Analysis Of The Impact Of Net Promoter Score On Financial Performance With Customer Loyalty As Mediation

Bayu Hardianto, Sieni Wijaya

¹Harapan Bangsa Business School, Bandung, Indonesia

*e-mail: [email protected], [email protected]

Keywords

ABSTRACT

Net Promoter Score; Business Continuity; Customer Retention

· If we look at the increasing competition and market growth in every field especially in the professional services industry, customers have many choices, which puts organizations in a dilemma to find out what makes customers loyal to the company. People tend to tell negative stories more than positive ones, so understanding and mobilizing Promoters is critical to success. Net Promoter Score (NPS) is used in companies to measure a customer's desire to recommend a company's overall product or service to their friends or colleagues. NPS reflects customer satisfaction & has been shown to be an indicator of business growth potential. In this research, we will explain the relationship between Net Promoter Score and financial performance analysis of the impact of NPS score in IT firm in the last 5 years. The data collection technique used is to make an NPS survey that will be disseminated to existing customers of IT firm in the last 5 years who have purchased services / services as many as 93 customers with 218 Respondents. For variable (X) of this study is NPS, for variable (Y) is Financial Performance and for intervening variable (Z) is Customer Loyalty. The results indicate that NPS has a significant influence on Customer Loyalty, then Customer Loyalty has a significant influence on Financial Performance, and it can also be seen that there is a significant influence of NPS on Financial Performance

INTRODUCTION

Especially in the digital era, changes in consumer behavior, especially in terms of purchasing goods and services, are things that need to be watched out for and how companies can monitor customer satisfaction (monitoring satisfaction). Many companies systematically measure how well they treat or provide services to customers by identifying the factors that make up satisfaction. However, the relationship between customer satisfaction and customer loyalty is less relevant (proportionate) (Farooq et al., 2019). The Net Promoter Score (NPS) has been widely adopted by companies as a measure of customer mindset and a predictor of sales growth in which potential customers will repeat orders and even recommend the company to friends or colleagues (Baquero, 2022).

Changes in consumer behavior in conveying information make the relationship between customer satisfaction (customer satisfaction) and customer loyalty (customer loyalty) disproportionate or less relevant. in addition, the increasing sophistication of technology allows customers to quickly spread good and bad news. Net Promoter Score (NPS) is an index that measures a customer's willingness to recommend a company's products or services to a friend or colleague (Baehre, O’Dwyer, O’Malley, & Lee, 2022).

Figure 1. NPS Mapping

NPS is a concept introduced by Fred to achieve customer loyalty. In the ultimate question (book) there are various methods of using NPS to gain profitability. NPS adds value to customers by taking customer feedback into account to improve services that can meet their expectations and exceed. (Reichheld, 2003). There is a relationship between company growth and client loyalty. Where customers who have high loyalty will immediately have the potential to repeat orders. So that an increase in the number of transactions will affect business growth (Korneta, 2018). Net Promoter Score does not only focus on quality, satisfaction, or value, but how customers recommend word of mouth. (Keiningham, Cooil, Andreassen, & Aksoy, 2007). A score between 0 and 30 is a good range to be in, however, there is still room for progress. If your NPS is higher than 30 that would indicate that your company is doing great and has far more happy customers than unhappy ones

Financial ratios are to assist a company in analyzing and evaluating financial reports. There are various financial ratios and with this diversity, companies will be able to examine various aspects of company operations (Brigham & Houston, 2017: 103). Brigham and Ehrhardt (2017:114-116) explain that this ratio provides useful guidance for the company's operating activities. The effects resulting from liquidity, asset management, and debt on operations company shown in this profitability ratio. Various types of profitability ratios such as: Gross Profit Margin (GPM), Net Profit Margin (NPM), Return on Total Asset (ROA).

According to (Reichheld, 2003) there are two types of profit, namely good profit, and bad profit. To achieve good profits the company must try to achieve customer loyalty. The key factor for organizational growth is customer loyalty. The results of hypothesis testing in research conducted by (Reichheld, 2003) in the Harvard Business Review Magazine show that there is an influence of the Customer Loyalty variable on Business Growth in Companies (Srirahayu, Anugrah, & Layyinah, 2021).

