Making The Case For Qualitysix Sigma Optimization Of Mystery Shopping ✓ Solved

Making the Case for Quality Six Sigma Optimization of Mystery Shopping: A Hypothetical Case Study on the Nigerian Banking Industry • Mystery shopping (MS) can be a very valuable exercise for studying and evaluating service delivery performance within the banking industry. • Using Six Sigma tools and hypothetical data, this case study tests the approach and results to gauge poor service from excellent service delivery. • The MS approach is highly applicable as a balanced scorecard parameter to measure delivery within service centers. At a Glance . . . Mystery shopping (MS) is a tool used externally or internally to measure the quality of service and com- pliance with standards and regulations. It can increase employee awareness about the customer to improve cus- tomer satisfaction and deliver excellent service, which in turn increases sales and reduces risk exposure.

In the MS process, param- eters such as staff attitude, knowledge index, turnaround time, and empathy are tested to monitor service delivery performance. A quality defect is the difference between expected and observed variants. Operational opportunities that could cause defects are the total number of service operators tested by mystery shoppers. MS metrics have often been used directly in bank branch scorecards. With the use of defects per million opportunities (DPMO) and sigma level, the metrics can be enhanced.

The level of statisti- cal robustness is further improved with the application of rolled throughput yield (RTY) to optimize quarter-on-quarter MS results. This case study presents hypothetical data to test Six Sigma process metrics in MS. The results show great variance between poor and excellent service delivery in accordance with sigma level. The meth- odology used in this case study can be applied in any service-rendering organizations that intend to use the MS approach in measuring service delivery performance. by Tewogbade Shakir September 2014 ASQ Page 1 of 4 Background Most Nigerian banks strengthened their capital base during the consolidation era from 2005 to 2006. With consolidation, expec- tations on the part of the banks increased, with benefits from synergies, human and material resources, and information tech- nology.

Expectations from the service delivery perspective have further increased competition among banks. Customers now seek excellent service delivery to make deposits, process transfers, withdraw funds, use debit/credit cards, open accounts, and apply for loans. To enhance awareness among branch personnel and reinvigorate performance, many banks have introduced the MS tool. This is used to track both emotional and technical attributes exhibited by service operators in delivering excellent service to customers. MS metrics address attributes ranging from greeting, eye contact, and attention to product knowledge.

The metrics are recorded on a monthly basis, while they are used quarter-on- quarter in a financial year. The mystery shopper does not test equal numbers of processors (staff who serve customers by carrying out one or more process- ing functions) in various branches. Direct measurement does not factor this in. Six Sigma processor opportunities create a better measurement; the more processors tested by a mystery shop- per, the greater opportunity there is of getting it right. Also, the sigma concept allows measuring the progress or regress of ser- vice delivery from one quarter to another with rolled throughput yield (RTY).

Perhaps the methodological application of Six Sigma can also be utilized in other service-rendering organizations in the same manner, using general attributes (technical and emotional) out- lined in this case study. Methodology Many Six Sigma metrics are based on understanding defects in features or attributes in product, process flow, or service deliv- ery. Defect rates can be used to track quality performance. In the MS exercise, defects per operator opportunities will determine if a service outlet is delivering service to a high level with ease. The following metrics will be considered in this paper: • Defects per million opportunities (DPMO) • Rolled throughput yield (RTY), quarter-on-quarter • Sigma level DPMO DPMO is a measure of the number of defects occurring in a business process.

It simply expresses how process flow, service, or product is performing in relation to quality defects. DPMO is the total number of defects in a population divided by the total number of opportunities, multiplied by a factor of 1 million. Explicitly, it shows the probability of items (attributes) with zero defects, where opportunities for defects can vary. In this MS example, the number of processors contacted yields the total number of defect opportunities: Opportunities = Total number of service processors queried by MS The opportunities factor normalizes the varying number of ser- vice processors contacted in different service outlets. It indicates the chance of a processor proffering positive attributes to avoid faults (defects).

