THE IMPACT OF ONLINE MARKETING ON THE CUSTOMER JOURNEY STAGES

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THESIS DEFENSE John J. Malkoun 24-06-2020

THE IMPACT OF ONLINE MARKETING

ON THE CUSTOMER JOURNEY

STAGES

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Understanding the

Customer Journey

and the role of

Marketing Tools

We will bridge the research gap and bring new practical

recommendations

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

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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

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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

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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 5

FIC 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

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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

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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

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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)

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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

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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

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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

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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

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Results

Hypotheses

- H1 partially confirmed

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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

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Email

- Journeys with higher overall transition probabilities

- Facilitator in journeys (Anderl, Becker, von Wangenheim & Schumann, 2016)

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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)

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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

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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)

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Email

- 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

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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

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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 &

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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

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Understanding the

Customer Journey

and the role of

Marketing Tools

Contributions

Consider using specific FICs Different FICs

according 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

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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

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Afbeelding

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