How SEO Specialists Help Small Businesses Compete Online

Fundamental changes in the economy, society and politics force marketing to revise its fundamental purpose, assumptions and models that have defined marketing for the past 50 years (Webster & Lusch, 2013, p. 389). Scholars argue that marketing departments increasingly face complexities such as altering consumer demographics, disrupting technologies and enlarging quantities of data (Bolton et al., 2013; Kumar et al., 2013; Wirtz et al., 2013), changing business models (Ehret et al., 2013) and a constant need to develop 

powerful value propositions as means of differentiation (Bolton et al., 2014; Payne and Frow, 2014). However, if marketing departments do not possess appropriate market-sensing capabilities, they are less likely to generate profitable growth (Wirtz et al., 2014, p. 175), which is believed to contribute to a lack of trust in marketing departments among CEOs and a reduction in the marketing departments’ responsibilities (Wirtz et al., 2014, p.175; Fournaise 

Group, 2012). A survey found that 80% of the responding CEOs to be “not very impressed” with marketers as well as perceiving them as “poor business performers” (Fournaise Group, 2012). Other research has shown that most employments of Chief Marketing Officer, hereafter referred to as CMO, do not last long (Nath & Mahajan, 2010, p. 66), in fact, they have the highest turnover in top management (Whitler & Morgan, 2017). Hanssens and Pauwels (2016, p. 173) argue this is not only a result of underperformance, but also due to the 

Difficulty in holding  financially accountable

Whitler & Morgan (2017) argue it mainly is a result due to the ambiguity of the CMO-role, which consequently lead to only 22% of job descriptions mentioning how the CMO would be measured or held accountable. due to the ambiguity of the CMO-role, which consequently lead to only 22% of job descriptions mentioning how the CMO would be measured or held accountable. Hanssens and Pauwels (2016, p. 187) state that due to the multifaceted nature 

of marketing, top management must rely on a variety of weakly interrelated performance metrics - attitudinal, behavioral and financial. This makes it complex to assess the value of marketing, hence resulting in a distrust and less of a focus on marketing at senior levels of decision-making (Hanssens & Pauwels, 2016, p. 187). Thus, marketing value assessment is 

essential to marketing having an influence at the level of top management, an influence that shapes its role in the organization, ranging from being responsible of short-term tactical decisions to growth strategizing (Whitler & Morgan, 2017). However, the core responsibility of marketing management has been described by Webster (1992, p. 14) as “... making sure that 

Every aspect of the business

is focused on delivering superior value to customers in the competitive marketplace”. A recent survey conducted by Christine Moorman (CMO Survey, 2019, p. 49), reported the following activities to be assigned to marketing departments of at least 50% of the responding CMOs; brand management, digital marketing, advertising, social media, public relations, promotion, positioning, marketing research, lead generation, marketing analytics, insight, as well as 

competitive intelligence. Moreover Marinchak et al. (2018, p. 22) state that “how, what and to whom to sell, as well as- what, how, and from whom to buy, is increasingly an all-digital, AI-augmented and automated process”. Indeed, the role of marketing managers seems to undergo substantial change, with new responsibilities as technology evolves. Promisingly, the rapid development of data quality and quantity along with new analytical methods, haveThe 

premise of the participators at the Dartmouth conference was that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it (Epstein, 2015, pp. 36-37). This implies a focus on computing machines to perform tasks otherwise restricted by humans. Since then, the visions of AI have transformed into; enhanced hybrid intelligence by combining machines and humans; new 

Crowd intelligence systems configured 

by humans, machines and networks; as well as more complex intelligence systems, combining e.g. humans, societies, physics and cyber systems (Pan, 2016, p. 410). In this study, we are mainly concerned with AI as a rational agent, in other words the ability of AI to do the “right thing” given what it knows (Russell & Norvig, 2014, p. 1). Consequently, AI has been defined by Nilsson (1998, referred in Russell & Norvig, 2014, p. 2) as being 

concerned with intelligent behavior in artifacts”. Moreover, throughout this this research we are solely concerned with so called narrow AI, in other words AI that has a narrow expertise in a particular area (Ayoub & Payne, 2016, p. 795). Narrow AI can play chess or drive but not both, and lacks common sense” (Hutson, 2017). This in oppose to general AI, which focuses on finding a “universal algorithm for learning and acting in any environment”, a focus that 

originates from the 1956 Darthmouth conference (Russell & Norvig, 2014, p. 27). Moreover, when AI is treated as a general phenomenon and there is no need for specification of the technologies or methods involved, the term AI will be used in this thesis. Otherwise, the aim is to use a more accurate terminology to avoid misunderstandings resulting from its ambiguity. Or as John McCarthy, the organizer of the Dartmouth conference and coiner of the term 

Conclusion

Artificial Intelligence put it  as soon as it works, no one calls it AI anymore” (Vardi, 2017, p. 5). The area of AI that probably has received the most attention regarding business and marketing (Schrage & Kiron, 2018a, b) is machine learning, which can be explained as programming computers to optimize a performance criterion using example data or past experience” (Alpaydin, 2014, p. 3). Machine learning uses statistics to build mathematical 

models with the core task of making inference from a sample, which are either descriptive - to gain knowledge from historical data, or predictive - to make predictions of the future, or both As machine learning is the perhaps most utilized technique in marketing, it is a central theme throughout this research. Research by Kardon (2019) has shown that machine learning is already becoming a practical tool near-term with capability of increasing the productivity and 

efficiency of the marketing team and its manager, including tasks such as lead scoring and predictive analytics, automated email conversations, customer insights, personalizing with data as well as with content creation to a certain extent. A concrete example of what AI can do in relation to marketing is one of a Harley Davidson dealership in New York that tripled its sales and increased its leads by 2930% by utilizing predictive analytics in an AI-driven marketing platform (Power, 2017). What the AI did was determining precisely how much and