The results of hypothesis testing in research conducted by (Reichheld, 2003) in the Harvard Business Review Magazine show that it is not surprising that customers "would recommend". For example, in the local telephone and cable TV businesses, population growth and economic expansion in the area determine the rate of growth, not how well customers are treated by their suppliers. And in some cases, we find smaller companies that are growing faster than a percentage of their net-promoters score well. But for most companies in most industries, getting customers enthusiastic enough to recommend a company seems critical to the company's growth (Rajasekaran & Dinesh, 2018).

Equine Global as a whole, which includes both Line of Business, ERP provider and consultancy, has a total of 172 customers including 42 customers with ongoing projects as of 2022. This number of customers does not include LoB System Integrator which was established before Equine Global sold SAP and strategic consulting solutions as it is today. Thus, it can be estimated that Equine Global's total customers exceed 225 customers per year 2022. Equine Global has covered 24 types of industries since 2013 with the five most industries covered, namely Financial Services 41 customers (23.8%), Professional Services 37 customers (21.5%), Oil, Gas & Energy 17 customers (9.9%), Cargo, Transportation & Logistics 9 customers (5.2%), and Manufacturing 8 customers (4.7%). As for projects that are still running as of 2022, the five most industries covered are Financial Services 7 customers (16.7%), Cargo, Transportation & Logistics 5 customers (11.9%), Oil, Gas & Energy 5 customers (11.9%), Manufacturing 4 customers (9.5%), and Professional Services 4 customers (9.5%).

Equine Global has 291 projects from 2017 to 2021, with the highest number of projects in 2021 at 98 projects and the lowest number of projects in 2019 at 35 projects. The average Project Growth Equine Global is 44%.

Table 1. Detail Projects Per Year

Year	Total Projects	Increase in Projects	Growth (%)
2017	50	18	56%
2018	60	10	20%
2019	35	-25	-42%
2020	48	13	37%
2021	98	50	104%
Average	58	13	41%

Source: Research Observation Results

New customer growth rate is the rate at which the company gets new customers within a specified period. The growth rate of new customers helps understand success in attracting new customers. To calculate the new customer growth rate, new customers obtained in a certain period are divided by the total customers in the previous period. This formula does not calculate churn. To measure net growth that calculates churn, the total customers in each period divided by the total customers in the previous period then subtracted by 1. Net growth and new customer growth rates can be very different depending on churn rate. If the company loses more customers than new customers, the new customer growth rate will be positive, but the net growth will be negative.

Overall, Equine Global has an average of 17 new customers per year, with the highest new customers in 2021 at 27 customers, while the lowest new customers in 2015 at 8 customers. Global existing customer equine per year is 28.6 customers, with the highest existing customers in 2022 at 51 customers, while the lowest existing customers in 2014 are 15 customers. Equine Global's average churned customer is 9.7 customers, with the highest churned customer in 2022, while the lowest churned customer in 2014 is 1 customer. Equine Global's overall average new customer growth rate was 42.86%, with the highest new customer growth rate in 2016 at 65.52%, while the lowest new customer growth rate in 2019 was 21.37%. Equine Global has an average annual net growth of 19.39%, with the highest net growth in 2014 at 56.25%, while the lowest net growth in 2019 was 1.96.

Customer Retention Rate is the percentage of existing customers who continue to use services in a certain period. Customer retention is calculated by dividing customer returns (existing customers who continue to use services in a certain period) by the total customers in the previous period.  Overall, Equine Global has an average customer retention rate of 82.48% from 2020 to 2022. The highest customer retention rate obtained by Equine Global in 2020 is 100%, while the lowest customer retention rate in 2022 is 72.86%.

Table 2. Customer Growth Per Year

Year	2017	2018	2019	2020	2021	Average
New Customer	9	11	6	8	21	11
Existing Customer	19	20	21	20	20	20
Churned Customer	4	8	10	7	8	7.4
New Customer Growth Rate	39.13%	39.29%	19.35%	32.00%	80.77%	42%
Net Growth	21.74%	10.71%	-12.90%	4.00%	57.69%	16%
Churn Rate	14.29%	25.81%	37.04%	25.00%	19.51%	24%

Source: Research Observation Results

From 2017 to 2021, Equine Global received an average NPS score of 40%. The NPS Score with the highest score was in 2021 with a score 47%, while the NPS Score with the lowest score was obtained in 2018 with a score of 28%. From this data, it can be concluded that the customer doing great and has far more happy customers than unhappy ones.

