Six sigma project_-_Call Centre Quality improvement
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Quality improvement project in Call Centre
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Six sigma project_-_Call Centre Quality improvement
- 1. Submitted by Name: Vijay Kumar Roll No. 150401047
- 2. DEFINE DELIVERABLES D Define M Measure A Analyse I Improve C Control Pre DMAIC Analysis Project Charter Terms & Acronyms Used ARMI/RASIC Process Map (Flow Chart) SIPOC/COPIS
- 3. PROJECT MAPPING D Define M Measure A Analyse I Improve C Control Customer Sample Comments Key Output Characteristics Important to Business VP Operations & Quality Assurance Need to initiate the project on service quality as from last 6 months i.e. Aug-17 to Jan’18 we are unable to meet the desired target of 90%. The objective is to increase the internal quality score without compromise on the process productivity. Service Quality Client Satisfaction R&P Customer Relationship Manager From last 3 months the average quality scores are stagnant at 87%, Need to work with all the team members and understand where is the gap so that the new target 90%(monthly) can be achieved and sustained. Quality Target Customer Service Customer Experience Management Manager (OPS) Urgent attention required on quality as we have not been able to achieve the target 90% from last 6 months despite the incentive program rolled out for quality. Weekly Quality Scores Monthly incentives on quality Number of agents meeting the target Productivity
- 4. Dec-17toFeb-18QualitySummary 0.980.840.700.560.420.280.140.00 Median Mean 0.8900.8850.8800.8750.870 1st Q uartile 0.85420 Median 0.88540 3rd Q uartile 0.91670 Maximum 1.00000 0.86917 0.87706 0.88540 0.89000 0.10866 0.11424 A -Squared 352.84 P-V alue < 0.005 Mean 0.87311 StDev 0.11138 V ariance 0.01241 Skewness -6.2990 Kurtosis 46.5711 N 3064 Minimum 0.00000 A nderson-Darling Normality Test 95% C onfidence Interv al for Mean 95% C onfidence Interv al for Median 95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for AQS 3 MONTHS DATA GRAPHICAL SUMMARY 1.41.21.00.80.60.40.20.0 99.99 99 95 80 50 20 5 1 0.01 AQS Percent Mean 0.8731 StDev 0.1114 N 3064 AD 352.844 P-Value <0.005 Probability Plot of AQS Normal - 95% CI • Since, P-Value is less than 0.05, hence data distribution is Non - Normal • Since data is Normal so, central tendency should be taken as Median
- 5. PROJECT CHARTER D Define M Measure A Analyse I Improve C Control Six Sigma Project Charter Belt Name: Vijay Kumar Project Name: Quality improvement in PHD process Division: PHD Process Champion (PO): Nitin Sachdeva Start Date: 1-Feb Master Black Belt: Nitin Sachdeva End Date: Team Members: Syed Ali Gaurav Kumar Vinay Sharma Shivangi Tripathi Kannu Bhardwaj Problem Statement: Objective & Scope Internal call quality, an important performance metric at Pizza Hut contact centre operations in inTarvo Technologies Pvt. Ltd, was dipping from 87.9% to 86.7% during Dec-17 to Feb-18. Analyzing the data from last three months the average quality was observed to be 86% which is 4% less than the desired target of 90%. The objective of the project is to improve the quality stats through the identification of the variables that influence the quality. The goal of the project is to sustain an incremental improvement in quality so as to reach to desired target of 90%, gain reward on its billing and enable maximum of its operational employees to achieve incentives. The scope of the project is centralized order taking centre at inTarvo Technologies Pvt Ltd connected by a call centre number 011-39883988. Primary Metric: Internal Quality Secondary Metrics: Average Call Handling Time Consequences Metrics:
- 6. PROJECT CHARTER: Primary & Secondary Metric D Define M Measure A Analyse I Improve C Control 90% 90% 90% 88% 87% 87% 85% 86% 87% 88% 89% 90% 91% Dec-17 Jan-18 Feb-18 AvgQTYscores Primary Metric Target Actual 165 165 165 179 178 179 150 155 160 165 170 175 180 185 190 195 Dec-17 Jan-18 Feb-18 AverageCallHandlingTime Secondary Metric Target Actual
