BizGrow Engine

M Seshu Vineeth(smatcha@deloitte.com)
P Chaanukya Babu(cbabu@deloitte.com)

1. http://www.sierra-cedar.com/wp-content/uploads/sites/12/2014/11/
Abstract
World’s greatest machination is an engine which is the heart of every evolution and revolution.
The soul of every engine is its equation.
In the current competitive environment an important aspect of growth is simply not an increased
headcount, rather it’s an increased market-relevant skilled headcount, moreover for a services
based organization employee salaries make up more than half operating expenses. Organizations
struggle to understand the potential of their existing workforce. It becomes complex to correlate
this to the organization’s strategy. Successful organizations adopt workforce analytics and have
8% higher sales growth, 24% higher net operating income growth, and 79% higher sales per
employee1.
The BizGrow engine is driven by single equation for Relative Organizational Growth (G):
The engine has the following modules employing mathematical techniques which generate the
necessary indices for the above equation:
Hiring Module: Hiring comprises experienced/fresher recruitment, this module computes the
Optimal Hiring Index (Hi) utilizing social media analytics (potential employee recognition etc),
text analytics (resume profiling etc).
Retention Module: This module computes the Quality Retention Index (Ri) using the survey
sentiment analysis and significant employee clustering for quality retention.
Project Delivery Module: This module computes the Relative Supply-Demand Equilibrium (SDEi)
and the Delivery Index (Di) using the planning information, project pipeline and utilization reports
as inputs.
In Essence, this engine calculates various indices to empower an organization to tackle each of
the business areas and also provides informed insights into the enhancement of each of them.

Why is evaluation of Growth important?
Profitability although the raison d’tere for a few firms, is an important goal for many organizations.
For a newly set up firm profitability can be a good index to measure the firm’s performance.
However, for seasoned organizations profitability alone is an ineffective measure. Growth
becomes a more accurate attribute to gauge the firm’s performance.
‘Growth’, apart from being one of the most lucid measures to gauge the success of a firm, is an
important indicator of the said firm’s survivability. The traditional definition of ‘Growth’ is high
profitability of a firm and an increased skilled workforce, however this definition has evolved over
time and in the current market-place, ‘Growth’ is sustainable profitability accompanied with
increased market-relevant skilled workforce along with optimized operational costs.
Although there are many abstract and definite aspects to ‘Growth’, they can be classified into two
broad categories:
a) Revenue Aspect:
1) Project Demand/Supply Equilibrium
2) Project Delivery
b) Workforce Aspect:
1) Quality Retention
2) Quality Hiring

Growth is often perceived abstract, so how does one even quantify growth? Consider the simple
scenario below which provides the basic details of a firm performance:
So how does one evaluate the performance of the firm over the three years? Can we conclude that
the firm’s growth is more in the period 2014-15 (owing only to the revenue generated)? Or is the
growth more in the period 2015-16 (owing to the revenue generated relative to the increase in the
workforce)? It would be incorrect to conclude just on basis of the available information as there
would many factors which would need to be considered before commenting on the firm’s relative
performance over the three years.
Each firm has its unique way of evaluating ‘Growth’, but wouldn’t it be an ideal scenario to design
a repeatable solution to evaluate the growth over a period of time? The answer would have to be
invariably YES! With leaps and bounds made in the field of data science and its fusion with
statistical/mathematical techniques, we now have many sophisticated instruments at our disposal
to envisage effective data solutions for various business requirements. As part of this paper, we
will detail our solution for the evaluation of the ‘Relative Growth’ of a services based organization
- BizGrow Engine.
Over the next pages we will demonstrate the development of our solution by using the latest
analytical procedures and mathematical techniques. A foreword, our intent was not to develop a
passive solution which would take certain inputs, perform calculations and simply give an output
index. Although our solution can do just that, however its true potential, as its name expounds, is
its utility to equip the end user with insights into relevant information from multitude sources and
make informed decisions to have better control over the growth trajectory. In essence, use the
BizGrow Engine periodically, act on the insights provided and see your business grow.
The BizGrow Engine
As previously mentioned BizGrow Engine is a comprehensive solution to evaluate the relative
growth of an organization, specifically it provides end user to evaluate the growth of the
organization relative to another period. This demands the question why relative organizational
growth? For which we would have a counter question, name just about any measure which has

