The Influence of Consumer Insights –
Utilizing Big Data to Reinforce a Marketing Strategy
Case Company Global Players
Report by: Jermaine Braumuller (114471915) Braumuller02@gmail.com
University of Amsterdam, MBA Fulltime 2017-2018 Supervisor: Prof. Dr. Edward I. Huizenga
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Abstract:
As the ability to store large quantities of data grows, so does our need to manage those large sets. As a result, mapping the consumer has become simpler with the ability to take data and predict what a consumer wants and when. The magnitude of these gargantuan sets of data has been given the name Big Data. In this paper, I will incorporate the use of this technological resource and create a new framework based on various literature to address the question of: How to utilize consumer insights to create a
marketing strategy using Big Data? I will apply this framework to Global Players, a study
abroad program for student athletes.
Technology helps capture rich and plentiful data on the consumer, gone are the days of assumption and speculation, todays consumer needs a tailored approach. Creating correlation has never been more important in grabbing the consumer’s attention, i.e. how does this affect me? This paper emphasizes the value that creates said correlation, and generates a method that is sustainable for revenue growth.
Considering the various elements of a business model, I will apply a method that facilitates Big Data towards a market need. Once the technology, Big Data, is managed through a supported environment I will apply a marketing strategy that utilizes the consumer insights to directly target the customer. Drawing from my time with Global Players, and personal research paired with the teachings from the MBA, I will provide recommendations that can be implemented to create value.
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Table of Contents
I. Introduction ... 4 II. Literature Review/Underlying Theory ... 8 A. What is a Business Model? ... 8 B. Finding Value in Big Data ... 9 C. Marketing value ... 12 III. The Framework/Tool: ... 13 A. Resource based theory and Big Data ... 13 1. Firm Resources ... 14 2. Big Data ... 15 3. Resource Characteristics ... 15 4. Consumer Insights ... 16 5. Adaptive/Dynamic Capabilities ... 16 B. The Transformative Business Model ... 17 1. Personalization ... 18 2. Closed Loop ... 19 3. Asset Sharing ... 20 4. Usage-Based Pricing Model ... 20 5. Collaborative Ecosystem ... 21 6. Agility ... 22 C. Value Pyramid ... 23 1. Functional Elements ... 25 2. Emotional Elements ... 25 3. Life Changing Elements ... 26 4. Self Transcendence ... 26 D. Data Marketing Strategy Application ... 27 1. Framework Synthesis ... 27 2. Global Players ... 28 3. Application ... 30 IV. Implications/Managerial Recommendations ... 32 V. Limitations/Conclusion: ... 344
I. Introduction
“It’s not what you look at that matters, it’s what you see” – Henry David Thoreau We live in the era of information, one that is incorporated heavily into our daily lives. In this digital renaissance we have reached an unprecedented capability to manage large quantities of data that was once thought inconceivable. In 1971, the 8 inch floppy disc held a maximum of 1.2 megabytes at a cost of $1000 per gigabyte(GB)1. Fast forward 40 plus years and our method of storing data on the cloud has increased to a capacity of over 16 Zeta bytes with 50GB available for free use. To comprehend that amount of data, imagine if we were to quantify every word ever spoken by a human being and multiply that by 200, that would be the equivalent of only one Zeta byte. With that sum in mind, we begin to ask ourselves, what is the limit? How much information is too much information? And how do we manage it?
To answer the first question, there does not seem to be a limit, as technology continues to advance so do our capabilities to collect and store data. However, with this technology also comes the aptitude to find meaning within large quantities of data. As humanity continues to supply this endless amount of information, the field of Big Data and it’s potential to supply new insights to the way we think has transformed itself from a useless waste into a valuable asset. It is within this report that I will be dissecting how a small or medium-sized enterprise (SME) can take advantage of this data and turn it into a marketing tool.
There are currently over 490,000 student athletes currently registered with the National College Athletic Association (NCAA) and who compete in approximately 24 sports every year in over 1,100 recognized NCAA colleges in the United States2. This
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http://www.inetservicescloud.com/how-to-evolve-your-storage-technology-in-2017/
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does not include an additional 10,000 plus athletic staff consisting of directors, coaches, and assistants, and a yearly turnover of incoming freshman. College sports in America is truly a big business.With a growing appeal of collegiate internationalization, the number of U.S. students studying abroad in the last 15 year has doubled3. Statistically, the benefits students receive from incorporating a study-abroad program in their college career are indisputable. A study done by the University of California found that 97% of study-abroad students graduates find employment within 12 months of graduating, when only 49% of college graduates find jobs within the same period4. Within their academic career, students GPA’s tend to rise as they approach the completion of their undergraduate degree, those who go abroad see their GPA’s rise twice as fast5. With the amount of statistical data showing the undeniable benefits a student’s career can have by studying abroad, what are the benefits for a student athlete?