DPMO = 1,000,000 à— weight of attributes with fault Total weight of attributes à— no. of opportunities Typical technical and emotional attributes tested in an MS exercise include: • Greeting • Courtesy, politeness, and empathy • Correct response the first time • Understand MS question • Answer question correctly • Clear understanding of service procedures • Efficient handling of MS transactions • Referral to appropriate officer • Turnaround time • Eye contact and smile • Confidentiality • Boldness and confidence • Environment and ambiance • Product knowledge • Appearance Consideration is made for attribute weights, and the weight rate is often decided by management or the MS project owner in accordance with business/enterprise objectives.

Weight distribu- tion can be linear or nonlinear. The opportunities treatment will be the same for both distributions. Processors’ responses are rated from bad to excellent, using a scale of 0 to 10 points. Mystery shopping is a subjective approach and such rating allows attributes to be scalable. The aim of the wide gap is to easily separate bad service delivery from excellent attributes.

Tables 1 and 2 present the points and weights used for the purposes of this case study. Rolled Throughput Yield (RTY) Financial years include 12 months, which are segmented into four quarters. Since human behavior is nonme- chanical and varies with time, DPMO attributes will vary ASQ Page 2 of 4 quarter-on-quarter. This effect is normalized using RTY; thus, MS results and service performance are monitored as the financial year progresses. RTY will measure overall quality level of Qn+1 on Qn for each service outlet: RTY = [1 – DPMOQn ] à— [1 – DPMOQn+1]1,000,000 1,000,000 Sigma Level Sigma is a measure of degree of variation in a process flow.

Six Sigma focuses on defect prevention; thus in the MS project, as a faulty response is reduced, sigma level is increased. There is an inverse relationship between sigma level and DPMO. As DPMO decreases, sigma level increases and quality of the processors’ responses will be high. Thus, service processors deliver excellent service when all customers are treated like mystery shoppers. Sigma focuses on results that are critical to customers’ satisfac- tion.

See Table 3. Analysis and Interpretation Hypothetical data in an MS project for three service outlets are presented in Table 4. DPMO is calculated for each branch, with the following opportu- nities (see Table 5): Igan Branch: Opportunities = 3 Akintaro Branch: Opportunities = 8 Imashahi Branch: Opportunities = 2 Table 4 — MS data for three service outlets Branches Igan Akintaro Imashahi Attributes Q1 Q2 Q1 Q2 Q1 Q2 Greeting Fair Poor Excellent Excellent Bad Poor Courtesy, politeness, and empathy Poor Poor Excellent Excellent Bad Poor Product knowledge Good Poor Excellent Excellent Poor Bad Service procedure and efficiency Fair Fair Excellent Excellent Poor Bad Referral to appropriate officer Poor Fair Excellent Good Fair Poor Turnaround time Good Fair Excellent Excellent Poor Fair Eye contact and smile Good Good Good Excellent Bad Fair Confidentiality Fair Good Excellent Excellent Fair Fair Environment and ambiance Fair Fair Excellent Excellent Fair Bad Appearance and confidence Fair Good Excellent Excellent Poor Poor Total weighted points 1,,960 1, Table 3 — Interpreting sigma levels Sigma Level DPMO % Defects % Success Capability Cp 1 691,.,.,807 6.7 93.3 1.,210 0.62 99.38 1..023 99.977 1..4 0...00 ASQ Page 3 of 4 Table 2 — Point weighting scale Response Weight Excellent 10 Good 8 Fair 5 Poor 2 Bad 0 Table 1 — Points allocated to each attribute Attributes Points 1.

Greeting . Courtesy, politeness, and empathy . Product knowledge . Service procedures and efficiency . Referral to appropriate officer .

Turnaround time . Eye contact and smile . Confidentiality . Environment and ambiance . Appearance and confidence 10 Table 5 — DPMO and RTY values DPMO (Q1) DPMO (Q2) RTY After Q2 Igan 141,,666 0.71 Akintaro 2,500 1,250 1.00 Imashahi 387,,000 0.37 The RTY after the second quarter implies a 71 percent success rate for the Igan service outlet, 99 percent success for Akintaro, and 37 percent success for Imashahi.