Table 3. Detail NPS Per Year

Year	Detractor (%)	Passive (%)	Promoter (%)	NPS (%)
2017	0%	57%	43%	43%
2018	0%	72%	28%	28%
2019	4%	52%	43%	39%
2020	0%	55%	45%	45%
2021	1%	51%	48%	47%
Average	1%	57%	41%	40%

Source: Research Observation Results

Table 4. Breakdown NPS By Industry

Industry	Detractor (%)	Passive (%)	Promoter (%)	NPS (%)
Building Materials	1	0	2	33
Cargo, Transportation & Logistics	8	0	7	53
Chemicals	0	0	1	0
Consumer Products	5	2	8	20
Engineering, Construction & Operations	4	0	3	57
Financial Services	30	0	30	50
Healthcare	2	0	0	100
Manufacturing	10	0	6	63
Mining	1	0	4	20
Non-Profit Organization	1	0	1	50
Oil, Gas & Energy	4	0	6	40
Power Plant	1	0	2	33
Professional Services	12	0	18	40
Public Sector	2	0	4	33
Telecommunications	3	0	8	27
Utilities	1	0	22	4
Wholesale Distribution	3	0	1	75
Retail	5	0	0	100

Source: Research Observation Results

Based on the background and limitations of the problems above, the formulation of the problem in this study are (1) How big is the influence of NPS measurements that have a direct impact on Customer Loyalty in IT consulting firms, (2) Identify and measure customer loyalty on financial performance in IT consulting firms, (3) Identify and measure NPS on financial performance in IT consulting firms.

Figure 2.  Research Model

H1 : Effect of Net Promoters Score on Customer Loyalty

H2: The influence of Customer Loyalty on Financial Performance

H3 : Effect of Net Promoters Score on Financial Performance

Financial performance is an analysis of the degree to which financial targets have been accomplished (Pham, 2021). This research determined the financial performance of IT consulting firm to describe their financial position during a specific period, including aspects of profitability ratio and liquidity ratio.

METHODS

This type of research uses a quantitative method which uses the SEM-PLS analysis technique to evaluate construct measurements and hypotheses where this method examines path coefficients in structural models which in general` have been widely used for social science purposes (Joseph F Hair et al., 2019). In SEM-PLS, there are two phases of analysis carried out. First, the researcher builds a hypothetical model that includes the dependent and independent variables and the relationships between these variables. This model is then tested using SEM-PLS to find out to what extent the data collected is in accordance with the model.

Quantitative research is a process of finding knowledge that uses data in the form of numbers as a tool to analyze information about what you want to know (Ferdinand, 2014: 229). According to Malhotra (2005: 161) quantitative research is a research methodology that seeks to quantify data and usually applies a certain form of statistical analysis. This research is focused on knowing the effect of NPS on Company Financial Performance through Customer Loyalty in an IT Consulting firm in Jakarta (Tarnowska, Bagavathi, & Ras, 2022).

The population in this study are customers at an IT consulting in Jakarta with a total of 176 customers. IT Consulting firm has covered 24 types of industries since 2013 standing with the five most industries covered namely Financial Services customers (23.8%), Professional Services customers (21.5%), Oil, Gas & Energy customers (9.9%), Cargo, Transportation & Logistics customers (5.2%), and Manufacturing customers (4.7%). And through the calculation of the Slovin formula above, the number of samples is minimum 77 customers.

In this study, the data used are primary data and secondary data. Primary data is data obtained directly from original sources (without intermediaries). In this study, the primary data used was the result of distributing questionnaires to a sample population that had been carried out at the IT Consulting firm. The secondary data used comes from the company's financial reports and project reports.

