Data Analytics
TMI Group
Building blocks
Case studies
1. Advantage of disaggregation
2. A Case Study: Retention vs.
Productivity
3. Impact of Training
4. Predictability of performance
Coverage
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3
Integrated supply chain for salesforce
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Focus Practices
Value-Added Staffing
• Many employment arrangements
• Visible performance tracking & sustenance
Large-scale Hiring & RPO
• Pan-India, Consortium-based
• Just-in Time
• Manning solutions with TATs
Learning Sciences
• Blended Learning, Assessments & HR Technology
• LaaS – Learning as a Service
Thick-Data based HR Analytics
• Partner Business & HR to answer Higher order Qs
• RAG-tagging performers
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Value-adding partner of Business & HR
Treasure Trove of Data
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500,000+ Connects/year
Recruitment Conversations
Onboarding conversations
30, 60, 90 day calling
Performance Data
Attrition DataTraining and
Learning Conversations
Anecdotal Data
Unstructured Data
Structured Data
Data Approach
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Building Blocks of TMI Data Analytics
Data Driven
Counter-
Intuitive
Hypotheses
Data
Disaggregation
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Analytics &
Testing of
Hypotheses
Data
Gathering
Data
Curation
Removal
of Outliers
Visualization
Individual
Productivity
Life Cycle
Advantages of Disaggregation – One Dimensional Study
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Aggregate Performance is showing a narrow growth with
stagnation (N=585)
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Aggregate performance has been an intuitive way to understand performance till now
Let’s drill down further
Aggregate Performance after bucket segregation (N=585)
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When we disaggregate into buckets it looks like everyone is performing at a steady level in each bucket
Let’s drill down further
Individual Performance is Chaotic with Multiple Peaks and Troughs
(N=585)Employees have good and bad months.
No one has steady performance. Everyone is zigzagging. Even low achievers in some cases cross the average.
Hence assumption that sharp reduction in monthly productivity is a characteristic of non-performers is invalid
Is this zigzagging valid for high performers?
Let’s drill down TMI Analytics division
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Super Achievers performance is not Uniform (N=52)
The graph shows the performance of employees who have on average delivered more than 140 % of target.
Zigzagging performance continues even for super achievers.
Interestingly they Zigzag above 100% above target
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Note: Super Achievers were selected using ‘’Slope calculation’’ where in All the employees who have achieved 100+ in M3 & M4. Slope is calculated based on average of all employees Month on Month post training (First month and the sixth month).
Non Achievers performance is not Uniform (N= 35)
The graph shows the performance of employees who have on average delivered (M2 and M3) less than 20% of target.Immediately after training (M1), performance improved significantly
Zigzagging performance continues even for non-performers.
Interestingly they Zigzag to maximum of above 100% of the target as well
Hence assumption that sharp reduction in monthly productivity is a characteristic of non-performers is invalid
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Note: Super Achievers were selected using ‘’Slope calculation’’ where in All the employees who have achieved 100+ in M3 & M4. Slope is calculated based on average of all employees Month on Month post training (First month and the sixth month).
Objective
Synopsis
Problem Definition
Solution
Return on Investment (ROI)
Training Case Study
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Objective
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To develop a methodology for calculating Return on Investment (ROI)To demonstrate the methodology using a live case study
Synopsis
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Financial services company with pan India Salesforce selling asset products
High Early Attrition and Low Productivity in the sales force
TMI conducted a study and found the root causes
Designed and implemented an induction training for all three levels in the sales channel
TMI measured the feet on street (FOS) pre- and post- training performance and attrition
ROI for the training investment was calculated to be over 300%
Problem Statement
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How can we turn around the performance of our Feet on Street Sales (FOS) team who are attriting early and have low productivity?
How can we calculate the Return on Investment (ROI) on Training conducted for the FOS sales team?
