Digital business technologies rapidly develop and make it crucial for companies to integrate novelties for maintaining competitiveness. Modern services are capable of including multiple innovations that can work simultaneously to optimise and speed up tasks that tend to be time-consuming. Moreover, automation is a powerful strategy to reduce costs and increase efficiency for any business that operates with digital tools’ assistance. The report describes customer relationship management software as a modern tool required for any firm’s optimised performance.
CRM-related services are crucial for businesses because they affect their structures, such as customer support, management, and strategy making. Softwares can gather, store, and analyse information about customers from multiple channels. Digital technologies utilised in CRMs are the key to automate data structuring and retrieve the evidence-based analytics necessary for managing sales. The report’s research is based on peer-reviewed studies and articles about CRM, innovations, and business processes improvement. The problem stated is the lack of customer-related data for the marketing teams necessary to launch effective campaigns and optimise business strategies.
The report includes a description of modern technologies applied in business and focuses on such innovations as cloud storage, big data tools, and machine learning. The effectiveness and benefits of CRM software were discussed based on the literature research. Then, the integration of technologies to the customer management system was explained for each innovation. The report outlines how technologies help businesses solve the problem with marketing and sales optimisation. Research for alternatives confirms that the offered tools are the most effective solutions, and the example of Cirque du Soleil CRM is provided as evidence. In conclusion, four recommendations were crafted based on information about digital technologies’ benefits for customers’ management and marketing.
Business today allows technologies to involve in many structures: digitalised features help firms increase the efficiency of communications, transportation, internal and external processes. The trend to replace old strategies with innovations helps businesses grow faster, automate the workflow, and achieve more with fewer investments (Walker, 2018). For example, machine learning can develop algorithms that make routine manual actions and is a much cheaper solution than a human worker. Cloud services utilisation is widespread among different companies as the most convenient data storage. Gathered information and machine-built algorithms are beneficial for management because of their simultaneous effect on multiple processes.
Moreover, recent technological development combines different innovations that together create even more opportunities for business optimisation. Combination of data collecting and machine learning develop customised analytics options that firms can utilise to work with clients (Santouridis and Veraki, 2017). The approach is widely applied in such application areas as customer relationship management (CRM) that requires a combination of algorithms and information about clients and leads. Amnur (2017, p. 17) states that “CRM solutions are multi-criteria decision-making analysis tools that do not require prior assumptions to explore the weights and performances among projects”. Businesses that are aware of their client’s necessary information and preferences provide higher quality products and experiences.
Customer data collection is an essential strategy for a firm as it allows them to determine the target consumer niche and forecast the representatives’ needs. However, managing the continuously growing volume of information might be challenging for a responsible person, and this is when the technologies involved automate the process. In modern understanding, customer relationship management includes data retrieval, collection, analysis, and strategies building based on the client’s information.
Digital tools like cloud services and big data assist in these basic functions, and innovations make working with the client base more efficient (Walker, 2018). This report aims to analyse how digital technologies improve and develop customer relationship management in modern businesses.
Problems that Customer Relationship Management Solves
Customer Relationship Management (CRM) is a software that collects all data about actual and potential clients. The approach used in most firms is relatively novel due to the necessity of cloud services that became accessible a decade ago (Remane et al., 2017). CRM is a valuable section of the working process for firms of any size as it helps establish proper connection with the clients, improve their experience, and craft strategies for expanding the base (Courchesne et al., 2019). The software’s primary function is to collect the data about a customer and their interactions with the company on all levels at one place. Such centralisation is vital for quick and practical work with clients and would not be possible without modern digital technologies.
CRM belongs to the area of communication with clients that influences firms’ marketing. The lack of customer data management leads businesses to significant sales problems, such as inaccurate forecasting, inability to provide good experience for a client, and inefficient issues’ analysis (Anshari et al., 2019). While the responsible team members can manage customer support and communication, marketing strategies cannot be properly crafted without a centralised relationship management system. The problem modern CRM systems are built to solve is the companies’ difficulties in considering clients’ demands and trends in the field where they operate to improve sales and grow.