- 7. TERMS & ACRONYMS USED D Define M Measure A Analyse I Improve C Control Indicators Definition AQS Average Quality Score QA Quality Analyst IVR Interactive Voice Response FCR First Call Resolution SL Service Level AHT Average Handling Time QTY Quality
- 8. STAKEHOLDER ANALYSIS: ARMI When Populating the Stakeholder, consider the ARMI: • A= Approver of team decisions • R= Resource or subject matter expert (ad hoc) • M= Member of team • I= Interested parties who are to be informed Key Stakeholders Define Measure Analyze Improve Control VP Operations & Quality Assurance - Intarvo CRM Head – DIL GM - Ops & Quality Project Mentor - IMT Customer Relationship Manager – DIL Operations Manager Quality Manager I/R I/R I/R I/R I/R Team Members M M M M M I/A I/A I/A I/A I/A I/A I/A I/A I/A I/A I/A I/A I/A I/A I/A I I I I I I/A I/A I/A I/A I/A I I I I I
- 9. RASIC D Define M Measure A Analyse I Improve C Control • Responsible (R) : Solely and directly responsible for the activity (Owner) - Includes approving authority (A) • Approve (A) : Reviews and assures that the activity is being done as per expectations • Support (S) : Provides the necessary help and support in the project • Inform (I) : Is to be kept informed of the status/progress being made • Consult (C) : Is to be consulted for this activity for inputs RASIC Chart for Define & Measure Activities VP Opera tions & Qualit y Assur ance- Intarv oCRM Head- DIL Projec t Ment or- IMTCusto mer Relati onshi p Mana ger- DIL Opera tions Mana ger Qualit y Mana ger Team Memb ers Memb ers (Assoc iates) Pre- DMAIC Collection of VOB from all stakeholders I I I I/A S/C/R R - - Data Collection for last 3 months I I I I/C R/S R S - Analysis of data I I I C I R S - Report out on the Pre DMAIC Analysis I I I I/C I/C R - - DEFINE Project Charter Creation I I A I/S/C I R - - Charter Approval from Executives/Project Guide A I A S/R - - - - Build SIPOC I I I I I R S - Build Process Map - - - S/C S/C R - - MEASURE Build the data collection plan I I I S/C S R - - Get the DCP Approval - - C A - R - - Approve DCP I I A - - R - - Collect Data - - I/C I S R - - Validate data - - C S/C S R - - Publish next steps to stakeholder I I I I I R - -
- 12. THE SIPOC PROCESS MAP D Define M Measure A Analyse I Improve C Control Suppliers Inputs Process Output Customer Requirements • Phones • Office depot • System • CRM • Customer • Store • Accounting team • Complete call < 3mins • Correct address • Correct price • Accuracy of punching • Customer’s query • Customer details • Address • Name • Time, date • Volume • CRM navigation • Training, Coaching & feedback • Shifts • Hold Call received by CC Associate Customer’s concern identified Customer details verified in CRM Information provided to customer Call is documented in CRM Call closed by cc Associate Quality Audit done next day/same day • Call completion • Case tagged • Data on cycle time • Process metrics data saved
- 13. MEASURE DELIVERABLES D Define M Measure A Analyse I Improve C Control Data Collection Plan Measurement System Analysis Process Capability
- 14. DATA COLLECTION PLAN D Define M Measure A Analyse I Improve C Control Y Operational Definition Defect Definition Required Performance Standard Specification Limit Opportunity Improvement in internal Call Quality Points Achieved/Points Available <85% 90% USL- NA LSL- 85% Weekly Y Data Type Unit of Measurement Decimal Places Database Container Existing/New Database If new, when would the database be ready Planned Start date for Data Collection Improvement in internal Call Quality Discrete % Yes Excel Existing NA 15h Apr Data Items Needed Formula to be Used Equipment Used for Measurement Equipment Calibration Info Responsibility Training Need Operator Information Quality data for last 3 months Points Achieved/Points Available Audit Legend Quality Lead NA Quality Team MODE OF COLLECTING DATA
- 15. MSA: MEASUREMENT SYSTEM ANALYSIS VinaySyedShivangi 95 90 85 80 75 70 Appraiser Percent 95.0% C I Percent VinaySyedShivangi 95 90 85 80 75 70 Appraiser Percent 95.0% C I Percent Date of study: 20-March-2018 Reported by: Vijay Kumar Name of product: Misc: Assessment Agreement Within Appraisers Appraiser vs Standard ATTRIBUTE AGREEMENT ANALYSIS • The sample of 60 units has been evaluated by 3 different evaluators and the audit variance of 3 appraisers is ranging between 85% to 87% • The appraiser vs standard agreement has also been observed between 80% to 81% • Above results are enough to conclude that the evaluation scores are accurate and precise, however appraisers need some calibration with master calibrator