Solution
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TMI did a study to identify the root causes:
Poor role clarity; Insufficient product knowledge; and, lack of functional skills
TMI designed an induction training module for the Relationship Officers (RO), Team Leaders (TL), and Sales Managers
Induction Training Components: demystifying the role (individuals who want to leave early, go); Identify recurring key activities with highest impact on their productivity; Identify and prepare a curriculum based on the best performer behaviours in the system
Training measurement: Collect and compare pre and post training performance data from the company; fine-tune on the basis of FOS performance; Calculate ROI based on post-training productivity
Note: The employees who have been trained is over past two yearsA select cohort of 585 executives from personal loan department: measured pre- and post- training performance
Trained FOS over 2 years (2017-19)
High Achievement :
Longest StayHigh Achievement :
Lowest Stay
Low Achievement :
Longest Stay
Low Achievement :
Lowest Stay
A Select cohort showing the concern pre-training: Individual Productivity &
Residency pre-Training (N=585)
Q1 = 92
Q2 = 80
Q3 = 148
Q5 =29
Q4 = 211
Q6 = 25
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1. Average productivity is at 60%
2. Early attrition was very high at 45%
3. Those who stayed and performed
(Q1) was only 16%
Same Cohort post-training: Individual Productivity & Residency
Post-training (N=585)1. Average productivity has gone up from
60% to 82%
2. Q1 (High performance and residency)
quadrant has doubled from 92 to 181
3. Key concern areas: Q3 (High retention
of non-performers) and Q6 (Early
attrition of non-performers)
4. Q1 can be maximized & Q3 can be
minimized if we study the employees’
attributes of Personality + Work in
these quadrants and used for:
1. Recruiting Right
2. Predictive RAG tag post training
assessment
3. It will help us build a predictive
model
Cohort Average
100% Target
Q1 = 181
Q2 = 46
Q3 = 159
Q4 = 17
Q5 = 9
Q6= 173
High Achievement :
Longest Stay High Achievement : Lowest Stay
Low Achievement : Lowest
Stay
Low Achievement :
Longest Stay
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26%
39%43% 43% 41% 38%
46%
65%
74%
81%
97%99%
103%
114%120% 123%
108%
87%
0%
20%
40%
60%
80%
100%
120%
140%
M-6
M-5
M-4
M-3
M-2
M-1
M0
M+
1
M+
2
M+
3
M+
4
M+
5
M+
6
M+
7
M+
8
M+
9
M+
10
M+
11
Average Monthly Productivity %
Training Month for the cohort
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Before and After – Average Monthly Productivity of the entire
Trainee population (N=15602)
1. P1: Weighted Average performance of the
trainee population before training = 35.64%
2. P2: Weighted Average performance of the
trainee population post training = 90.22%
3. P3: Peak Productivity in a month before
training = 43%
P3: Peak performance pre-training
P1: Weighted Avg. Performance pre-training = 35.64%
P2: Weighted Avg. Performance post-training = 90.22%
X = Month
Y = Productivity %
Productivity post training is significantly
higher than Productivity pre training
ROI on Training Calculation
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• Measure weighted average pre-training productivity (P1)STEP 1
• Measure weighted average post-training productivity (P2)STEP 2
• Measure peak pre-training productivity in a month (P3)STEP 3• Calculate productivity increase assuming 100% productivity =
Employee SalarySTEP 4• Case 1 Training Benefit: [{((P2-P1)/100)* (Assumed Monthly
Salary) * (Avg. Retention in months post training)} –11000]/11000 = 411.93%
STEP 5 (a)• Case 2 Training Benefit: [{((P2-P3)/100)* (Assumed Monthly
Salary) * (Avg. Retention in months post training)} –11000]/11000 = 338.30%
STEP 5 (b)
Cohort Cohort #
Avg. Tenure in
months before
training
Avg. monthly
weighted
productivity (in %)
Pre Training (P1)
Avg. Tenure in
months Post training
Avg. monthly
weighted
productivity (in %)
Post Training (P2)
ROTI Case 1
(P1=cohort average)
ROTI Case 2
(P1=cohort peak in
graph)
Loan sales officers with tenure (0-6 months)
before attending onboarding Training15278 1.48 35.64% 5.16 90.22% 411.93% 338.30%
Predicting Performance
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High attrition and low
performance of BDEs
(Front Line Sales) is a
problem
Define what it takes to
be a successful BSM
and factors which
influence a successful
BSM
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Problem Statement – For a Banking major
BSMs (Front Line
Managers) are
responsible for
managing a group of
BDEs
No Variables which
define the
performance of BSM
BDE Attrition
Sales Dispersion
Alternate Model
Scored attrition:
Attrited Num/ BDE Under
BSM , modelled using number
of variables given in appendix
Sales Dispersion: Sales PM
Std/ Sales PM Mean –
Dispersion of Sales per month
among the BDEs reporting to
a BSM as a ratio of the
Average Sales per BDE
reporting to a BSM, modelled
using number of variables
given in annexure
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Approach
Factors that potentially create a Good BSM
1. Ability to manage Attrition
2. Generate consistent Sales
Based on these variables we created TMI ranking.
TMI Ranking derived by Equal Weightage to both variables
Q1 = 190 Q4= 30
Q3 = 150Q2 = 385
Note
The colour represents the
ranking of BSM and size
represents the total sales
Standard Deviation of Percentage Scored Attrition (Annual)
Avg
. M
on
thly
Sa
les D
isp
ers
ion
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Sales Dispersion vs. SD of Percentage Scored Attrition
(12 months) - New BSM Ranking Score
TMI
Conclusion
TMI and Internal Customer
Ranking are not aligned.
