THESIS DEFENSE John J. Malkoun 24-06-2020
THE IMPACT OF ONLINE MARKETING
ON THE CUSTOMER JOURNEY
STAGES
Understanding the
Customer Journey
and the role of
Marketing Tools
We will bridge the research gap and bring new practicalrecommendations
Problem
Studies on specific channels and conversion but few on multichannel attribution
and effect of marketing tools on purchase funnel) Problem Evaluating effectiveness and attributing credit challenging (Nelsin & Shankar, 2009) Marketing Tools Advertising essential part of marketing-mix budget (Raman
et al. 2012) Customer Journey
Different Channels Different Purposes
- We classified touch points in two categories (Li & Kannan, 2014; de Haan, Wiesel & Pauwels, 2016)
- We look at how FICs influence the customer journey (moves between CICs)
Pushes message to customer (Shankar & Malthouse, 2007)
e.g. Affiliate, Retargeting
FIC
Requires a customer’s action (Li & Kannan, 2014)
e.g. Comparison app, firm website
CIC
Firm-Initiated
Customer journey map
AWARENESS CONSIDERATION DECISION SERVICELOYALTY
STAGES Acquiring basic information about a market/brand Collecting Enough information about a market/brand to be able to select a preference Choosing and purchasing a product Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. STEPS - Many models - AIDA model conceptualizes stages consumer goes
Literature
LOYALTY Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. Green marketing is a practice where companies seek to go above beyond. 2 5FIC Stage Article
Paid Search Awareness, Consideration Jansen & Schuster (2011) Rusmevichientong &
Williamson (2006) Affiliate Awareness, Consideration Malaga, 2007).
Fox & Wareham (2010 Display Awareness, Consideration Ghose & Todri (2015)
Urban, Liberali, MacDonald, Bordley & Hauser (2013) Email Awareness, Consideration Ellis-Chadwick & Doherty
(2012)
Anderl, Becker, von
Hypotheses
SERVICELOYALTY Green marketing is a practice where companies seek to go above beyond.- H1: Probabilities of moving from one CIC to another are
significantly different when the FIC is used in the same journey than when it is not.
- H2: Probability of moving from a CIC to conversion is
higher in journeys that include the FIC.
CIC CIC Conversion
Data
- 2,456,414 observations between 01-06-2015 and 31-09-2016 - Travel agency focus brand, GfK web tracker
Description
- Journeys with only one touchpoint removed > multichannel study
- Touchpoints 11 and 17 removed > no data on them
- Outliers and similar touchpoint in succession were kept > no impact on model
- Dummies for the FICs > subsets for journeys with specific FICs
Cleaning
26,507 2,080
Journeys With at least one FIC
189
Method
- Set of states:
S = {s1, …, sn}
- Transition probabilities:
pij = P(Xt+1 = j | Xt = i) for i,j∈
S, t = 0,1,2,…
Model Characteristics
- Ability to represent dependencies between sequences (Anderl, Becker, von Wangenheim &
Schumann, 2016) - Here sequence of
touchpoints
Markov Chains
Value lost by removing a touchpoint Removal Effects First-order: better
balance between accuracy and stability (Anderl, Becker, von Wangenheim & Schumann, 2016)
Fit models
- On subset without any FIC - On subsets with at least one FIC
T-test for all transition probabilities
- Compare subsets with FICs and the one without
T-test for specific stages
- Compare each stage in subsets vs control
Manual Comparison of specific stages
0 Compare matrices of journeys vs control
0
1
2
3
Results
0 2 4 6 8 10
Control Email Journeys
Average Probability (%)
T-test for all transition probabilities
- Only email journey significant - On average probabilities in journeys with email are
Significant Stage Direction
Affiliate Generic search Less likely
Banner
Accommodation website Less likely
Email Generic search Less likely
Pre-Roll None
Retargeting Generic search Less likely
Results
T-test for transition probabilities at all stages
Affiliate Banner Email Pre-Roll Retargeting Accommodation website - Competitor website - Info/comp website - Accommodation app - Generic search - Competitor web - Info/comp app - Flight tickets web
- Accommodation web - Competitor web - Info/comp web - Competitor website - Info/comp app - Flight tickets app
- Competitor web - Flight tickets web
- Focus brand web - Info/comp web
Competitor
website - Accommodation app - Info/comp app - Flight tickets app
- Info/comp web - Accommodation
app
- Accommodation web
- Focus brand web
- Competitor app - Focus brand web
- Accommodation web - Competitor search - Competitor app - Info/comp web - Info/comp app Flight tickets
website - - Flight tickets search Focus brand
website - Focus brand search - Competitor web- Info/comp web
- Start - Competitor web - Focus brand search
