Ecosystem Hub for Starbucks

Proposed Ecosystem Hub for Starbucks
Overview
Each day, technologists working at Starbucks, in collaboration with other stakeholders, work on innovations that could only be described as groundbreaking. This is the team responsible for the unique Starbucks experience. However, without this level of dedication and inventiveness, Starbucks technology-centeredness would not have been possible. Thanks to these efforts, Starbucks now boasts of superior customer connection with the enterprise and continues to further promote customer experience. In line with Starbuck’s digital business transformation strategy, there is need for the development of a robust and effective Ecosystem Hub solution comprising of the software tools listed below:
a) Asana (Collaboration Portal)
b) Dropbox (Enterprise Content Management)
c) Board (Platform combining business intelligence tools with predictive analytics, simulation, as well as corporate performance management capabilities)
d) Mailchimp (Digital Content Marketing Management)
In an attempt to underline its commitment to digital business transformation, the company continues to hire competent and talented staff in the areas of data science. Indeed, in keeping with its trend to promote solutions that are forward-thinking, Starbucks has in the past successfully pioneered “projects such as Mobile Order & Pay and [the] Starbucks Rewards™ loyalty program” (Starbucks, 2020). With the Mobile Order & Pay, for instance, it is possible for customers to make purchases without necessarily having to queue.
To a large extent, the present proposal to develop an Ecosystem Hub solution is essentially a continuation of the company’s forward-thinking resolve and decision to monetize its digital platform.
Starbucks Digital Business Transformation Strategy
According to Rahman (2020), Starbucks is a data tech company, and not a coffee business. While this statement could seem farfetched at first instance, the way the company has used its digital platform makes it what Rahman refers to as “a textbook example of how to strategically use data to stay competitive” (Rahman, 2020). It is important to note that in addition to offering for sale cold and hot drinks, Starbucks also engages in the collection and gathering of voluminous data sourced from its customer interactions. The company’s utilization of the said data would give us a sneak preview of its digital business transformation strategy. Towards this end, it would be prudent to assess the company’s utilization of technology and data analytics in the recent past to enhance its competitive advantage and fuel business development.
a) Customer Experience
To begin with, Starbucks’ competitive advantage is largely based on its exclusive and innovative reading (and subsequent utilization) of customer data. For instance, on the Starbucks Rewards and loyalty program, the total number of people signed up stands at approximately 14 million (Whitten, 2017). In 2017, for instance, Whitten (2017) points out that “rewards represented 36 percent of U.S. company-operated sales in the second quarter and mobile payment was 29 percent of transactions.” What this effectively means is that the company can be able to accurately chart customer orders and make the relevant offering adjustments. For instance, it as far as loyalty customers are concerned, it is easy to determine their most frequent orders. This enables the company to ensure that customers are sent offers that are relatively personalized so as to further enhance sales.
b) Alignment of Product Lines with Customer Preferences
Data analytics have also come in handy in the further enhancement of the company’s product line and menu optimization. Marr (2019) points out that data from various stores was instrumental in the company’s rollout of bottled beverages and k-cups. Additional customer market research played a supplementary role in this particular case. For instance, based on available data from its stores, the company was aware of the fact that most of those who took tea routinely avoided adding sugar into the same. Towards this end, the company deemed it necessary to introduce unsweetened tea k-cups. According to Marr (2019), the company also pioneered a move to utilize its digital menu boards in the strategic enhancement of the bottom-line. Essentially, this being yet another example of the targeted operationalization of data analytics, the company can in this case be able to adjust prices throughout the day in response to a wide range of factors – and thus stimulate demand.
c) Other Strategic Decisions
It is important to note that following the return of Howard Schultz in Starbucks as CEO in the year 2008, a decision was made to embrace an analytical approach in decisions relating to store locations. This was in response to the closure of numerous store locations as a consequence of dwindling sales. An enterprise by the name Esri, and specializing in location analytics, was roped into the plan (Thau, 2014). Thanks to this move, Starbucks was able to identify and set up stores in better locations. All this was made possible by the sound application of data on traffic patterns, income levels, as well as population density. This is yet another example of Starbuck’s utilization of data analytics as the secret ingredient to stir up or drive up sales.
