Chapter 2 Literature Review
2.4 Analytical Maturity Model
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have become a popular marketing tool to encourage loyal customer behavior (Tuzovic and Mangold, 2008).
Loyalty scheme may play two roles in CRM, first, to generate data that can be used to guide customer acquisition, retention and development and secondly, loyalty scheme may serve as an exit barrier (Buttle 2009).Alternatively, Minami and Dawson (2008) define CRM as the integration of relationship technology (i.e. data consolidating and data mining) with loyalty schemes.
In many cases, loyalty scheme is considered as a marketing tool and has become a critical component of firms’ overall CRM efforts. Studying the
literature in the fields of CRM and loyalty scheme, it becomes clear that loyalty scheme is always involved with CRM. Until loyalty scheme evolves into a state capable of using data and relational technologies for getting, growing and keeping customers, loyalty scheme becomes a key component of CRM, a company-wide business strategy that assumes the role for customer acquisition, development and retention.
Therefore, this study believes the proper definition of loyalty scheme is a scheme that is capable of using data and relational technologies for getting, growing and keeping customers.
2.4 Analytical Maturity Model
Davenport (2006) was the first to point out the phenomenon of competing on analytics and also identified the characteristics shared by analytical
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competitors in his study of 32 organizations that have made a commitment to quantitative, fact-based analysis.
His study shows that analytical competitors are the leaders in their varied fields—consumer products, finance, retail, and travel and entertainment among them. Analytics has been instrumental to Capital One, which has exceeded 20% growth in earnings per share every year since it became a public company. It has allowed Amazon to dominate online retailing and turn a profit despite enormous investments in growth and infrastructure. In sports, the real secret weapon is not steroids, but statistics, as dramatic victories by the Boston Red Sox, the New England Patriots, and the Oakland A’s attest (Davenport 2006).
Organizations are competing on analytics not just because they
can—business today is awash in data and data crunchers—but also because they should. At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the last
remaining points of differentiation. And analytical competitors wring every last drop of value from those processes (Davenport 2006).
Four pillars of analytical competition
Researching 371 medium to largest firms in 2005 and detailed analysis of data allows Davenport and Harris (2007) to define the four key attributes of an most analytically sophisticated and successful analytical competitor: (1) Analytics supported a strategic, distinctive capability; (2) The approach to and
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management of analytics was enterprise-wide; (3) Senior management was committed to the use of analytics; and (4) The company made a significant strategic bet on analytics-based competition. These four pillars are not independent of each other but supporting one another to make an analytical platform. However, Davenport and Harris (2007) believers that true analytical competitors have all four; less advanced organizations may have only one or two at best.
Figure 2-5 Four pillars of analytical competition (Davenport and Harris 2007)
Support of a strategic, distinctive capability
To support company’s competitive strategy, analytics must be in the support of an important and distinctive capability first. Distinctive capability is considered to be the decisive factor for a company in getting beyond its competitors and making it successful in the marketplace. Without a distinctive capability, it is impossible to be an analytical competitor as there is no clear process or
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activity for analytics to support.
An enterprise-level approach to and management to management of analytics
Davenport and Harris (2007) found that companies and organization that compete analytically do not delegate analytical activities just to one group within the company or to a collection of disparate employees across organization. They manage analytics as an organization or ensure that no process or business unit is optimized at the expense of another. In addition, they make the management of analytics an organization-wide activity ensuring that that the data and analysis are made available broadly through the
organization.
Senior Management Commitment
To adopt an analytics approach to business, a company requires the changes in culture, process, behavior, and skills for multiple employees. To achieve that, senior management must commit to lead the changes as well as have passion for analytics and fact-based decisions. Davenport and Harris (2007) found that it is rare to find a firm making the cultural changes necessary to become an analytical competitor without the push from the top.
Largest-Scale Ambition
The final way to define analytical competitors is by the results they aspire to achieve. There are many ways to measure the results of analytical activity but the most obvious is with money, i.e. savings or revenue increases. The results
‧
of analytical comp
etition can also be measured in overall revenues and profits, market share, and customer loyalty. It is considered not really competing on analytics if a company cannot see any impact on such critical measures of its nonfinancial or financial performance (
Five stages of analytical competition
Davenport and Harris (
2007
analytical competition and thus proposed an
in figure 2-6. These stages can
follow from non-
analytical competitors to true analytical competitor while true analytical competitors exhibit all four factors and less advanced organization may have only one or two at best:
Figure 2-6
Five stages of
2007)Stage 5 organizations, “the analytical competitors”, with four factors described above. They, supported by analytics, have distinctive capabilities that set them
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etition can also be measured in overall revenues and profits, market share, and customer loyalty. It is considered not really competing on analytics if a company cannot see any impact on such critical measures of its nonfinancial or financial performance (
Davenport and Harris2007
Five stages of analytical competition
2007) has exemplified the four factors that define the analytical competition and thus proposed an
analytical maturitymodel, as seen
. These stages can describe the path that an organization can analytical competitors to true analytical competitor while true analytical competitors exhibit all four factors and less advanced organization may have only one or two at best:
Five stages of
analytical maturitymodel (Davenport and Harris,
Stage 5 organizations, “the analytical competitors”, with four factors described above. They, supported by analytics, have distinctive capabilities that set them
Stage 5
etition can also be measured in overall revenues and profits, market share, and customer loyalty. It is considered not really competing on analytics if a company cannot see any impact on such critical measures of its
2007
).) has exemplified the four factors that define the model, as seen describe the path that an organization can analytical competitors to true analytical competitor while true analytical competitors exhibit all four factors and less advanced organization
model (Davenport and Harris,
Stage 5 organizations, “the analytical competitors”, with four factors described
above. They, supported by analytics, have distinctive capabilities that set them
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apart from competitors, they are taking an enterprise-wide approach, their executive are committed to analytics and their analytical initiatives are ambitious enough to produce financial o nonfinancial results. Davenport and Harris (2007) estimated that no more than five percent of large firms would be in this category and it is difficult to generalize about industries for analytical competition.
Stage 4 organizations, “the analytical companies”, are in transition to analytical competition but still face some challenges to get to stage 5. They have skills but lack the will to compete on this basis, do not consider analytics as firm’s strategic competencies yet, or senior management are not passionate about competing on this basis.
Stage 3 organizations, “the analytical aspirations”, realize the importance of analytics, have visions and ambitions to compete on this basis, yet have not started the implementation. Often, organizations at this stage still have difficulties mounting a cohesive approach to analytics across the enterprise.
Stage 2 organizations, “the localized analytics”, emphasize on reports but they do not measure up to the standard of analytical competition. Their analytical activities produce economic benefits but not enough to affect the company’s competitive strategy. Lacking of vision of analytical competition from senior execution vision is a primary characteristic for organizations at stage. Some company may have some of same technology as firms at higher stages of analytical activity, but they have not put it to strategic use.
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Stage 1 organization, “the analytically impaired”, have some desire to become more analytical, but lack both the intention and the skill to do so and thus far the goal of analytical competition. They lack either human talents, technical skills to analytical competition or even interest in analytical competition from senior executives. Therefore, they are still focused on putting basic, integrated transaction functionality and high quality data in place, i.e. they do not have a single definition of the customers and hence cannot se customer data across the organization to segment and select the best customers. In shorts, they are not even on the path to becoming analytical competitors.
Davenport and Harris (2007) believe that most organizations need to go through each one of the stages but an organization, with sufficiently motivated sensor executive, may be possible to skip a stage or at least move rapidly through them. However, organization change will still be the most difficult part to overcome.