FRAMEWORK FOR SUCCESSFUL ADOPTION OF ML IN DIGITAL MARKETING
Analysis and interpretation of results from both qualita-tive and quantitative research enabled the construction of aframework aimed fostering the utilization and adoption ofML-based analytical tools in digital marketing. The frame-work consists of two main components: 1) Enablers, i.e. fac-tors of organizational culture and management contributingto the atmosphere and conditions, where such a project canbe initiated and successfully finalized, and 2) Process map,i.e. a map of processes of such a project consisting of fourmain phases (Fig. 2).The absence of the enabler and/or following the suggestedpath may negatively affect the success of ML-driven tools’adoption.Firstly, the project needs top management support. Dueto its character (cost, cross-departmental reach etc.), adop-tion of ML tools cannot be pursued without top managers’commitment. There have been cases where projects fueledby regular employees and lower level of management weresuccessful. However, in general, a top-bottom driven innova-tive climate is required for such complex project to succeed.The company’s managers should also act as leaders whoare aware of the importance of continuous improvement, thecontribution of detailed analytics to the business and perfor-mance of their project teams. They should introduce ideas,adopt new solutions and test them. Secondly, introducinginnovative solutions, measures, and tools needs to be embed-ded in the company culture and connected with its competi-tiveness and process efficiency. Thirdly, the company needsto realize that technologies form the base for its effectiveoperation success. A knowledgeable CIO and other C-levelmanagers should have at least a high-level understandingof recent technological innovations and their advantages forbusiness. It is advantageous if the company has an inter-nal IT team and/or has developed steady and sustainablerelationships with technical partners and developers. Lastly,the company needs to foster frequent and detailed use of datafor the sake of deploying its services, analyzing the impactof marketing strategies, and for internal management. With-out this component, the initiative to automate data-intensiveprocesses and reporting would not be justifiable by hard data,suggesting huge time savings and efficacy boosts.The process map (Fig. 3) outlines the phases and neces-sary steps for introducing automation of digital marketingprocesses and ML-driven analytical tools into companies.Following the process map will guide the organizationthrough the stages of the project to pay attention to all neces-sary aspects, enabling the minimization of risk and increasingthe chance of project success. The processes of implemen-tation and adoption of ML-based analytical tools in digitalmarketing are included in one of four main phases. The focusof the process map is the identification of periodical, time-consuming processes, predominantly in the area of data anal-ysis that be supplemented by intelligent software solutions,utilizing the power of ML.The process map builds on the knowledge generatedby this research. The implementation process, as outlinedin Fig. 3, starts with the Education phase. The possible lack oknowledge about the opportunities of utilizing of ML andAI in marketing prevents companies from developing ideason deploying these technologies. Companies are encouragedto raise knowledge and acquire know-how in this area. Fourpotentially valuable and accessible groups of knowledgeresources have been identified. Quality literature from ver-ified peer-reviewed sources should be used to naturally filterincorrect or misleading content. Marketing managers areused to participating in conferences and they represent apopular source of knowledge. Relevant materials can be alsoobtained from free internet resources on topical blogs, vlogs,websites of institutes, and associations and their newsletters.Finally, a number of educational massive open online courseplatforms, both free and paid, can be utilized to develop theknowledge on data analytics, ML and AI by company’s keystaff Digital Marketing Company Kumbakonam
marketing strategies. The analytical tools serve as the mainsource of information for marketing analysis, on which mar-keting managers base their strategic decisions. Working withanalytical tools has many advantages, such as: an overviewof competitor activities and market mapping; acceleratingthe decision-making process; possibility of importing internaldata into third party analytical tools; possibility of visualiza-tion of obtained data; rich targeting capabilities; instant avail-ability of information; the ability to track real-time data inongoing campaigns; the ability to identify space for optimiz-ing ongoing processes; the accuracy of the observed parame-ters; possibilities of target group segmentation by behavioralprofiles; often assisted analytical tool implementation or intu-itive user interface.On the other hand respondents referred to disadvantages ofusing these tools, such as: data access can be limited in spe-cific analytical tools (some analytical tools require additionalcharges for full data access); the implementation of analyticaltools is often expensive; inaccurate data (incorrectly mea-sured) leads to misrepresented decisions; the correct use ofanalytical tools increases the pressure on additional know-how, thus generating costly additional staff training; theimplementation of analytical tools is often a complicated andtime-consuming process; many analytical tools use samplesof respondents based on which gained information prevailacross the whole market (often it is questionable whether asample is representative); different analytical tools use dif-ferent metrics that are often not compatible with each otherand additional calculations are needed to achieve a summaryresult; the metrics are often adjusted, according to who ownsthe analytical tool; the absence of a comprehensive view ofthe results achieved across all analytical tools used.The discussion about the use of data and big data formarketing analysis can be summarized in the following state-ment: large companies in the Slovak market will manage anduse data within their custom developed IT systems. This hasbeen the results of advancements in the technological possi-bilities of data-oriented approaches and the decreasing costsfor the development and implementation of own softwaresolutions. Small businesses, on the other hand, will probablynot have enough money to pay for custom development. The option of implementing standardized solutions and applica-tions can also prove difficult due to the diversity of digitalmarketing activities.Based on respondents’ answers, the level of awareness ofML and AI-related concepts, and partially also their currentuse rate, were identified. Discussions about the current appli-cation of ML to digital marketing processes and identifyingareas where ML can be applied led to the conclusion thatthe greatest use of ML is to provide better quality data andprocess automation. Automation can be applied to a recurringprocess such as reporting, creating and optimizing adver-tising campaigns, and even communication with customers.When introducing innovative technologies, the focus is pri-marily on process automation and data processing. Theseactivities are currently performed manually https://arudhrainnovations.com/