Global Players, a study abroad program tailored for student athletes is a scalable enterprise that has established itself in a niche market with a unique value proposition. Their aim is to offer an immersive and culturally significant experiences for student athletes. With few competitors and a growing appeal for internationalization within U.S. colleges, the Global Players model can expect much growth with a proper marketing strategy in place
The focus of this report lies in synthesizing a framework that emphasizes a sustainable use of technology to create value for Global Players. Value that can be managed and refined to create a targeted marketing strategy. Businesses’ today are surrounded with an abundance of new technologies that have created an upcoming event horizon of technological applications. If these organizations do not update their business models to facilitate the use of these technologies, they are doomed to become stagnant
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https://www.forbes.com/sites/sergeiklebnikov/2015/07/30/more-u-s-students-are-studying-abroad-but-is-it-enough/#2c5c6691f8f1
4https://studyabroad.ucmerced.edu/study-abroad-statistics/statistics-study-abroad#resources
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and face the risk of losing their competitive advantage. Therefore, it will be imperative that a business model that facilitates a sustainable use of these technologies be implemented.I begin by discussing the literature that defines “what is a business model?” What are the core ideas that are incorporated to make a successful and sustainable business model? Once a model is in place to facilitate new technologies, I move to define Big Data and its components. On its own Big Data is just endless amounts of raw information, I will discuss the process for management and how it is refined to become a valuable resource. Once usable data has been collected, I then move to the components of a business strategy, specifically focusing on those that utilize consumer insights toward a marketing strategy, one that targets users based on their wants and needs.
Building on the reviewed literature, I will generate a data driven marketing strategy that combines the previously mentioned concepts to facilitate Big Data. Because Big Data is not used in this market and it provides a unique perspective for growth I will need to combine several frameworks to create a new strategy. This synthesized framework is based on Kavadias (2016), Erevelles (2016) and Almquist’ (2016).
Kavadias’ (2016) business model framework introduces new technology to facilitate “Keys of Innovation” in order to satisfy a market need. I will build on this business model using a resource based theory framework introduced by Erevelles (2016), which centers around the process of managing Big Data into consumer insights. Erevelles’ framework will map out the internal systems that need to be in place in order to properly manage Big data into a valuable resource. Finally, I apply the framework from Almquist’ (2016) value pyramid which allows for consumer insights that are output from the previous models to be incorporated toward a tailored approach to building out services market directly to consumers.
Once the new strategy has been established I will outline key insights and actions that can be implemented to the application of the strategy to the case of Global players.
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Their aim is to map their customer profile in order to directly appeal to a targeted consumer base. I will lay out a new marketing strategy that allows to better analyze the targeted consumer while appealing to their insights in order to gain a competitive advantage. My recommendations will include a streamlined model that can almost run autonomously and discuss the levels scaling that are made possible as an SME gains more resources to expand and develop. Lastly, the report will conclude with a review of the limitations of this custom marketing strategy.8
II. Literature Review/Underlying Theory
In this literature review section, I begin with describing the meaning of business models. I start with the most pertinent pillars of a business model that aim to interact directly with the consumer. I then define big data through its three dimensions: Volume, Velocity, Variety, which will directly influence consumer analytics. Finally, I conclude this section explaining marketing strategy and its influence on the business model. It is imperative to indicate how developing a strategy differs from creating a business model as they both have similar principles but act distinctly within their own realms.
A. What is a Business Model?
“There has never been as much interest in business models as there is today; seven out of 10 companies are trying to create innovative business models, and 98% are modifying existing ones” (Masanell et al. 2011 p.5)
So what is a business model? The term business model can sometimes be thrown around as corporate jargon, its terminology is misconstrued through generic terms that overall have the same concept. Some might say a business model is how a company makes money, others may use a company’s value proposition as a short one-line answer. The reality is that a business model is not one concept, and no general definition has emerged (Masanell et al. 2011). However, more often a business model has been referenced as an architecture, blueprint or method. In an analysis done by Morris et al. (2005), a content analysis of keywords in 30 definitions led authors to identify three general categories based on their principle emphasis. These categories can be labeled economic, operational, and strategic, each with a unique set of decision variables. See
Appendix 1 for for “Components of a Business Model as cited in Morris et al 2011.
Shafer et al. (2005) sees the creation of a business as the sum of the strategic choices that have been made thus far within an organization. That is not to be confused with strategy, “While business model does facilitate analysis, testing, and validation of a firm’s
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strategic choices, it is not itself a strategy.” (Shafer et al. 2005 pg. 203). Within its common
theme lies a general idea of value, specifically how an organization create and delivers this value. The authors continue by defining a business model as a representation of a firms underlying core structure and strategic choices to create this value. Ultimately, this stated model must be as comprehensive as possible and not one or two components.
As its multitude of definitions leaves a business model’s interpretation vague, this report will focus on Shafer’s value adding business model. We can define a model with a set of managerial choices and the consequences of those choices, Masanell (2011) describes these pillars as the logic of a company – how it operates and creates and captures value for stakeholders in a competitive market place.
B. Finding Value in Big Data
Gupta (2012) describes Big Data as data that exceeds the processing capacity of conventional database systems, meaning the data size is either too large or the values change too quickly for traditional database management systems. Big Data can be defined through 3V’s (Hassani 2017), which are presented as high-volume, high-velocity, and high-variety information assets that require new forms of processing to make enhanced decision making.
• Volume: Refers to large amounts of data from any source.
• Variety: Refers to different types of data collected from multiple sources; sensors, smart phones, social networks, surveys etc.
• Velocity: Refers to the speed of data transfers, the data content is constantly changing. (Hassani 2017 pg. 743)
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Hassani (2017) discusses how data must go through several steps before it becomes observable, this process is called data management, it allows the data to be formatted into for a statistical analysis. Once in its observable form, Big Data can be used to find correlations between variables, we use Big Data to learn and predict. Big Data’s counterpart, Small Data offers a different insight. Small Data, is the use of surveys and observable collection tools such as focus groups and questionnaires to gain insight. In comparison, Small data can be used to find causation and Big data is used to find correlation.Data has little to no meaning on its own, data is raw and unfiltered as Balar (2013) describes. Once categorized through a proper system we can break down the step by step approach that ultimately leads to analytics. Balar (2013) references the knowledge pyramid:
Fig. 1 The knowledge pyramid (Balar 2013 pg.1)
Breaking down the process of data from low to high value, we begin with data at its collected infancy as low value, going through its refinement to become information, knowledge, and at its most valued, wisdom.
