The impact of external negative news on the customer purchase journey within the travel industry.

Brace for impact?

The impact of external negative news on the customer purchase journey within the travel industry.

Marc van Eck

MSc Marketing Intelligence Thesis defense

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Introduction

Customer well-being

More and more important for firms to make the customers’

experience excellent.

Customer purchase journey

Part of the experience. Internal touchpoints are well investigated.

Externalities are known to exist but little research investigate them.

Context: travel industry

This thesis investigates the customer purchase journey within the travel industry.

Negative news

External to the customer journey.

Negative news will be investigated

in how it has impact on the journey.

Research questions

Main question

What is the impact of negative news (plane crashes) on the customer purchase journey in a travel industry context?

Sub question one

What is the impact of negative news on the attribution of customer journey touchpoints?

Sub question two

How enduring is the effect of negative news on the customer journey?

Literature

1. Firms are not isolated

According to Tax, McCutcheon & Wilkinson (2013, p. 461).

Research by Lemon & Verhoef (2016, pp. 78–79) proves that past experiences can influence current experiences

2. News as externality lacks research

Research that exists focusses for example on the effect of news on stock volatility (Conrad et al., 2002; Eagle & Victor, 2013)

3. Risk aversion

Possible explanation for altered behavior when crises happen is risk aversion (Eeckhoudt & Hammit, 2004;

Kahneman & Tversky, 1984; Pandey & Natwani, 2004). 4. Customer journey stages

Focus in this research is on the pre-purchase stage and purchase stage (Lemon & Verhoef, 2016).

5. Touchpoints

Touchpoints are categorized in two different types: the firm-

initiated touchpoint (FIT) and the customer-initiated

touchpoint (CIT) (Wiesel, Pauwel & Arts, 2011).

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Conceptual model

FITs

Purchase CITs

Pre-purchase stage Purchase stage

Negative news External of the

customer journey

Internal of the customer journey

Time

(H1, H4)

-

-

Touchpoints

Non-linear-(H3)

-

Research design

• Event-based, online data from Dutch travel agencies collected by GfK à internal data

• Google Trend data as proxy for the impact of negative news à external data

• No missing data. Tested for outliers, one journey excluded

• Dataset is transformed from longitudinal panel data to time-series data

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Research design

• Vector Autoregression (VAR) models to investigate impact of shocks

• VAR assumptions: non-stationarity &

cointegration, optimal lag length and non- autocorrelation

• Visual outcomes with IRF plots

• Bayesian Causal Impact (BCI) model as controlling technique

• Does not use external data, models synthetic counterfactual instead

𝑦 _" = 𝛼 + 𝛽 _' 𝑥 _'" + 𝛽 ₎ 𝑥 _)" + 𝛽 _* 𝑥 _*" + 𝛽 ₊ 𝑥 _+" + 𝛽 _, 𝑥 _," + 𝜀 _"

𝑌 _" = Α + 0

12',..,5 5

𝛽 ₆₁ 𝑌 _"71 + 0

12',…,5 5

𝛿 ₁ 𝑅𝑆𝐼 _"71 + 𝜀 _"

𝑡 = 1, 2, … , 𝑇

𝑌 _" =

𝑃𝑂 _"

𝑃𝐶 _"

𝑆𝐹 _"

𝑆𝐶 _"

𝐴𝐹 _"

𝐴𝐶 _"

Where for time 𝑡,

𝑃𝑂

= amount of purchases (own, focal brand)

𝑃𝐶

= amount of purchases (competitor brands)

𝑆𝐹

= amount of FITs

𝑆𝐶

= amount of CITs

𝐴𝐹

= attribution of FITs

𝐴𝐶

= attribution of CITs

Results: VAR

• Dickey-Fuller test: multiple variables show non-stationarity à solved by taking the first difference

• Optimal lag length is defined at (𝑝 = 6), but model has autocorrelation (p < .05)

• VAR (𝑝 = 8) has no autocorrelation and better AIC (table 5)

• All VAR equations are significant (table 6), no differences found between attribution methods

à Relatively little variance explained for RSI and sales of focal brand

à Sales of competitor brands, Amount of FITs and Markov Attribution of FITs are explained considerably well

Table 5. Edgerton-Shukur F test and AIC scores

Model (lag) Edgerton-Shukur F test p-value AIC

VAR (! = 1) 3.5898 .000*** 45,241.11

VAR (! = 2) 2.5244 .000*** 44,984.61

VAR (! = 3) 2.3351 .000*** 44,836.57

VAR (! = 4) 2.1739 .000*** 44,751.5

VAR (! = 5) 1.8081 .000*** 44,631.21

VAR (! = 6) 1.308 .001** 44,441.21

VAR (! = 7) 1.1609 .049** 44,364.58

VAR (! = 8) 1.1541 .057* 44,296.71

* p<.1, ** p<.05, *** p<.01

Table 6. Estimation results per equation for VAR (! = 8)