Figure 2.  Path Diagram of Latent Variables

Table 5. Definition of Operating Variables

Variable 1	Indicator	Formula	Measurement Scale
Independent Variables X = Net Promoters Score (NPS)	Detractors = 0-6 Passives = 7-8 Promoters = 9-10	NPS(%) = (Promoters – Detractors) / Total  Respondent * 100	Percentage
Mediation Variables Z = Customer Loyalty	Customer loyalty is measured whether the customer has made a purchase / repeat order in the last 2 years or not	0 = Customer Not Active 1 = Customer Active	Nominal
Dependent Variables Y = Financial Performance	Gross Profit Margin Ratio	Gross Profit / Net Sales Revenue	Ratio
	Return on equity ratio (ROE)	Net Income / Average Total Stockholders' Equity	Ratio
	Current Ratio	Current Assets / Current Liabilities	Ratio

Source: Research Observation Results

Data Analysis Methods

Testing the research hypothesis was carried out using the Partial Least Square (PLS) based Structural Equation Model (SEM) approach. According to Latan and Ghozali (2012), PLS is an alternative approach that shifts from a covariance-based SEM approach to a variant-based one. Covariance-based SEM generally tests causality. Researchers perform data processing which is then grouped into independent variables and dependent variables. Financial ratios based on profitability ratios; Gross Profit Margin Ratio, Net Profit Margin Ratio, Return on Assets Ratio (ROA)

Test Instruments

Convergent Validity

This stage aims to ensure that all indicators used to measure the same latent variable (construct) have a high degree of correlation (Laitinen, 2018). By testing convergent validity, researchers can strengthen the reliability and validity of measuring instruments. The general method used is to analyze the correlation between the measuring instrument being tested and a valid measuring instrument. The importance of convergent validity is seen in various fields of research. From this information, all research indicators can be included in the research model. This can be seen in table 6, from each indicator, it has a significant and high Average Variance Extracted (AVE) value (> 0.7), where the required AVE with a value of AVE > 0.5. Then the latent variable is declared valid.

Table 6. Test The Validity with Convergent Validity

	Average Variance Extracted (AVE)
NPS	1.000
Financial Performance	0.787
Customer Loyalty	1.000

Source: SmartPLS Result

Reliability Test

This test is used to determine the consistency of each variable. Cronbach's Alpha value and Composite Reliability are two methods used to measure the internal reliability of a construct consisting of several items in a measurement scale. Composite Reliability and Cronbach's alpha values are higher than the required value of 0.7, thus indicating how well construct indicators reveal latent constructs (Joe F Hair, Risher, Sarstedt, & Ringle, 2018). In table 7 alpha value above 0.7 is considered good, while a value above 0.8 is considered very good.  As in Cronbach's Alpha, a Composite Reliability value above 0.7 is considered good, and a value above 0.9 is considered excellent.

Table 7. Test The Reliability and Validity

	Cronbach’s alpha	Composite reliability (rho_a)
NPS	1.000	1.000
Financial Performance	0.857	0.909
Customer Loyalty	1.000	1.000

Source: SmartPLS Result

Discriminant Validity

This stage aims to ensure that a construct has a higher degree of correlation with the indicators that should be related to it than with indicators from other constructs. This can be done by calculating the Fornell-Larcker Criterion value, which compares the correlation value between the construct and other indicators in the model with the correlation value between the construct and other constructs in the model as shown in table 8. In table 8 we can see that variables have unique contributions and can be distinguished from each other

Table 8. Discriminant Validity – Fornell-Larcker criterion

	Customer Loyalty	Financial Performance	NPS
Customer Loyalty	1.000
Financial Performance	-0.908	0.887
NPS	0.588	-0.841	1.000

Source: SmartPLS Result

Hypothesis Testing

Test the Coefficient of Determination (R²)

The coefficient of determination (R²) is used to measure how far the model's ability to explain the variation in the dependent variable. The coefficient value of R² of determination is between 0 and 1. The small value of the coefficient of determination means that the ability of the independent variables to explain the variation of the dependent variable is very limited. A value close to 1 means that the independent variables provide almost all the information needed to predict the variation in the dependent variable (Ghazali, 2013: 97).

