Business Finance - Operations Management Week 7 assignment

2

Starbucks Corporation Solution Assessment

Francis Nicaragua

Keiser University

BUS510G3-107072025

Instructor Lisa Kramer

08/10/2025

Starbucks Corporation Solution Assessment

This analysis examines Starbucks Corporation and a specific problem that affects performance and growth: a decline in customer visits and engagement with the Starbucks Rewards program in key markets. Starbucks remains one of the largest global coffeehouse brands, yet recent quarters show pressure on traffic and loyalty that warrants structured analysis and targeted action. In late 2024, financial coverage reported falling visits and a need to restore the core experience, which frames the business case for intervention (PYMNTS, 2024). The company simultaneously positions Rewards as a critical lever for growth and experience innovation, underscoring the importance of diagnosing the current state and testing solution paths.

Company Problem and Context

The immediate problem centers on lower repeat visits and softening engagement among Rewards members, even as the program remains very large. Market analysts have noted that adding more discounts in the app did not reverse the trend, and they argue the program’s scale now exposes design flaws that may depress perceived value or relevance (Starbucks, 2025). Moreover, customers increasingly expect personalized value and exclusive experiences rather than generic offers, which raises the bar for program design across retail categories (Boston Consulting Group, 2024). Finally, the current terms of use include star expiration rules that can reduce perceived fairness if members do not redeem frequently, which suggests friction that may erode engagement among value-sensitive segments.

Stakeholders and Status Quo Map

The core problem owner is the Chief Marketing Officer, who is accountable for the Rewards strategy, brand equity, and customer lifetime value. However, the broader stakeholder network includes store managers, baristas, the technology and data teams, and the customer community that uses the app. The status quo diagram in Appendix A visualizes current flows among the CMO office, analytics and technology teams, the data platform, stores, customers, and pain points around engagement and perceived value. The Starbucks Rewards page and growth strategy announcements confirm that digital experience and membership are central to the brand agenda, so mapping the current state is a necessary precursor to solution testing (Tan, 2023).

Analytical Frame and Decision Approach

Because this problem spans people, process, technology, and value propositions, the analysis uses standard business analysis practices from the BABOK Guide to structure discovery, options analysis, and stakeholder evaluation. The BABOK knowledge areas support tasks such as stakeholder analysis, current state assessment, future state definition, and risk management, which align directly with this project’s needs (International Institute of Business Analysis, 2015). In addition, the analysis draws on peer-reviewed research about loyalty and gamified engagement so that solution design is grounded in evidence, not conjecture. For example, personalization and gamification can strengthen engagement and reduce switching, which is directly relevant to the crowded coffee category.

Alternative One: Personalized Rewards and Adaptive Offers

The first alternative deploys a personalization engine that uses first-party data to tailor rewards, challenges, and recommendations to each member’s purchase history and behavioral patterns. Research on loyalty programs demonstrates that tailored benefits meaningfully increase satisfaction and program value, which supports this direction for brands with rich data assets (Myftaraj & Trebicka, 2023). Further, recent empirical work shows that gamified elements tied to meaningful goals can drive sustained engagement when they satisfy utilitarian and experiential motives, and personalization makes those goals feel relevant (Zhang, 2025). In practice, the Starbucks team would classify members into dynamic segments and deliver adaptive offers, expiring boosts, and timely reminders before stars expire, which reduces the friction of the current terms. The diagram in Appendix B shows the flows among the CMO office, the personalization engine, the data platform, the privacy and compliance function, the app, and the store network.

Alternative Two: Partner Rewards and a Gamified Ecosystem

The second alternative expands value beyond coffee by creating a partner-enabled reward marketplace and community challenges that connect Starbucks visits to cross-brand perks. Strategy communications from the company have already highlighted partnerships as a growth lever, which indicates organizational readiness for this path if designed with clear value exchange and store friendliness (Tan, 2023). In parallel, recent work on tokenized or partner-backed rewards suggests that new reward forms can raise intention and retention when the benefits are easy to understand and redeem, strengthening the case for a curated marketplace and simple redemption ladders (Boukis, 2024). The diagram in Appendix C maps the flows among the CMO office, partner brands, a reward marketplace service, a social gamification layer, the app, and the store network, with special attention to operational simplicity at the counter.

