Evaluating the Effectiveness of Marketing Policies at Acb – Perspective from Customer Satisfaction

2.3. Evaluating the effectiveness of marketing policies at ACB – Perspective from customer satisfaction

2.3.1. Design of research on the impact of marketing activities on customer satisfaction

a) Research method:

The research was conducted through a combination of two methods: qualitative research method and quantitative research method.

Qualitative research: The author conducted direct interviews with 02 bank marketing experts, 5 branch/transaction office managers through group discussions. They are the people who directly implement the bank's marketing policies. The discussion aims to explore, adjust, and supplement the scales of research concepts.

Quantitative research: In this study, the author established a questionnaire consisting of 30 observed variables to evaluate how marketing policies affect customer satisfaction; then used the convenience sampling method and direct interview technique with customers at the bank through a detailed questionnaire.

b) Research process:

Building the scale: The author bases on the theoretical basis of the 7P marketing model of Commercial Banks, customer satisfaction and studies following this model in the domestic market. On this basis, the research model and a set of observed variables are built to measure the latent variable (research concept).

Qualitative research: By using the group discussion technique with marketing experts and branch/transaction office managers of ACB bank, the author collected opinions on the preliminary scale and designed a survey questionnaire. Through this qualitative research, the author chose a convenient sampling method with the subjects approached being customers who have used/are using the services of Asia Commercial Joint Stock Bank with any product/service and completed the survey questionnaire to serve the quantitative research.

Quantitative research: The sample size chosen was n = 200 and was selected by convenience sampling method. These scales were adjusted through the main techniques: (1) cronbach alpha reliability coefficient method and (2) exploratory factor analysis EFA method.

Determine the overall research: All customers who have used/are using the services of Asia Commercial Joint Stock Bank.

Research sample size: there are 30 observed variables, so the survey sample must satisfy the formula: M >= nx 5 + 50. Based on the number of observed variables in the study, the required sample size is 200.

Survey period: from November 1, 2016 to January 1, 2017. Survey location: Ho Chi Minh City.

Sampling method: In this study, the research sample was selected by convenience sampling method. The author interviewed customers who came to transact at the bank during working hours at 5 branches including: Ho Chi Minh City Branch, Saigon Branch, Phu Lam Branch, An Suong Branch, Thu Duc Branch.

Building metrics and analyzing data

Based on the theoretical basis of the 7P marketing model of Associate Professor, Dr. Nguyen Thi Minh Hien, the measurement of concepts by researchers in the world and in the country, the author builds and encodes a scale to evaluate customer satisfaction with ACB's marketing policy. (Appendix 5)

The questionnaire consists of five parts: Part one is the introduction; part two is the screening part; part three is the demographic profile of the sample including age, gender, marital status, income and transaction time with ACB Bank; part four is the survey questions; part five is the thank you. (Appendix 6)

There are 30 questions divided into 8 groups including: 7 independent factor groups and 1 dependent factor group according to the presented theoretical basis. The observed variables in the components all use a 5-point Likert scale.

The author collected survey forms, then coded the data, entered and processed the data using SPSS 20.0 statistical software.

Preliminary assessment of the scale through Cronbach's Alpha

In this section, the author can eliminate inappropriate variables and limit junk variables during the research process. Normally, observed variables with a variable-total correlation coefficient of less than 0.3 will be eliminated and the standard for selecting a scale is when the Cronbach's Alpha coefficient is 0.6 or higher.

Factorial test

A redundant observation variable is a variable measuring a concept that is almost identical to another measurement variable. According to Nguyen Dinh Tho [8], a Cronbach's Alpha coefficient of 0.6 or higher can be used. However, if a scale has a Cronbach's alpha reliability coefficient that is too high (> 0.95), there is a possibility that redundant observations will appear in the scale. In that case, the redundant variable should be eliminated.

Exploratory factor analysis EFA

This technique is used to reduce and group variables into related groups of variables for consideration and presentation as a small number of underlying factors. In exploratory factor analysis EFA, the author uses Varimax rotation procedure to analyze the data structure.

The KMO (Kaiser-Meyer-Olkin) coefficient and the Bartlett criterion are two indices used to evaluate the appropriateness of EFA. Accordingly, the hypothesis H0 (variables are not correlated with each other in the population) is rejected and therefore EFA is called appropriate when: 0.5 ≤ KMO ≤ 1 and sig < 0.05. In case KMO < 0.5, factor analysis is likely not appropriate for the data. The scale is accepted when the total variance extracted is greater than or equal to 50% and the eigenvalue coefficient has a value greater than 1.