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Erevelles (2015) calls this era in data management a “Big Data Revolution”, one that has the potential to lead to entirely new ways to understanding consumer behavior. As data management technology continues to grow at such a fast pace, this unprecedented amount of information being gathered has led to an almost clairvoyant method of targeting consumers directly. The ability to manage insignificant data that was once seen as useless has shifted to an advantageous capacity to map the consumer identity. In its core consumer analytics is the value gained from managing big data.Once we have found the value in the data it can then be defined as an asset, thus bringing our data into the realm of a resource. In Barney (1991)’s resource based model, an organization must first define whether they have a resource or capability, the former being defined only if its attributes can lead to a sustainable competitive advantage. These advantages, as defined in Barney’s VRIO framework, are Valuable, Rare, Inimitable, and supported by the organization that can exploit this resource.
Fig. 2 VRIO Framework (adapted from Barney 1991)
As shown in the figure above, without its core characteristics a resource loses its competitive advantage and is categorized as a capability rather than a resource. The challenge then from Big Data is whether a firm can fully utilize its capabilities and define it as a resource.
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C. Marketing valueAs Shafer (et al. 2005) mentioned, a business model is not a strategy, a strategy can be a part of a business model. Much like a blueprint is prepared by an architecture, the preparation to use this blueprint is the strategy. “Strategy can be viewed in at least four different ways: as a pattern, plan, position, or perspective. Specifically, in a backward-looking context, strategy is sometimes viewed as a pattern of choices made over time.” (Shafer et al 2005 pg. 203).
Similarly, we can also look at Kim’s (2009) two types of strategy: Structuralist strategies that assume the operating environment is given and reconstructionist strategies that look to change the environment. Kim’s further discusses that choosing between the two options is dependent on the environmental attractiveness, the resources available to the organization, and the organizations strategic position for competing or innovating. Structuralist strategies requiring a position of either low cost or differentiation, and reconstructionist strategies aiming to deliver on both. Moreover, Kim (2009) discusses how the value created by choosing a strategy needs to target different sets of stakeholders which include buyer, shareholders, and employees.
Marketing is the process via which a firm creates value for its customers (Silk 2005). Value is created by meeting the customers needs. “Thus a firm must define itself not by the product it sells, but by the customer benefit provided” (Silk 2005 Pg. 3). Within this definition Silk (2005) continues the main focus on managing value; Marketing is what an organization must do to exchange and create value to its customers. Silk views marketing to have two major activities: determining the desired positioning of the product within the target customers mind and specifying the strategy to achieve those desired positions.
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III. The Framework/Tool:
In this section, I will expand the frameworks that are to be implemented in order to create a synthesized model that uses consumer analytics to strengthen a marketing strategy. I have several frameworks in place to build upon each other, as each framework builds from an internal to external approach.
A. Resource based theory and Big Data
Erevelles’ (2016) resource based view on big data, takes a deeper dive into technologies and how they are utilized to build value. The author calls this era in data management a “Big Data Revolution”, one that has the potential to lead to entirely new ways to understand consumer behavior. At its core, consumer analytics is at the center of the big data environment, as technology continues to allow us to capture large amounts of data in real time. This resource based theory framework helps us understand the impact of big data allowing organizations the ability to utilize its benefits. The three resources described in this process are physical, human, and organizational capital, all of which will be used to moderate the following: (1) Collecting and storing evidence of consumer activity as Big Data as a process, (2) extracting insight from Big Data, and (3) the process of taking the consumer insight to improve core competencies.
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Fig. 3 A resource based theory Erevelles 2016 pg.299
1. Firm Resources
The first framework will discuss the ability to gather data from consumer activities. Using these activities, we define the resources that are available to an organization by gathering and processing the data received. Our first intersect in Fig 3. are the physical capital resources, Human Capital resources, and Organizational capital resources, each within the context of managing the data retrieved from the consumer activities. Physical capital resources include platforms that firms use to collect, store and analyze big data. This platform should be capable of handling Big data within its three V’s, Volume – Large amounts of data, Velocity – continuously flowing in real time, and Variety – form several sources.
The second resource a firm must apply to big data are its human capital resources, these include the insight from managers and strategists that know how to capture information from Big Data. Finally, companies will need to include an organizational structure that permits these insights into the business strategy. As
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shown in Fig. 3 Physical, human, and organizational capital resources process consumer activities into a sustainable competitive advantage.2. Big Data
Once the consumer’s activities have been filtered through a firm’s resources do we actually have our Big data categorized into three divisions; structured data, semi-structured, and unstructured data. From this point the data is filtered as a resource characteristic before it can be viewed as a tangible insight of the consumer activity. How data is viewed through these characteristics is essentially the frame of mind the data processed in, these include characteristics of Ignorance and Creative intensity.