Decision Criteria and Weights

A multi-criteria approach evaluates the two alternatives using criteria that reflect brand strategy, feasibility, and risk. The criteria are customer impact at 35 % weight, strategic fit at 15% weight, implementation feasibility at 15% weight, cost efficiency at 10% weight, time to value at 10% weight, privacy and compliance risk at 10% weight, and operational complexity at 5% weight. These criteria align with BABOK guidance on value assessment and risk analysis for future state options and are informed by external evidence on what drives loyalty adoption and engagement (IIBA, 2021). Moreover, the weights reflect Starbucks' communications about the centrality of customer experience and speed to deliver improvements at the store level. This keeps the evaluation tied to the brand’s stated priorities.

Scoring the Alternatives and Explaining the Results

Using a one to five scale, where higher is better, the analysis assigns each criterion a score for both alternatives and then multiplies by its weight to produce a total. Personalized rewards scores as follows: customer impact five, strategic fit five, feasibility four, cost efficiency three, time to value four, privacy and compliance three, and operational complexity three, for a weighted total of 4.25 out of five. By comparison, partner rewards receive: customer impact four, strategic fit four, feasibility three, cost efficiency four, time to value three, privacy and compliance two, and operational complexity three, for a weighted total of 3.50 out of five. Consequently, the personalized path leads because it creates immediate relevance with data that Starbucks already holds, whereas the partner path adds extra coordination and privacy complexity that can slow impact.

Evidence-Based Rationale for the Preferred Alternative

The personalized rewards alternative best addresses the engagement problem within a reasonable timeline and risk profile. Academic studies consistently show that tailored offers and gamified tasks increase perceived value and reduce switching, which fits a market where customers can easily choose local alternatives or other chains (Zhang, 2025). In addition, program critiques in the trade press suggest that simply increasing generic discounts does not fix the underlying issue, so more relevant and dynamic value is needed for members with many choices and high expectations (Starbucks, 2025). Finally, the company’s strategy statements emphasize digital features that help partners and stores run smoothly, which aligns with a solution that enhances the app but keeps the store experience simple for baristas and managers (Tan, 2023).

High-Level Implementation Roadmap for Personalized Rewards

The implementation begins with design and governance so that data use and privacy safeguards are explicit. The first six to eight weeks define the personalization objectives, the segmentation logic, the reward and challenge catalog, and the consent and opt-out flows, all in consultation with privacy and compliance. The next twelve to sixteen weeks will build and test the learning models, the in-app experience, and the operational playbook for store partners, followed by a three-city pilot with weekly measurement and improvement. Research indicates that gamification works best when goals are meaningful and feedback is timely, so the pilot should include clear micro goals, visible progress indicators, and easy redemption to translate intent into visits (Zhang, 2025). As the pilot scales, the team can add simple social features that allow members to accept friendly challenges or share progress, but only when store impact and privacy controls remain tight.

Risks, Mitigations, and Success Metrics

The main risks are data quality gaps, model bias, customer fatigue, and the possibility that added complexity slows barista routines. Therefore, the mitigation plan includes conservative model rollouts, frequent A and B tests, clear caps on message frequency, and a store partner feedback loop to surface friction quickly. Since star expiration can frustrate light users, the program should add pre-expiry reminders and limited-time rescue boosts that help members redeem with dignity, directly addressing a known friction point. Success metrics include increased active Rewards members, monthly actives to total member ratio, redemption rate, frequency per member, and a lift in same-store sales in pilot markets relative to matched controls. In addition, the company should track barista task time and satisfaction to ensure the store experience remains warm and efficient, which the brand has identified as central to its identity and future growth.

Conclusion

In conclusion, this analysis concludes that Starbucks should prioritize a personalized rewards and adaptive offers strategy to counter loyalty decline and stimulate repeat visits. The decision follows a structured evaluation grounded in BABOK practices and informed by research that links personalization and gamification to higher engagement and lower switching. Although a partner-enabled reward marketplace promises future waves, it likely delivers slower impact and introduces more operational and privacy complexity in the near term. By focusing on member relevance, store simplicity, and measurable learning, Starbucks can translate brand scale into daily value that feels personal and timely. Finally, the attached diagrams map the current state and the two solution paths so stakeholders can align quickly and proceed with disciplined execution.