- 16. MSA: MEASUREMENT SYSTEM ANALYSIS D Define M Measure A Analyse I Improve C Control Process Sigma Level Calculator - Discrete Data Sample Data (user inputs): Number of units n 3,064 Total number of defects observed d 1,889 Number of defect opportunities per unit o 1 Sigma Shift (typically +1.5 for long term data) 1.5 Results: Defects per Unit dpu 0.61651436 Defects per Million Opportunities dpmo 616,514.4 Defects per Opportunity dpo% 61.65% Yield yield% 38.35% Process Sigma Level sigma 1.204
- 18. D Define M Measure A Analyse I Improve C Control Hypothesis Testing – Overall Summary Key Input Variables Input Type Data Collection possible Data Type - X Data Type - Y Data Normality (Y) Test Used Outcome Call Type N Yes Discrete Continuous No Kruskal-Wallis test Reject Null Hypothesis Duration P Yes Continuous Continuous No Spearman Correlation Reject Null Hypothesis Trainers C Yes Discrete Continuous Yes ANOVA Fail to reject the null hypothesis Feedback Delivery C Yes Discrete Continuous No Wilcoxon rank sum test Reject Null Hypothesis Team leader wise QTY scores C Yes Discrete Continuous No Kruskal-Wallis test Reject Null Hypothesis Shift Stretch/Overtime P Yes Discrete Continuous No Wilcoxon rank sum test Reject Null Hypothesis FCR C Yes Discrete Continuous No Wilcoxon rank sum test Reject Null Hypothesis Gender C Yes Discrete Continuous Yes Two Sample t-test Reject Null Hypothesis Peak non peak N Yes Discrete Continuous No Wilcoxon rank sum test Reject Null Hypothesis Age C Yes Continuous Continuous Yes Pearson Correlation Reject Null Hypothesis Different Shift C Yes Discrete Continuous No Kruskal-Wallis test Reject Null Hypothesis Part time/full time C Yes Discrete Continuous No Wilcoxon rank sum test Reject Null Hypothesis Associate's Tenurity P Yes Continuous Continuous Yes Pearson Correlation Reject Null Hypothesis Experienced vs Fresher Associate C Yes Discrete Continuous Yes Two Sample t-test Reject Null Hypothesis High hold usage/avg hold C Yes Continuous Continuous Yes Pearson Correlation Fail to reject the null hypothesis Day Hours N Yes Discrete Continuous No Kruskal-Wallis test Reject Null Hypothesis Week Day N Yes Discrete Continuous No Kruskal-Wallis test Reject Null Hypothesis Education background C Yes Discrete Continuous Yes Two Sample t-test Reject Null Hypothesis Hiring Source C Yes Discrete Continuous Yes ANOVA Reject Null Hypothesis Rushing on call C Yes Discrete Continuous No Wilcoxon rank sum test Reject Null Hypothesis Training, feedback & coaching C Yes Continuous Continuous Yes Pearson Correlation Fail to reject the null hypothesis Call Length C Yes Discrete Continuous No Wilcoxon rank sum test Reject Null Hypothesis Attrition C Yes Discrete Continuous Yes Two Sample t-test Fail to reject the null hypothesis Pressure of Other Metrics N Yes Continuous Continuous No Pearson Correlation Fail to reject the null hypothesis Top Call Drivers C Yes Discrete Discrete No Pareto Analysis NA Tagging % N No Discrete Discrete NA NA NA Technical issues N No Discrete Continuous NA NA NA Process Complexity N No Discrete Continuous NA NA NA Floor Environment C No Discrete Continuous NA NA NA Training Material N No Discrete Continuous NA NA NA
- 19. D Define M Measure A Analyse I Improve C Control Paramatric Testing – Data Normality Shapiro-Wilk normality test data: Agentwise_data_Monthly$QTY_Score W = 0.98135, p-value = 0.07097 Anderson-Darling normality test data: Agentwise_data_Monthly$QTY_Score A = 0.48113, p-value = 0.2284 Null Hypothesis: The agent wise quality score follows a normal distribution Alternate Hypothesis: The agent wise quality score does not follow a normal distribution Result: As the p-value in both the test (Shapiro-Wilk normality test & Anderson-Darling normality test) is greater than 0.05, we fail to reject the null hypothesis of data follows a normal distribution.