absolute units, if you are quick to suggest time (seconds), mass (grams), distance (meters). Did
you know each of these units has its own definition? For E.g.: 1 Meter is the distance travelled by
light in 1/299,792,458th of a second, and 1 second would be the time elapsed between
9,192,631,770 cycles of the radiation produced by the transition between two levels of the
Caesium-133 atom. So when you actually say that you have walked a kilometer you have just
traversed 1000 times the distance which light travels in 1/299,792,458th of a second. Hence every
measure has a unit which serves the purpose of determining the quantity relative to another
quantity of the same measure. Hence it would only make sense to evaluate Growth in relative
terms. However, unlike the standard measures of time, distance, mass and so on Growth is
subjective to an organization under consideration. Hence it would be sensible to evaluate the
Growth of an organization in a period against Growth of the same organization in a previous
period. The tool imposes no limitation in terms of the duration of the period, the duration of the
period can be an year, a month, a week, even a day, if you would want to see how much your
organization has grown over the previous day. Hence, Relative Organizational Growth would be
the Growth of an organization across two different periods, it would be sensible to have both
periods of the same duration.
The BizGrow Engine is driven by a single equation:
The equation is comprised of four indices:
1. Optimal Hiring Index (Hi)
2. Quality Retention Index (Ri)
3. Relative Supply-Demand Equilibrium (SDEi)
4. Delivery Index (Di)
And each of these indices are computed from three individual modules of the BizGrow Engine,
each representative of the previously mentioned aspects of ‘Growth’. Below is the high-level
segregation of the Engine and its various modules:

The next pages will detail each of the modules, the theory behind the creation of each of them and
the calculation of the respective indices from each of these modules.

For each of the modules below would be the template of presentation:
1. The typical (most commonly followed) approach
2. A brief explanation of the incumbent approach and its shortcomings
3. The BizGrow Engine’s approach
4. A model of the module
5. The calculation of the module’s index
Hiring Module:
The name of the module needs no further disambiguation, hiring for an organization is akin to
inhalation of oxygen, a ceaseless function executed at regular intervals of time and necessary for
sustenance, it is invariably one of the founding pillars of firm’s human resource department and a
process with great ramifications to the overall survival of the organization both in short and long
run.
Typical Approach:
Almost every organization approaches hiring in two ways:
 Intrinsic Hiring: The firm invests its resources, personnel to conduct entire hiring procedure
including aptitude examination, resume profiling, and interview. The actual number of
rounds vary from firm to firm. Below are the salient instances of this hiring strategy:
o Campus Hiring, the firm seeks out prospective colleges and establishes MoU with
the respective institutions to hire the best-fit of fresh minds. Typically multiple
rounds are conducted before the actual interviews. Usually the firm invests heavily
on the initial examination, intermediate rounds, and personal interviews and usually
intake varies from organization to organization however typically multiple
recruitments are made in this approach.
o Walk-Ins, although this approach is slowly losing out on relevance owing to the
existence of various third parties to advertise job interviews. The firm usually
advertises job vacancies in various avenues such as Newspapers Ads etc and
undertakes the typical firm specific recruitment stages. Usually the rounds are more
in number in comparison to Campus hiring and significant investment is made in
terms personnel to profile various candidatures.
 Extrinsic Hiring:

o Third Party hiring, where-in the firm outsources in profiling the candidates to a
third party, with presence of many job-search engines such as Naukri, Monster etc.
Advertising job vacancies has become hassle-free.
o Employee Referrals, referrals is another effective mechanism where-in existing
employees refer candidates to specific job vacancies which are made visible
through intranet. The investment for the firm here is significantly less.
In spite of the differences in each of these approaches, they have the following shortcomings in
common:
 Acute sensitivity to miscommunication
 High reliance on the responsiveness of both the organization and the candidate
 High risk of skill mismatch in the applications resulting in significant loss to the firm in
processing all the candidatures
 Sensitivity to large volume of applications
Although these traditional approaches cater to the current hiring appetite of the firm, there is large
friction between various stages resulting in significant investments from the firm and lower
returns.
The BizGrow Approach
BizGrow Engine reorganizes the investments at various stages, reducing investment on third party
services and multiple stages of recruitment, an initial investment should be made on mining the
relevant data. Owing to social media explosion currently there innumerous data rich avenues to
mine the desirable data.
 Make a one-time investment in programmers with expertise in NLP (Natural Language
Programming), Web data mining.
 Mine data from the following sources:
o Job seeking groups etc from Facebook, LinkedIn etc.
o Various technical forums, forums especially with technology in which the firm has
already invested or plans to invest in. There are millions of online forums, however,
they can be profiled based on the traffic to each forum or the number of users
visiting the forums.
o Post online advertisements on reputed websites such as LinkedIn, create groups on
Facebook dedicated to the firm’s recruitment.
 Once all the data is obtained, comes the crucial stage of profiling and the profiling can be
performed by utilizing the below analytical procedures:

