https://dinastipub.
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DOI: https://doi.
org/10.
38035/dijefa.
https://creativecommons.
org/licenses/by/4.
Effects of Omnichannel Marketing.
Product Quality and Customer Engagement on Uniqlo Customer Loyalty Desi Listiyani1.
Danang Kurniawan2.
Nurliya Apriyana3.
Paramadina University.
Jakarta.
Indonesia, desilistiyani92@gmail.
Paramadina University.
Jakarta.
Indonesia, danangkurniawan654@gmail.
Paramadina University.
Jakarta.
Indonesia, nurliya.
apriyana@paramadina.
Corresponding Author: desilistiyani92@gmail.
Abstract: The fashion retail industry in Indonesia is experiencing rapid growth, mainly driven by lifestyle changes and the development of digital technology.
However, the COVID-19 pandemic has had a huge negative impact on this sector.
A decline in sales occurred due to social restrictions and the shift of consumers to online shopping.
To survive, many retail companies, including fashion industry players, adopted an omnichannel strategy that integrates online and offline sales.
With a combination of physical stores, e-commerce platforms, and strong promotional strategies on social media, it is expected to reach more consumers and increase customer loyalty.
In addition, high product quality and good customer engagement is one of the supporting factors in building customer loyalty.
Uniqlo, being a Japanese appareal brand that is quite successful in Indonesia.
Moreover, it was able to survive amid the COVID19 pandemic that hit at that time.
Through empirical testing and data analysis, this study aims to prove the positive and significant influence between Omnichannel Marketing.
Product Quality, and Customer Engagement with Customer Loyalty on Uniqlo clothing products.
Testing was carried out using quantitative methods to analyze all existing variables and sampling techniques using purposive sampling techniques.
The respondents totaled 102 Primary data was collected through an online questionnaire (Google For.
for one Data analysis using multiple linear regression with the help of SPSS software version 30 of 2024.
The results of this study indicate that Omnichannel Marketing.
Product Quality and Customer Engagement have a positive influence on Customer Loyalty.
With these positive significance results and understanding the relationship between these factors is expected to provide insight for companies to develop more effective marketing strategies and increase market share.
Keywords: Omnichannel Marketing.
Product Quality.
Customer Engagement.
Customer Loyalty.
UNIQLO.
INTRODUCTION
In this era of globalization, business competition is getting tougher, where consumers are smart enough to choose superior products that are considered suitable and comfortable for 992 | P a g e https://dinastipub.
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them to use.
The fashion retail industry is one of the fastest growing industries in Indonesia.
This is driven by changes in lifestyle and the development of digital technology, which is certainly one of the driving lines of the national economy.
Based on statistical data made by Ahdiyat .
(Ahdiat, )2023shows that the stretching of the retail industry rose in the first quarter of 2023, compared to the past 2 to 3 years, around the beginning of 2020-2021.
Figure 1: Indonesia's Real Sales Index (RSI) Source : https://databoks.
The decline in the sales index that occurred in 2020-2021 was due to the global COVID19 pandemic.
Where all industrial sectors are affected, the fashion retail industry is no According to Roy Mandey.
Head of the Indonesian Retailers Association (APRINDO), the retail sector only grew by 3-3.
5% in 2020.
The pandemic shutting down physical stores and services, and consumers' intention to shop contactlessly made online shopping services an indispensable part, (Nguyen, 2.
The implementation of large-scale social restriction policies over a long period of time has resulted in complex negative consequences for companies, including internal and external crises that potentially threaten business continuity.
Thus, many companies have begun to integrate their products into digital platforms to maintain their business in order to stay connected with their customers, either by creating their own website and implementing it, or through social media platforms and marketplaces.
This concept is better known as Omnichannel.
According to (McKinsey.
Company, 2.
Omnichannel marketing is the process of interacting with customers through their preferred channels in the form of offline stores, online, text messages or social media.
It is intended to be able to interact 24/7, to be able to reach the desired product anywhere and anytime.