Which is the better ranking model?
Validating the new
approach
Check for the ability of the model to predict Sales
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Validation of New Approach
Days in Previous Employment
Branches in a Pin CodeBranch Category
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Factors influencing BSM
% Women
BSM Sales = Constant + X1 * Avg. Monthly BDE Sales Dispersion + X2 * SD of
% of scored attrition (12 months) + X3 * Tenure in Bank (in Months) + X4 *
Education + X5 * Age + X6 * Circle Ranking & Cluster Ranking (Client given data)
+ X7 * Branches in the Same Pin Code (RBI Data) + X8 * % of Women in the
branch + X9 * % of Trained (BDEs under the BSM) + X10 * Branch Potential
Category (Client categorization of branches)
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Regression Equation
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Regression Equation
PARAMETER DEFINITION
BSM SalesTotal sales per month for each BSM (sum of average sales per month of all BDEs under a BSM)
Monthly BDE Sales DispersionDispersion of Sales per month among the BDEs reporting to a BSM as a ratio of the Average Sales per BDE reporting to a BSM
SD of % Scored Attrition SD of % of Attired Num/ BDE Under BSM
Tenure in Bank Tenure of BSM in bank
Education Education Qualification of the BSM (discreet values)
Age Age of BSM
Circle Ranking Circle Rank – given by Client
Cluster Ranking Cluster Rank – given by Client
Branches in the Same Pin Code All Banks’ branches in the same Pin Code (RBI Data)
% Women Percentage of BDEs under a BSM who are Women
% Trained Percentage of BDEs under a BSM who are trained
Branch Potential Category Metropolitan, Urban, Semi-Urban, Rural, Rural-Unbanked (Client Category)
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Regression DetailsPARAMETER P-VALUE Coefficient of Correlation
Monthly BDE Sales Dispersion 0.000 -0.7133977
SD of % Scored Attrition 0.000 -3.190028
Tenure in Bank 0.000 0.002513
Education (Graduate) 0.175 -0.6893111
Education (Masters (Others)) 0.225 -0.6223869
Education (MBA & Other PG) 0.205 -0.64527
Age 0.036 -0.0126821
Circle Ranking 0.653 -0.0013211
Cluster Ranking 0.385 0.0003224
Branches in the Same Pin Code 0.680 -0.0001922
% Women 0.140 0.1707008
% Trained 0.185 -0.0995709
Branch Category (Metropolitan) 0.000 -0.4859517
Branch Category (Urban) 0.000 -0.5133281
Branch Category (Semi-Urban) 0.012 -0.3351569
44% of Std. Deviation of BSM Sales performance isexplained by:
Avg. Monthly BDE Sales Dispersion
SD of % of scored attrition
Branch Potential Category
Rest of the variables used in our model were insignificant
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Outcome
1. Using our model we check oneach branch type
2. Below we discuss thespecific action items that canbe taken for each branchtype
Model Factors
Metropolitan
UrbanSemi-Urban
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Does our model work for all branch categories?
Variables which are significantly impacting BSM
1. Sales Dispersion
Coefficient= -0.435 & P>|t| =0.057
2. Percentage of Scored attrition
Coefficient =-3.312 & P>|t| = 0.000
3. % Women
Coefficient = 0.457 & P>|t| = 0.046
Conclusions
1. Sales Dispersion and Percentage of Attrition are the major contributors to Sales Performance by the model
2. Gender of BSM, Women, in particular, in metropolitan branches have an impact in predicting sales
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Model – Metropolitan Branches – R squared – 38%
Variables which are significantly impacting BSM
1. Sales Dispersion
Coefficient= -0.583 & P>|t| =0.007
2. Percentage of Scored attrition
Coefficient = -3.989 & P>|t| = 0.000
3. Age
Coefficient = -0.022 & P>|t| = 0.089
Conclusions
1. Sales Dispersion and Percentage of Attrition are the major contributors to Sales Performance by the model
2. Hire younger BSMs in Urban Branches
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Model – Urban Branches – R squared – 55%
Variables which are significantly impacting BSM
1. Sales Dispersion
Coefficient= -0.987 & P>|t| =0.002
2. Percentage of Scored attrition
Coefficient =-3.482 & P>|t| = 0.000
Conclusions
1. Sales Dispersion and Percentage of Attrition are the major contributors to Sales Performance by the model
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Model – Semi Urban Branches – R squared – 57%
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Conclusions
• TMI Model predicts the standard deviation of BSM performance very well in Semi-urban and Urban branches
• It predicts reasonably well in Metropolitan branches• The ranking based on TMI model could yield better results in the long
term
Thank You
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For any queries about this presentation or HR data analytics, reach out to:Sanjay Suri, General Manager, TMI
[email protected]; 88610 03308