Goals and Objectives
On the basis of the discussion above, it is clear that Starbucks is committed to the strategic use of data to further promote its competitiveness. For this purpose, the company has lots of data on which to rely upon. As a matter of fact, in the words of Rahman (2020), the company “has over 30,000 stores worldwide and completes close to 100 million transactions per week… this gives it a comprehensive view of what its customers consume and enjoy.” It therefore follows that this is something that the proposed Ecosystem Hub will be able to tap into in an attempt to drive sales and remain relevant in an increasingly competitive marketplace. A total of four (4) considerations have been explored on this front.
a) Workplace Analytics
The relevance of collaboration in workplace settings cannot be overstated. This is more so the case given that this not only promotes creativity, but also drives productivity and increases the number of opportunities available for learning. Data analytics could come in handy in seeking to measure collaboration. Thanks to its abstract nature, team collaboration is inherently difficult to measure. For this reason, we need a tool or approach that is capable of evaluating overall collaboration so that meaningful insights can be drawn. This is where continuous team collaboration assessment and analysis comes in. With data analytics, it will be possible to identify misbalances in team activity. This is particularly the case when it comes to poor planning and ineffective time management. These can, for instance, be defined on the basis of how much time is spent by teams on video conferencing. Too much time would be indicative of minimal time spent on the collective execution of core tasks. It is also possible to use data analytics to determine team underloads or overloads (Adams, 2019). This is made possible via the assessment of the load pipeline. Lastly, thanks to data analytics, it will be possible to assess collaboration paths so as to further enhance team relationships. In basic terms, this is done by way of determining how team members collaborate - with the overall intention of highlighting the relationships that are most productive. This could result in facilitating better engagements between the trios or duos deemed to be most effective.
b) Predictive Analytics
In as far as the utilization of data analytics to predict is concerned, it is important to note that as Adams (2019) observes, predictive analytics come in handy in the formulation of forecasts relating to unknown future events. Indeed, in the words of the author, “predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future” (Adams, 2019, p. 87). It therefore follows that with predictive analytics, a company like Starbucks can see to it that data is transformed into meaningful future insights. This the company would be able to accomplish via the embrace of not only machine learning (an analytics technique), but also active utilization of historical data. As it has already been pointed out elsewhere in this text, Starbucks has a huge repository of customer data that it can make use of to not only forecast upcoming events, but also detect trends so that it can better serve its legion of adherent customers. The company has performed amazingly well on this counter in the past. This is more so the case when it comes to exploiting customer buying patterns to better position offerings. Even so, there are more opportunities for improvement. Examples in this case include, but they are not limited to, better configuration of store layouts during specific times or periods and improved forecasting of inventories.
c) Content Management
Effective content management is often an unattainable goal for most enterprises. This is more so the case given the sheer amount of data that the said enterprises have to deal with from time to time as a direct consequence of the ever growing number of applications that could be classified as customer-facing. Data analytics could also come in handy in this regard due to its ability to provide insight. In the case of Starbucks, data analytics would enable the company to better engage with content following the capture, analysis, as well as activation of the said content. In the end, this could enable the management to come up with greater insights that could be used to direct meaningful action.
d) Marketing Analytics
According to Marr (2019), Starbucks conducts close to 90 million transactions on a weekly basis. From each and every one of these transactions, the company derives some data which could come in handy in its sales and marketing efforts – if put to effective use. Some of the most important tools that the company could use on this front are smart phones. This is more so the case given that as Adams (2019) points out, ‘mobile order and pay’ presently has a significant share of the commercial transactions pie. Already, Starbucks has in place a mobile app that has seen its user-base grow significantly since its launch. With proper utilization of data analytics, the opportunities for targeted marketing in this case are endless. The transaction data that the company collects ought to be harnessed alongside social media data to accurately chart customer (both existing and new customers) interests. From this, effective digital advertising campaigns can be rolled out with the most potent tools in this regard being classified internet, display internet, and mobile internet.