3. Resource Characteristics
Erevelles (2016) discusses the idea of ignorance as a resource characteristic and the added value of categorizing the data as an unknown variable. “Understanding
what we do not know, referred to ignorance, is as important as understanding what we do know… The pursuit of knowledge sometimes requires that researches recognize ignorance” (Erevelles 2015 pg. 899). Viewing data through the lenses
of ignorance enables knowledge, because the realization of not knowing is critical for new knowledge. This approach allows for a cultural orientation for facilitating creativity within an organization and allows for the data analysis to fill any open chasms within the current model.
The other resource characteristic, Creative intensity, accelerates the speed of transforming Big data into hidden insights that lead to extreme innovations. Erevelles (2016) describes this intensity as being essential to harnessing the benefits of Big Data and gaining a sustainable competitive advantage. This characteristic lies the firm’s human capital resources and organizational culture
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that allows for a firm to utilize new and innovative ideas. When utilized, this intensity helps a firm broaden its knowledge based resources that create the rare, inimitable, and valuable attributes towards a sustainable competitive advantage. 4. Consumer InsightsOnce raw data has been filtered through a firm’s resources and viewed through the firm’s resource characteristics does a firm have its first look at consumer insights. At this stage, consumer insights that were once unknown enable marketers to predict consumer behavior more efficiently. These improved capabilities enable for a more proactive response to market environments. This improved foresight allows for better analysis of market drivers and changes a firm’s adaptive capabilities, backed by consumer trends.
5. Adaptive/Dynamic Capabilities
Completing this framework, a firm must utilize the information gained from the consumer insight to enhance either its dynamic or adaptive capabilities. A firm’s dynamic capabilities are defined as a firm’s ability to respond to change, and its adaptive capabilities are a firm’s ability to be predictive and proactive to changes to a markets needs (Erevelles 2016).
As illustrated in Fig. 3, both adaptive and dynamic capabilities are achieved through consumer insights from big data and facilitate value creation towards various marketing activities. This value is specifically created as a result of the insights available through big data. This allows for a sustainable or temporary competitive advantage. Both capabilities stimulate innovation and enable a firm to create value using big data. These innovations can be seen in forms of incremental innovation and radical innovation. These insights enable marketers to have a better measurement and improve the effectiveness of their advertising campaign
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while having a more complete understanding of the customer through the use of Big Data.Value created from adaptive and dynamic capabilities that are improved by insights from Big Data can be added to value creation, including product, price, place, promotion, etc. Dynamic Pricing for example enables a firm to enact a flexible pricing strategy based on Big Data to improve revenue management. Many variables and sources can be integrated to reach the optimum frequency for target price matching, Erevelles (2016) uses a baseball game as an example of collected data that can effect ticket prices including the timing of ticket sales, weather conditions, construction around the ball park, teams on the rise, popular players, even the potential for a record-setting event, or social media chatter about the game.
As a result of this resource based theory we come back to Barney’s (1991) resource based framework and its overall goal to create a sustainable competitive advantage. Using Big data as a direct resource allows for a firm to capture great insights to a consumer, however without the proper channels, an organization can still fail to utilize these consumer insights to facilitate adaptive capabilities.
B. The Transformative Business Model
Kavadias’s (2016) transformative business model is a business model that maximizes value creation and that can link a new technology to a market need. As no new technology can transform an industry without having the blueprint formed by a business model that links it to an emerging market. Kavadias’ (2016) business model serves as an interface between what technology enables and what the marketplace wants, it is through this link that allows for a business model that is transformative. Within this model are six features that enable a transformative structure. It is noted that it is not needed to display all six, in order for a business model to be transformative it would need three or more of these features continuously recurring and that a higher number of these features would usually
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correlate with a higher chance of transformation.Fig. 4 Transformative Business Model Kavadias 2016 pg. 95
These “Six keys to Success” as seen in Fig 3., labeled as keys to innovation success, are all linked to a recognized technology trend and funneled through to a market need. The currently placed trends have been identified through an analysis of regularly published industry reports and reputable consulting companies as of Fall 2016 (Kavadias 2016).
1. Personalization
The first key to innovation success on the transformative model will be a more personalized product or service. As a staple of any model that looks to be flexible and continuously changing to meet the target customers’ needs, having
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a model that allows for a personalization is an essential feature in a business model. Companies often leverage technology for products and services that are better tailored and connect to a market need. These tech trends include sensing, interfacing and material technologies, managing platforms for connecting devices, Mobility and cloud technologies, and decentralized small-scale Manufacturing, all of which are used to create more diversity of consumer preferences (Kavadias 2016). An example in business today, would be Amazon’s Deep Scalable Sparse Tensor Network Engine (DSSTNE)6, a Deep Learning (DL) Machine Learning (ML) that recommends products to customers based on their search history and online presence.2. Closed Loop
The second key is creating a closed-loop process of the supply chain. As a majority of companies currently use a linear process in their supply chain, i.e. one in which products are made, used, and then disposed of, a closed loop replaces this model with the ability for products to be recycled. The tech trends used to innovate a closed loop cycle are identical to those that offer a personalized product or service, however a closed loop targets several other market needs. The first is the ability to handle the increase in demand for products and services, having a closed loop feature in a business model will allow for reusable resources. Another market need is the management of rising input costs that include resources, labor and transportation costs, all of which are reduced when recycling resources (Kavadias 2016). Finally, creating a closed loop and creating a more sustainable presence will manage rising regulatory pressures and create a corporate responsibility presence. A current growing trend among organizations, creating sustainable solutions using a closed loop format can be felt from large organizations like Dell, which supports
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an extensive e-waste collection program that include free recycling options in 78 countries which will recycled into their products by 20207.3. Asset Sharing
A third key feature is Asset sharing, a feature that has been a growing trend with companies such as Airbnb and Uber. Kavadias (2016) describes how these innovations succeed because they enable sharing of costly assets. For example, Airbnb allowing home owners to share with travelers. This type of sharing can reduce entry barriers to many industries, much like Uber within the taxi and limousine commission in the U.S.8, because the assets do not need to be owned by the business entrant, they only have to act as the intermediary service. Tech trends that support this innovative feature in a business model are almost identical to those of the first two keys to innovation success without the need for a decentralized small scale manufacturing since the assets are shared, and with the inclusion of optimization technologies such as AI, big data, and robotics. Asset sharing also covers the same market needs as a closed loop feature.