- 20. D Define M Measure A Analyse I Improve C Control 2 Sample T-Test – Gender wise QTY scores Null Hypothesis: Average quality score are equal in male & female both categories Alternate Hypothesis: Average quality score for females is higher than that of males Result: As the p-value for t-test is less than 0.05, we reject the null hypothesis of mean quality scores are equal and conclude at 95% confidence level that we have enough evidence of assuming that mean quality scores of female employees are higher than the male employees.
- 21. D Define M Measure A Analyse I Improve C Control 2 Sample T-Test – Experience wise QTY scores Null Hypothesis: Average quality score for fresher & experienced employees are equal Alternate Hypothesis: Average quality score of experienced employees is higher than that of fresher employees Test result: As the p-value for t-test is less than 0.05, we reject the null hypothesis of mean quality scores are equal and conclude at 95% confidence level that we have enough evidence of assuming that mean quality scores of experienced employees are higher than the fresher employees.
- 22. D Define M Measure A Analyse I Improve C Control 2 Sample T-Test – Attrition Null Hypothesis: Average quality scores are equal for attired and non attired employees Alternate Hypothesis: Average quality scores are not equal for attired and non attired employees Test result: As the p-value for t-test is greater than 0.05, we fail to reject the null hypothesis of mean quality scores are equal and conclude at 95% confidence level that we have enough evidence of assuming that mean quality scores of both category of employees are same.
- 23. D Define M Measure A Analyse I Improve C Control 2 Sample T-Test – Educational Background Null Hypothesis: Average quality scores are equal for graduate and under graduate employees Alternate Hypothesis: Average quality scores of graduate employees are greater than of under graduate employee Test result: As the p-value for t-test above is less than 0.05, we reject the null hypothesis of mean quality scores are equal for both level of education background and conclude at 95% confidence level that we have enough evidence of assuming that mean quality scores of graduate employees are greater than under graduate employees.
- 24. D Define M Measure A Analyse I Improve C Control ANOVA - Trainers Null Hypothesis: There is no significant difference in trainer wise (3 trainers) average quality scores Alternate Hypothesis: At least one trainer trained trainees’ quality scores are different Test result: As the p-value for t-test above is greater than 0.05, we fail to reject the null hypothesis of trainer wise mean quality scores are equal and conclude at 95% confidence level that we have enough evidence of assuming that mean quality scores are same irrespective of the batch trained by any trainer.
- 25. D Define M Measure A Analyse I Improve C Control Trainers – Residuals vs Fitted Plots
- 26. D Define M Measure A Analyse I Improve C Control ANOVA - Hiring Source Null Hypothesis: There is no significant difference in average quality scores of employees hired from different sources Alternate Hypothesis: At least one group of employees’ quality scores are different Test result: As the p-value for t-test above is less than 0.05, we reject the null hypothesis of mean quality scores are equal for associates hired from different hiring sources and conclude at 95% confidence level that we have enough evidence of assuming that mean quality scores of at least one hiring source employees is different.
- 27. D Define M Measure A Analyse I Improve C Control Hiring Source Pairwise Probabilities Matrix Pairwise Probabilities Campus Drive Job Portal Referral Walk in Campus Drive 0.0000 0.0002 0.0058 Job Portal 0.1605 0.0039 Referral 0.2086 Walk in As the P-Values in pairwise probabilities matrix for campus drive are less than 0.05 against the other sources of hirings, we concluded that scores are poor for employees those who have been hired from campus drive. Similarly walk in.