Table 9. Value of Coefficient of Determination (R2)

	R-square	R-square adjusted
Customer Loyalty	0.346	0.343
Financial Performance	0.968	0.968

Source: SmartPLS Result

Table 9 shows the result of the value of R². The significance of the resulting R² value on the R² value shows the magnitude of the influence of the latent variable of intellectual capital on financial performance of 0.968. This shows that the correlation between intellectual capital and financial performance is 0.968, which is obtained from the root value of R² of 0.968 and signifies a substantial correlation. The value of R² financial effect of latent variables of performance on customer loyalty is 0.346. This shows that the correlation between financial performance and financial sustainability is estimated at 0.588 obtained from the root R² 0.346 in addition to showing moderate.  From these measurements, construction in the Customer Loyalty model has a predictive ability of 34.6% and in the Financial Performance model of 96.8%. The results of this measurement are considered to have met the provisions of R² of at least 0.10.

Path Coefficient

The path coefficient shows how much change in the dependent variable is expected to occur when the independent variable changes one unit, by controlling for other variables in the model.

Table 10. Value of Coefficient of Determination (R2)

Path	Standard Beta	Standard Error	T-value	P-values	Result
NPS  Financial Performance	-0.470	0.026	17.837	0.000	Accepted
NPS  Customer Loyalty	0.593	0.039	15.151	0.000	Accepted
Customer Loyalty  Financial Performance	-0.629	0.034	18.660	0.000	Accepted

Source: SmartPLS Result

T-value and P-value are statistical measures used in path analysis or structural equation models to evaluate the statistical significance of path coefficients between variables in the model.  In general, the commonly used P-value limit is 0.05 or 0.01. If the P-value is less than this limit (for example, the p-value < 0.05), then the path coefficient is considered statistically significant.  Table 4.14 shows the strong influence of NPS on the company's performance, with a P value of 0.000 which is < the value of the α from 0.05. NPS has also been shown to have a strong impact on Customer Loyalty, as P obtained a value of 0 which is < a value of α 0.05. Finally, customer loyalty is proven to mediate a strong influence on financial performance, which is denoted by a P value of 0.000 which is < a α value of 0.05. So, the conclusion is that the path coefficient has a direct influence on the NPS, Financial Performance, and Customer Loyalty variables.

Changes in R² values occur when exogenous latent variables are omitted from the model. This procedure can be used to estimate the impact that variables eliminate on endogenous latent variables and is called effect size f² (Joseph F Hair et al., 2019). Table 11 shows the magnitude of the influence of f² for this study. From table 11, removing the latent variable of intellectual capital from the pathway model resulted in an effect of 4.531 on the latent variable of financial performance which was classified as a strong influence. In addition, elimination produced a small effect of 0.530 on the latent variable customer loyalty. The removal of financial performance from the pathway model creates a moderate influence of 8,280% on the latent variable customer loyalty. Based on the analysis of the size of securities, f², the latent variable of financial performance, is determined as an important variable affecting financial sustainability, since its elimination produces a moderate effect on the path model

Table 11. Effect size (f²)

	NPS	Financial Performance	Customer Loyalty
NPS	4.531	0.530	4.531
Financial Performance
Customer Loyalty	8.280		8.280

Source: SmartPLS Result

In path analysis or structural equation models, Q² (coefficient of cross-determination) is a measure used to measure the prediction and predictability of models against out-of-sample observed endogenous variables or on data not used in model construction.

The value of Q² is generated from the blindfolding process which shows the accuracy of the path model in predicting the observed value. The slight difference between the predicted and observed values translates into higher Q² values to produce higher prediction accuracy (Joseph F Hair et al., 2019). The criterion for Q² is that the value must be greater than 0, which indicates an adequate reconstruction of the observed values and the prediction of the accuracy model. As a benchmark, Q² values above 0, 0.025, and 0.50 describe small, medium, and significant predictive relevance pathway models (Joseph F Hair et al., 2019). Table 12 presents the Q² values generated in this study. The Q² values at table 12 are all greater than zero, meaning that the formed structural model is congruent with the observation of the data and can be used for prediction.