4. Usage-Based Pricing Model
Fourth, is having a usage-based pricing system, rather than requiring them to buy something outright, these models charge customers when they use a product or device. Kavadias (2016) makes the distinction that the customers will benefit because they incur costs only as an offering generates value and the company benefits because with no continuous service fee, the number of customers will likely grow. Here, mobility and cloud technologies and platforms for connecting devices are noted as an influence on this innovative feature. The application (Apps) model is one that thrives in this innovation, as obtaining the
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https://www.forbes.com/sites/heatherclancy/2014/05/28/dells-supply-chain-brings-closed-loop-recycling-advantage/#7004b98e3836
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program needed to use the service rarely incur a fee for large scale enterprises that have a usage based pricing feature. As mentioned previously, the customer will benefit because they incur costs only when the offerings create value, as a reflection of this business model feature the only market need that is satisfied is the increase in demand for products and services. A simple model that many recognize as usage based pricing would be a pay-as-you-go cell phone plan that frequently charge on usage such as minutes used or the amount of data spent.5. Collaborative Ecosystem
The next feature in a transformative business model is creating a more collaborative ecosystem, which is using innovations in technology to improve collaboration with supply chain partners. This feature also opens up the opportunity for future collaboration beyond the traditional supply chain, working with non-traditional partners in other sectors and industries, and competitors. The only technology trend supporting this current feature is optimization technologies such as big data, AI, and robotics, and the utilization of big data opens up a wider scope of opportunities in discovering potential connections within a business landscape. This will open up new networks that are usually not seen as useful. When meeting these market needs a collaborative ecosystem fulfills a need to manage a rise of input costs for resources and labor by helping allocate business risks more appropriately, making cost reductions possible, and increasing the demand for products and services. Healx corporation uses this feature in their business model, by leveraging big data technology and machine-learning to match patients biological information by data collaborating across multiple pharma companies to offer personalized treatment options9.
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6. AgilityHaving an agile and adaptive organization is the final feature in Kavadias’ (2016) transformative business model. Here innovators will sometimes use technology to move away from traditional hierarchical models of decision making. Instead decisions are made based on how the market is reflected and allows for a real-time adaptation of those needs. Kavadias (2016) mentions that the result of these decisions often offer a greater value for the customer and lowers the cost for the company. Here we see a tech influence from optimized technology and decentralized small scale manufacturing. This allows for speeds to meet the demands of a more diverse consumer preference within the marketing needs. It also allows for a faster adaptation to a greater regulatory presence. An example of a company not being adaptive is Blackberry, which failed to follow upcoming trends involving touch screens and better Wi-Fi capabilities led to the ultimate downfall of company that once held the greatest market share within the mobile device industry10.
With all six features examined through its influences of popular technology trends and the marketing needs that they fulfill, it’s important to emphasize that there is a level of cohesion that must be built between these key innovations. Although each key can operate discreetly on its own, as noted in Fig 4., the level of overlap for the use of tech trends will lead to a web of value creation that meets multiple market needs. Karvadias’ (2016) research suggests that business models with a transformative potential tend to only have three or more of these six features.
As seen in Appendix 2, companies like Uber ticks a total of five boxes, their business model being built on asset sharing, the drivers use their own cars. Uber has developed a collaborative ecosystem in which the driver takes on the risk of picking up fares, while the platform helps minimize that risk with the application of big data. The
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platform creates agility through its internal decision-making system that responds to market changes in real time, setting up system of usage based pricing directing drivers to locations with a higher probability of finding a fare. Uber’s last feature of personalization via the big data platform allows customers to rate drivers and use this rating to decide whether or not customers will take that fare depending on the drivers rating (Kavadias 2016).When applying the transformative business model, the first step is to rate the organization on how well the current model performs on each one of these six features. If these six features are not leveraged against the competition, the chances of success lowers. Each feature must first be defined within its own industry, once this definition is expressed through the industry, can an organization develop metrics to evaluate its business model and develop a method to differentiate itself using new technologies.
C. Value Pyramid
As mentioned prior in the literature review, a strategy can either come from a
structuralist approach requiring a position of either low cost or differentiation, or a reconstructionist approach aiming to deliver both, this Framework focuses on the latter of the two. What consumers truly value can be hard to pinpoint without proper sourcing, and once this information is gathered, how can managers determine the best way to add this value to their products. Almquist' (2016) value pyramid framework utilizes consumer insights to measure and deliver what consumers want (See Fig.5). This framework focuses on creating a model containing universal building blocks of value, one that allows a company to come up with new combinations of value that its products and services can deliver (Almquist, 2016). In his research Almquist has identified 30 “elements of value” which act as fundamental attributes in their most essential and discrete form that when combined increase customer loyalty and revenue growth. These attributes fall into 4 categories, functional, emotional, life changing, and social impact.