Online sales from the retail sector increased by 15% but 8% of them represent the total sales, the data obtained from Aprindo indicates the online transformation (Maranti, 2.
In a study conducted by McKinsey, that during the COVID19 pandemic the use of omnichannel increased with more consumers turning to e-commerce, as well as the high demand for shopping without physical contact during the peak of the pandemic caused 20 to 30 percent of grocery store businesses in the US to switch to online (McKinsey It can be seen that omnichannel is the right step taken by the company in maintaining its business when the world economy is unstable.
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The growth of fashion and apparel in Indonesia continues to increase after the COVID19 pandemic.
Where changes in consumer lifestyles and awareness of the latest fashion trends encourage increased demand for fashion products.
In line with GDP growth data and IKI values.
Bank Indonesia (BI) also reported an increase in performance in the first quarter of Based on the BI manufacturing index (PMI-BI), the textile and apparel industry has increased and is in the expansion phase with an index reaching 57.
40 percent.
(Hidranto, 2024 Uniqlo is a Japanese appareal brand that is quite successful in Indonesia.
Especially being able to survive amid the COVID19 pandemic that hit at that time.
As well as being one of the fashion retail products that applies omnichannel marketing in its business.
With physical stores spread almost throughout Indonesia, as well as 24/7 website access equipped with ecommerce facilities which we can use for online purchases anytime and anywhere.
There is also an active role of promotion carried out on social media through an Instagram account which currently has reached 1.
8 million followers.
UNIQLO also provides newsletters facilities for customers who want to get the latest information about UNIQLO, both new product launches and price promotions.
Uniqlo is quite aggressive in carrying out various promotional activities on various platforms.
In addition to intense promotion through social media.
Uniqlo also works with public figures and social media influencers.
As of January 2023.
Uniqlo has 56 stores across Indonesia.
Based on fiscal 2020 data.
UNIQLO Southeast Asia & Oceania experienced a significant decline in revenue of 13%, where Indonesia and the Philippines needed extra time to recover from COVID19.
However, in the coming year after the COVID19 pandemic subsided.
UNIQLO's revenue increased again.
The peak was in 2023.
Recorded during fiscal year 2023.
UNIQLO International .
ll stores outside Japa.
reported very good revenue and profit growth with revenue ofAu 1.
4371 trillion and profit ofAu 226.
9 billion.
Specifically.
Uniqlo South Korea and Uniqlo Southeast Asia & Oceania became the second largest profit contributor after Uniqlo China.
Table 1.
Uniqlo's Group Operation Performance source : fastretailing.
These results show an indication of the intensity of purchasing UNIQLO products which cannot be separated from the role of UNIQLO consumer loyalty.
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Consumer loyalty arises when the customer is satisfied with the quality of the product he buys, as well as the company's role in satisfying its customers.
Therefore, it is important for many companies to provide quality products and convenient services for their customers.
Product quality is a combination of various unique features and characteristics that make the product able to meet market demand, (Khadka & Maharjan, 2.
In the midst of intense competition, superior product quality is a determining factor for the success of a business.
This study aims to analyze the effects of omnichannel marketing, product quality and customer engagement on customer loyalty for UNIQLO clothing products.
METHOD
This research uses quantitative methods.
Purposive sampling is the data collection technique used by researchers in analyzing case studies.
Respondents involved in this study were men and women with an age range of 15-45 years.
Respondents come from Jabodetabek with various types of jobs, namely ASN, housewives, private employees, students, entrepreneurs and others.
The research uses primary data derived from questionnaires and distributed via a google form link.
The sampling method was carried out for 1 month.
A total of 102 respondents were involved in this study.
The analysis technique used is multiple linear Using SPSS version 30 of 2024 as a data processing tool.
Closed questions were asked in a questionnaire with a Likert scale of 1-4 consisting of: Strongly Disagree.
Disagree.
Agree and Strongly Disagree.
