Advanced Innovation Group | Improvement in Quality Score
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Transcript of Advanced Innovation Group | Improvement in Quality Score
The Voice of the Customer - VOC
2
Customer Comments Critical to Quality-CTQ’s
ABC Bank was formed in 1969 through the merger of two separate banks, the AB of British South Africa and the TT of India, Australia and China.
Listed on the London, Hong Kong and Mumbai stock exchanges, and rank among the top 20 companies in the
FTSE-100 by market capitalization.
Voice Of Head Of Operations North US Banking :
There has been a significant dip in QC scores for voice associates.This would have possible impact on overall customer satisfaction and related SLAs.
The voice QC score of an associate should be > 85%
Project Charter
Business case:ABC Bank is Listed on the London, Hong Kong and Mumbai stock exchanges and ranked among the top 20 companies in the FTSE-100 by market capitalization.Currently the Contact Center caters to the Customers Of the bank for Debit Card and related services.
Team
VP – Shishir (Cust Service Operations) Manager Ops – Pradeep BB – Naveen G.B & SME – CS-Ops Team (Ram) Quality Coach – Jerry Team Lead – Tom
Problem StatementThe overall Voice QC scores for Cust_ service operations from (Jan-Mar’12) has been 64% vs. required of 85% .More than 75% of the population of associates, scores have been below the required QC score benchmark of 85%.
Goal Statement
To achieve Voice QC scores for Cust_ service operations to 87% per month by end of Aug,12 and sustain .
In Scope :
•Voice QC scores for Cust_ service operations •QC team (Voice)
Out Scope :• Non Voice Team•Admin Team•Client Support Team (Data ,Admin, Tech support)
Milestones Target Date Actual date
D 17-Sep-12 M 26-Sep-12 A 17-Oct-12 I 26-Nov-12 C 17-Dec-12
Critical To Quality Tree
Improvement in Call QC scores / month for ABCBank Contact Center Operations
Improvement in Call QC scores / month for ABCBank Contact Center Operations
CTQs
Product KnowledgeProduct Knowledge
EffectiveTraining and CoachingEffectiveTraining and Coaching
Number of calls handled(Call Volume) /associateNumber of calls handled(Call Volume) /associate
Technology & IT( Headsets ,networks , PCs & Other applications/ softwares
Technology & IT( Headsets ,networks , PCs & Other applications/ softwares
Tenure of associatesTenure of associates
SIPOC
•ABC (Bank Cards)
•Card Holders (Customers) of XYZ Bank
• Resolve Callers
(Customers) query.
•Update Customer’s account
with current status .
•Update notes on Customer’s
accounts.
•Customer accounts getting
routed to appropriate
unit/department for further
resolution.
•Update required info on
Account # on which contact
established
•Update notes on Account
•Route Account as per
procedure (SOP)
•Ask for purpose of call
•Ask details of query
•Attempt to provide on call
solution to caller as per
guidelines (SOP)
Contact withCustomer3rdparty,vendorCustomer’s representative
•Call received-Inbound /
•Call made – Outbound
•Verify details of Party on
phone
•ASSOCIATE PHONE & SYSTEM
LOGIN
•ASSOCIATE READY FOR
INBOUND & OUTBOUND CALLS
•Customer Accounts :
Call / Query
• IT & Technology Support
• ABCBank (Bank Cards) – Contact Center operations team members
•Customer Accounts volume from ABCBank (Bank Cards) strategies team.
ABC Bank (Bank Cards) – Customer Service Delivery Team
•Call recording Tool –
Captures (Voice & Screen) of
sample calls daily.
Data Collection Plan What To Measure ?
mMEASURE
Project Y / KPI Operational Definition Defect Def Performance StdSpecification Limit
OpportunityLSL USL
Improvement of Call QC score for ABC Bank-Contact Center
ABC Bank-Contact Center , caters to all incoming customer queries on Toll Free # and provides resolution to Customer queries. Accounts are updated with appropriate notes and information.
Call Score %age is less than 85%
Voice QC scores Greater than OR
equal to 85%85% 100%
•Calls handled by Contact
Center
Data Collection Plan How To Measure ?
mMEASURE
KPI Data Type Data Items Needed
Formula to be used Unit
Plan to collect Data Plan to sample
What Database or Container will
be used to record this data?
Is this an existing
database or new?
If new, When will the
database be ready for use?