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of value have direct psychological meaning to the consumer. Elements of the pyramid can be focused more inwardly, focusing on addressing a consumer’s personal needs, and others can have a more outward focus; these elements can help a customer interact with there day to day needs. Conceptually, Maslow’s argument of human actions arises from the requirement to fulfill basic needs like food, water and sleep, to the more complex like altruism. Similarly, the bottom of the value pyramid contains safety and convenience while the top provides a more mental stimulus of hope and self actualization.
Fig. 5 Value Pyramid Almquist 2016 pg. 5111
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1. Functional ElementsStarting from the bottom of the pyramid a functional element of value offers a variety of components to assist and provide the customers with products and solutions that are practical and useful. These outward focusing elements help a user’s productions and have less of an emotional impact. Here, products that offer such elements (saving time organization, connectivity, reduction of effort, etc.) appeal to a more pragmatic psychological connection from the consumer. Marketers in industries that want products to appeal to this functional application can range, companies that exclusively accumulate elements in this category ordinarily derive from industrial or labor intensive industries. Almquist (2016) notes, in order to deliver on higher-order elements toward the top of the pyramid, a company must provide at least some of the functional elements required by a particular product category.
2. Emotional Elements
Moving up the pyramid, product elements start to have a more inward appeal to the consumer, providing an emotional connection. Here products hit a more dopamine level of value within the consumer, products that have an emotional element can provide fun and entertainment, a reduction in anxiety, as well as a therapeutic value. These components begin to focus on the more passionate side of the consumer, concentrating on how the user feels when interacting with the product. When coupled with functionality, emotional elements add an enthusiastic and entertaining feel to more practical functions. This combination is best seen in the marketing of mobile devices, which exclusively covers nearly every functional element and now has developed to have a more emotional appeal through its design aesthetics and entertaining values.
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3. Life Changing ElementsProceeding to life changing elements, within this consumer need we begin to see less elements defining our categories. Products and services including these elements start to appeal to a consumer’s desires, and provide an element of encouragement. Life changing elements like motivation can be seen in products such as exercise tracking tools for example Fit-bit, that monitor a user’s level of health and fitness. Others provide a sense of hope with stores like GNC corporation that sell a wide range of weight loss and nutritional products designed to improve a consumer’s health and fitness. Whether it be stimulating people to achieve their goals, or helping them become a part of a group to feel an affiliation or belonging, life changing elements of a consumers needs begin a transformative process that consumers look for internally. Combined with previous elements, a specific reasoning can be perceived behind the emotional and functional elements of products and services.
4. Self Transcendence
The final consumer need, social impact, is served by only one element of self-transcendence. The consumers’ need for a product to have a level of social impact can have a greater meaning for a companies’ position with the consumer than the product or service offered. These look to appeal to a customers philanthropic need to help other people or society more widely. Companies that use a one-for-one giving model are notorious for hitting this element of value, for instance, when TOMS shoes sells a pair of shoes, a new pair of shoes goes to an impoverished child in developing countries. Companies like Smile Squared that fund wish trips to children facing life-threatening medical conditions through its sales of toothbrushes and travel journals.
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As mentioned previously, these elements are not mutually exclusive per service or product to cover one consumer need. The impact of the value pyramid is felt through the coupling of different elements spread across all four consumer needs. Almquist (2016) implies that in order for an organization to deliver in higher-order elements, they must first provide some of the functional elements. Some elements do matter more than others, the element of quality for instance affects a customer’s advocacy more than any other element. Products and services must attain a certain minimum level of quality and no other elements can make up for a significant shortfall (Almquist, 2016).Much like finding hidden consumer insights using Big Data, the value pyramid’s potential lies in developing new types of value to provide. It is important to note the correlation of providing more elements with the achievement of a high level of sustainable revenue growth model and greater customer loyalty. Putting these elements to work, in addition to creating value for the consumer, can provide a greater use to solving business challenges and growing revenue for the organization. It is only when managers make value a priority and realize their product features as a growth opportunity do these elements of value work best.
D. Data Marketing Strategy Application
1. Framework Synthesis
In order to properly apply the reviewed frameworks to Global Players, it is important to perform a proper synthesis of the models and devices. Each framework is an important tool to properly manage and integrate Big Data as a valuable marketing tool. Every framework previously mentioned highlights the ability to recognize and exploit value. Here we take that same concepts and use Big Data as a catalyst for that value. In order to manage big data within an organization as an asset, first the business model must allow for a technology such as big data to flourish, it is not enough to have a new technology but have
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a system in place for a sustainable implementation. With the proper business model as the framework background a system of managing and using Big Data must be applied to properly gain usable consumer insights. Finally, a method to implement those consumer insights into a marketing strategy will allow for the proper application. This method is referred to as the “Data Marketing Strategy” (see Fig. 6), and will be the framework applied to Global players. Its purpose is to manage, and apply consumer insights to a marketing strategy using Big Data.Fig 6 “Data Marketing Strategy framework”
2. Global Players
By teaming with study abroad offices and athletic departments on a global scale, Global players is able to offer an immersive and culturally significant
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experiences for student athletes. In its offerings, students with an athletic curriculum can study, intern, and play abroad all while maintaining an athletic schedule.Global players targeted customer includes students and faculty within a colleges athletic Department. Its programs offered include:
• Study abroad – Students study with international universities and earn credits toward their current studies
• Service abroad – Student give back by volunteering in underdeveloped countries
• Intern abroad – Students work for international companies and develop their hard skills toward building their CV
• Team Travel – Coaches and their respective teams will be sent abroad and will participate in cross training with foreign teams, tournaments, and team building
• Faculty-Led Programs / Athletic Director (AD) Retreats – Faculty and AD’s come abroad for cultural immersion and work on team building
Global players unique approach to these offerings is that every single one is tailored to match the needs and wants of the team or individual. This is done through a qualification process that Global players facilitates through individual meetings prior to creating the program. For this reason, it is imperative that Global Players incorporates a business model that can optimize this qualification process using Big Data analytics and consumer insights.