Factor loadings represent simple correlations between variables and factors, used to assess the significance of EFA. According to Hair et al. [14], Factor loading > 0.3 is considered to be at the minimum level; Factor loading > 0.4 is considered important; Factor loading > 0.5 is considered to have practical significance.

2.3.2. Quantitative research

Customer characteristics of the sample

The author conducted a survey with 200 questionnaires distributed to 200 random customers, and the number of ballots collected was 200. (Appendix 7)

About age

The survey results show that the majority of customers transacting at ACB Bank are between 30 and 40 years old (96 people, accounting for 47.27%). The rest are under 30 years old and over 40 years old, accounting for 39.48% and 13.25%, respectively.

About gender

Of the 200 survey participants, 89 men and 111 women responded to the questionnaire, with males accounting for 44.5% and females accounting for 55.5%.

About marital status

According to the survey results, there were 123 married people, accounting for 61.5%, and 77 unmarried people, accounting for 38.5% .

About average monthly income

With 200 respondents, the majority of customers have an average monthly income of 7 to 15 million VND, with 109 people (54.5%). The group with an average monthly income of less than 7 million VND, has 47 people (23.5%); from 15 million VND or more, has 44 people (22.0%);

About transaction time with ACB bank

Among those surveyed, the author obtained the result that there were 105 people who had transacted with ACB bank from 1-3 years (accounting for the majority of 52.5%). The remaining were from less than 1 year with 31 people (accounting for 15.5%); from 3 to less than 5 years

There are 45 people (22.5%) and 19 people (9.5%) from 5 years or more .

Sample descriptive statistics of 7P marketing factors affecting customer satisfaction

Descriptive statistics of factors

In the total sample surveyed, the author obtained the following statistical results: Customers rated the factors above average. While the two factors product and distribution

For the marketing mix, customers rated the lowest (mean = 3.62 and 3.59), while for the human factor, customers rated it at the highest level (mean = 3.94). In general, the average values of the components of the 7P marketing mix differed quite a bit (from 3.59 to 3.94), which shows that there are different assessments of the importance between these components.

Table 2.5: Sample descriptive statistics for the 7 main components of the marketing mix

Descriptive statistics

	N	Smallest	Biggest	Medium	Standard deviation
Product	200	1.00	5.00	3.62	0.83
Interest and fees	200	1.00	5.00	3.74	0.88
Distribution	200	1.00	5.00	3.59	0.82
Promotion mix	200	1.00	5.00	3.81	0.73
Human	200	1.00	5.00	3.94	0.69
Procedure	200	1.00	5.00	3.67	0.89
Tangible means	200	1.00	5.00	3.85	0.72

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(Source: Author's field investigation in 2016)

Descriptive statistics for observed variables of each factor (Appendix 7)

The “product” factor

The survey results show that most of the observed variables are rated at an average level ranging from 3.24 to 3.61. In particular, customers mostly agree with the product policy "ACB Bank's services are very diverse" (sp1, mean = 3.63), "ACB Bank's services meet customers' needs well" (sp2, mean = 3.58) and "ACB Bank's services are easy to use" (sp3, mean = 3.62). These policies help customers when transacting at the bank to have many product choices and easily use the service as they wish. The ability to flexibly meet customers' needs is the advantage of the bank. However, customers are neutral with "ACB Bank's services have many utilities". This explains the lack of connection with partners that provide additional service features for banks, so orienting effective marketing strategies to meet increasingly demanding customer needs is a big challenge in the current period.

The “interest and fees” factor

Most of the observed variables were rated above average on the 5-point Likert scale. The observed variable “Reasonable service fees” reached the highest level (mean = 3.81), which shows that customers find it reasonable when comparing fees with other banks. In addition, most customers agree with “Service fees are lower than competitors” (mean = 3.70) and “ACB Bank has preferential service fees and loan interest rates” (mean = 3.71). Customers will be more satisfied when the bank offers preferential service fees and interest rates, showing that the bank's interest and fee policies pay great attention to and care for its customers. In this way, it has created an advantage in word-of-mouth marketing, increasing the bank's brand and reputation.

The “distribution” factor

The observed variables were scored at an average level ranging from 3.72 to 3.89. Specifically, the observed variable "ACB Bank's system of transaction points is widespread" has a high average value (mean = 3.89) and "ACB Bank's branches/transaction offices have enough transaction counters to serve customers even during peak hours" reached 3.80. From this, it can be seen that the distribution channel is focused on expanding the number of transaction points, and aiming to make customers realize that the waiting time for their transactions is short. This will be an advantage to help the Bank and customers save time and money when making transactions.