Equation y-variable R

Adj. R

F-statistic p-value

RSI .374 .297 4.856 .000***

POS competitor .852 .834 47 .000***

POS focal brand .344 .264 4.271 .000***

Amount of CITs

.546 .489 9.776 .000***

Amount of FITs .863 .846 51.08 .000***

Markov Attribution of CITs

.467 .402 7.147 .000***

Markov Attribution of FITs .789 .763 30.47 .000***

* p<.1, ** p<.05, *** p<.01 |

First difference variable

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Results H ₁

• Decrease expected in amount of touchpoints in pre-purchase, not found in IRF plots à H ₁ not supported

• Amount of CITs decreases over time, but Amount of FITs shows a small positive trend on long-term

à H ₃ only fully supported for Amount of CITs

Results H ₂

• Decrease expected in amount of sales in purchase stage, not found in IRF plots à H ₂ not supported

• Small positive trends on long-term

à H ₃ therefore not supported for these

variables

Results H ₄

• Attribution for both FITs and CITs will decrease as a result of a shock, only found for CITs à H ₄ partially supported

• Attribution of CITs decreases to almost zero over time, but Attribution of FITs shows a small positive trend on long-term à H ₃ only partially supported for Attribution of CITs, not for FITs

Results H ₅

• CITs in the pre-purchase stage will decrease more severely in attribution effectiveness as a result of a shock than FITs,

found in IRF plots à H ₅ fully supported

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Results: Bayesian Causal Impact

• No significant outcomes

Table 8. Bayesian Causal Impact model outcomes Model response

variable Observed

response value

Predicted response

value

Absolute effect

p-value POS competitor 6.3 (319.0) 6.2 (316.2) 0.054 (2.779) .470 POS focal brand 0.31 (16.0) 0.32 (16.45) -0.009 (-0.448) .480 Amount of CITs

2.4 (122.0) -3.6 (-183.5) 6 (305) .477 Amount of FITs 46 (2332) 78 (3989) -32 (-1657) .331 Attribution of CITs

3,179 (162,128) -609 (-31,079) 3,788 (193,206) .480 Attribution of FITs 2,301 (117,354) 2,615 (133,379) -314 (-16,025) .414

1

First difference variable |

average value (cumulative value)

Discussion

• Impact of shocks on attribution is different for CITs and FITs

• Endurance of the effect of a shock is variable dependent. On average:

• There is a significant impact of negative news on the customer journey:

CITs

Short term Long term

FITs

Short term Long term

(Mortality) risk aversion? Customers happy to view presented FITs in first days.

Positive contribution to conversion as attribution increases

15.2 days till stable and calm

(min. 10 days, max. 20 days)

11 days intense period

(min. 8 days, max. 15 days)

Variable specific Increase in perceived societal risks (Janic, 2000)

has a shorter effect than expected

Indeed an effect on population as a result of shock events, in line with Von Dem Knesebeck (2015)

Altered behavior is very volatile following Eagle & Victor (2013) but short in duration

𝑥

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Implications

• This research adds to the available literature and contributes to academia. The Marketing

Research Institute (2018) noted the effect of macro-economic trends on the customer journey as a subject of further research. This research contributes to this call.

• This research can aid marketing strategies and help marketing executers in detecting the effect of external negative shocks.

• Firms are recommended to investigate how their business and variables of interest react to negative (external) shocks.

à Such investigation can capitalize on this research by using it as a guide.

• Firms can also consider the implementation of the Bayesian Causal Impact model. This state of

the art model might prove beneficial for firms as the BCI model can be applied in situations where

it is infeasible to collect (proxy) data containing (external) shocks.

Limitations & future research

• This research has reasoned from a risk aversion perspective. Other explanations, such as socially inappropriateness to buy, might also be possible.

à Future research can investigate such explanations by gathering insights of customers’ feelings and emotions after such shock event

• This research has collectively investigated CITs and FITs, aggregating multiple touchpoints into one variable

à Future research can investigate individual touchpoints to get more detailed insights

• Dataset somewhat restrictive with no price information and few observations for the focal brand and FITs

à The investigation of shock impact on price variables and/or more observations is suggested

• Research is limited to a certain time-period, industry and country

à Generalizability is questionable. Research in other time-periods, industries and countries is encouraged

• Expanding the amount of variables in the VAR model could lead to better results and the possibility to implement BCI model as well

à Such future research would yield new methodological options for (academic) research

Thank you!

Questions?