Questionnaire Question List Variables Definition Indicator Code Number of Questions Supporting Literature Omnichannel Marketing According (Mosquera et Omnichannel marketing is a channels with the aim of behavior, such I find Uniqlo's various online platforms .
ebsite and mobile ap.
easy to use.
OM1.
(Ayensa, et al, 2.
I am satisfied with Uniqlo's omnichannel marketing.
OM1.
(Vanitha et , 2.
Uniqlo's saves customers time.
OM1.
(Vanitha et , 2.
Omnichannel makes me buy uniqlo OM1.
(Vanitha et , 2.
According Kotler Keller .
Product quality is the ability of a Uniqlo clothes have good PQ1.
Product Quality (Dhasan Aryupong , 2.
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Uniqlo clothes are durable to use.
PQ1.
(Dhasan Aryupong , 2.
Uniqlo comfortable to wear.
PQ1.
(Dhasan Aryupong , 2.
Uniqlo clothing designs pay attention to detail and are innovative to the latest PQ1.
(Nisa and Ahmadi.
I follow the news of the Uniqlo CE1.
I frequently visit the Uniqlo CE1.
(Dhasan Aryupong , 2.
I would like to learn more about this brand.
CE1.
(Dhasan Aryupong , 2.
I often pay attention to everything related to Uniqlo CE1.
(Dhasan Aryupong , 2.
Sharma & I will continue to use clothes Sharma Sourabh .
state that loyal customers have long-term I consider myself loyal to the Uniqlo clothing CL1.
According Kotler Keller .
, engagement is the extent of active attention participation in which shows a response than impression and tends to create value for the CL1.
(Dhasan Aryupong , 2.
(Dhasan Aryupong , 2.
(Dhasan 996 | P a g e https://dinastipub.
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company and this is one of the strategic goals.
Aryupong , 2.
I buy more products at Uniqlo than other brands for similar products CL1.
(Cotarelo et , 2.
I would recommend Uniqlo to others.
CL1.
(Cotarelo et , 2.
RESULTS AND DISCUSSION
Descriptive Test The purpose of using descriptive statistics in this study is to examine data characteristics by determining extreme values .
aximum and minimu.
, central tendency .
, and dispersion .
tandard deviatio.
Descriptive statistics are statistics used to analyze data by describing the data that has been collected as is without intending to make general or generalized conclusions (P.
Sugiyono, 2.
Descriptive statistics provide a brief summary of the main characteristics of a data set.
this case, there are four variables:
Customer Loyalty as variable Y .
Omnichannel Marketing as variable X1 .
Product Quality as variable X2 .
Customer Engagement as variable X3 By including the average value of customer loyalty (Mea.
then the distribution of customer loyalty data around the average (Std.
Deviatio.
and the number of respondents (N).
Table 3.
Descriptive Statistics Test Results Source: Results of data processing using SPSS 30 .
In the table, it can be analyzed that Product Quality has the highest average of 13.
followed by Omnichannel Marketing at 12.
Customer Loyalty at 11.
3039, and finally Customer Engagement at 10.
This shows that in this sample, perceptions of product quality tend to be higher than other aspects.
Then for Data Distribution (Std Deviatio.
Product Quality also has the highest standard deviation of 2.
73093, followed by Customer Engagement 72003.
Customer Loyalty of 2.
59012, and finally Omnichannel Marketing of 2.
This means that Product Quality and Customer Engagement data are more varied or dispersed than Customer Loyalty and Omnichannel Marketing data.
Omnichannel Marketing data tends to be more homogeneous or collected around the average.
And for Value (N) indicates the number of respondents involved in this study.
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Reliability Test According to (P.
Sugiyono, 2.
the reliability test is used to measure a questionnaire which is an indicator of the variable.
Reliability is important because unreliable measurement results cannot be relied upon for making valid decisions or drawing conclusions.
if the Cronbach Alpha value is more than 0.
60, the statement items in the questionnaire are declared reliable (P.
Sugiyono, 2.
Table 4.
Reliability Test No.
