”

- Yes, at least partly.

 Recommendations:

- Showrooming  target showroomers with correct search ad - Webrooming  Optimize process of finding product online and

purchase it offline

- Interpret findings with caution, take into account type of product - No single variable determines browsing behavior

- Identified showrooming segment corresponds to literature

› Limitations & Further research

 Data structured on session ID  run analysis on client ID

References

› Bucklin, R. E., & Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3), 249–267. › Gensler, S., Neslin, S. A., & Verhoef, P. C. (2017). The Showrooming Phenomenon:

It’s More than Just About Price. Journal of Interactive Marketing TA - TT -, 38, 29– 43. https://doi.org/10.1016/j.intmar.2017.01.003 LK -

https://rug.on.worldcat.org/oclc/7023156168

› Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing TA - TT -, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420 LK -

https://rug.on.worldcat.org/oclc/6884830692

› Skinner, C. (2010). The complete customer journey: avoiding technology and business barriers to measure the total value of media LK -

https://rug.on.worldcat.org/oclc/646824231. Business Strategy Series TA - TT -,

11(4), 223–226.

› Zheng, Z. (Eric), Fader, P., & Padmanabhan, B. (2012). From Business Intelligence to Competitive Intelligence: Inferring Competitive Measures Using Augmented Site-Centric Data. Information Systems Research, Vol. 23, pp. 698–720.