When is the planned start date for data collection?
Improvement of Call QC score for ABCBank-Contact Center
Discrete Data
QC scores
•Team Wise•Associate Wise•Week Wise
% Call QC score =Sum of Weight age of call
parameters / 100
%age QC score
•Call QC database
maintained by QC team
Existing N/A 4-July-12 All Call QC performed :
Validation Measurement System throughEffectiveness & Efficiency
mMEASURE
Effectiveness
Efficiency
Volume
Opportunities (Samples Taken) 30
Errors 4
Result 86.67%86.67%
Volume
Opportunities (Samples Taken) 30
Errors 5
Result 83.33%83.33%
Efficiency & Effectiveness is successful, hence the data (measurement system) is all right for further analysisConsent has been received on above Measurement System - benchmarks to be treated as effective from Business Leaders.
KPI Data Type
QC Score %age Discrete
Microsoft Office Excel 97-2003 Worksheet
Data Sheet
Current Capability - Process Sigma Level mMEASURE
Since (Y) Quality Score is Discrete Data Type hence DPMO method DPMO method is used to calculate current sigma Value of Process.
(Y) Data Type : Discrete
# Of Opportunities 181
Pass 39
Fail 142
DPO 0.784530387
DPMO 784530.387
Current Sigma Level Of Process is : Current Sigma Level Of Process is : 0.71 0.71 δδ
Stability Check Of Process – Run Chart
180160140120100806040201
100
90
80
70
60
50
40
30
20
Observation
Call Qualit
y (In
%)
Number of runs about median: 81Expected number of runs: 91.5Longest run about median: 8Approx P-Value for Clustering: 0.059Approx P-Value for Mixtures: 0.941
Number of runs up or down: 120Expected number of runs: 120.3Longest run up or down: 3Approx P-Value for Trends: 0.476Approx P-Value for Oscillation: 0.524
Run Chart of Call Quality (In % )
Since P-value is more than 0.05 for all four behaviors hence the is data is considered to be stable.
Normality Test
Normality: P value >
Shape: Non-Normal or Normal
Measure of central tendency :data is normal measure of central tendency will be Mean / Median
Aim: The project shall focus on shifting the central tendency and reduction in variation.
mMEASURE
Null and alternate hypothesisHO – Data is normalHA – Data is non normal
Normality: P value is 0.005 , hence data is non-normal
Measure of central tendency :SInce Data is non-normal measure of central tendency will be Median.
OBSERVATION
Graphical SummaryGraphical Summary to check Centering and Variation. Path : Stats > Basic Stats > Graphical SummaryIf target is met then problem with VariationIf target is not met then problem with CenteringSince P-value < 0.05 data is not normal hence we consider MEDIAN Target: 85% , Median: 64% (Target is not met), i.e.: Problem is for centering
9075604530
Median
Mean
72706866646260
1st Quartile 46.500Median 64.0003rd Quartile 83.000Maximum 100.000
61.170 67.317
59.838 71.000
18.996 23.368
A-Squared 2.08P-Value < 0.005
Mean 64.243StDev 20.955Variance 439.107Skewness -0.04708Kurtosis -1.17315N 181
Minimum 25.000
Anderson-Darling Normality Test
95% Confidence I nterval for Mean
95% Confidence I nterval for Median
95% Confidence I nterval for StDev95% Confidence I ntervals
Summary for Call Quality (In % )
Organize Potential Cause – CE or Fish Bone Diagram
Factors identified through brainstorming
aAnalyze
scoresQuality
Environment Methods
Personnel
Qualification
Trainer
Team Leader
Experience Type
Tenure
Age
Gender
Process Complexity
Process Knowledge
Location
Shift
Cause-and-Effect Diagram
Potential Xs and hypothesis Tests
# Potential Xs Description Data Type Test Reason
1 Age Age of FTEsContinuou
s BLR
2 GenderGender sub-grouped into male &
female DiscreteChi Square - Cross
TabulationSince Y is Categorical, X is
Categorical
3 ShiftShifts sub-grouped into morning ,
evening & night DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
4 Process Knowledge process knowledge scores of FTE's DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
5 Tenure Tenure of FTE's in years Continuous BLR
6 Experience Type Domain experience DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
7 Team Leader All TL's in the project DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
8 Trainer All Trainers in the project DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
9 Location Location of Service Delivery DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
10 Process Complexity Process complexity for DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
11 QualificationEducational and Professional
Qualification of FTEs DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
Sl. No Potential Xs Description Data Type Test Reason
1 Age Age of FTEs Continuous BLR
Result / Observation
P Value 0.598; p_value >0.05 hence Ho(Null Hyp) which means there is No significant impact of Age of FTEs on Y (Qc Scores)
Sl. No Potential Xs Description
Data Type Test Reason Result / Observation
2 GenderGender sub-grouped into male &
female DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X
is Categorical
Tabulated statistics: SLA Met Status, Gender
Rows: SLA Met Status Columns: Gender
F M All
Fail 63 79 142Pass 16 23 39All 79 102 181
Cell Contents: Count
Pearson Chi-Square = 0.139, DF = 1, P-Value = 0.709Likelihood Ratio Chi-Square = 0.139, DF = 1, P-Value = 0.709
Would it be correct to refer Pearson Value and infer Correlation between X and Y in terms of impact ,Magnitude of impact, Regression Equation ?