Variables Cornbach's Alpha
Status
Omnichannel Marketing 0,796 Reliable Product Quality 0,886 Reliable Customer Engagement
0,843
Reliable Customer Loyalty 0,819 Reliable Source: Results of data processing using SPSS 30 .
The reliability test results in this case for all variables show the Cornbach's Alpha number> 0.
6 where the results are reliable.
Normality Test According to (Ghozali, 2.
thenormality test is used to test whether in the regression model, variables or residuals have a normal distribution.
The data normality test is fundamental in many statistical tests, especially in parametric tests such as the t-test.
ANOVA, and linear Figure 3.
Histogram of normality test Source: Results of data processing using SPSS 30 .
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In this case, the normality test is visualized with a histogram and P-P Plot, the results of which in Figure 4.
1 display the distribution of Regression Standardized Residuals with the dependent variable Customer Loyalty.
This histogram provides strong enough evidence that the assumption of residual normality in regression analysis is met.
By having a Mean value = 78E-16 which in scientific notation is very close to 0.
This value is very small and can be considered zero, which is as expected for standardized residuals.
As well as the value of Std.
Dev.
= 0.
985 which in scientific notation is very close to 1.
This value also corresponds to what is expected for standardized residuals.
Although there are some minor deviations from perfect normality, such as there is a slight positive skew, which is indicated by slightly more extreme positive residuals .
o the right of the grap.
, that is, there are some data whose actual values are much higher than the model predicts and the histogram bars do not form a perfect bell There are some small "bumps" and it is not completely smooth.
However, the deviations are not very large and are unlikely to significantly affect the validity of the regression results, especially with a large enough sample size (N = .
In many cases, with samples larger than 30, small violations of normality can often be ignored due to the Central Limit Theorem.
Normal P-P Plot of Regression Standardized Residuals Source: Source: Results of data processing using SPSS 30 .
Unlike the histogram which shows the frequency distribution, the P-P plot compares the cumulative distribution of the sample data with the theoretical normal cumulative Figure 4.
2 shows the results that this P-P plot provides visual evidence supporting the assumption of normality of the residuals, albeit imperfectly.
Since the dots mostly follow the diagonal line, this indicates that the standardized residual distribution is close to a normal Although most of the points are close to the diagonal line, there are some small deviations, especially at the top and bottom of the plot.
These deviations indicate that there are some differences between the distribution of your data and the theoretical normal distribution.
However, these slight deviations are not very serious and will not significantly affect the validity of the regression results, especially if they are supported by a large enough sample size and the previous histogram interpretation results.
Homoscedasticity Test The homoscedasticity test is a test that aims to test the regression model whether there is an inequality of variance of the residuals for observations in the regression model (Priyatno.
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the regression line is consistent across the entire range of values of the predictor variable.
this degree of spread varies, it is called heteroscedasticity.
Figure 4.
Scatterplot Source: Results of data processing using SPSS 30 .
In this case, the scatter diagram becomes a measuring tool in testing Homoscedasticity.
Figure 4.
3 shows the results that the homoscedasticity assumption in the regression model for Customer Loyalty is likely to be met.
The random distribution of points indicates that the residual variance is consistent across the range of predicted values.
In general, the pattern of dots in Figure 4.
3 appears randomly scattered around zero on the vertical axis (Standardized Predicted Valu.
There are no obvious patterns, such as fan-shaped patterns .
idening or narrowin.
, curved patterns, or other patterns.
The absence of this clear pattern is a good indication that the assumption of homoscedasticity is met.
This means that the variance of the residuals is more or less constant across the range of predicted values, which is one of the important assumptions in linear regression.
Multicollinearity Test The regression model ideally consists of independent variables that do not affect each The multicollinearity test aims to determine or test whether in the regression model there is a correlation or relationship between independent variables .
ndependent variable.
(Ghozali, 2.
To determine whether there is multicollinearity in the table, it can be seen through the VIF (Variance Inflation Facto.
VIF < 5 is considered no multicollinearity Meanwhile, if VIF> 5, there is an indication of multicollinearity.