References

Boukis, A. (2024). The effect of tokenized rewards on customer loyalty programs.  Annals of Tourism Research,  109, 103851. https://doi.org/10.1016/j.annals.2024.103851

International Institute of Business Analysis. (2021). A guide to the business analysis body of knowledge (BABOK Guide) (3rd ed.). IIBA.

Myftaraj, E., & Trebicka, B. (2023). Analyzing the impact of loyalty card programs on customer behavior: insights from the Albanian market.  F1000Research,  12, 1028. https://doi.org/10.12688/f1000research.138185.3

PYMNTS. (2024). Starbucks plans to revive its strategy as customer visits decline. PYMNTS.com. https://www.pymnts.com/earnings/2024/starbucks-plans-revival-strategy-as-customer-visits-decline-and-sales-struggle/

Starbucks. (2025). Starbucks® rewards terms of use: Starbucks Coffee Company. Starbucks Coffee Company. https://www.starbucks.com/rewards/terms/

Tan, T. (2023). What’s next? Starbucks unveils long-term growth strategy for a limitless future. Starbucks Corporation. https://about.starbucks.com/stories/2023/whats-next-starbucks-unveils-long-term-growth-strategy-for-a-limitless-future/

Zhang, J., Zou, Y., Wang, S., & Xiang, Y. (2025). Consumer experience and perception in gamification marketing: evidence from the user survey of POPMART.  Frontiers in Communication,  10, 1388020. https://doi.org/10.3389/fcomm.2025.1388020

Appendices

Appendix A: Status Quo Diagram

Appendix B: Alternative One Diagram

Appendix C: Alternative Two Diagram

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Liu, Y., Liu, X., Wang, M., & Wen, D. (2021). How to catch customers’ attention? A study on the effectiveness of brand social media strategies in digital customer engagement.  Frontiers in Psychology,  12, 800766. https://doi.org/10.3389/fpsyg.2021.800766

PYMNTS. (2024). Starbucks plans to revive its strategy as customer visits decline. PYMNTS.com. https://www.pymnts.com/earnings/2024/starbucks-plans-revival-strategy-as-customer-visits-decline-and-sales-struggle/

Starbucks. (2025). About Starbucks. https://about.starbucks.com/mission/

Appendix

2

Starbucks Corporation Solution Assessment

Francis Nicaragua

Keiser University

BUS510G3-107072025

Instructor Lisa Kramer

08/10/2025

Starbucks Corporation Solution Assessment

This analysis examines Starbucks Corporation and a specific problem that affects performance and growth: a decline in customer visits and engagement with the Starbucks Rewards program in key markets. Starbucks remains one of the largest global coffeehouse brands, yet recent quarters show pressure on traffic and loyalty that warrants structured analysis and targeted action. In late 2024, financial coverage reported falling visits and a need to restore the core experience, which frames the business case for intervention (PYMNTS, 2024). The company simultaneously positions Rewards as a critical lever for growth and experience innovation, underscoring the importance of diagnosing the current state and testing solution paths.

Company Problem and Context

The immediate problem centers on lower repeat visits and softening engagement among Rewards members, even as the program remains very large. Market analysts have noted that adding more discounts in the app did not reverse the trend, and they argue the program’s scale now exposes design flaws that may depress perceived value or relevance (Starbucks, 2025). Moreover, customers increasingly expect personalized value and exclusive experiences rather than generic offers, which raises the bar for program design across retail categories (Boston Consulting Group, 2024). Finally, the current terms of use include star expiration rules that can reduce perceived fairness if members do not redeem frequently, which suggests friction that may erode engagement among value-sensitive segments.

Stakeholders and Status Quo Map

The core problem owner is the Chief Marketing Officer, who is accountable for the Rewards strategy, brand equity, and customer lifetime value. However, the broader stakeholder network includes store managers, baristas, the technology and data teams, and the customer community that uses the app. The status quo diagram in Appendix A visualizes current flows among the CMO office, analytics and technology teams, the data platform, stores, customers, and pain points around engagement and perceived value. The Starbucks Rewards page and growth strategy announcements confirm that digital experience and membership are central to the brand agenda, so mapping the current state is a necessary precursor to solution testing (Tan, 2023).

