Buttressing customer relationship management through digital transformation: perspectives from zimbabwe’s commercial banks

Abstract As a way of buttressing customer relationship management, it is prudent that banks integrate digital transformation as a matter of urgency. To this end, our study’s objective was to establish the contribution of digital transformation on customer relationship management within the banking sector. Our study was hinged on Technology Acceptance Theory and the Identify, Differentiate, Interact and Customise Theory. The sample (348) for the study was drawn from 5 commercial banking employees, and these were randomly selected. A structured questionnaire with Likert type questions was used to collect data, which was analysed using SPSS. Our research findings indicate a positive relationship between digital transformation and customer relationship management. The current study recommends measures that guide the development of digital transformation processes within the banking sector. This research is important as it provides solutions, which can assist management and contact centres for commercial banks to manage customer relationships in modern business. Specifically, our research introduces a new e-customer relationship management system that is technically embedded into e-banking channels.


PUBLIC INTEREST STATEMENT
This paper was written from the studies of commercial banks. Buttressing customer relationship management is now required as a matter of urgency within commercial banks around the globe. This is because the enormous cost of poor customer relationship management is a continuous loss of customers in modern business and in the future. For banks, customer relationship management is an especially useful tool for meeting sales and marketing goals and exceeding customer expectations. The current study recommends measures that guide the development of digital transformation processes within the banking sector. Our research is important to banking practitioners, and banking policymakers as well as central governments as it brings up solutions that can help management and contact centres of commercial banks on how to manage customer relationships in modern business. Specifically, we introduced a new e-customer relationship management system that is technically embedded into e-banking channels. Our research contributes to theory, practice and future research.

Introduction
Technology allows marketers to have global reach in their marketing strategies, which enables them to effectively manage their customers (Chikazhe & Makanyeza, 2022;Mashapure, Nyagadza, Chikazhe,Msipa, Ngoroma & Gwiza 2022). For the purposes of customer relationship management within the banking sector, technological investments give insights into customer behaviour (Gil-Gomez, Guerola-Navarro, Oltra-Badenes & Lazano-Quilis, 2020). Ideally, in customer relationship management, digital transformation through automated categorization and interactions with customers enables banks to come up with products that suit personalised customer needs and wants (Castagna, Centobelli, Cerchione, Esposito, Oropallo & Passaro, 2020). Customers are able to share their input towards product development (Alshurideh, 2022). This also provides insights through continuous monitoring of customer behaviours and it improves customer intelligence through customer risk analysis, customer centricity and customer retention (Chikazhe, Siziba, Bhebhe & Nyagadza, 2023;Kumar & Reinartz, 2018). Other than categorisation and interactions with customers, e-customer relationship management is applied in forecasting, algorithmic trading and organizational intelligence.
Even though, banks especially in developed countries are enjoying such benefits from digital transformation. Other banking sectors in Africa like Zimbabwean have not yet fully unlocked the benefits of digital transformation. The technological advancement, which has been rapidly accepted by Zimbabweans also poses pressure to the banking sector (Hapanyengwi, 2018). The introduction of mobile money platforms like Telecash, Ecocash and Netcash, services make some of the banking services so unnecessary to date. With these platforms, people can substitute the banking services with the much convenient mobile money transfer services. The mobile money solutions operate 24/7 (Matsvai, Mushunje & Tatsvarei, 2021). However, they are not yet fully implemented to carter for all banking services, so people still need banks regardless of the limitation of inconvenience. The nature of the banking industry poses great challenges for investors and entrepreneurs. The regulated capital level is one of the major barriers to entry into the industry. The banks have to undergo a thorough scrutiny by the Reserve Bank of Zimbabwe before they can be licensed to operate.
Due to the seemingly limited digital transformation, proactive analysis of customer dissatisfaction, advanced segmentation, customer profiling and customer journey evaluation in commercial banking in Zimbabwe is seemingly not viable (Matsvai et al., 2021). The banks are not flexible enough to timely introduce products and services in response to the ever-changing operating environment. With the advancement in technology and the volatility of the business environment in Zimbabwe, expectations of customers are also fast changing making flexibility of banks necessary. The introduction of the civil servants Nostro account saw banks such as the Commercial Bank of Zimbabwe and ZB Bank failing to introduce timely systems where clients could self-liquidate their Nostro funds on mobile banking or internet banking (Matsvai et al., 2021). Customers use manual means to liquidate Nostro accounts. Foreign-owned banks were the last to come up with the self-liquidating service. Most civil servants had moved to other banks that had self-liquidating platforms, such as Nedbank and Standard Chartered Bank. Thus, the quality of service delivery has been compromised (Chikazhe & Makanyeza, 2022). Had the banks adopted e-customer relationship management, they could be able to predict and resolve their customer needs.
The situation within the banking sector in Zimbabwe is that digital transformation is slow compared to other African countries. As a result, banks within Zimbabwe are suffering from a low monthly activity ratio of around 15% and have never gone beyond 35% since 2019. The customer bases of some banks have dropped by an average of at least 2.7% since 2016. This gives the need for ways to buttress customer relationship management. Banks are not fully digitalised as they are not enjoying the benefits of e-customer relationship management like other international banks that have digitally transformed. If not properly addressed with the urgency it deserves, some Zimbabwean banks may continue to lose customers in the future. Customers may switch to other banks that have adopted e-customer relationship management to their benefit and that of their customers. Studies conducted to date to try and address this problem have been conducted in developed countries and in stable economies (Afifi & Amini, 2021;Ahmad & Methe, 2021;Alshurideh, 2022;Gunawan & Rustiadi, 2021;Mariia, Natalia, Popkowski & Kateryna, 2020). However, none of the studies included e-retailing, e-customer profiling, e-customer journey and e-segmentation in buttressing customer relationship management within the banking sector especially in an emerging economy like Zimbabwe. Therefore, the current study sought to establish the effect of digital transformation (e-retailing, e-customer profiling, e-customer journey and e-segmentation) on buttressing customer relationship management within the banking sector in morden business.
Our specific objectives were to: establish the effect of e-segmentation, e-customer profiling, e-customer journey evaluation and e-retailing on customer relationship management. The other sections provide the literature review that guides the study followed by the research methodology. After the methodology section, results are presented, interpreted and discussed. The last section provides conclusions and recommendations for the future.

