MKTG3002: Evaluating Service Opportunities and Challenges in Modern Banking Operations

Introduction

This report aims to evaluate (BANKS NAME), the service marketing issues and challenges, and provide extensive analysis and critiques with innovative solutions to rectify the challenges.

The main problems when introducing service robots (AI) into an organisation like a bank are:

• Customer dissatisfaction – mixed reviews from Baby Boomers and Gen X with varying occupations, stating that some prefer talking to a human when dealing with bank inquiries, while others prefer the efficiency of a robot.

• Finding the perfect middle ground between implementing AI and interacting with a human is fundamental for any bank’s success, as this caters to the previous mixed reviews of their customer service.

• Advanced equipment does not guarantee service excellence – a leading bank incorporated a state-of-the-art machine learning program to identify fraudulent transactions automatically. Although the implementation increased the volume of fraud resolved by 30% in less than four months, employee and customer experience deteriorated.

  • Client satisfaction = 2/5

• To address this issue, the bank sought support from a team in Intelligent Automation (IA) to review and redesign not only the fraud identification activity but the entire end-to-end process, with an emphasis on employee and customer experience.

  • Client satisfaction = 4/5

Case Analysis

• Three-Stage Model of Service Consumption → The bank considered varying customer reviews regarding service robots and used this feedback to find a balanced approach toward implementing AI. The first launch was unsuccessful; however, the IA incorporation strategy was reevaluated and redesigned for improved outcomes.

• Service Profit Chain → The bank focused on enhancing customer experience to increase satisfaction and build loyalty. The second attempt at incorporating IA proved more successful as customer experience significantly improved.

• Customer Complaints and Service Recovery → Client satisfaction during the first IA launch dropped to 2/5 because employee and customer experience was not prioritised. Service recovery was achieved by shifting focus to customer needs and overall experience when interacting with IA systems.

Critical Assessment

• The bank should have prioritised customer experience during the initial launch of IA services.

• It was a strong decision to reevaluate the first launch and identify reasons for its failure.

• Considering reviews from customers of diverse backgrounds was an effective approach to understanding public perception of IA in banking services.

Brief summary of assessment requirements

This assessment (MKTG3002 A2 : Professional Practice: Service Opportunities and Challenges) requires a concise, evidence-based report that evaluates a bank’s service marketing issues when implementing intelligent automation (service robots / AI). The report should:

• Introduce the organisation and clearly state the service problem(s) being investigated.
• Present a case analysis applying relevant service marketing frameworks to explain what occurred. Possible frameworks include the Three-Stage Model of Service Consumption, the Service Profit Chain, and models of Service Recovery.
• Provide a critical assessment of the bank’s actions (what went wrong, what went right), supported by specific evidence such as customer feedback and client-satisfaction indicators.
• Offer practical, evidence-based recommendations for resolving issues and improving customer and employee experience with AI/IA.
• Use objective, academic tone and clear structure (introduction, case analysis, critical assessment, recommendations).
• Include measured outcomes where available (e.g., “fraud resolved ↑ 30%”, client satisfaction = 2/5 then 4/5) and interpret their managerial implications.

Key pointers to cover in the submission:

1. Problem statement: customer dissatisfaction, generational differences, and employee impact from AI rollout.

2. Evidence: performance metrics, customer feedback, and timeline of interventions.

3. Framework application: explain behaviour and outcomes via established service marketing models.

4. Critical analysis: why the first launch failed; what was learned; role of employee experience.

5. Recommendations: balanced AI–human service mix, pilot testing, stakeholder engagement, continuous monitoring.

6. Implementation considerations: change management, training, communication strategy, KPIs for monitoring.

7. Conclusion: reconciled value proposition and how improvements restored client satisfaction.

How the Academic Mentor guided the student

The mentor used a scaffolded coaching approach to build the student’s analytical, practical and communication skills. Each stage below maps to the sections the student was required to produce.