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3. Application
Within the Data Marketing Strategy, it is important to observe the foundation in which the solution is run. This approach uses Kavadias (2016)’s transformative business model and its “Keys to Success” framework as a substructure for using an emerging technology such as Big Data. I begin by locating how the model can benefit from utilizing Big Data as a tech trend and link that towards an innovation that satisfies a market need of the consumer. When applied to Global Players, the Data Marketing model uses consumer insights from student-athletes and faculty to populate these market needs. This model should be viewed as a continuous avenue for data to be improved upon. Once Global players has established a business model to facilitate the use of Big Data, the proper management applications must be put in place. Erevelles’ (2016) resource based theory allocates Global Players data management systems as a resource that takes raw data and refines into consumer analytics. To properly manage its data asset, Global Players will need to allocate its resources toward its physical, human, and organizational. Managing a company’s resources can lead to the outsourcing of some capabilities such as data management systems. When applied to a small enterprise as Global Players, outsourcing certain data management solutions may be beneficial while scaling. This can also be applied to the insights of managers and strategists. With the Data Marketing Strategy in effect there will be no need to focus on the organizational capital resources since the business model and strategy already permit the use of data insights within the company.
To begin with, a true understanding of the data will need to be facilitated prior to its use. Erevelles’ (2016) resource based theory demonstrates the importance to identify the two frames of mind to view the filtered data before labeling as a consumer insight. Through Global Players’ assigned human capital resource, a view of ignorance can be used to determine to determine its resource characteristic. Using this perspective, the managers can determine
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where the gaps lie in our current understanding of the student-athletes, i.e. what don’t we know. Proceeding this perspective, Global players will then need to view the data through a creative intensity characteristic in order to fill those previously mentioned gaps with innovative ideas.After successfully determining the resource characteristics of our data output, Global Players will then have its consumer insights. These insights provide the backbone to the entire framework utilization, from here Global players will determine the adaptive capabilities of these insights, and the dynamic capabilities to facilitate value creation within the product. These capabilities can influence pricing, destinations offered abroad, product itinerary management, and promotional content.
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IV. Implications/Managerial Recommendations
Global Players specializes in study-abroad programming for a niche market, while there are many study-abroad programs there are only a handful of programs that offer services designed for student-athletes. With few competitors and a growing appeal for internationalization within U.S. colleges, the Global Players model can expect much growth with a proper marketing strategy in place. In order to properly manage the Data Marketing Strategy in Global Players current upscale, there will be quite a few adjustments in its infancy to achieve an optimal level of value creation. When implementing this approach, they must first consider the allocation of resources that are available to implement certain solutions. Its first step will be to financially evaluate what pillars of the framework will need to be outsourced and which ones can be facilitated internally.
When resourcing Big Data, I recommend that Global players outsource these capabilities with a data management company with similar solutions such as SAS, or SAP. This outsourcing will allow Global Players to reduce infrastructure costs that would come with building out data servers and statistical staff management. Outsourcing these capabilities will also allow for an improved sourcing of data sets that Global players cannot currently support.
When applying the Data Marketing Strategy, Global players will need to populate the market needs of the collegiate athlete environment with consumer insights. In the the created frameworks infancy, this will need to be populated with forms of “Small Data” – Data that is sourced from more traditional forms of research such as surveys and focus groups. Small data can be a valuable resource when populating consumer insights toward marketing capabilities, it delivers insights on causation and reasons why. It is important to note that this strategy cannot be relied on for marketing capabilities for long periods, it is a short term solution for Global players to start with since Global players uses a strategy to correlate consumer elements to its solutions and not a causality of why. Here I would implore Global Players to use past research polling over 800 college coaches and their
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perspective on study abroad and team travel (Appendix 3).Once Global Players consumer insights have been collected, either through an outsourced data management system or its “Small Data” sample gatherings, they can adapt this information through dynamic capabilities or adaptive capabilities to determine value internal methods of value creation, an example of those methods are:
• Dynamic capability potential for Global Players can be used to influence internal organizational activities such as direct marketing outlets. Using consumer insights can result in optimum pricing strategies and can vary from determining when the peak season of students purchasing study abroad programs would be (Summer, spring etc.) and develop a sliding price scale, or determining seasonally the most attractive locations for students to visit (winter can result in a fluctuation of warmer climate destinations). These capabilities can also determine what types of marketing tools to use and when, for example if a coach is in season, they might not be available for a phone meeting versus a coach that is off season and more willing to book an abroad trip immediately post season.
• Adaptive capabilities of consumer insights can lead to proactively predicting levels of consumer behavior. Global players should use consumer insights to notice trends that can be exploited for customer interaction. For instance, insights gained from certain sports teams can predict where they go and when and for what reason. Hypothetically, if a football team losses during a season, they may want to do a service abroad trip, or if a team wins, they may prefer a more sight seeing team travel. Adaptive capabilities allow for collected data on the travel habits based on their win/loss percentages utilizes exclusive predictive data.
