Chapter 5 Conclusion and Suggestion
5.2. Strategic marketing advice
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The rolling of strategic 4C analysis on analytical maturity model:
The maturity of analytics has impact on the availability of CRM program choices as earlier sections discussed. Therefore, as retailers are capable to migrate to next analytical stage one, the priority of 4C for CRM program should be adjusted accordingly as well. By doing so, retailers are able to take an inner cycle to accumulate 4C advantage at each stage and roll over again until the retailers are migrated to next stage.
To answers research objective 3: Develop a practical framework that allows retailers for self-diagnosing their CRM programs in terms of analytical maturity and strategic marketing focus, this research adopted literature of strategic 4C model (Chiou 2010) and analytical maturity model (Davenport and Harris 2007) to form a two-stage diagnostic framework for retail CRM program. This
framework would help business executives and practitioners to self-diagnose their CRM programs in terms of analytical maturity and strategic marketing focus.
5.2. Strategic marketing advice
Companies should always have strategic purposes to launch their CRM programs. Once they launch their CRM programs, they should periodically review where they are now and how they do in terms of performance as the external competition is not static so should be CRM programs.
There are three steps suggested for any retailer to self-diagnosis their CRM
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programs:
1. Identify the analytical stage that a retailer belongs to (company level)
Davenport and Harris’ analytical maturity model is the first step that a company could use as a framework to understand its maturity stage in terms ofanalytical competition. A company can make better decision about whether to evolve its CRM program according to analytical maturity model
2. Review CRM program strategy from strategic 4C perspective:
(program level)
Secondly, a company is suggested to further use strategic 4C model to review its program focus and thus fine-tune its resource. Another indication is that when acquiring a new customer through CRM programs, retailers need to think about lower switching cost of customers, C4 cost and once customers are acquired, the priority is shifted to how to increase the switching cost of
customer, building up C4 cost. These switch cost refer to the asset specificity cost in this study as the hold-up incurs only when customers already or C4 cost introduced in strategic 4C model.
3. Align CRM tactics with CRM program strategy (tactic level)
Each tactic deployed has its objective and expectation. However, amis-focused tactics not only means resource misallocation but also means opportunity lost.
In the future, retailers should be on trend with any emerging technologies,
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ahead of rivals, embed them intho their CRM program, and find new ways of connecting customers.
Many sales cycles start online today and yet finish offline.
Search-online-purchase-office becomes a norm today. More and more people tend to use one channel for research and another for purchase. New
technologies are emerging everyday and so are the channels and types of customers.
A study conducted McKinsey & Company ( Chappuis, Gaffey and Parvizi 2011) has shown that consumer behavior is shifting rapidly as more people use digital devices and platforms intensively. Three key trends includes: (1) social networks, particularly Facebook, are emerging as the dominant
digital-communications channels; (2) As the power and functionality of devices grow, mobile devices are invading the domains of single-purpose gear such as game consoles and portable media players, as well as PCs; (3) As digital platforms multiply, consumer video-viewing habits continue to change that challenge traditional TV role.
Internet has become part of daily life, from information sharing, communication, shopping, doing business, daily necessities, entertainment, to social
networking. The opportunities to collect data about customers become numerous than ever.
Amazon.com, a stage five organization defined by Davenport and Harris
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(2007), already masters its skills of using data mining techniques to distill customer insights from behavioral data. Whenever customer comes back online, Amazon provides personalized information to customer; therefore, customer may feel understood and valued and be pleased to revisit the websites.
Likewise, Tesco, UK’s largest physical giant supermarket chain also knows the tricks of magic, now is planning to tap into Amazon’s territory (Thompson 2012).
Retailers, physical or online, have opportunities to leverage the online-offline data for winning analytical competitions, depending on who knows better about their customers and engage in a long-term relationship.
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