- 28. D Define M Measure A Analyse I Improve C Control Correlation Test – Hold and Feedback Null Hypothesis: There is no correlation between average quality scores and average hold time Alternate Hypothesis: There is a correlation between average quality scores and average hold time data: Avg_Feedback_Break and QTY_Score t = 1.0884, df = 128, p-value = 0.2785 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.07770563 0.26360135 sample estimates: cor 0.09576174 data: Avg_Hold_Time and QTY_Score t = -1.3789, df = 128, p-value = 0.1703 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.28718967 0.05228902 sample estimates: cor -0.1209864 Test result: As the p-value for t-test above is greater than 0.05, we fail to reject the null hypothesis and conclude at 95% confidence level that we have enough evidence of assuming that there is no correlation between average quality scores and average hold time. Test result: As the p-value for t-test above is greater than 0.05, we fail to reject the null hypothesis and conclude at 95% confidence level that we have enough evidence of assuming that There is no correlation between average quality scores and average feedback breaks. Null Hypothesis: There is no correlation between average quality scores and average feedback breaks Alternate Hypothesis: There is a correlation between average quality scores and average feedback breaks
- 29. D Define M Measure A Analyse I Improve C Control Correlation Test – Age and Tenurity Null Hypothesis: There is no correlation between average quality scores and age of the associates Alternate Hypothesis: There is a correlation between average quality scores and age of the associates Null Hypothesis: There is no correlation between average quality scores and tenurity of the associates Alternate Hypothesis: There is a correlation between average quality scores and tenurity of the associates Test result: As the p-value for t-test above is less than 0.05, we reject the null hypothesis that there is correlation between quality scores and age of the associates and conclude at 95% confidence level that we have evidence of assuming that there is a moderate correlation between average quality scores and age of the associates. Test result: As the p-value for t-test above is less than 0.05, we reject the null hypothesis that there is correlation between quality scores and tenurity of the associates and conclude at 95% confidence level that we have enough evidence of assuming that there is a correlation between average quality scores and tenurity of the associates.
- 30. D Define M Measure A Analyse I Improve C Control Linear Regression - Age Null Hypothesis: There is no significant prediction of quality scores through age of the associates Alternate Hypothesis: There is a significant prediction of quality scores through age of the associates Result: Since the R sq value is 13%, associates age predict only 13% variation in quality scores. We can not consider the model as R sq value is below 50% and conclude at 95% confidence interval that there can not be a significant prediction of quality scores through age of the associates. RegressionModel:QTY_Score = (0.739839) + (0.005364)*Age
- 31. D Define M Measure A Analyse I Improve C Control Regression Null Hypothesis: There is no significant prediction of quality scores through tenurity of the associates Alternate Hypothesis: There is a significant prediction of quality scores through tenurity of the associates Regression Model: QTY_Score = (0.822761) + (0.000262) * AssociatesTenurity Result: Since the R sq value is 51%, associates tenurity predict 51% variation in quality scores. We can consider the model as R sq value is above 50% and conclude at 95% confidence interval that there can be a significant prediction of quality scores through tenurity of the associates.
- 33. D Define M Measure A Analyse I Improve C Control Pareto Analysis – Top Call Drivers 1. As shown in above Pareto graph 80% of the defects on the calls are due to 20% of the parameters available in quality sheet. 2. Majorly these defects are on soft skills parts only 3. No major process knowledge gap identified
- 34. Non-Parametric Testing 0.980.840.700.560.420.280.140.00 Median Mean 0.8900.8850.8800.8750.870 1st Q uartile 0.85420 Median 0.88540 3rd Q uartile 0.91670 Maximum 1.00000 0.86917 0.87706 0.88540 0.89000 0.10866 0.11424 A -Squared 352.84 P-V alue < 0.005 Mean 0.87311 StDev 0.11138 V ariance 0.01241 Skewness -6.2990 Kurtosis 46.5711 N 3064 Minimum 0.00000 A nderson-Darling Normality Test 95% C onfidence Interv al for Mean 95% C onfidence Interv al for Median 95% C onfidence Interv al for StDev 95% Confidence Intervals Summary for AQS 1.41.21.00.80.60.40.20.0 99.99 99 95 80 50 20 5 1 0.01 AQS Percent Mean 0.8731 StDev 0.1114 N 3064 AD 352.844 P-Value <0.005 Probability Plot of AQS Normal - 95% CI Null Hypothesis: The agent wise quality score follows a normal distribution Alternate Hypothesis: The agent wise quality score does not follow a normal distribution Result: As the p-value in both the test (Shapiro-Wilk normality test & Anderson-Darling normality test) is negligible & less than 0.05, we reject the null hypothesis of data follows a normal distribution and conclude with 95% confidence level the we have enough evidence of assuming non normality of the data.