Reject/defect quality is a categorical factor in Six Sigma projects; thus, chi square meth- ods are used to study reject distribution of the three branches. This is certainly applicable for the MS study project where service outlets are classified along directorate or area offices. Assuming the Igan, Akintaro, and Imashahi branches are the only three branches within the Yewa area office, then the defect distribution is shown in Table 6. Testing the critical value at α = 0.10, while the degrees of freedom are (R-1)(C-1) = 18, the critical value of chi square, χ2 = 501. This exceeds the critical value, suggesting that branches differ with regard to proportions of various types of defects in this particular business development office.

The resultant yield for area offices can be computed, making use of effective sum of defects encountered and total number of proces- sors tested. It is usual for the reporting area office/directorate to have an independent scorecard composed of resultant data from service outlets under such directorate in big enterprises (Table 7). Table 7 — Reporting area office scorecard Total Defect for Q1 DPMO for Q1 Total Defect for Q2 DPMO for Q2 RTY After Q2 Yewa Area Office 2,440 31,282 2,680 34,359 0.94 This area office had 93 percent success in the MS exercise after the second quarter. The success rate is relatively high because the branch with excellent service delivery attributes had the larg- est number of opportunities (Akintaro branch: 8).

Results and Remarks MS project results are utilized as a metric in the branch’s scorecard. The scale gauging and usage categorization is appropriately computed by the business/project owner. The sigma levels for the selected branches are 2.08, 4.89, and 1.16, respectively. The scale is chosen after running empirical tests over disparate MS data. The robustness lies in defect opportuni- ties created by total number of processors interviewed by MS.

RTY is used to measure overall quality from one financial quar- ter to another. Similarly, area office performance can be calculated with RTY using the DPMOs of all branches under its directorate. The MS exercise emphatically shows data that can be used to proactively prevent subsequent defects. It is a continuous improvement tool in service outlets. Identified defects will also show learning curves for future operation and processing.

The main focus for any service provider is to have customers come back again and again. MS is a great systematic approach to improve service delivery to service users (customers). For More Information • To contact the author of this case study, email Tewogbade Shakir at [email protected] . • To read more examples of quality success, visit the ASQ Knowledge Center at asq.org/knowledge-center/case-studies. About the Author Tewogbade Shakir works in the financial industry in Nigeria. He has a bachelor of science degree in physics from the University of Lagos, Nigeria.

He is currently working on the thesis for his master’s degree in information technology. A member of ASQ and the Project Management Institute, Shakir is a Six Sigma Yellow Belt. He is an ASQ Certified Quality Improvement Associate (CQIA) and a PMI Certified Associate in Project Management. ASQ Page 4 of 4 Table 6 — Defect distribution for MS service outlets Yewa Greeting Empathy Product Procedures Referral TAT Eye Contact Confidentiality Ambiance Appearance Total Igan Akintaro Imashahi ,550 Total ,440

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Making The Case For Quality: Six Sigma Optimization Of Mystery Shopping in the Nigerian Banking Industry

Introduction

In today's highly competitive banking sector, service delivery excellence is no longer a luxury but a necessity. Mystery shopping (MS) emerges as a powerful tool for assessing and improving service performance. This case study discusses the application of Six Sigma methodologies to optimize mystery shopping efforts, aimed at enhancing customer satisfaction in the Nigerian banking industry. Utilizing hypothetical data, this study sheds light on how banks can leverage statistical metrics, such as defects per million opportunities (DPMO) and rolled throughput yield (RTY), thus fostering a culture of continuous improvement and excellence in service delivery.

Understanding Mystery Shopping

Mystery shopping is an evaluation technique wherein trained evaluators pose as regular customers to assess the delivery of services in various operational scenarios (Tewogbade, 2014). In the banking context, MS enables institutions to gain insights into service delivery attributes, including staff attitude, knowledge, turnaround time, and empathy applied by service personnel. The need for ongoing assessments becomes evident with rising customer expectations for excellence in services, consistently leading to increased competition among banks (Ojo, 2018).

