Leveraging Marketing Automation to Enhance Customer Relationship Management and Improve Management Performance in MSME B2B

This study aimed to explore the impact of using marketing automation in customer relationship management strategies and ultimately improving management performance in the context of B2B Micro, Small and Medium Enterprises (MSMEs). The research method involved primary data collection using a quantitative approach and a non-probability sampling method, specifically purposive sampling, on 105 samples that actively use marketing automation tools. The analysis technique used was Structural Equation Modeling (SEM) with the help of Lisrel 8.80 software. The results show that marketing automation has a positive and significant impact on B2B customer relationship management, with reach of message as the most dominant indicator, contributing 65.61%. Furthermore, B2B customer relationship management also has a positive and significant effect on Marketing Performance, where improving the quality of customer relationships is the most dominant indicator at 77.4%, and the most dominant indicator in shaping marketing performance is having a larger market, accounting for 42.25%. The conclusion of this study is that the use of marketing automation in customer relationship management strategies can improve the marketing performance of B2B-based MSMEs.


Introduction
Business-to-business (B2B) interactions form the backbone of global commerce, with micro, small, and medium-sized enterprises (MSMEs) playing a pivotal role in this dynamic landscape.However, B2B MSMEs often encounter challenges in maintaining and fostering customer relationships in the digital era, potentially leading to a loss of trust and hindering long-term sustainability (Humairo & Abidin, 2023).Additionally, the integration of existing technology with emerging digital tools and effective data management can pose significant hurdles due to limited technological expertise.Customer relationship management (CRM) has emerged as a transformative tool for B2B MSMEs to navigate these challenges and embrace digital transformation (Yunusa et al., 2023).CRM empowers MSMEs to effectively manage customer data, track customer interactions, and provide personalized customer service, ultimately enhancing customer relationships and driving sales and marketing effectiveness (Aqiilah, 2022;Latifah et al., 2022;Ramadhani, 2022;Saura et al., 2020).
Despite the potential benefits of CRM, its adoption among Indonesian MSMEs remains relatively low.CRM implementation can be complex and resource-intensive, requiring substantial financial investment and digital literacy.Furthermore, convincing colleagues to adopt changes and overcoming the lack of digital literacy among MSME players can pose additional hurdles (Benjamin, 2019;Corsaro et al., 2021;Hanaysha et al., 2022;Harini et al., 2023;Mero et al., 2022;Parker, 2020).
Marketing automation offers a promising solution to address these challenges.Marketing automation streamlines repetitive marketing tasks, reduces manual labor, and optimizes operational costs.Additionally, it leverages data-driven insights to gain valuable customer information (Harisandi et al., 2023).By synergizing marketing automation with CRM strategies, B2B MSMEs can effectively manage customer interactions, enhance customer satisfaction, and improve overall business performance.This research is based on the limitations of research that focuses on B2B MSMEs because most studies focus more on large companies (Ika Purnama et al., 2021;Novita & Yuliani, 2016;Rosalina, 2019).
This research aims to explore the impact of marketing automation on customer relationship management strategies and ultimately improve management performance in the context of B2B MSMEs.Specifically, the research will: Identify the barriers hindering the adoption of CRM and marketing automation in B2B MSMEs, Develop strategies to enhance the digital capabilities of MSMEs and improve their technological understanding, Analyze how CRM can be effectively implemented in B2B MSME businesses by synergizing with marketing automation (Sudarmanto et al., 2023), Evaluate the real impact of implementing marketing automation in CRM on MSME business performance, and Contribute to the knowledge gap on how technology can improve MSME business results and overcome digital transformation barriers to CRM implementation.The findings of this research will provide valuable insights for B2B MSMEs seeking to leverage technology to enhance their customer relationship management practices, improve business performance, and thrive in the digital era (Njiru & Thoronjo, 2023).