Table 12. Value Q²

	SSO	SSE	Q2 (=1 SSE/SSO)
NPS	218.000	218.000
Financial Performance	654.000	159.834	0.756
Customer Loyalty	218.000	142.936	0.344

Source: SmartPLS Result

Discussion

The Effect of Net Promoters Score (NPS) Model on Customer Loyalty

Based on the results of data processing through SmartPLS and analysis of the value of the coefficient of determination from the test, it was obtained that the Net Promoters Score (NPS) has a direct effect on Financial Performance. This means that Net Promoters Score (NPS) is certainly a variable and an important aspect in the business world, especially related strategies to increase satisfaction. Citing one of the previous studies conducted (Prakoeswa et al., 2022)stated that high NPS results indicate customer loyalty to service.  Although the service is considered good, there are some parts that need to be improved. Special recommendations from respondents are needed to improve services. The results of this study provide additional evidence that NPS is not the only  "one number you need to grow"  (Reichheld, 2003) when measuring customer loyalty and satisfaction, and a multidimensional metric that includes emotions, attitudes, and quality-based factors are needed (Keiningham et al., 2007)to capture the amount of customer experience of the company's products and services

The Effect of Customer Loyalty Model on Financial Performance

After checking the fulfillment of the provisions of the model construction, a hypothesis testing procedure as formulated in this study is carried out. From the test results it was found that all hypotheses were declared accepted customer loyalty clearly affects profitability. Loyalty also drives top-line growth. Obviously, loyal customers can increase the level of income by repeat-ordering.  Truly loyal customers tend to buy more time, as their income grows, or they devote a larger share of them to the company they like (Reichheld, 2003).

Based on the results of the research above, it was obtained that the percentage of direct influence of Customer Loyalty on Financial Performance. Most growing companies focus on retaining customers and motivating them to make more transactions and get them to recommend your product or service and it is believed to be a driver for business growth and increased profitability (Keiningham et al., 2007).

The Effect of Net Promoters Score Model on Financial Performance

NPS was first introduced by (Reichheld, 2003), which is a simple tool to predict the profitability growth of a company with the results / scores of NPS.  NPS is assessed based on the results of answers to a single question, and some appeal is based on its simplicity and ability to provide a quick and decisive trail in decision-making (Reichheld, 2003).

NPS can be assumed to be future growth and profitability very quickly gaining popularity among n companies (Arie & Suryandari, 2023).

From the results of this study, the results show that NPS has an influence on Financial Performance and is a valid predictor of Financial Performance. By focusing on NPS, companies will know that they will increase the value of NPS as well as improve their business performance and create more value for customers (Hassenzahl & Sandweg, 2004). Reichheld also advocated the company's most reliable growth indicators compared to other loyalty metrics, such as customer satisfaction and retention. One path to sustainable and profitable growth starts with creating more promoters and fewer detractors.  This number is the one number you need to develop (Reichheld, 2003).

CONCLUSION

Based on the results of research that has been conducted on IT companies, especially in ERP service providers regarding the effect of Net Promoter Score on Financial Performance with Customer Loyalty as an intervening variable by analyzing data with a period of 5 years (period 2017 to 2021), the following conclusions can be concluded:

· Net Promoter Score has an influence on Financial Performance

· Net Promoter Score has an influence on Customer Loyalty

· Customer Loyalty has an influence on Financial Performance.

This research shows that under the right conditions, NPS can predict future sales growth, which has relevance to financial performance.  However, as evidenced in our research, companies need to focus more on how to leverage NPS, and companies need to pay special attention to the following points.

1. Given that NPS is most effective in predicting short-term sales growth, it is best thought of as a measure that can validate whether current marketing activities such as digital marketing, physical events, SAP info day, are having the effect expected by consumers. Nevertheless, in long-term sales growth, it is necessary to consider other factors that require more time to change, for example, product positioning, service strategy and product innovation (Bennett, n.d.).

2. The research also shows where changes in NPS can predict sales growth. Therefore, companies need to focus on improving NPS. Consistency in conducting NPS surveys (at least once a year) is needed to continue to maintain and monitor this NPS value. NPS is also a metric to measure satisfaction, reputation, brand awareness, employee performance, and customer retention. Some points to improve are:

· Placing more Customer Executive Engagement (CEE) especially in large types of companies.

· Increase knowledge for the latest product (SAP Cloud) and the right learning path.

· Conduct more aggressive marketing activities for SAP Cloud Products (Webinars, Events, SEO, etc.)

Therefore, companies should explore the importance of NPS in their own industries and organizations. Our proposed research methodology will enable them to operationalize this effectively (Bennett, n.d.).

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