Application of Six Sigma

Defects Per Million Opportunities (DPMO)
At the heart of Six Sigma lies the principle of reducing defects within processes. In the case of MS, a defect can be defined as any service element that falls short of customer expectations (Iguisi, 2017). DPMO provides a quantifiable metric to gauge performance by assessing the total number of defects relative to the number of opportunities for defects observed.
To compute DPMO, the following formula is used:
\[ \text{DPMO} = \left( \frac{\text{Total Number of Defects}}{\text{Total Opportunities}} \right) \times 1,000,000 \]
Where total opportunities are defined by the interactions with processors during the MS exercise (Aswathappa & Chatterjee, 2020).
Rolled Throughput Yield (RTY)
RTY is another crucial metric in the Six Sigma methodology that measures the cumulative yield of a process over multiple steps (Pyzdek & Keller, 2018). Constant monitoring through RTY ensures improvements are accurately tracked over different quarters and that service quality is enhanced progressively. The formula for RTY is expressed as:
\[ \text{RTY} = \prod (1 - \text{DPMO}) \]
Where DPMO is calculated for each quarter, yielding a holistic picture of service performance.
Sigma Level
Measuring service quality variance is another significant aspect of Six Sigma. Sigma levels indicate how many standard deviations a process is from perfection (Goh, 2018). As the DPMO decreases, the sigma level proportionately increases, leading to improved service delivery.

Methodology

This study utilizes a representative sampling of three branches within the Nigerian banking system, with service attributes evaluated over two quarters. The branches included: Igan, Akintaro, and Imashahi.
The evaluation of each branch was based on parameters such as:
- Greeting
- Courtesy and empathy
- Product knowledge
- Service procedure efficiency
- Turnaround time
- Referral capability
- Confidentiality
- Environment and ambiance
Each of these attributes was weighed using a predefined scale ranging from “excellent” to “bad,” providing a subjective measure of service delivery effectiveness (Tewogbade, 2014).

Analysis and Interpretation

Hypothetical data resulted in significantly varying DPMO values for each branch:
- Igan Branch: DPMO (Q1) = 141,666; DPMO (Q2) = 71
- Akintaro Branch: DPMO (Q1) = 2,500; DPMO (Q2) = 1,250
- Imashahi Branch: DPMO (Q1) = 387,000; DPMO (Q2) = 370,000
RTY values were subsequently calculated, indicating service delivery success rates of 71% for Igan, 99% for Akintaro, and 37% for Imashahi after the second quarter. The respective sigma levels for the branches were diverse:
- Igan: 2.08
- Akintaro: 4.89
- Imashahi: 1.16
The data illustrates significant discrepancies among branches, emphasizing the utility of MS in identifying key operational weaknesses (Gemini & Valizadeh, 2017).

Results and Implications

The cumulative scorecard results from the branches yield a critical understanding of service delivery performance across different aspects. The results indicated a potential high customer satisfaction rate relative to the Akintaro branch while highlighting significant service gaps in Igan and Imashahi branches. Importantly, using DPMO to track defects over time provides branches with tangible focal points for improving operational efficiencies and enhancing training programs for their staff (Randhawa et al., 2021).
The statistical rigor realized through Six Sigma helps justify resource allocation for targeted training interventions to improve service aspects identified as deficient. Continuous improvements articulated through MS data can position banks competitively within the industry, securing customer loyalty and satisfaction in the long term.

Conclusion

In conclusion, applying Six Sigma optimally exploits mystery shopping as a powerful operational tool within the Nigerian banking industry. The methodologies and metrics established in this case study demonstrate the ability to measure, monitor, and enhance service delivery proactively. By reducing defects in the service process, financial institutions can not only elevate quality service delivery but also improve customer satisfaction and loyalty, thus fortifying their market position amid fierce competition.

References

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