Tabulated statistics: SLA Met Status, Gender
Rows: SLA Met Status Columns: Gender
F M All
Fail 63 79 142Pass 16 23 39All 79 102 181
Cell Contents: Count
Pearson Chi-Square = 0.139, DF = 1, P-Value = 0.709Likelihood Ratio Chi-Square = 0.139, DF = 1, P-Value = 0.709
Would it be correct to refer Pearson Value and infer Correlation between X and Y in terms of impact ,Magnitude of impact, Regression Equation ?
Result / ObservationChi Square Cross Tabulation Checks Expected Vs Observed Values.
P_value is 0.7.09,which is > 0.05. Hence Ho(Null hypo) which means No significant difference between expected and observed values.
17
Contribution to Chi-square
Pearson Chi-Square = 0.585, DF = 2, P-Value = 0.746Likelihood Ratio Chi-Square = 0.579, DF = 2, P-Value = 0.749
P>0.05 (0.746) i.e. Ho(null hypothesis)
No significant difference between expected vs observed
Sl. NoPotential
Xs DescriptionData Type Test Reason Result / Observation
3 ShiftShifts sub-grouped into morning ,
evening & night DiscreteChi Square -
Cross TabulationSince Y is Catergorical,
X is Categorical
p_value is 0.746 which states p_value > 0.05. Hence Ho(Null Hypo) which means No significant difference between expected and observed values.
18
Contribution to Chi-square
Pearson Chi-Square = 6.666, DF = 1, P-Value = 0.010Likelihood Ratio Chi-Square = 6.688, DF = 1, P-Value = 0.010
P value,0.05, ha(alternate hyp)Significant difference between expected vs observed value
Sl. No Potential Xs Description Data Type Test Reason Result / Observation
4Process
Knowledge process knowledge scores of FTE's Discrete
Chi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
P value,o.o5, ha(alternate hyp)Hence significant difference between expected vs observed value
Result / Observation
BLR P_value is 0.001 which is < 0.05. Hence Ha(alternate hypo) which means Significant impact of X(Tenure) on Y (Quality)
5 Tenure Tenure of FTE's in years Continuous BLR
Binary Logistic Regression: SLA Met Status versus Tenure
Link Function: Logit
Response Information
Variable Value CountSLA Met Status Pass 39 (Event) Fail 142 Total 181
Logistic Regression Table
Odds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant -1.03871 0.315958 -3.29 0.001Tenure -0.0782532 0.0826167 -0.95 0.344 0.92 0.79 1.09
Result / ObservationChi Square Cross Tabulation Checks Expected Vs Observed Values.
P_value is 0.457, which is >0.05. Hence Ho(Null hypo) which means No significant difference between expected and observed values.
Contribution to Chi-square
Pearson Chi-Square = 2.604, DF = 3, P-Value = 0.457
Likelihood Ratio Chi-Square = 2.788, DF = 3, P-Value = 0.425
6 Experience Type Domain experience DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
21
7 Team Leader All TL's in the project DiscreteChi Square -
Cross TabulationSince Y is Catergorical, X
is Categorical
Pearson Chi-Square = 2.869, DF = 4, P-Value = 0.580Likelihood Ratio Chi-Square = 3.077, DF = 4, P-Value = 0.545
Since p_value is 0.580 which is > 0.05, hence (Ho) Null Hypo .