Deployment of Digital Business in a BICC
To a large extent, the deployment of new technology is often a challenging exercise for many enterprises. This is more so the case given that technical innovation is not a straightforward undertaking – much like plug and play activities. Deployment would in this case comprise of four critical steps. These will be highlighted below.
a) Selection of the Appropriate Tools
In this case, hardware and software requirements ought to be adequately addressed. These are based on the unique business needs as well as the problems or concerns being addressed. In the present scenario, the software tools have been identified as Asana, Dropbox, Board, and MailChimp. The company must meet the key infrastructure requirements so as to ensure that deployment is successful.
b) Employee Involvement
Deployment cannot be effective without the active involvement of all employees. Ansoff (2016) points out that failure to seek the perspective of employees or other key stakeholders in an undertaking of this nature could result in resistance to change. In the words of the author, “the role of employee involvement in change management is crucial” (Ansoff, 2016). It therefore follows that in the present scenario, employees will be actively engaged so as to ensure that they are not only open to the overall idea, but also supportive of the entire implementation process.
c) Training
Most undertakings of this nature run into problems because of failure to provide effective training to users as well as all those who actively interact with the proposed solution. Hitt, Ireland, and Hoskisson (2014) are categorical that successful adoption of new systems begin with effective and well-designed training programs. Training ought to be designed in such a way that in addition to inseminating the essential skills, it also addresses (and displaces) misplaced concerns.
d) Pilot Operation
In basic terms, pilot operation has got to do with structured implementation of solutions. This is to say that the solution is operationalized in various segments first before rollout across the entire organization. To a large extent, this helps promote credibility (specifically among stakeholder with a high level of interest and great power) and brings down the level of risk. It therefore follows that following successful operationalization in other segments, we can talk of proof of concept in readiness for organizational rollout.
Balanced Scorecard Deployment
There is always need to keep track of how well the solution embraced is being executed. This is where a balanced scorecard will come in handy in as far as the description of the digital business transformation is concerned. The balanced scorecard could in this realm be conceptualized as performance management tool in the context of strategy operationalization. In the present scenario, therefore, it would be prudent to focus of a number of markers including, but not limited to; the strategic initiatives, and the performance measures as well as targets. In as far as strategic initiatives are concerned, of great relevance are the various drivers of results, i.e. action plans. On the other hand, performance targets as well as measures would come in handy in seeking to define the level at which the enterprise is performing. We could also go further and incorporate various strategic objectives as well as strategy maps into the balanced scorecard equation. These would ideally be the activities deemed to be the most important strategy components.

Ends
In reference to the company’s continued investment in digital innovation, Starbucks CEO has in the past categorically stated that “no traditional brick-and-mortar retailer is better positioned to navigate and flourish in the global retail industry of today or better positioned to lead in the digital retail world of tomorrow” (Marr, 2019). At present, the company is well-positioned to exploit available opportunities in its industry owing to its robust digital platform. In essence, it has mounds of data, a dedicated and skilled cast of employees, and worthy partners to put its strengths to good use. Available data in this case ought to be used to leverage the company’s position in the market via the further promotion of customer experiences so as to grow market share.
References
Adams, R. (2019). Data Analytics for Businesses 2019. Mason, OH: Cengage Learning.
Ansoff, H.I. (2016). Strategic Management. New York, NY: Springer.
Hitt, M.A., Ireland, R.D. & Hoskisson, R.E. (2014). Strategic Management: Concepts, Competitiveness and Globalization (11th ed.). Stamford, CT: Cengage Learning.
Marr, B. (2019). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Hoboken, NJ: John Wiley & Sons.
Rahman, W. (2020). Starbucks Isn’t a Coffee Business — It’s a Data Tech Company. Retrieved from https://marker.medium.com/starbucks-isnt-a-coffee-company-its-a-data-technology-business-ddd9b397d83e
Starbucks (2020). Explore the Possibilities, Drive Innovation. Retrieved from https://www.starbucks.com/careers/technology
Thau, B. (2014). How Big Data Helps Chains Like Starbucks Pick Store Locations -- An (Unsung) Key To Retail Success. Retrieved from https://www.forbes.com/sites/barbarathau/2014/04/24/how-big-data-helps-retailers-like-starbucks-pick-store-locations-an-unsung-key-to-retail-success/#432e7edd16db
Whitten, S. (2017). Starbucks Rewards Members Can Now Earn Even More Stars at the Grocery Store. Retrieved from https://www.cnbc.com/2017/05/04/starbucks-reward-members-can-now-earn-stars-at-the-grocery-store.html