data analytics - tmi groupanalytics •partner business & hr to answer higher order qs...

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

TMI Analytics division Confidential & Proprietary

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Integrated supply chain for salesforce

15-10-20194

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

15-10-2019 5

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

Confidential & Proprietary12

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%


1. Sales Dispersion

Coefficient= -0.583 & P>|t| =0.007


Coefficient = -3.989 & P>|t| = 0.000

3. Age

Coefficient = -0.022 & P>|t| = 0.089

Conclusions


2. Hire younger BSMs in Urban Branches


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Model – Urban Branches – R squared – 55%


1. Sales Dispersion

Coefficient= -0.987 & P>|t| =0.002


Coefficient =-3.482 & P>|t| = 0.000

Conclusions



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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

mailto:[email protected]

data analytics - tmi groupanalytics •partner business & hr to answer higher order qs...

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