Literature review
This section reviews existing literature on the concepts understudy.

Technology acceptance theory
Technology Acceptance Theory explains how commercial banks accept and use a technology. It is an adoption of the theory of reasoned action. The theory argues that perceived usefulness and perceived ease of use determine the intention of users in using a system. In relation to the current study, customer relationship management comes as one aspect of the perceived usefulness of adopting digital transformation processes critical to this study. Bank customers in Zimbabwe in services, such as opening of a bank account, money transferring and financial assistances high bargaining power (Hapanyengwi, 2018). This is because of the existence of other ready service providers. For opening a bank account, a customer who is a student can choose to go to any bank such that the account can be opened within minutes, one can choose to use Standard Chartered Bank for the reputation that the bank has as well as confidence that they provide to depositors (Matsvai et al., 2021). One can choose Ecobank Zimbabwe for free opening and a very low transaction cost 0.4% of a student account. This therefore shows that the buyer, if not treated as they expect, can choose from other financial service providers.

Identify, differentiate, interact and customise theory
The identify, differentiate, interact and customise theory explains why firms should use technology infrastructure not only to identify but also differentiate, interact, and customize the services they provide to customers. Thus, the technological advancement which has been rapidly accepted by Zimbabweans also poses pressure to the banking sector (Hapanyengwi, 2018). The introduction of mobile money platforms which are Telecash, Ecocash and Netcash services makes some of the banking services so unnecessary. With these platforms, people can substitute the banking services with the much convenient mobile money transfer services. The mobile money solutions operate 24 h a day (Matsvai et al., 2021). However, they are not yet fully implemented to carter for all banking services so people still need banks regardless of the limitation of inconvenience. The nature of the banking industry poses great challenges for investors and entrepreneurs to target. The regulated capital level is one of the major barriers to entry into the industry. The banks have to undergo a thorough scrutiny by the Reserve Bank of Zimbabwe before they can be licensed to operate.