Research Method
This study employed a quantitative approach utilizing purposive sampling, a nonprobability sampling method that involves selecting subjects based on predetermined criteria.The target population consisted of B2B MSMEs that use Marketing Automation from MarkasBot.id.The sample size was determined using Hair & Ringle( 2014) method, resulting in 105 respondents.The instruments used to measure the variables were: Marketing Automation (Benjamin, N & Paraskevi, 2019).B2B Customer Relationship Management (CRM) (Cao & Weerawardena, 2023) and Marketing Performance (Cao & Weerawardena, 2023).Data were collected through a closed online survey or questionnaire distributed via WhatsApp and Telegram.Structural Equation Modeling (SEM) analysis was employed to analyze the collected data.Due to the reflective nature of the variables, SEM analysis was conducted using Lisrel 8.80

Results
Respondent Characteristics, This stage will show the characteristics of respondents from respondents who filled out this research questionnaire.Respondent characteristics based on gender, age, and education.

Confirmatory Factor Analysis (CFA)
CFA is carried out to test a construct that is carried out before testing.CFA is an initial measurement model to test the validation and reliability of all indicators forming latent constructs (Gunarto, 2018;Haryono, 2016;Yanti, 2022).CFA model testing in this study was carried out with one stage (first order).

CFA Model on Variable of Marketing Automation (MA)
The third variable tested with the CFA model is the marketing automation (MA) variable.Below are the results of the Standardized Solution CFA model obtained with the Lisrel 8.80 program.The results of the CFA X2 model validation and reliability test are described in Table 1.Table 1 shows that the marketing automation variable CFA model which has 3 indicators is declared valid.This is because all indicators on this variable have factor loading (λ) > 0.5.In addition, in the reliability test, the MA variable has CR> 0.5 (0.833) and AVE> 0.5 (0.624), which means that marketing automation is valid and reliable.

CFA Model on B2B Customer Relationship Management Variable
The fourth variable tested with the CFA model is the B2B CRM variable.Below are the results of the Standardized Solution CFA model obtained using the Lisrel 8.80 program.The results of the validation and reliability test of the B2B CRM CFA model are described in Table 2. Table 2 shows that the CRM B2B variable CFA model which has 4 indicators is declared valid.This is because all indicators on the CRM B2B variable have a loading factor (λ)> 0.5.Then, the results of the CFA model reliability test obtained a CR value> 0.7 (0.873) and AVE> 0.5 (0.633) which means that the CRM B2B variable is valid and reliable.

CFA model on Marketing Performance (MP) Variable
The fifth variable tested with the CFA model is the MP variable.Below are the results of the Standardized Solution CFA model obtained using the Lisrel 8.80 program.The results of the validation and reliability test of the CFA MP model are described in Table 3. From Figure 3, it can be seen that MP2 is less than 0.5, namely 0.30 so it must be eliminated, the results can be seen in Figure 6.Table 6 shows that 3 indicators have a loading factor (λ)> 0.5, which means that 3 indicators in the MP variable are declared valid.Then, the CR value> 0.7 (0.783) and AVE> 0.5 (0.548).Therefore, it can be stated that the MP variable is valid and reliable.Initial Structural Analysis, In the CFA measurement, all variables have been declared valid and reliable.This means that all latent variables are measured properly.The next step is to establish a structural model to find out how the latent variables are related.From Table 4 in testing the overall structural model that 21 indicators tested have been declared valid.This is because all indicators have λ more than 0.5.

GOF on Model Initial Structural
GOF results data on the initial model of the entire structural model in this study are shown in Table 5.From Table 5 shows that 6 criteria out of 11 criteria qualify the measurement model that is not good (not fit), especially RMSEA.Based on consensus, the RMSEA criterion is more widely used to determine the suitability of a model (Gunarto, 2018).so that the structural model of this research must be modified.The modified results in this study can be seen in Figure 9.  6 .From Table 6, it can be seen that all indicators in the final model of the entire model have λ> 0.5 so that the 20 indicators in this final model have been declared valid.Table 4.10 shows that 5 variables in the final model have a CR value> 0.7 and AVE> 0.5, which means that the 5 variables in this study are valid and reliable.

