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Gautam Jaiswal
A QUANTITATIVE EVALUATION OF CUSTOMER MARKETING
IMPACT BY ARTIFICIAL INTELLIGENCE IN HOSPITALITY
INDUSTRY
Gautam Jaiswal 1
Abstract
Artificial Intelligence (AI) is often overhyped and confusing language has made
it complex to understand. But its
unavoidable use is making it more
popular. AI helps the ability of the
technology to improve and make smart
decisions. AI helps in better online
product search and customizes services.
Present paper is an effort to estimate the
applicability of AI in customer marketing
in the context of luxury hotels. It is based
on survey of hotel employees and
customers. The tool used for the purpose
of study has Cronbach’s alpha value above
0.600. This shows the reliability of
research tool used in the present study.
KMO value for sampling adequacy has
been considered. The research has tried
to find out contribute of different aspects
of AI in customer satisfaction in hotel
industry.
Keywords: AI, Customer Marketing, Hotel
Industry.
1.
Assistant Professor, Integrated Academy
of Management and Technology
(INMANTEC) Ghaziabad, Uttar Pradesh,
India.
10.5958/0974-0945.2020.00011.4
40
Introduction
Businesses that stay inflexible to incorporating
new technologies are well on the way to decline
in their course of advancement. Organizations
over the globe have acknowledged that it is so
essential to incorporate contemporary
computerized innovation to drive consistent
development and income. The most recent decade
has seen mind blowing developments and
achievements in the scene of advanced
arrangements. One of such convincing advances
is called Artificial Intelligence (AI).
Frequently misinterpreted as a swap for human
force, the idea of AI as a mechanical guide is a lot
bigger, more extensive and inescapable. It has
made more noteworthy trust, be that as it may, for
the cordiality business, indicating the guarantee
of changing its procedures, administrations and
offices through AI-controlled mechanical
autonomy.
Today, the hotel business, where comfortcharacterizing progressions are most quickly
consolidated, has advanced its whole framework
with the selection of numerous creative strategies
utilized for giving fulfilling client care. Hotel
industry is service based industry. In the service
based industries customer expectations are very
different from one to another. The complexity in
customer marketing and maintaining service
quality is difficult in this industry. With offering
at different level hotel industry is known as
‘HoReCa industry’. HoReCa is an abbreviation for
Pranjana: Vol 23, No 2 Jul-Dec, 2020
the food service industry. The term is a syllabic abbreviation of the words Hotel, Restaurant
and Café. The term is mostly used in Dutch. In English, this term is often known as (Hotel
and) Catering Industry. For delivery purposes drone are now a day’s used by restaurants
and food selling industry. Present study explores the level of impact of AI based marketing
efforts on overall customer marketing for hotel industry.
Review of Literature
Burger et al. showed already in 2001 how artificial intelligence can be used to forecast
tourism demand in Durban, South Africa (Burger et al. 2001). Since then uncountable
tourism demand forecast models, based on artificial intelligence algorithms, have been
developed and proved useful (Peng, Song, and Crouch 2014). Besides demand forecasting,
recommender systems were another research focus(Borràs, Moreno, and Valls 2014). The
utilization of artificial intelligence systems by the travel industry as well as the
accompanying costs and benefits were not further discussed. This changed remarkably
during the year 2017. Scientific papers were published which took first steps in answering
questions such as ‘what are costs and benefits of artificial intelligence systems for travel
companies’ (Ivanov and Webster 2017) or ‘how service robots will be utilized in the
hospitality sector’ (Murphy, Gretzel, and Hofacker 2017).
Methodology
The present research hypothesis was tested with the help of a questionnaire with 20
items. The core survey was conducted with multiple objectives and the research paper
presents only a part of whole research. The research tool was tested for validity and
reliability. The tool has been developed with a primary survey of 160 people. The cronbachs’
alpha value of the tool is 0.825 which is reliable. Final data was collected with a survey
of 400 people.
The research consist 4 variables. ‘Customer marketing impact’ is the dependent variable
representing customer satisfaction. Rests three are independent variables i.e. Guest
Expectations Met, Guest Personalization and Supplementing hotel Staff. These variables
show the impact that AI generates in service quality.
Hypotheses
1- Ho (Null Hypothesis): There is no significant effect of AI based ‘Guest Expectations Met’
on Customer marketing impact.
Ha (Alternate Hypothesis): There is significant relation between AI based ‘Guest
Expectations Met’ on Customer marketing impact.
2- Ho (Null Hypothesis): There is no significant effect of AI based ‘Guest Personalization’
on Customer marketing impact.
Ha (Alternate Hypothesis): There is significant relation between AI based ‘Guest
Personalization’ on Customer marketing impact.