Digital transformation processes
Technological advancement has disrupted the financial industry and this requires firms to be innovative. According to Dash and Chakraborty (2021) digital transformation is the introduction of new technology, systems and models as an attempt to improve customer service and experience. In another definition, Matarazzo, Penco, Profumo & Ouaglia (2021) defines digital transformation as a process through which an organization reorganizes customer value proposition by making use of new technologies as a way of enhancing customer experience. According to Zeithaml (2018) customer service automation refers to a process of reducing human intervention when serving customers through new technologies. Therefore, the adoption of digital transformation processes in banks is based on the fact that banks intend to enhance their quality of service delivery. The rationale behind the adoption of digital transformation processes within organisations is explained in the Identify, Differentiate, Interact and Customise Theory and Technology Acceptance Theory as presented.

Customer relationship management
Customer relationship management as put by Munyimi (2019a) as cited in Munyimi (2022) is managing all of your company's relationships and interactions with current and prospective customers. The goal is straightforward: to strengthen business relationships. A Customer relationship management system assists businesses in staying connected to customers, streamlining processes, and increasing profitability (Nasr & Safira, 2021). Chikazhe, Jecha, Nyagadza, Bhebhe & Manyeruke (2022) added that customer relationship management makes use of emerging technology to provide fast and effective customer service through the development of a relationship using information collected from customer databases. It aims at bringing on board clients for the organization, maintaining the existing customer and growing the relationship using the current customers (Brink & Berndt, 2009;Chigunhah, Svotwa, Mabvure, Munyoro & Chikazhe, 2020). Ideally, in customer relationship management, digital transformation through automated categorization and interactions with customers enable banks to produce products that suit personalized customer needs and wants. Customers are able to share their input towards product development (Castagna, Centobelli, Cerchione, Esposito, Oropallo & Passaro, 2020; Munyimi and Chari, 2018). This also provides insights through continuous monitoring of customer behaviours and it improves customer intelligence through customer risk analysis, customer centricity and customer retention. Other than categorization and interactions with customers, e-customer relationship management is applied in forecasting, algorithmic trading and organizational intelligence.

E-segmentation
Market segmentation involves deconstructing one heterogeneous market into smaller homogeneous markets in response to preferences due to desires for specific satisfaction by customers in different homogenous markets . The process of dividing a customer base into sections based on demographics, interests, behaviours, consumer behaviour, and history is known as e-segmentation (Mharidzo, . It is, without a doubt, one of the most important data sources a business owner can use to strengthen and optimize their marketing strategy (Mwandifura, Chikazhe, Manyeruke & Mashavakure, 2022). Marketers have long recognized the importance of segmentation to tailor their marketing mixes per the specific needs of different customer groups. Recognizing the different needs of your customers is essential to a productive marketing plan (Sivarajah, Kamal, Irani & Weerakkody, 2017). Sub-segments are created by different companies based on needs, demographics, priorities, shared interests, and behavioural and psychographic criteria (Wedel & Kannan, 2016). The process of identifying segments will differ from one company to the next. Market segmentation, for example, may include four categories: geographic, demographic, psychographic, and behavioural (Chigunhah et al., 2020).

E-customer profiling
Customer profiling is a marketing strategy that employs data to create a mental image of the ideal customer who will interact with your product or service (Chikazhe & Makanyeza, 2022). A useful customer profile, when done correctly, will serve as a guide for your marketing and advertising to reach your ideal customers (Sivarajah et al., 2017). A customer profile, also known as a consumer profile, is a comprehensive description of your current customers . In order to target similar customers in your sales and marketing campaigns, you would identify purchasing behaviours, pain points, psychographic data, and demographic data in a customer profile.
Whenever a business acquires a new customer through marketing activities, the customer determines what the value of each activity received from the business. E-customer profiling has the abilities for tracking purchase histories and their online conversations about their products (Sivarajah et al., 2017). The impact of e-customer profiling is significant, on the distribution of marketing channels between service providers and customers, whereas suppliers or service providers are engaging with customers directly, threatening the sustainability of intermediaries marketing agencies (Mharidzo et al., 2022). Ideally, in customer relationship management, digital transformation through automated categorization and interactions with customers enable banks to produce products that suit personalized customer needs and wants.