CRM allows an employee from any department to connect with clients to gather data about the buyer’s behavioural patterns, preferences, jobs, locations, and other details necessary to build a portrait. Such a significant amount of information is hard to be collected manually, and the software allows companies to automate it and make the base reachable (Remane et al., 2017). When a firm does not include a detailed analysis of the existing customer base into its business strategy, it risks losing money on ineffective campaigns and decrease revenue. The problem of marketing’s cooperation with information about clients is worth solving because it is crucial for a firm’s growth.
CRM is involved in multiple areas: sales increase, client’s experience improvement, leads retention, and overall business strategy optimisation. The software appliance helps sell more by pointing to the particular needs of existing buyers. Customer support becomes quicker and more efficient when the team has access to centralised data about a person that needs assistance (Zerbino et al., 2018). It is easier to provide new leads with proper and timely retention that will turn them into new clients when the basic information and behavioural patterns are collected (Anshari et al., 2019). Business strategy wins from having a detailed portrait of a recent and potential client, and CRM software is the most convenient approach to crafting it.
Marketing is the main objective where CRM operates, considering its influence on a firm’s success in that field. It assists a company’s sales team on each stage of interaction with a client, from purchasing decisions to crafting advertising campaigns. As CRM collects necessary information about a new client and then saves data about the products they bought, a marketing team can retarget them by customising recommendations (Kitchens et al., 2018). Moreover, the system allows to identify customers’ behavioural patterns or product preferences, and that information is crucial for understanding the audience and forecasting trends in the business field. Lastly, CRM is capable of analysing the efficiency of a marketing strategy to help businesses make timely improvements.
Solutions Used in The Application Area
The problem of marketing addressing customer relationship is that the lack of information or access to it can harm sales and slow a company’s growth. Moreover, that application area requires systematic strategies optimisation, which is impossible without data about clients’ experiences collected in the centralised system. Multiple solutions are being utilised in small companies and enterprises, and most of them are time and workforce-consuming. For example, a company might hire a separate specialist to manage customer relationships and gather the data necessary to improve campaigns and increase sales.
Another solution for marketing strategies improvement with customer experience involved requires sales and client support teams to work together, and one of the solutions is to conduct meetings where the representatives exchange the collected information and observations. A human factor might affect the results that experience and misunderstandings might lead to other campaigns to worse outcomes.
The problem might also be solved by periodical data retrieving on the demands of marketing teams when a particular piece of information like the time between initial visit and purchase is required to optimise a strategy. The approach takes time and further manual analysis of gathered data, and is irrational due to the existence of many software that accesses and collects it automatically.
CRM is the profound approach to solving the problem marketing teams discover while interacting with customer experience due to its ability to store and structure significant amounts of data. Kitchens et al. (2018, p. 543) state that “to maintain sustainable competitive advantage, firms must achieve agility in combining rich data across the organisation to deploy analytics that sense and respond to customers in a dynamic environment”. Indeed, the CRM software that collects information about customers in an accessible and convenient way is the missing tool to manage a company’s growth.
Customer Relationship Management and Technology
Technologies that rapidly develop from year to year influenced the data gathering approaches of CRM. The system not only became automated, but it also sorts information without assistance, analyses it, and even capable of forecasting customers’ demands and marketing trends. The innovations that make the modern CRM powerful are cloud services, big data, and machine learning applied to work with information about a company’s clients. Anshari et al. (2017, p. 97) claim that “the recent developments of technologies and big data analytics have optimised process, growth, and generate an aggressive marketing strategy and delivering value for each customer and potential customer”. Each innovation is responsible for different parts of the process, and the combination increases CRM’s efficiency by moving it beyond the basic data collection functionality.