Information app - Flight tickets app - Flight tickets app - Flight tickets app - Flight tickets app - Flight tickets app
Results
Manual comparison at all stages
Results
Hypotheses
- H1 partially confirmed
How Result
Objective Model specification Fair credit attribution
Predictive validity
- 10-fold cross-validation accounts for variability (Kohavi, 1995)
- ROC/AUC better than classical metrics (Baesens et al. 2002; Fawcett 2006)
AUC = .85
Robustness - Run model on different subsets using
cross-validation Consistent results
Interpretability Specification and estimation Clear variables and methodology + visualization
Versatility Specification and estimation Model easy to update
Algorithm Efficiency Estimation Around 220 lines of code and 94 seconds
Validation
- Journeys with higher overall transition probabilities
- Facilitator in journeys (Anderl, Becker, von Wangenheim & Schumann, 2016)
Affiliate, Email and
Retargeting
- Lower likelihood to lead to a generic search
- Online search involves search cost summarized as time and effort (Kumar, Lang & Peng, 2005)
- May reduce that cost as they serve as a direct way to push information to a customer (Shankar & Malthouse, 2007)
and a personalized message to a previous visitor of the website (Lambrecht & Tucker, 2013)
Affiliate
- Higher likelihoods of leading from an accommodation app, an information app or a flight tickets app to a competitor’s
website
- Purpose is to directly link to the focus brand’s website (Malaga, 2007)
- Could be coincidental correlations or the consequence of other factors not included in the data
Banner
- Focus brand search is more likely to lead to the focus brand’s website
- Would make sense if banner ads are located in the journey either before a customer searches the name of the brand
or after
- Low likelihood to go to focus brand’s website by directly clicking the banner ad may add to argument that this form
of advertisement may not always be wanted (Blattberg, Kim, & Neslin, 2008)
- Higher probability of leading from a competitor’s website or an information/comparison website to the focus
brand’s website
- Email can increase website visit (Ansari & Mela, 2003) - When email appears after visiting a competitor’s website
or information website, it serves as a recall or provider of information and the next step is to go to the focus brand’s
Pre-Roll
- Decrease in likelihood to visit a competitor’s website after visiting the focus brand’s website
- Little direct impact on the visit of the focus brand’s website
- Metric for measuring video ad effectiveness is the attitude toward that video (Belanche, Flavián & Pérez-Rueda, 2017) - Attitude important mediator of brand consideration and
Retargeting
- Higher likelihoods to move from start of the journey, the focus brand’s search and a competitor’s website to the
focus brand’s website
- May mean that customers who search for the focus brand, or are at the beginning of their journey and have visited
the firm’s website in a preceding journey, may be more likely to revisit this website when retargeted (Lambrecht &
Closer to Conversion ?
Even though we lack conversion data, we can speculate that certain stages are closer to conversion than others
Some stages closer
Which stages
A consumer is closer to the conversion square when visiting the focus brand’s website, than an information/comparison website or app, a generic search or a competitor’s website, search and app
The accommodation, competitor, flight tickets and focus brand’s
respective channels might, by definition, be part of the final route toward conversion
Which stages
Which FICs work best
Affiliates, emails and retargeting can be
effective in driving traffic from some CICs
Understanding the
Customer Journey
and the role of
Marketing Tools
Contributions
Consider using specific FICs Different FICsaccording to importance, budget and effectiveness given the customer stage
Practice
Recommend practitioners to view the customer journey
as a sequence of touchpoints with different purposes Data-Driven
The use of real big data and advanced statistical
models
New Research New research on the
effect of marketing tools on the multichannel online
Understanding the
Customer Journey
and the role of
Marke8ng Tools
Limitations &
Future Research
Long-Term Effects
Long-term effects of the touchpoints can offer a more realistic and time-varying view of marketing
efforts and budget allocation (Breuer, Brettel,
& Engelen, 2011)
Future Research
Segmentation using customer data could add value by improving
the targeting efforts of FICs (Cambra-Fierro, Melero-Polo, Sese &
van Doorn, 2018) CICs
Focus not put on CICs but touchpoints particular to travel
industry
Limita>ons Unbalanced distribution