E-customer journey evaluation
Customer journey evaluation covers assessment of attitudinal or psychographic, behavioural and market insights of customers throughout their lifetime with the organisation (Sivarajah et al., 2017;Wedel & Kannan, 2016). For you and your customers, evaluation is most likely the most important part of the customer journey. During this time, you have the opportunity to strengthen a relationship, close a sale, and lay the groundwork for a long term, profitable account (Foltean, Trif & Tuleu, 2019). Here are some practical points to consider when designing each stage of the customer journey to help address customer journey challenges: awareness, consideration, purchase, retention, and advocacy (Anshari, Almunawar, Lim & Al-Mudimigh, 2019).
Ideally, in customer relationship management, digital transformation through automated categorization and interactions with customers enables banks to produce products that suit personalized customer needs and wants. Customers are able to share their input towards product development . This also provides insights through continuous monitoring of customer behaviours and it improves customer intelligence through customer risk analysis, customer centricity and customer retention.

E-retailing
Electronic retailing is the sale of goods and services through the internet (Homburg, Jozic & Kuehnl, 2015). It includes business-to-business and business-to-consumer sales of products and services (Homburg et al., 2015). Companies worldwide have resorted to conducting business using online platforms like the Internet . This development gives a competitive advantage to firms that are technologically advanced especially within developed states (Chikwere, Chikazhe & Tukuta, 2022). Ideally, in customer relationship management, digital transformation through e-retailing and interactions with customers enable companies to produce products or services that suit personalized customer needs and wants. Customers are able to share their input towards product development (Chikazhe & Makanyeza, 2022). This also provides insights through continuous monitoring of customer behaviours and it improves customer intelligence through customer risk analysis, customer centricity and customer retention.

Empirical literature and hypotheses development
Literature confirms positive relationship between customer relationship management and digital transformation (Anshari et al., 2019;Foltean et al., 2019;Ulas, 2019). Foltean et al. (2019) examined customer relationship management capabilities and social media technology use. The study results indicate that customer relationship management capabilities indirectly mediate the relationship between social media technology use and firm performance.
Using a quantitative approach, Yang (2012) investigated the link between e-segmentation and customer relationship management of Taiwan commercial banks. The results indicate that information knowledge management, conjoint service capabilities, and marketing knowledge positively influenced the effectiveness of customer relationship management performance. The research, however, did not examine the adoption of digital transformation processes, with a focus on customer relationship management, which was the focus of the current study. Thus, the current study sought to narrow this gap in literature by investigating whether the selected digital transformation dimensions have an influence on customer relationship management. Hence, it can be proposed that:

H 1 : There is a positive relationship between e-segmentation and customer loyalty in the banking sector
In a related study, Anshari et al. (2019) investigated the influence of big data in bringing a new wave of customer relationship management. The study found out that customer relationship management with big data enables an industry to become more aggressive in terms of marketing approach to their possible target audiences. Also, Arachchi, Karunarathne, Perera, Bandara & Ganegoda (2020) examined customer profiles and textual behaviour for the transport sector. The study concluded that customer profiling positively impacts firm performance. The current study is unique in that it focuses on e-customer profiling, an area not included in earlier studies. Thus, it can be hypothesized that: There is a statistically significant relationship between e-customer profiling and customer recovery in the banking sector A study by Vakulenko, Shams, Hellstrom & Hjort (2019) investigated how service innovation affects e-customer behaviour and presents a basic map of the e-customer journey. The findings also lay the groundwork for better customer experience management and managerial decision-making when developing new e-commerce last mile services. Pantouvakis & Gerou (2022) studied customer journey research in services and highlight its importance for service sustainability. According to the findings of the study, the customer journey influences managers to better understand their customers' decision-making process and better allocate their resources by establishing a sustainable service design throughout the various service phases. Likewise, Araujo, Goncalves, Costa, Dias & Pereira (2022) conducted a study to better understand the current state of consumer interaction with artificial intelligence during the digital customer journey in the fashion industry. People who interact with artificial intelligence are younger and more self-assured, and they want a quick response to their requests, primarily through recommendation systems, chatbots, and virtual testers. However, none of these studies focused on e-customer journey evaluation within the banking sector. Hence, it is wise to assume that: H 3 : There is a statistically significant relationship between e-customer journey evaluation and customer acquisition in the banking sector Kazancoglu & Demir (2021) investigated the impact of flow experience on repurchase intention. E-customer satisfaction was discovered to have a significant effect on repurchase intention. Jafarzadeh, Tafti, Intezari & Sohrabi (2021) investigated the factors that influence effective recovery from service failures encountered during the delivery phase of e-commerce. Our findings provide empirical insights into how perceived recovery effort interacts with situational criticality and brand equity to mitigate the negative consequences of service failure during the delivery phase. Therefore, it was hypothesised that: H 4 : There is a statistically significant relationship between e-retailing and customer retention in the banking sector Based on the preceding debate, there are no studies that investigated the effect of digital transformation dimensions, i.e., e-segmentation, e-customer profiling, e-customer journey and e-retailing on customer relationship management. Thus, the current study is unique and first of its kind within the banking sector of a developing nation like Zimbabwe. Also, considering the debate above, the conceptual framework below was proposed. See Figure 1.

Research philosophy, design and target population
These researchers adopted positivism research philosophy that accommodates a quantitative approach. The adoption of the research philosophy was based on the stipulations by Balnaves (2020) which state that this philosophy is best for research studies that are neither purely quantitative nor purely qualitative. Explanatory research design was relevant as it was consistent with the positivist research philosophy adopted. The study population encompassed employees from five digitalised commercial banks in Zimbabwe. These were: Steward Bank, ZB Bank, Commercial Bank of Zimbabwe, Banc ABC and Ecobank Zimbabwe. In total, there were nine commercial domestic banks operating in Zimbabwe, of which the five of the selected banks for the study were digitalised. We settled for only digitalised commercial banks as they had employees with the required information for the study. The target population was 11, 500 employees as broken down in Table 1.

Sampling and data collection
A sample of 380 commercial banking employees was adopted for the current study. We used a RAOSOFT sample size calculator to come up with sample size form the targeted population. Respondents were first grouped into strata (digital bankers, risk and compliance, information systems and service desks). Stratified sampling was first employed so as to make sure that there is equal representation of employees in the study. The respondents were then selected from the different population categories using simple random means as justified by Harkiolakis (2020) that statistical analysis need to be done using randomized samples. We ensured that respondents were selected from all strata within all the five banks. Thus, 86 respondents were selected from each bank.
A structured questionnaire with six sections (e-segmentation, e-customer profiling, e-customer journey evaluation, e-retailing and customer relationship management) was constructed. Under each variable, items were developed and measured used using Likert type questions. The Likert type questions ranged from 1 = Strongly Disagree to 5 = Strongly Agree. The e-segmentation section asked questions meant to understand the basis of segmentation for all respondents. The second section, which focused on e-customer profiling, addressed questions to do with the understanding of current customers, identifying purchasing behaviours, pain points as well as psychographic information. The section on e-customer journey evaluation focussed on the assessment of attitudinal or psychographic, behavioural and market insights of customers throughout their lifetime with the organisation's awareness, consideration, purchase, retention, and advocacy. As of the e-retailing section, questions addressed goods and services sold by the organisation through the internet, as well as the respondents' interactions with employers to enable banks to produce goods that suit personalized customer needs and wants. Lastly, on customer relationship management, questions sought to address issues to do with a technology that allows a firm to manage all of the company's relationships and interactions with current and prospective customers. A Customer relationship management systems assist businesses in staying connected to customers, streamlining processes, and increasing profitability. Items used in coming up with the research instrument were borrowed from related studies (Chikazhe et al., , 2023Gunawan & Rustiadi, 2021;Mariia et al., 2020;Matarazzo et al., 2021), and they were modified to suit the requirements of the current study.
A self-administered structured research questionnaire was used to collect data from commercial banking employees since it allows collection of fresh and original data that resolves the research problem at hand (Saunders, Lewis & Thornhill, 2017). The pretesting of the research instrument was done with a sample of 30 commercial banking employees who participated in the main research survey and the instrument was refined accordingly. For reliability, Cronbach's Alpha measure was used.