GOF Analysis of the Final Model
The GOF results on the final structural model are described in Table 7. Table 7 shows that the structural model formed has met several GOF statistical criteria.7 criteria out of 11 criteria have met the criteria for a good measurement model (fit), especially RMSEA has met the statistical criteria ≤ 0.08 (RMSEA: 0.079).
R-Square, R-square result this research are shown on the below: In table 11, it can be seen that the R-square of B2B CRM is 85%, and Marketing Performance is 70%.
Hypothesis Test, All parameters are tested with the t-test statistic.The variable is declared significant if the t-count value is more than 1.96 and if the t-count is less than 1.96 then the variable is not statistically significant.The results of T-Values testing the full model are shown in Figure 10.Based on Table 12, it can be concluded as follows: H1 = There is a positive and significant influence between Marketing Automation (MA) and Customer Relationship Management (CRM), the T-value is 7.76 which is more than 1.96 as the limit value if the variable is significant (accepted).H2 = There is a positive and significant influence between Customer Relationship Management (CRM) and Marketing Performance (MP), the T-value is 5,88 which is more than 1.96 as the limit value if the variable is significant (accepted).

Discussion
The Impact of Marketing Automation and Customer Relationship Management (CRM) on Business Performance in B2B Marketing automation (MA) and CRM are two interconnected concepts that can positively influence each other.MA implementation in CRM enhances customer data management and interactions, leading to improved business performance.This aligns with research by Bandem Bandem Mahatma Wicaksana et al., (2022), who found that VTiger CRM integration positively impacts business performance.Additionally, MA integration with CRM enhances customer interaction personalization.Hasan et al., (2023) analyzed customer relationship management in Moex Ice Cut MSMEs, revealing that MA aids in presenting more relevant customer content, fostering a more personalized and satisfying experience.

The Effect of Customer Relationship Management (CRM) on Marketing Performance in B2B
CRM positively and significantly impacts marketing performance, with a t-value of 5.88.This study demonstrates that MSMEs capable of identifying attractive customers and establishing and maintaining relationships with them to enhance profits tend to exhibit superior marketing performance.This aligns with research by (Cao & Weerawardena, 2023) The theory underlying CRM's influence on marketing performance is based on Teece's dynamic capabilities concept.Companies, or MSMEs in this context, can leverage their dynamic capabilities to generate new knowledge applicable to pursuing their primary competitive strategies.In the CRM context, these capabilities include identifying potential customers, initiating and maintaining relationships with them, and utilizing these relationships to boost customer-level profitability.

Conclusion
Based on the explanation above, it can be concluded that marketing automation (MA) and customer relationship management (CRM) have a positive and significant impact on business performance, especially for B2B businesses.MA can improve efficiency in managing customer data and interactions with customers.This can be achieved by automating marketing tasks, such as sending emails, scheduling events, and tracking leads.MA can also improve personalization in customer interactions, so customers feel more valued and appreciated.
CRM, on the other hand, can help businesses to identify potential customers, start and maintain relationships with them, and use those relationships to increase profitability.CRM can also help businesses to understand customer needs and behavior, so businesses can provide better and more relevant service.
When MA and CRM are integrated, these two concepts can complement each other and increase their positive impact on business performance.This integration can help businesses to: Automate marketing and sales tasks more efficiently, Improve personalization in customer interactions, Identify and maintain relationships with potential customers, Understand cutomer needs and behavior, Increase customer satisfaction, and Increase sales and profits.Therefore, MA and CRM are essential tools for B2B businesses to improve their performance.

Figure 1 .
Figure 1.Standardized Solution CFA Model Variable Marketing Automation Source: Data processed in Lisrel 8.80, 2023

Table 1 .
Validation and Reliability Test of Marketing Automation Variable

Table 2 .
Validation and Reliability of CRM B2B Variable

Table 3 .
Validation and Reliability Test of Marketing Performance Variable

Table 4 .
Validation and Reability Test Data on the Initial Model

Table 5 .
GOF on the Initial Model

Table 6 .
Validation and Reliability Test Data on the Final Model

Table 7 .
GOF in the Final Model

Table 8 .
R square results

Table 9 .
Hypothesis Test Results