There is no significant difference between expected Vs. observed
22
Contribution to Chi-square
Pearson Chi-Square = 0.585, DF = 3, P-Value = 0.900Likelihood Ratio Chi-Square = 0.581, DF = 3, P-Value = 0.901
P_value is > 0.05 i.e. (0.900) stating Ho(Null hypo). Hence there is no significant difference between expected Vs. observed
Sl. No Potential Xs Description Data Type Test Reason Result / Observation
8 Trainer All Trainers's in the project DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorial
P_value is 0.001 which is < 0.05. Hence Ha(alternate hypo) which means Significant impact of X(Tenure) on Y (Quality)
23
Contribution to Chi-square
Pearson Chi-Square = 1.453, DF = 1, P-Value = 0.228Likelihood Ratio Chi-Square = 1.435, DF = 1, P-Value = 0.231
P-Value = 0.228 which is > 0.05, which means Ho(Null hypo). Hence there is no significant difference between expected vs observed values
Sl. No Potential Xs Description Data Type Test Reason Result / Observation
9 Location Location of Service Delivery Discrete Chi Square - Cross TabulationSince Y is Catergorical, X is
Categorical
P-Value = 0.228 which is > 0.05, which means Ho(Null hypo). Hence there is no significant difference between expected vs observed values
24
Contribution to Chi-square
Pearson Chi-Square = 0.265, DF = 1, P-Value = 0.606Likelihood Ratio Chi-Square = 0.264, DF = 1, P-Value = 0.607
P-value > 0.05 , which is (0.606) stating Ho (Null Hypo)Hence there is no significant difference between expected vs. observed values
10 Process Complexity Process complexity for Discrete Chi Square - Cross Tabulation Since Y is Catergorical, X is Categorial
P-value > 0.05 , which is (0.606) stating Ho (Null Hypo)Hence there is no significant difference between expected vs. observed values
25
11 QualificationEducational and Professional
Qualification of FTEs DiscreteChi Square - Cross
TabulationSince Y is Catergorical, X is
Categorical
Contribution to Chi-square
Pearson Chi-Square = 3.380, DF = 2, P-Value = 0.184Likelihood Ratio Chi-Square = 3.173, DF = 2, P-Value = 0.205
P-value > 0.05, states Null hypo (Ho).Hence there is no significant difference between expected vs actual values
26
Sl. No Potential Xs Data Type Test P-Value Impact
1 Age Continuous BLR 0.598 No
2 Gender Discrete Chi Square - Cross Tabulation 0.709 No
3 Shift Discrete Chi Square - Cross Tabulation 0.746 No
4 Process Knowledge Discrete Chi Square - Cross Tabulation 0.01 Yes
5 Tenure Continuous BLR 0.001 Yes
6 Experience Type Discrete Chi Square - Cross Tabulation 0.457 No
7 Team Leader Discrete Chi Square - Cross Tabulation 0.58 No
8 Trainer Discrete Chi Square - Cross Tabulation 0.9 No
9 Location Discrete Chi Square - Cross Tabulation 0.228 No
10 Process Complexity Discrete Chi Square - Cross Tabulation 0.606 No
11 Qualification Discrete Chi Square - Cross Tabulation 0.184 No
Out of the POTENTIAL X’s listed and Hypothesis tests performed ,
VITAL X’s can be determined as Process Knowledge & Tenure
[ VITAL X’s ] OBTAINED FROM HYPOTHESIS TESTS PERFORMED[ VITAL X’s ] OBTAINED FROM HYPOTHESIS TESTS PERFORMED
27
PRIORITIZING VITAL X’s PRIORITIZING VITAL X’s
COST
LOWLOW MEDIUMMEDIUM HIGHHIGH
IMPACT
LOW Fitness Levels
MEDIUM
•Tenure•Process Knowledge
Morale
HIGH
Vital X’s are Process Knowledge & Tenure, basis brain storming 2 more Vital X’s have been added which could not have been Quantified : (Fitness Levels, Morale)In order to prioritize Vital X’s factors considered are (IMPACT & COST)
CCOSTOST
IIMPACTMPACT
TTIMEIME
EEFFORTFFORT
28
QFD >> Quality Function Deployment / Screening SolutionsScreening Solutions
QFD will help prioritizing the CTQs to perform appropriate actions / activities.