Data analysis and ethical considerations
Data were presented in the form of tables. These researchers ensured that respondents showed their consent before the collection of data and their engagement in data collection. Right to privacy and confidentiality to protect against victimization was maintained throughout our study.

Results
This section presents, interpret and discuss the findings of the study

Response rate and reliability results
The response rate calculated by the formula borrowed from Saunders et al. (2017) that of dividing the number of returned questionnaires completed and free from errors by the total number of research questionnaires issued is presented in Table 2 below: The results indicated in Table 2 show a response rate of 91.6% on commercial bank employees. According to Kothari (2014) a response rate of at least 70% is valid enough to render the research findings representative of the target population. The response rate meets the minimum results as recommended by various authors (Kothari, 2014;Saunders et al., 2017). This then confirms that the findings of our research permitted for data analysis to be carried out.

Reliability test results
Reliability was tested using the Cronbach's alpha and the results are presented in Table 3 below: Our results indicated in Table 3 indicate that minimum conditions for a reliability test were satisfied. Thus, all individual reliabilities and the overall Cronbach's alpha coefficient in the constructs were above the minimum cut-off point of 0.7 as recommended by Saunders et al. (2017) as cited by Munyimi (2019b).
As for descriptive statistics for the primary data, Table 4 presents the results for all the five variables for our study. Table 4 , shows an average response of 4.02, standard deviation = 0.981 on e-segmentation. They also agreed that e-customer profiling (mean score 4.12 and SD 0.893), e-customer journey evaluation (mean score 3.80 and SD 1.026) and e-retailing (mean score 4.25 and SD 1.006) all influence customer relationship management within the banking sector. Our results imply that respondents approved of the view that e-segmentation, e-customer journey evaluation, and e-retailing all positively influence the enhancement of customer relationship management within the banking sector.

Normality tests results
These researchers ran normality test on all variables that were meant to be correlated and regressed and are presented hereunder Table 5.  Table 5 shows that the Shapiro Wilk and Kolmogorov-Smirnov significance values are below the level of significance used, the data is not normally distributed, implying that there is sufficient evidence against the null hypothesis, which claims that the data for the variables is normally distributed. We used 5% level of significance for hypothesis testing. The Shapiro-Wilk and Kolmogorov-Smirnov significance values for all variables under study were greater than 0.05, showing that all the data was not normally distributed. This resulted in the performance of the Spearman's rank correlational analysis by researchers. Table 6 indicates correlation results of the relationship between e-advanced segmentation and customer relationship management in commercial banks. Table 6 indicate that the correlation value between e-segmentation and customer relationship management in commercial banks is 0.971. This is a largely strong positive relationship. This suggests that e-segmentation influences customer relationship management. Table 7 which follow presents regression outcomes for e-advanced segmentation and customer relationship management. Table 7 are the basis of accepting and rejecting the alternative hypothesis, which states that there is a statistically significant relationship between e-segmentation and customer relationship management in commercial banks. E-segmentation and customer relationship management in commercial banks have a positive relationship (β = 0.97) and level of significance of 0.01. Our results indicate that e-segmentation contributes 97% towards improving customer relationship management in commercial banks. Therefore, we accepted the research hypothesis that there is a statistically significant relationship between e-segmentation and customer relationship management in commercial banks. This entails that if banks segment their market, chances are high that they will deliver the correct information to customers and this will create a strong relationship in modern business.    Table 8 indicates correlation results of the relationship between e-customer profiling and customer relationship management in commercial banks.