- 35. D Define M Measure A Analyse I Improve C Control Kruskal-Wallis rank sum test – Team Leader wise QTY scores Pairwise comparisons using Wilcoxon rank sum test Null hypothesis: There is no difference in team leader wise quality scores’ distributions Alternate hypothesis: At least one team leader wise quality scores’ distribution is different Result: reject the null hypothesis
- 36. D Define M Measure A Analyse I Improve C Control Wilcoxon rank sum test – PT/FT Null hypothesis: Median of quality scores for part timers and full timers are equal Alternate hypothesis: Median of quality score of part timers is lesser than that of full timers Result: As the p-value is less than 0.05, we reject the null hypothesis that median of quality scores for part timers and full timers are equal and conclude with 95% confidence level that we have enough evidence of assuming median of quality score of part timers is lesser than that of full timers.
- 37. D Define M Measure A Analyse I Improve C Control Kruskal-Wallis rank sum test – PT/FT Null hypothesis: There is no difference in quality scores’ distributions of different categories of calls Alternate hypothesis: At least one category of calls has different quality scores’ distribution Result: As the p-value is negligible & less than 0.05, we reject the null hypothesis of different categories call quality scores’ distributions are indifferent and conclude with 95% confidence level that we have enough evidence of assuming at least one call category quality scores’ distribution is different. As shown in box plot the QTY scores for complaint category of calls are pretty poor
- 38. D Define M Measure A Analyse I Improve C Control Kruskal-Wallis rank sum test – Different Shifts Null hypothesis: There is no difference in quality scores’ distributions of different shifts Alternate hypothesis: At least one calls audited for one shift has different quality scores’ distribution Result: As the p-value for Kruskal-Wallis test is negligible & less than 0.05, we reject the null hypothesis of call quality scores’ distributions in all shifts are indifferent and conclude with 95% confidence level that we have enough evidence of assuming at least one shifts’ quality score distribution is different. Distribution of 19:30-23:30 shift quality scores is different than others.
- 39. D Define M Measure A Analyse I Improve C Control Wilcoxon rank sum test – Peak vs Non-Peak Null Hypothesis: Median of quality scores are equal in peak and non peak hours Alternate Hypothesis: Median of quality score in peak hours is lesser than in non peak hours Result: As the p-value is negligible & less than 0.05, we reject the null hypothesis that median of quality scores are equal in peak and non peak hours and conclude with 95% confidence level that we have enough evidence of assuming peak hours quality scores are less than non peak hours quality scores.
- 40. D Define M Measure A Analyse I Improve C Control Wilcoxon rank sum test – Call Length Null hypothesis: Median of quality score for long calls is less than or equal of median of short calls Alternate hypothesis: Median of quality score for short calls is greater than of long calls Result: As the p-value is 1, we fail to reject the null hypothesis that Median of quality score for long calls is less than or equal of median of short calls and conclude with 95% confidence level that we have enough evidence of assuming that long calls quality scores are less than short calls.
- 41. D Define M Measure A Analyse I Improve C Control Wilcoxon rank sum test – Overtime Null Hypothesis: Median of quality scores in overtime hours is less than or equal to non-overtime hours Alternate Hypothesis: Median of quality scores in overtime hours is greater than to non-overtime hours Result: As the p-value is 1, we fail to reject the null hypothesis that Median of quality score for overtime hours is less than or equal of median of non-overtime hours and conclude with 95% confidence level that we have enough evidence of assuming that overtime hours quality scores are less than non-overtime hours quality scores.