3- Ho (Null Hypothesis): There is no significant effect of ‘Supplementing hotel Staff’ by AI
on Customer marketing impact.
Ha (Alternate Hypothesis): There is significant relation between Supplementing hotel Staff’
by AI on Customer marketing impact.
41
Gautam Jaiswal
Findings of the Study
1- Endeavouring ceaselessly from the conventional effect of artificial intelligence
frameworks, the inquiry remains how travel and the travel industry associations can
utilize such frameworks. As some other mean of an association artificial intelligence
frameworks can be utilized to build its upper hand. Early AI adopters with a proactive AI
methodology will be benefited and will have leverage on others. AI will help service
industry to meet service quality and avoid service gap.
2- Study related to celebrity endorsement, brand image and brand loyalty with consumer
buying is shown as follows:
Customer
marketing
impact
Guest Expectations
Met
Guest
Personalization
Supplementing
hotel Staff
Pearson Correlation
.436**
.532**
.507**
Sig. (2-tailed)
.000
.000
.000
N
400
400
400
Correlation of Guest Expectations Met, Guest Personalization and Supplementing hotel
Staff with Customer marketing impact is 0.436, 0.532, and 0.507 respectively. It shows
that the effects of all three variables are very high on consumer buying.
3- Coefficient of multiple correlations (R)
Model Summary table shows following values when consumer buying is predicted by rest
of three variables:
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.657a
.432
.430
3.12384
The above table shows the R value is 0.657 which shows strong correlation among all
three considered variable in the model. R square value shows that the predicted model is
moderately fit. There is significant relation between Guest Expectations Met, Guest
Personalization and Supplementing hotel Staff and AI based Customer marketing impact.
4- The coefficients table shows following details:
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Pranjana: Vol 23, No 2 Jul-Dec, 2020
Coefficients
Unstandardized
Coefficients
Model
1
B
Std. Error
Standardized
Coefficients
Beta
t
Sig.
(Constant)
3.880
.566
6.860
.000
Guest Expectations
Met (X1)
.280
.020
.340
13.803
.000
Guest
Personalization (X2)
.254
.023
.254
10.915
.000
Supplementing hotel
Staff (X3)
.289
.026
.277
11.112
.000
a. Dependent Variable: AI based Customer marketing impact (y)
The proposed model considers AI based Customer marketing impact as Dependent variable
and Guest Expectations Met, Guest Personalization and Supplementing hotel Staff as
independent variables. The linear relation between the variables ac be predicted as
following:
Predicted variable (Dependent variable) =slope*independent variable + intercept
Y = ß0 + ß1X1 + ß2X2 + ß3X3……………… (i)
Dependent Variable= Customer marketing impact =(y)
Guest Expectations Met = (x1)
ß1 = 0.280
Guest Personalization = (x2)
ß2 = 0.254
Supplementing hotel Staff = (X3)
ß3 = 0.289
Constant (ß0) = 3.880
Putting the values in equation (i) we get our prediction equation as follow:
Y = 3.880 + 0.280X1 + 0.254X2 + 0.289X3
5- t-value in coefficients table
t-value column in the coefficients table shows all the values are above 1.96. This supports
the hypothesis testing in finding 1,2and3. t-values are above 1.96 shows that the variables
have explainable correlation.
6- sig value in coefficients table
Sig column in the coefficients table shows all the values are below 0.05. This supports the
hypothesis testing in finding 1,2and3. Sig values are below 0.05 shows that the variables
have explainable correlation.
Conclusion
With regards to using AI, the lodging business was lingering out of date. In any case, as
this investigates study appears, that is not true anymore, especially as for the visitor
experience. Today the business is moving quick and irately toward not just reflecting AI43
Gautam Jaiswal
empowered home and office encounters in visitor rooms, yet outperforming those
encounters with a scope of creative capacities intended to additionally hoist the general
visitor experience over the whole property and past. Coming up next is only a couple of the
key takeaways of this examination for lodging administrators to remember:
AI dramatically reduces the need for human assistance when it comes to answering
questions and resolving problems that commonly arise during a guest stay, resulting
in substantial cost savings.
AI is driving the evolution of hotel messaging; increasingly, the conversation between
hotels and guests is shifting from a request-based one to a two-way dialogue, which
is far more valuable.
AI significantly enhances the in-room guest experience by seamlessly integrating
technologyenabled amenities. This integration translates into increased guest
satisfaction and loyalty.
By automating “best next actions” based on guest data, AI gives hotels the opportunity
to enable personalization in ways not previously possible — driving increased
RevPAR in the process.
AI can identify and resolve issues that could potentially erode guest satisfaction
without the need for human intervention. It can also suggest new innovations to
further improve guest satisfaction.
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