Once Global players has used these consumer insights internally, I would recommend to adapt these consumer insights to the services and product offerings of Global players itself. Using the value pyramid, I recommend highlighting the current elements of value in their product offerings. Once these elements are defined, I would then suggest Global
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Players to use gained consumer insights to update their product offerings to add new elemental functions within their tailored product model.V. Limitations/Conclusion:
I have explored the conceptual frameworks of using Big Data as a valued resource, and surveyed the relevant literature regarding business models, Big Data, and strategy. Within those topics, the underlying philosophy towards the process of constructing an
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optimal business approach lied in the presence of creating value. I have then explored Big Data’s development from its raw unfiltered inception into a refined valuable resource as a consumer insight mechanism.As the application of big data is more of a recent application, in its infancy there are still a few limitations of its implementation into an organizations strategy. Starting with management, depending on the levels of data that are being processed, large sets of data sometimes require an infrastructure of data servers and hardware that would need to be outsourced by a smaller enterprise like Global Players. Once this level of private data being outsourced reached, a risk assessment will be needed to be taken to use a data management company. High priorities will lie within safety and the security features of the managed data. As Erevelles (2016) proclaims, the existence of team-specific assets is handled with higher productivity within an organization than outside an organization. Outsourcing data processing capabilities can be monitored to a certain extent, however for Global players it would be suggested to facilitate as much of the data refining process from within the organization before resources are extended.
Without the proper models in place, Big Data can tell you what a consumer did and not why those actions were taken in the first place. Although these observed behaviors are useful to marketers, Hofacker (2016) suggests that a solution to solve this problem would be to supplement Small Data from a more traditional research method such as a survey. For smaller sample sizes Global players can revert to questionnaires and surveys to add a why factor to back up larger sets of data. In this limitation, we also see the potential of Big Data to show associations but not causation.
Hofacker (2016) describes a very unique limitation associated with causality created through customer treatment levels. For example, Hofacker (2016) explains how customer relationship marketing strategies often invest more marketing resources into better customers than weaker ones. As a result, all high value customers receive more marketing, and all low level customers receive less. This level of marketing activity is limited to customer quality, and it becomes impossible to unravel separate effects within
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the data outputs. To avoid this issue, an organization withholds some of its marketing from its better customers, and invests more marketing in some of its weaker ones (Hofacker 2016) but many would see this as a waste of resources. As Big Data is often observational and subjects are not randomly assigned treatment levels such techniques can lead to skewed data samples.Big Data sets also may not be representative; Marketers should question where their data was sampled in order to filter potential biases that can be created. When potentially outsourcing from data management companies, it will be imperative to manage the sampling procedure to avoid wasted resources. For example, Hofacker (2016) discusses how a company’s data may be detailed and have a large quantity but this data may only be representative of long term customers. This can result in a survival bias of data, a logical error of concentrating on people that make it through the process and ignoring those that haven’t, in this case the short term customers.
Sentiment analysis, which is based on opinions expressed online, is a popular Big Data application source. A risk assessment of this data set shows that opinions are sometimes not fully represented in this format. The choice of anonymity within these sample sets can be left open to saboteurs, one that an organization will not be able to distinguish between a potential unsatisfied customer or potentially a disgruntled employee or even a competitor sabotaging the brand (Hofacker 2016). There are many companies that offer social-media tracking services within their data management solutions, ones that produce reports on how well a company’s presence online will correlate to their unit sales. Hofacker (2016) discusses how sometimes there are situations where there is no relationship, and it can be easy to to cherry pick data in order to show correlation.
This report has provided a method for a business to capitalize the use of a new technology, in sum, it has provided a broad overview on the question of how an SME can utilize Big Data within its marketing strategy. The customized framework I have merged provides an outline to manage this technology. Using this strategy, I was able to employ a practical method of application in the case of Global players.
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In conclusion, I was able to extract recommendations that gives a smaller enterprise like Global Players access to a marketing strategy. One that sources its business intel from its consumer, allowing for a sustainable implementation as the company continues to grow. In regards to Global Players’ potential for scaling and its ability to gain access to new resources, I recommend several ways to manage its current resources to maximize the value gained from the “Data Marketing Strategy”. Using these consumer insights applied to Global Players’ product model, I suggest highlighting more functional elements in the marketing campaign toward a customers need in order to increase appeal. Overall, these recommendations provide a hypothetical internal framework for utilizing consumer data for Global Players. Once populated with sourced consumer insights refined from Big Data, this strategy can assist Global Players to achieve an effective competitive advantage.38
Appendix 1: Business models39
Appendix 2: How many Boxes should a model tick40
Appendix 3: Global players Infographic41
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• Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120.
• Dawar, N. (2013) When marketing is strategy. Harvard Business Review, 1-17.
• Erevelles, S., Fukawa, N., & Swayne, L. (2016) Big Data consumer analytics and transformation marketing. Journal Business Research, 69, 897-904.
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• Hassani, A., Gahnouchi, S.A. (2017) A framework for business process data management based on big data approach. Science Direct, 121, 740-747
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• Kavadias, S., Ladas, K., & Loch, C. (2016) The transformative business model: and how you know if you have one. Harvard Business Review, 91-98
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toward a unified perspective. Journal of Business research, 58, 726-735• Shafer, S. M., Smith, H.J., & Linder, J. C. (2005). The power of business models. Business Horizons, 48(3), 199-207.