- 42. D Define M Measure A Analyse I Improve C Control 1. Quality scores of part timers are lesser than that of full timers and they are contributing more on lower side of quality scores 2. Complaint category quality scores are extremely poor and demands immediate attention 3. As mentioned in point no 2 part timer who majorly come in 19:30-23:30 shift are impacting the quality scores severely 4. Quality scores improves for next call if feedback is given for previous call within 24 hours of call audit 5. Peak hours which are generally observed between 06:00 PM to 10:30 PM are having greater impact on lesser side of quality scores 6. During the peak hours 08-09 PM & 09-10 PM are the major hit area and an strategy must be formulated for these hours 7. Wednesday quality scores distribution is different than others. 8. Associates are unable to score good on long calls and these calls are accounting for lower quality scores 9. Associate are not active, energetic or serious during their overtime hours and scoring poor in quality scores during overtime hours 10. FCR calls quality score are higher than non FCR, which clearly shares a message that if the query of a customer is not resolved in first go it becomes an unpleasant experience for guest and tough for agents to handle such calls 11. TL Sidhartha Kapoor’s team has been scoring poor in quality scores 12. Quality scores of female employees are higher than the male employees 13. Quality scores of experienced employees are higher than the fresher employees 14. 80% of the call defects are due to lack of the soft skills of associates 15. Quality scores of graduate employees are greater than under graduate employees 16. Associate tenurity can be modeled to predict the quality scores, higher the tenurity, higher the quality scores CONCLUSIONS
- 43. D Define M Measure A Analyse I Improve C Control 1. Employees who are having experience from same industry are scoring good in terms of quality 1. Refresher training for fresher employees every week 2. Fresher Employees are scoring poor quality scores 2. Stretching training days for fresher employees 3. Fresher Employees are adding more fatals to the calls 3. Conducting briefings on top call drivers 4. Sharing feedback on aily basis so that the employees can work on defaulting areas 1.Female employees are scoring good quality scores 1. Conducting refresher training for male associates 2. Male employees are scoring poor quality scores 2. One-o-one session with the employess so that they can be motivated toward their work 3. Call listening sessions on floor so that best practice can be followed across. 1. Quality Scores vary team leader wise 2. Atleast one team leader has low quality scores CallType 1. Poor quality scores in complaint calls 1. Team Leaders will share feedback on real time basis 2. Daily pre/post briefings will be conducted by Team leaders 2. Hiring less number of part timers 1.Agents would be coming to office 15 mins prior to their shifts starts time so that they can be given briefing on the basis of Day-1's observations 2. Refreshers and complaint handling session to be conducted for defaulter 1. Part timers are having low quality scores in comparison to full timers PT/FT 1. It is observed that part timers attend less number of refresher and other sessions so the number of sessions to be increased Gender ExperiencedEmployeesvs FresherEmployees 1.More no. of female employees to be employed 1. Hiring Experienced candidates so that the good quality can be ensured Defaulting Areas Factors Corrective Preventive TeamLeaders RECOMMENDATION
- 44. D Define M Measure A Analyse I Improve C Control 1. Short calls are having good quality scores 2. Long calls are having poor quality scores Overtime 1. Employees who are doing overtime are having poor quality scores HiringSource 1. Associate hired from campus drive are scoring poor in quality 1. Associates who are scoring poor consistently are to be indentified and must be issued a perforance improvement plan 1. More number of employees are to be hired from Job portal and employee referral. Tenurity 1. As the agent is tenured in he system his QTY scores improves 1. Management to work on 0-90 days tenurity bucket in term of attrition WeekDay 1. Wednesday having special offers which increases the demand and stores lack on scalability which turns customers unhappy and contribute in lower quality scores 1. To push client to align more resources to store 2. To align 100% manpower at call centre Call Length 1. Team of knowledge holders will be prepared for handling calls efficiently 2. Arranging call back for the customers 1. Effcetive manpower/batch palnning to be done DifferentShifts 1. Associates in shift 19:30-22:30 are scoring poor quality score 1. Refresher training for employees 2. Conducting call play sessions for the associates 3. Conducting pre shift briefings to motivate the employees Defaulting Areas Factors Corrective Preventive RECOMMENDATION
Editor's Notes
- This data includes the critical errors/defects which are rated 0%
- This data does not include the critical errors/defects which are rated 0%