Step 1 : Clarify the brief and define scope

• What the mentor did: Reviewed the assignment rubric and learning outcomes with the student. Helped the student define the report boundary (focus on AI/IA implementation in bank service delivery) and identify the primary problems to investigate.

• Student outcome: Clear problem statement (customer dissatisfaction; operational gains vs experience losses).

Step 2 : Gather and organise evidence

• What the mentor did: Advised on data sources and evidence to collect: customer reviews segmented by cohort (e.g., Baby Boomers, Gen X), internal performance metrics (fraud resolution rates), and client-satisfaction ratings before and after IA redesign. Recommended documenting timelines and any communications or pilot notes.

• Student outcome: Compiled key facts: 30% increase in fraud resolved; client satisfaction fell to 2/5 after first launch and improved to 4/5 post-redesign.

Step 3 : Select and apply relevant theoretical frameworks

• What the mentor did: Explained the Three-Stage Model of Service Consumption, the Service Profit Chain, and service recovery theory. Demonstrated how each framework maps to observed events (e.g., why technical success did not equal customer success).

• Student outcome: Case analysis that links evidence to theory (e.g., IA improved operational metrics but violated elements of the Service Profit Chain such as employee engagement and customer experience).

Step 4 : Conduct critical assessment

• What the mentor did: Guided the student to interrogate root causes (insufficient user testing, poor communication, omission of employee workflows) rather than only reporting outcomes. Encouraged balanced critique : identifying both mistakes and corrective strengths (rapid IA redesign with IA team).

• Student outcome: Concise critical assessment that identifies prior omission of customer experience in initial rollout and praises the rapid iterative redesign.

Step 5 : Develop practical, justified recommendations

• What the mentor did: Facilitated brainstorming of actionable solutions: human-AI hybrid workflows, phased pilots, co-design with frontline staff, targeted customer segmentation strategies, and monitoring KPIs. Prioritised recommendations by feasibility and expected impact.

• Student outcome: A set of implementable recommendations tied to evidence and frameworks (e.g., pilot with representative demographic segments; include customer experience KPIs; staff training).

Step 6 : Drafting, feedback and polishing

• What the mentor did: Reviewed drafts for structure, clarity, and academic tone. Corrected logical flow, ensured third-person objective voice, and advised on concise presentation of metrics. Suggested linking each recommendation back to specific failures identified earlier.

• Student outcome: A cohesive report with clear sections, supported arguments, and actionable conclusions.

Step 7 : Presentation preparation (optional)

• What the mentor did: If a speaking component is required, coached the student on succinctly presenting the problem, evidence, and top three recommendations, and how to anticipate likely questions.

• Student outcome: Prepared speaking notes summarising the valuation and defence of recommendations.

Final outcome and learning objectives covered

Final outcome delivered:
A structured, evidence-based professional report that: introduces the bank and the IA service problem; analyses the case using service marketing frameworks; presents a critical assessment of the initial IA rollout and subsequent redesign; and provides practical, prioritized recommendations to reconcile operational efficiency with customer and employee experience. The report uses the supplied client-satisfaction metrics and operational outcomes (fraud resolution rate) to substantiate findings and recommendations.

Key learning objectives and skills demonstrated

1. Problem identification & evidence synthesis : locating, summarising and interpreting both quantitative (30% fraud resolution improvement; satisfaction scores) and qualitative (customer cohort feedback) evidence.

2. Application of theory to practice : using the Three-Stage Model, Service Profit Chain and Service Recovery frameworks to explain real outcomes and guide solutions.

3. Critical thinking and root-cause analysis : moving beyond surface metrics to diagnose why a technically successful IA rollout produced poor customer experience.

4. Solution design and prioritisation : proposing feasible interventions (pilot testing, hybrid human–AI workflows, stakeholder co-design, KPIs) with justification.

5. Professional communication : producing an objective, logically structured report in academic/professional tone, with evidence-linked recommendations.

6. Change and stakeholder management awareness : appreciating the roles of employees, customers, and IA teams in successful service innovation.

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