The “promotion mix” factor

All observed variables are evaluated at a significance level close to 4 (ranging from 3.89 to 3.94). The survey results show that the observed variable "ACB brand creates trust" has the highest significance (mean = 3.94), the observed variable "ACB bank employees regularly contact customers" (mean = 3.92) Customers often receive customer care calls from ACB employees, the observed variable "Customers often receive promotional program information via email and mass media" (mean = 3.90) Customers often see ACB brand image/advertisement on popular media, the observed variable "ACB bank's advertising is diverse and impressive" (mean = 3.89). This shows that

We see that the bank invests a lot in promotional programs as well as advertising activities/building brand image in the minds of customers.

The “process” factor

The observed variables of the process factor have a mean value ranging from 3.73 to 3.89. In which, most customers agree with "Customer transaction information is confidential" (mean = 3.89), "Fast transaction processing time" (mean = 3.85), "Simple transaction procedures" (mean = 3.81) and "Easy-to-use forms and documents" (mean = 3.79), which shows that customers feel secure when transacting with ACB Bank. However, the observed variable "ACB Bank staff are ready at the reception counter to serve customers" (mean = 3.75), so ACB needs to improve the customer reception process to be more suitable to better meet customers' wishes.

The “human” factor

Most of the observed variables were rated at levels ranging from 3.75 to 4.09. Customers participating in the survey agreed with the professionalism and reliability of ACB employees with the highest mean (mean = 3.97) as well as "ACB employees are capable of consulting and answering customers' questions satisfactorily" with a significance level (mean = 3.98). However, it can be seen that the observed variable "ACB employees are honest" only reached a significance level (mean = 3.75). The human factor plays an important role in marketing strategies, so banks should invest more in training human resources, culture and professional ethics of employees.

The “tangibles” factor

Most of the observed variables have a mean level of agreement ranging from 3.77 to 3.90. Typically, the observed variables "using modern, smart transaction software" (mean = 3.90) and "ACB's parking lot is spacious and comfortable" (mean = 3.89) have the highest significance level. Meanwhile, the remaining two observed variables "The ATM system operates well and safely" and "ACB's service area is arranged and decorated with modern equipment" have significance levels of 3.77 and 3.78, respectively. This shows that banks need to focus on investing in technical infrastructure as well as understanding and satisfying customer psychology.

Descriptive statistics on customer satisfaction (Appendix 7)

The survey results show that the observed variables in the descriptive statistics table have values above average (mean = 3.82; 3.80; 3.89). It can be said that customers are mostly satisfied with the service quality at ACB Bank. More specifically, the observed variable "ACB is not much different from the ideal bank that customers dream of" has the highest level (mean = 3.89), showing that customers highly appreciate and really want to maintain long-term transactions with the bank both in the present and the future.

Scale evaluation using Cronbach's Alpha reliability coefficient (Appendix 8)

According to some researchers, Cronbach's Alpha reliability can be tested first, then put into EFA or vice versa. However, according to Nguyen Dinh Tho

[8] Studies should test Cronbach's Alpha before entering EFA factor analysis. In this study, the reliability of the scales was tested through observed variables to eliminate insignificant variables.

Through the results of Cronbach's Alpha calculation, it shows that the factors of the 7P marketing mix scale have Cronbach's alpha coefficients of product (0.805), service fee/interest rate (0.793), distribution (0.797), promotion (0.756), people (0.822), process (0.845), tangible (0.812) are all greater than 0.6 and the total correlation coefficients of the observed variables are all greater than 0.3, so the scale achieves reliability.

The customer satisfaction factor includes 3 observed variables. When testing the reliability of this factor, the result was Cronbach's alpha = 0.878 (>0.6) and the total correlation coefficients of the observed variables were all greater than 0.3, so the customer satisfaction scale achieved reliability.

The research concepts have Cronbach's alpha results less than 0.95, so there is no redundant variable in the scale. Therefore, the author can continue to use the scale for EFA analysis.

Exploratory factor analysis EFA (appendix 9)

EFA analysis of the scale of components in the marketing mix

Through the results of the first EFA factor analysis, the KMO value = 0.841 (satisfies the condition 0.5 < KMO <1), proving that factor analysis is suitable for the research data.

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