Analytical Frame and Decision Approach

Because this problem spans people, process, technology, and value propositions, the analysis uses standard business analysis practices from the BABOK Guide to structure discovery, options analysis, and stakeholder evaluation. The BABOK knowledge areas support tasks such as stakeholder analysis, current state assessment, future state definition, and risk management, which align directly with this project’s needs (International Institute of Business Analysis, 2015). In addition, the analysis draws on peer-reviewed research about loyalty and gamified engagement so that solution design is grounded in evidence, not conjecture. For example, personalization and gamification can strengthen engagement and reduce switching, which is directly relevant to the crowded coffee category.

Alternative One: Personalized Rewards and Adaptive Offers

The first alternative deploys a personalization engine that uses first-party data to tailor rewards, challenges, and recommendations to each member’s purchase history and behavioral patterns. Research on loyalty programs demonstrates that tailored benefits meaningfully increase satisfaction and program value, which supports this direction for brands with rich data assets (Myftaraj & Trebicka, 2023). Further, recent empirical work shows that gamified elements tied to meaningful goals can drive sustained engagement when they satisfy utilitarian and experiential motives, and personalization makes those goals feel relevant (Zhang, 2025). In practice, the Starbucks team would classify members into dynamic segments and deliver adaptive offers, expiring boosts, and timely reminders before stars expire, which reduces the friction of the current terms. The diagram in Appendix B shows the flows among the CMO office, the personalization engine, the data platform, the privacy and compliance function, the app, and the store network.

Alternative Two: Partner Rewards and a Gamified Ecosystem

The second alternative expands value beyond coffee by creating a partner-enabled reward marketplace and community challenges that connect Starbucks visits to cross-brand perks. Strategy communications from the company have already highlighted partnerships as a growth lever, which indicates organizational readiness for this path if designed with clear value exchange and store friendliness (Tan, 2023). In parallel, recent work on tokenized or partner-backed rewards suggests that new reward forms can raise intention and retention when the benefits are easy to understand and redeem, strengthening the case for a curated marketplace and simple redemption ladders (Boukis, 2024). The diagram in Appendix C maps the flows among the CMO office, partner brands, a reward marketplace service, a social gamification layer, the app, and the store network, with special attention to operational simplicity at the counter.

Decision Criteria and Weights

A multi-criteria approach evaluates the two alternatives using criteria that reflect brand strategy, feasibility, and risk. The criteria are customer impact at 35 % weight, strategic fit at 15% weight, implementation feasibility at 15% weight, cost efficiency at 10% weight, time to value at 10% weight, privacy and compliance risk at 10% weight, and operational complexity at 5% weight. These criteria align with BABOK guidance on value assessment and risk analysis for future state options and are informed by external evidence on what drives loyalty adoption and engagement (IIBA, 2021). Moreover, the weights reflect Starbucks' communications about the centrality of customer experience and speed to deliver improvements at the store level. This keeps the evaluation tied to the brand’s stated priorities.

Scoring the Alternatives and Explaining the Results

Using a one to five scale, where higher is better, the analysis assigns each criterion a score for both alternatives and then multiplies by its weight to produce a total. Personalized rewards scores as follows: customer impact five, strategic fit five, feasibility four, cost efficiency three, time to value four, privacy and compliance three, and operational complexity three, for a weighted total of 4.25 out of five. By comparison, partner rewards receive: customer impact four, strategic fit four, feasibility three, cost efficiency four, time to value three, privacy and compliance two, and operational complexity three, for a weighted total of 3.50 out of five. Consequently, the personalized path leads because it creates immediate relevance with data that Starbucks already holds, whereas the partner path adds extra coordination and privacy complexity that can slow impact.

