The Effect of Touchpoints on Booking
Conversion, Moderated by Timing of Booking
Alif Edris - S3375412
Table of Content
● Introduction and Research Question
● Literature Review and Conceptual Model ● Data Description
Introduction
The customer purchase journey has become more complex throughout the years with the increasing number of touchpoints in different media and channels
(Lemon & Verhoef 2016)
Companies with greater digital touchpoints understanding can convert sales 2.5 times greater than those who lack this understanding
Introduction
The change in users’ browsing behaviour is subjected to timing variable.
(Bucklin & Sismeiro 2003)
Each touchpoint has different peak time of usage which consequently affects its exposure to consumers.
Research Question
‘How is the influence of customer touchpoints and of the
dependencies between touchpoints on booking conversion?’
1. How strong are the dependencies between touchpoints?
2. What is the influence of touchpoints on own-, competitor-, and overall- booking
conversion?
Literature Review
● Any contact a customer has with a firm (touchpoint) can be divided into
customer-initiated and firm-initiated (Bowman & Narayandas 2001)
● Positive direct impact of CICs on firm’s performance (Jesus, Melero, & Sese 2017; Bowman & Narayandas 2001).
● Positive effects of firm-initiated customer engagement on firm’s performance (Kumar & Pansari 2016; Joshi & Hanssens 2010).
● The pooled information that customers gain from previously accessed websites would induce positive cross-effect by lowering the risk of adoption (Li et al. 2016).
● CICs and FICs, affect one another (Lemon & Verhoef 2016)
Travel Data Description
15 CICs and 5 FICs are used in the study.
In general, CICs have a higher frequency
Timing of Booking
This timing of booking would be divided into 3 variables: Morning, Daytime, and Evening.
Result: Factor Analysis
● Preliminary checks:
○ KMO (Kaiser-Meyer-Olkin’s) measure of sampling adequacy. All KMO measure is above 0.5 ○ Bartlett’s test of sphericity. Bartlett’s test with p-value of 2.22e-16 (Appendix 2B). H0 of “no
correlations can be established” is rejected.
○ The results indicate that Factor Analysis is appropriate to do.
● Model Quality
○ Cumulative variance of 0.29 (p-value of 5.97e-69). ○ Not all the communalities are above 0.4.
○ Cronbach Alpha of 0.3-0.6.
Result: Binomial Logit - Direct Effect
Result: CICs Effect on Conversion
Result: CICs Effect on Conversion
Result: FICs Effect on Conversion
Result: Touchpoints Effect - Effect Size
Biggest effect size for own- conversion is from Factor 1 (Focus Brand’s website). ● For own-conversion: odds ratio of 3.3 and marginal effect of 0.0034
● For competitor-conversion (cross-effect): odds ratio of 0.721 and marginal effect of -0.033
Result: Timing of Booking Effect
● ‘Evening’ timing variable gives significant positive impact on all of the three conversions. H6, 6a, and 6b are accepted.
Result: Timing of Booking Effect - Effect Size
● The biggest effect size is the direct effect of ‘Evening’ variable on overall- conversion with odds ratio of 1.006 and marginal effect of only 0.0006.
All significant direct effects and moderation effects of timing variables, in general, have considerably low impact
Discussion
● Grouping of touchpoints indicate that dependencies between touchpoints exist, which is consistent with hypothesis 5
● Spill-over effect between contacts with focus brand’s website and search (CICs) and exposure of e-mail and retargeting (FICs) is positive
● There may be some degree of consistency in users’ behaviour towards
utilizing App platforms
Discussion
● Each company’s platform is the most effective touchpoint to increase
conversion. Negative cross-effect is found from focus brand’s platform to competitor’s performance (ref: Danaher, Bonfrer, Dhar 2008)
● Non-existent effect of App platform on own-conversion and negative effect on competitor’s conversion may indicate inability of engaging app users (Lee 2013)
● Accommodation web, flight ticket web, and comparison web positively affect competitor’s conversion (Factor 3 and 5), indicating working pooled
Managerial Implication
1. Allocate the marketing budget to each touchpoint with respect to their impact and its significance, i.e. developing efforts for engaging customers to visit focus brand website
2. Giving incentives to penetrate CICs through FICs, which would utilize the positive spill-over effect
3. Develop strategies to keep app users engaged, given positive growth in app users traffic and conversion
Limitations and Suggestions
1. Exclusion of dynamic pricing policy (Chen & Schwartz 2008) 2. Exclusion of sequence of contact
3. Role of psychological determinants
4. More detailed data concerning users’ activity that corresponds with each row of event may help researcher to determine users’ intention for contacting the touchpoint
Factor Selection
1. Eigenvalue criteria: 7 factor solutions (Eigenvalue > 1) 2. Scree plot criteria: 5 factor solutions
3. Cumulative variance criteria: inconclusive
● Compare 5 and 7 factor solutions.