Cloud storage is a relatively old digital technology applied in customer management systems. The main benefit of keeping the data in a cloud is that it is accessible wirelessly and from multiple devices. Most modern businesses implement cloud services like DropBox to store information and integrate them into the CRM software to retrieve the necessary bites of data at any time. Indeed, the technology solves the problem for marketing by providing an option to work with clients’ profiles when required.
However, Patidar and Bansal (2019, p. 34) claim that “the increasing adoption of cloud-based applications, owing to several advantages such as cost savings, availability, time savings, etc., have also posed many issues and challenges”. One of such issues is the security of the data storing services because an unsafe CRM system can be hacked. It is crucial to encrypt cloud services to support safety while applying that convenient way of managing relationships with the customer base.
Another innovation that improves CRM and helps managers solve the problem of lacking collected and accessible information about clients is big data. Many types of information about a customer can be retrieved from the Internet, and modern CRM systems can find a place to apply it for different purposes. Big data allows us to combine everything related to a particular category, product, or characteristic in internal customers base and external research, and analyse that scope to make data-driven decisions (Raguseo, 2018). Companies can eliminate differences in exploring clients’ demands once they apply the technology of analysis described above.
Although big data solution is beneficial for optimising sales and marketing to help a firm grow, it should not be overestimated. The decisions based on the results collected that way might be broader than necessary for a company’s purposes, thus it’s recommended to apply big data only for well-known projects (Zerbino et al., 2018). CRM can be successfully integrated with that novel digital technology while the results of its analysis still need to be evaluated by managers.
The latest digital technology applied to improving CRM in businesses is machine learning. The innovation is based on the ability of applications or devices to learn and form algorithms based on the new information instead of explicit programming (Amnur, 2017). In connection with CRM, machine learning allows identifying types of clients and customising their experience with the company. Indeed, the technology indicates customers’ purchasing and behavioural patterns, separates leads, and helps predict how to turn them into clients (Amnur, 2017).
Moreover, the strategies marketing teams apply to increase a company’s sales can be remembered by the machine learning, and retrieve only the most crucial CRM-based data. The disadvantage of the system is that it might give wrong information, incorrect predictions, and harm the overall structure of CRM software in the case of technical or algorithmic issues. Machine learning integrated with CRM can identify the most beneficial marketing and sales optimisation approaches without time-consuming analysis performed by employees.
Applied to digital business, the solutions mentioned above can work to solve CRM’s problem of providing useful information for marketing purposes. Companies might develop their own software or purchase an integrative service that works on a combination of cloud storage, big data, and machine learning. The examples of such applications are Zendesk, SugarCRM, Salesforce, or Dynamic 365: they collect information about leads and clients and can sort it by categories or analyse it based on multiple factors. These management decisions help companies grow and maintain awareness of their customers’ demands and market trends.
Solutions and Alternatives in Customer Relationship Management
Customer relationship and digital technologies like cloud storage, big data, and machine learning are the best management solutions because the alternatives do not provide companies with the same benefits simultaneously. The modern CRM systems allow businesses to have a centralised and accessible workspace capable of automative data gathering (Patidar and Bansal, 2019). The collected information is necessary to improve communication and retention with a customer, add leads, analyse purchase decisions, behavioural patterns, and optimise sales strategies.
The alternatives are less effective and might not be capable of working simultaneously. Device or ethernet local network-based systems might be safer for the data, yet harder to access and retrieve by employees (Patidar and Bansal, 2019). Moreover, localised servers as an alternative to cloud storage are more expensive and require time to be set appropriately and manages. An option to big data technology is decentralised information collection based on specific categories forwarded by the manual search for trends and coincidences (Zerbino et al., 2018).
It requires customer relationship and marketing departments to work together and consumes time that could be spent on more productive research. Machine learning cannot be replaced with another tool because of its complicated nature (Amnur, 2017). Alternative solutions require manual work and time – the inconvenient condition when the number of customers grows, and marketing trends change.