The higher the VIF value, the stronger the level of multicollinearity.
And if VIF> 10 the indication is that there is very strong multicollinearity.
Table 5.
Multicollinearity Test Source: Results of data processing using SPSS 30 .
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For this case, the VIF values for the three independent variables (Omnichannel Marketing.
Product Quality, and Customer Engagemen.
are below 5.
The highest VIF value 47 for the Customer Engagement variable, which is VIF value > 1.
So, it can be concluded that there is no significant multicollinearity problem in this analysis.
Multiple Linear Regression Analysis Multiple linear regression analysis aims to identify and measure the relationship between several independent variables and one dependent variable.
Table 6.
Multiple Linear Regression Analysis Test Results Source: Results of data processing using SPSS 30 .
Based on the B (Unstandardized Coefficient.
value, we can form the regression equation as follows: Customer Loyalty (Y) = -0.
217*Omnichannel Marketing (X.
264*Product Quality (X.
486*Customer Engagement (X.
where the value (Constan.
This value indicates the value of Customer Loyalty when all independent variables are zero.
In this case, -0.
026 can be interpreted as the baseline or starting point of Customer Loyalty if there is no Omnichannel Marketing.
Product Quality, or Customer Engagement.
However, since the value is very close to zero, and in the context of customer loyalty it is difficult to imagine a negative value, this constant can be ignored in practical interpretation.
It is more important to focus on the relative influence of the other Then for the value of Omnichannel Marketing has a beta value of 0.
Every one unit increase in Omnichannel Marketing.
Customer Loyalty is predicted to increase by 21.
assuming Product Quality and Customer Engagement are constant.
Furthermore.
Product Quality has a beta value of 0.
So, every one unit increase in Product Quality.
Customer Loyalty is predicted to increase by 26.
4%, assuming Omnichannel Marketing and Customer Engagement are constant.
Furthermore.
Customer Engagement has a beta value of 0.
Every one unit increase in Customer Engagement.
Customer Loyalty is predicted to increase by 6%, assuming Omnichannel Marketing and Product Quality are constant.
Partial Hypothesis Test .
Based on Table 4.
4 on hypothesis testing in the form of the influence of independent variables (Omnichannel Marketing.
Product Quality and Customer Engagemen.
on the dependent variable (Customer Loyalt.
which is carried out separately using the t test.
The hypothesis is accepted if the significance value is <0.
It is concluded that Omnichannel Marketing has a Sig value.
= 0.
030 then Significant .
< 0.
Omnichannel Marketing has a significant effect on Customer Loyalty.
Then it is also concluded that Product Quality has a Sig.
value <0.
001, meaning that the effect is very significant .
<0.
Then.
Customer Engagement has a value of Sig.
< 0.
001, this figure is also considered very significant .
< So, the three independent variables (X1 X2 and X.
have a significant positive influence on the dependent variable (Y).
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Simultaneous Test (F Tes.
The F-test evaluates the appropriateness of the regression model in predicting the dependent variable.
Similar to the T-test, this test uses a significance level of 0.
05 as the decision limit.
If the significance value of F (Sig F) > 0.
05, this indicates that the regression model is feasible and able to predict the dependent variable.
If the significance value of F (Sig F) is more than 0.
this indicates that the regression model is not feasible to use and is not able to predict the dependent variable.
Table 7.
Simultaneous test results (F tes.
Source: Results of data processing using SPSS 30 .
The results of the simultaneous significance test shown in Table 4.
5 show the Sig.
of <0.
001 that the two-way analysis with an alpha significance level of 0.
05 is undoubtedly Then, we can compare the Ftable value with Fcount is how the F test is used to test the data.
With the value of Fcount .
> F table .
H0 is rejected, meaning that the regression model is significant overall.
Determination Coefficient Test The Coefficient of Determination test is used with the aim of measuring how far or how much the ability of the independent variable is in explaining the dependent variable (S.
Sugiyono, 2.