Link between e-customer profiling and customer relationship management
The results in Table 8 indicate that the correlation value between e-customer profiling and customer relationship management is 0.828 which proves to be a largely strong relationship. Thus, commercial banks can capitalise on e-customer profiling to improve customer relationship management. Table 9 presents regression outcomes between e-customer profiling and customer relationship management. Table 9 are that E-customer profiling and customer relationship management in commercial banks have a positive correlation (β = 0.83) and a level of significance of 0.08. The results indicate that e-customer profiling offered contributes 83% in explaining customer relationship management in commercial banks. These researchers accepted the hypothesis that there is a positive relationship between e-customer profiling and customer relationship management within commercial banks. This means that banks can go far beyond basic demographics but getting as close to consumers as possible so as to create a stronger relationship with customers in modern business.   Table 10 indicates correlation results of the relationship between e-customer journey evaluation and customer relationship management in commercial banks.

Link between e-customer journey evaluation and customer relationship management
The results in Table 10 indicate that the correlation value between e-customer journey evaluation and customer relationship management in commercial banks is 0.800 which proves to be a highly strong relationship between e-customer journey evaluation and customer relationship management within commercial banks. Table 11 which follows presents regression test outcomes. Table 11 are that E-customer journey evaluation and customer relationship management in commercial banks have a positive correlation (β = 0.80) and level of significance of 0.10. The results also indicate that e-customer journey evaluation contributes 80% in explaining customer relationship management in commercial banks. The hypothesis that there is a statistically significant relationship between e-customer journey evaluation and customer relationship management in commercial banks was accepted by researchers. The study results mean that banks could ensure e-customer journey evaluation in order to improve customer relationship management in modern business.   Table 12 indicates correlation results of the relationship between e-retailing and customer relationship management in commercial banks.

Link between e-retailing and customer relationship management
The results in Table 12 indicate that the correlation value between e-retailing and customer relationship management in commercial banks is 0.780 which proves to be a highly strong relationship between e-retailing and customer relationship management in banks. Table 13 presents regression outcomes for e-retailing and customer relationship management. Table 13 show that e-retailing and customer relationship management in commercial banks have a positive correlation (β = 0.780) and a level of significance of 0.10. The results also indicate that e-retailing contribute 78% in explaining customer relationship management in commercial banks. The hypothesis that there is a statistically significant relationship between e-retailing and customer relationship management in commercial banks was accepted. This implies that a bank can do more with digital marketing platforms than just distribute coupons and news about new products. Banks may increase customer loyalty and retention by offering valuable materials that provides value, such as advice about the business or ideas for creative ways to use products.

Conclusions and implications
Our research concludes that e-segmentation and customer loyalty in commercial banks have a positive relationship. Therefore, an improvement towards e-segmentation improves customer loyalty in banks. Moreover, these researchers conclude that e-customer profiling and customer recovery have a strong positive relationship in commercial banks. Customer journey evaluation helps in customer acquisition in banks since they have a strong positive relationship. It is concluded that e-retailing and customer retention have a strong positive correlation. This implies that an investment towards e-retailing results in customer retention in commercial banks. Overall, we conclude that customer relationship management within commercial banks is buttressed through digital transformation in modern business.