Technologies based on CRM integration are an effective solution for marketing improvements for different types of businesses. For example, Cirque du Soleil has recently integrated digital a CRM system to collect and automatically analyse information about clients and their social networks (Courchesne et al., 2019). The gathered data then was applied to optimise marketing strategies and even change the programme of artists’ performance to increase the audience’s interest and sales (Courchesne et al., 2019). Moreover, one digital decision is a cheaper and faster solution than woking hours spent on expensive analysis.
Summary and Recommendations
Digital technologies are becoming crucial participants of firms’ internal and external processes. Businesses can significantly improve their competitiveness by optimising the strategies and maintaining awareness of trends and demands in their field. All information about the present and potential customers is crucial for companies, and innovative approaches like CRM are an effective solution to store it. The software can collect, sort, analyse, and manage data that can then be applied to solve problems or help a business grow. Based on the research, CRM can assist in optimising marketing processes leading to better reach to potential clients and a better understanding of customers’ expectations.
Digital innovations integrative with CRM are cloud services, big data tools, and machine learning. These approaches are practical because of the capability to automate processes like data collection and analysis that usually is time-consuming and might be ruined by errors that appeared due to the human factor. The problem of the demand in the accessible and structured customer information for marketing purposes and strategies’ optimisation can be solved with these technologies integrated into a company’s CRM software.
Several recommendations about how businesses can improve marketing and increase sales by utilising CRM can be developed based on the research. Firstly, information must be collected in a structured manner to ease the analysis and integrate sorting options when necessary. Secondly, more sources of data are beneficial for crafting a better vision of the audience, therefore, CRM needs to include approaches to gather records from multiple channels such as social networks, support chats, browsers. Thirdly, with big data tools included, a marketing team might be able to compare and contrast competitors’ decisions and create effective campaigns. Lastly, machine learning integration is useful for businesses to recognise customers’ behavioural patterns and apply particular actions to engage them or improve their experience with a company.
Amnur, H. (2017) ‘Customer relationship management and machine learning technology for identifying the customer’. JOIV: International Journal on Informatics Visualization, 1(1), pp. 12-15. Web.
Anshari, M., Almunawar, M.N., Lim, S.A. and Al-Mudimigh, A. (2019) ‘Customer relationship management and big data enabled: Personalisation & customisation of services’. Applied Computing and Informatics, 15(2), pp. 94-101. Web.
Courchesne, A., Ravanas, P. and Pulido, C. (2019) ‘Using technology to optimise customer relationship management: The case of Cirque du Soleil’. International Journal of Arts Management, 21(2). Web.
Kitchens, B., Dobolyi, D., Li, J. and Abbasi, A. (2018) ‘Advanced customer analytics: Strategic value through integration of relationship-oriented big data’. Journal of Management Information Systems, 35(2), pp. 540-574. Web.
Patidar, M. and Bansal, P. (2019) ‘Log-based approach for security implementation in cloud CRM’s’, In Data, Engineering and Applications, pp. 33-43. Singapore: Springer.
Raguseo, E. (2018) ‘Big data technologies: An empirical investigation on their adoption, benefits and risks for companies’. International Journal of Information Management, 38(1), pp. 187-195. Web.
Remane, G., Hanelt, A., Nickerson, R.C. and Kolbe, L.M. (2017) ‘Discovering digital business models in traditional industries’. Journal of Business Strategy, 38(2), pp. 41-51. Web.
Santouridis, I. and Veraki, A. (2017) ‘Customer relationship management and customer satisfaction: the mediating role of relationship quality’. Total Quality Management & Business Excellence, 28(9-10), pp. 1122-1133. Web.
Walker, M. (2018) ‘Technology fads: Weighing risks and benefits. Web.
Zerbino, P., Aloini, D., Dulmin, R. and Mininno, V. (2018) ‘Big Data-enabled customer relationship management: A holistic approach’. Information Processing & Management, 54(5), pp. 818-846. Web.