The Coefficient of Determination (RA) serves as an indicator to assess the performance of the regression model and provides an overview of the extent to which the model is able to explain variations in the data.
Table 8.
Coefficient of determination test results Source: Results of data processing using SPSS 30 .
Seeing in table 4.
6 that the results show a fairly strong positive correlation between Customer Engagement.
Product Quality, and Omnichannel Marketing with Customer Loyalty, where the three independent variables together are able to influence Customer Loyalty by 7% (R Squar.
with a relatively good level of prediction accuracy and an average prediction error of 1.
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H1 Omnichannel Marketing has a positive effect on Customer Loyalty Based on the results of research and data testing on Omnichannel Marketing (X.
variables on Customer Loyalty (Y), where the results of multiple linear regression tests show that Omnichannel Marketing (X.
has a beta value of 0.
217, where every one unit increase in Omnichannel Marketing.
Customer Loyalty is predicted to increase by 21.
And has a calculated T value of 2.
206 with a Sig value.
= 0.
< 0.
These results indicate that Omnichannel Marketing (X.
has a positive influence on Customer Loyalty (Y).
H2 Product Quality has a positive effect on Customer Loyalty Based on the results of research and data testing on the product quality variable (X.
on Customer Loyalty (Y), where the multiple linear regression test results show that Product Quality (X.
has a beta value of 0.
So, every one unit increase in Product Quality.
Customer Loyalty is predicted to increase by 26.
And has a calculated T value of 3.
with a Sig.
value <0.
<0.
These results indicate that Product Quality (X.
has a positive influence on Customer Loyalty (Y).
H3 Customer Engagement has a positive effect on Customer Loyalty Based on the results of research and data testing on the Customer Engagement (X.
variable on Customer Loyalty (Y), where the multiple linear regression test results show that Customer Engagement (X.
has a beta value of 0.
486, then every one unit increase in Customer Engagement.
Consumer Loyalty is predicted to increase by 48.
6% assuming Omnichannel Marketing and Product Quality are constant.
And has a calculated T value of 6.
935 with a Sig.
value <0.
<0.
These results indicate that Customer Engagement (X.
has a positive influence on Customer Loyalty (Y).
CONCLUSION
Based on the results of research and data processing, it can be concluded that Omnichannel Marketing.
Product Quality, and Customer Engagement significantly and positively affect Customer Loyalty of Uniqlo products.
The results of this study provide a strong foundation for Uniqlo to continue to improve its business performance.
Seeing the omnichannel strategy implemented by Uniqlo so far has been quite effective in providing a consistent shopping experience for customers.
Uniqlo can also continue to improve integration between online and offline channels, for example by improving inventory systems, personalizing product recommendations, and loyalty programs that apply across channels.
feels that digital marketing optimization needs to be continuously optimized including SEO.
SEM, and social media marketing, to increase brand visibility and attract more customers.
High reliability on Product Quality shows that customers highly value Uniqlo's product quality.
Therefore.
Uniqlo needs to maintain and even improve its product quality standards by continuously innovating in developing new products that meet the latest market needs and The implementation of a strict quality control system is also needed to ensure that the products produced always meet high quality standards and actively listen to customer feedback and use it to improve product quality.
Then in terms of Customer Engagement.
Uniqlo can enrich its loyalty program by providing more attractive and relevant benefits for customers and providing more personalized and relevant communication, for example through email marketing or application notifications.
Not only about optimized digitization, other things such as responsiveness to complaints quickly and effectively can increase customer satisfaction.
terms of measuring customer loyalty more comprehensively.
Uniqlo can use various metrics that include quantitative aspects such as purchase frequency and transaction value as well as qualitative aspects such as NPS/Net Promoter Score.
In addition, by running a referral program.
Uniqlo can drive business growth and increase customer loyalty.
In short, by focusing on 1003 | P a g e https://dinastipub.
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strengthening omnichannel strategies, improving product quality, and increasing customer engagement.
Uniqlo can maintain existing customer loyalty and even attract new customers.
REFERENCES