Evidence-Based Rationale for the Preferred Alternative

The personalized rewards alternative best addresses the engagement problem within a reasonable timeline and risk profile. Academic studies consistently show that tailored offers and gamified tasks increase perceived value and reduce switching, which fits a market where customers can easily choose local alternatives or other chains (Zhang, 2025). In addition, program critiques in the trade press suggest that simply increasing generic discounts does not fix the underlying issue, so more relevant and dynamic value is needed for members with many choices and high expectations (Starbucks, 2025). Finally, the company’s strategy statements emphasize digital features that help partners and stores run smoothly, which aligns with a solution that enhances the app but keeps the store experience simple for baristas and managers (Tan, 2023).

High-Level Implementation Roadmap for Personalized Rewards

The implementation begins with design and governance so that data use and privacy safeguards are explicit. The first six to eight weeks define the personalization objectives, the segmentation logic, the reward and challenge catalog, and the consent and opt-out flows, all in consultation with privacy and compliance. The next twelve to sixteen weeks will build and test the learning models, the in-app experience, and the operational playbook for store partners, followed by a three-city pilot with weekly measurement and improvement. Research indicates that gamification works best when goals are meaningful and feedback is timely, so the pilot should include clear micro goals, visible progress indicators, and easy redemption to translate intent into visits (Zhang, 2025). As the pilot scales, the team can add simple social features that allow members to accept friendly challenges or share progress, but only when store impact and privacy controls remain tight.

Risks, Mitigations, and Success Metrics

The main risks are data quality gaps, model bias, customer fatigue, and the possibility that added complexity slows barista routines. Therefore, the mitigation plan includes conservative model rollouts, frequent A and B tests, clear caps on message frequency, and a store partner feedback loop to surface friction quickly. Since star expiration can frustrate light users, the program should add pre-expiry reminders and limited-time rescue boosts that help members redeem with dignity, directly addressing a known friction point. Success metrics include increased active Rewards members, monthly actives to total member ratio, redemption rate, frequency per member, and a lift in same-store sales in pilot markets relative to matched controls. In addition, the company should track barista task time and satisfaction to ensure the store experience remains warm and efficient, which the brand has identified as central to its identity and future growth.

Conclusion

In conclusion, this analysis concludes that Starbucks should prioritize a personalized rewards and adaptive offers strategy to counter loyalty decline and stimulate repeat visits. The decision follows a structured evaluation grounded in BABOK practices and informed by research that links personalization and gamification to higher engagement and lower switching. Although a partner-enabled reward marketplace promises future waves, it likely delivers slower impact and introduces more operational and privacy complexity in the near term. By focusing on member relevance, store simplicity, and measurable learning, Starbucks can translate brand scale into daily value that feels personal and timely. Finally, the attached diagrams map the current state and the two solution paths so stakeholders can align quickly and proceed with disciplined execution.

References

Boukis, A. (2024). The effect of tokenized rewards on customer loyalty programs.  Annals of Tourism Research,  109, 103851. https://doi.org/10.1016/j.annals.2024.103851

International Institute of Business Analysis. (2021). A guide to the business analysis body of knowledge (BABOK Guide) (3rd ed.). IIBA.

Myftaraj, E., & Trebicka, B. (2023). Analyzing the impact of loyalty card programs on customer behavior: insights from the Albanian market.  F1000Research,  12, 1028. https://doi.org/10.12688/f1000research.138185.3

PYMNTS. (2024). Starbucks plans to revive its strategy as customer visits decline. PYMNTS.com. https://www.pymnts.com/earnings/2024/starbucks-plans-revival-strategy-as-customer-visits-decline-and-sales-struggle/

Starbucks. (2025). Starbucks® rewards terms of use: Starbucks Coffee Company. Starbucks Coffee Company. https://www.starbucks.com/rewards/terms/

Tan, T. (2023). What’s next? Starbucks unveils long-term growth strategy for a limitless future. Starbucks Corporation. https://about.starbucks.com/stories/2023/whats-next-starbucks-unveils-long-term-growth-strategy-for-a-limitless-future/

Zhang, J., Zou, Y., Wang, S., & Xiang, Y. (2025). Consumer experience and perception in gamification marketing: evidence from the user survey of POPMART.  Frontiers in Communication,  10, 1388020. https://doi.org/10.3389/fcomm.2025.1388020

Appendices

Appendix A: Status Quo Diagram

Appendix B: Alternative One Diagram

Appendix C: Alternative Two Diagram