Implications to theory
We established a positive relationship between e-segmentation and customer relationship. This implies that e-segmentation influences customer relationship management within the banking sector. The finding here agrees with Yang (2012), Khanna & Gupta's (2015), Mohamud (2016) and Chege et al. (2020) who found out that e-segmentation largely positively influences customer relationship management. These researchers' findings are therefore confirming the position established by past researchers.
Likewise, the study found out that there is a positive relationship between e-customer profiling and customer relationship management in commercial banks. The findings here agree with those by Orenga-Rogla & Chalmeta (2016) and Krishna & Vadlamani (2016) that e-customer profiling strongly positively enhances customer relationship management in banks. Also, Nderi (2014) findings indicate that e-customer profiling in commercial banks actively improved the performance of commercial banks.
Also, the study established significant relationship between e-customer journey evaluation and customer relationship management in commercial banks. However, Sivarajah et al. (2017), Wedel & Kannan (2016, Bottomley & Doyle (1996) and Mengesha (2016) determined that e-customer journey evaluation and customer relationship management have no clear relationship. Githuku & Kinyuru (2018) found that customer with low perceptions of the value of e-customer journey evaluation results in turn with low satisfaction. Although there appears to be contradiction on findings by other researchers about the contribution of e-customer journey evaluation on customer relationship management, these researcher's findings put forward the position that e-customer journey evaluation has positive effect on customer relationship management.
Lastly, we concluded that e-tailing has a positive relationship with customer relationship management. This corroborates results in a study by Githuku & Kinyuru (2018) who found out that e-retailing influences customer relationship management. Also, Wanjau (2011) discovered a positive relationship between e-retailing and customer relationship management.
Moreover, the study results show that digital transformation positively influences customer relationship management, validating the two theories underpinning our study. Thus, the Technology Acceptance Theory explains how commercial banks accept and use a technology, which our study results supported. The identify, differentiate, interact and customise theory explains why firms should use technology infrastructure not only to identify but also differentiate, interact, and customise the services they provide to customers, which our study also confirmed.

Managerial implications
The Reserve Banks of developing countries are recommended to come up with several measures that guide the development of digital transformation processes that can improve customer relationship management in commercial banks. This is done by, for example, automating the auto settlement of queries between banks as well as between customers. The Bankers Associations and ZimSwitch have been pushing the banking industry to restructure its operations to fight competition from mobile money operators and other microfinance institutions. The research findings are therefore critical to such initiatives since these can be advanced to enhance customer satisfaction and retention. The research findings can also support the individual banks in identifying the key digital transformation areas that are critical for customer relationship management.
In light of the position established with respect to e-segmentation and customer loyalty in commercial banks, we recommend commercial banks to improve the utilization of e-segmentation. These can go a long way to retain customers. Commercial banks should improve utilization of e-segmentation to enhance customer loyalty at the bank. This can be achieved through developing and utilizing customer data to align the respective relationship management strategies. For example, data analytics that can be used in segmentation such that personalized services can be provided to customers as a way of enhancing the bond with customers. This is also in line with the mandatory Know-Your-Customer by various central banks in Africa. E-segmentation can be a long way of ensuring Know-Your-Customer in commercial banks. Also, recommended is that commercial banks should expand their capacity to profile their customers using digital means. This can be done through leveraging on data science. We further recommend that commercial banks should improve investment in e-customer profiling tools. These can improve efficiency of service provision that is integral to customer satisfaction. Commercial banks can also leverage the growth in internet penetration to develop dashboards and platforms, which can improve e-customer profiling through over-the-internet customer engagement.

Areas for further researches
The research successfully accomplished the research objectives through a quantitative analysis that has its own pitfalls. This could have limited the depth of the research by suppressing any possible qualitative insights that the research could have tabled out. This gives room for further researchers to explore the use of digital transformation on customer relationship management among commercial banks using an interpretivism philosophy. This can be accomplished through focus group discussions (FGDs) and indepth interviews with the managers of commercial banks. The current research analysed only commercial banks. This gives a limited view of the banking operations. We recommend that future researchers use savings banks and investment banks as well as microfinance institutions to establish a wider view of the usefulness of digital transformation in customer relationship management among banks. Panel data can be analysed to establish the usefulness of digital transformation in customer relationship management among commercial banks. Future research can expand on digital transformation variables since the current study was restricted to e-segmentation, e-customer profiling, e-customer journey evaluation and e-retailing. Also, future researchers might use multiple-regression model instead of separate regressions on each variable for comparison purposes.