i) Sentiment Analysis of the users’ posts, to narrow the potential candidates, who are
more likely quit their current jobs owing to professional dissatisfaction. Caution
should be exercised in identifying potential conflicts of interest. However this can
be an additional layer of filtering.
ii) Obtain professional summaries from the user posts to identify false positives in
terms of skill mismatch, culture mismatch etc and create master database of
employees to not invest on, in short create an Invest-Not database. The Invest-
Not will be accompanied with the specific skill-mismatch doesn’t override a
possible skill match.
iii) Perform text analytics of the data from the selected forums, to created database of
professionals to lookout for. For E.g.: Tableau is one of the technologies which a
firm wants to heavily invest on owing to business requirements and is looking out
for tableau-skilled lateral hires. The forums of Tableau provide bountiful
information on the users who are active respondents to various queries posted based
on the number of likes or number of people who find the response useful. All these
users can be profiled into an Invest-In database.
iv) Also perform a statistical analysis for the Invest-In database employees, to gain an
insight into their loyalty to the firm in terms of the number of years they have
remained with the firm. Typically 2 or maybe 3 years is a good number, and 1 or
less than 1 points to the flight-risk and this would increase the risk on investment
on such candidates.
v) Utilize the NLPs, data analytics to benchmark resumes to create a resume score.
This would slightly differ for lateral hires/campus hires:
1) Lateral hires’ resumes can be benchmarked and scored based on
originality of the resume and relevance of the application based on the text
analytics of the resume to identify the keywords indicating the requisite
skillset.
2) Analytics of the extra-curricular achievements to identify candidates
with decent communication skills.
3) For Campus Hires, since significant investment is made in the initial
examination, resume benchmarking and scoring should be performed
directly on the specific academics such as mathematics, English to profile
for candidates with better aptitude rather than solely relying on
examination which is subjective to various conditions resulting in loss of
Potential hires.

Hiring Module Model:
Each of the five areas mentioned above are not just abstract areas which have no tangible
weightage, rather each of these broad areas are variable (depending on the firm’s requirement)

with rating points. And the specifics of each of these criteria need to be input to the engine so that
scores are generated for each of the candidates which is an accumulation of the scores in each of
these areas.
In the above formula for each of the five areas the rating points of the respective areas are
represented by (i), (ii), (iii), (iv), (v). Wi, Wii, Wiii, Wiv, Wv represent the total weightage for each
of these areas subject to change by the firm based on its requirement.
The above calculation is the weighted average of the Potential Selection Rating which is
representative of the hiring quality.
Overall quality hiring for any period is calculated by:
The hiring index module’s output the Optimal Hiring Index (Hi) is calculated as follows:

Retention Module:
At a firm level moderate attrition is considered healthy, as this opens up avenues for inflow of
fresh pool of talent, thereby refreshing the firm from a grass-root level. However, the existing
workforce of an organization is the firm’s identity and a personification of the very brand of the
firm. Seasoned employees represent the investment of the most important and universal resource,
time. Each seasoned employee of the organization represents the time investment of the firm, and
time is only resource which cannot be gained once lost. Although the hiring makes up for
headcount, various organizational characteristics such as firm’s culture, operational methodology,
valuable client relationships, vast knowledge of the firm historic approach to business approach
these cannot be made up for by hiring. Poor retention results in hampering the delivery to client as
many a times owing to the time spent a professional ends up being the cornerstone of an
engagement. And when a firm fails to retain performing seasoned professionals the impact
cascades directly to profitability. Hence it is imperative to employ a skilled (quality) retention
strategy, as this in synergy with quality hiring greatly improves the growth/profitability of the
organization.
Typical Approach:
Although retention is a necessity it is very difficult to approach this aspect, as there are very few
instruments predicting possible skill erosion. Many-a-times retention strategy comes into the foray
in the face of impending attrition. And the attrition mitigation usually is not fool proof with 100%
results. Currently below are the typical retention mechanisms:
 HR Surveys, these surveys are regularly rolled out by the human resource division of the
firm to take stock of the employee perception/satisfaction. And poor survey performance
usually points in the direction of the issue for the employees if their responses are mostly
consistent. However, satisfaction is an abstract quality and the survey questions around
these areas usually tend to be broad. For E.g.: A typical survey question would be around
Managerial Effectiveness with corresponding rating points to be assigned against each
issue. However, the responses usually only point in the direction of the issue rather than
the exact concern of the employees and it becomes difficult for the firm to address this
issue to the employee’s satisfaction which usually results in attrition.
 The management only becomes cognizant of possible attrition once the employees actually
quit, usually then the firm goes into retention mode. However, this is not foolproof strategy
as the success ratio is usually low.
 Periodically making note of the employee’s needs to roll out benefits to cater to employee
satisfaction.

BizGrow Engine refurbishes the existing tool of HR Survey to facilitate the use of programmers
with expertise in NLP (Natural Language Programming), data analysis. Designed in the right
fashion the survey can provide rich relevant data which can greatly aid the retention strategy.
Typically employee concerns usually fall into the following buckets:
 Work-life fit
 Recognition
 Well-being
 Market relevant skill based deployment
Currently the surveys have responses ranging from Strongly Disagree, Disagree, Neutral, Agree,
and Strongly Agree. Although these are representative of the employee opinion of each of the
survey questions, this doesn’t actually point towards the core issue. The BizGrow Engine aided
survey would mandate all negative responses should be accompanied by textual explanation of the
employee concerns against each question.
Apart from analyzing the trends of the ratings, intensive sentiment/text analytics should be
performed on the textual responses, which would give a deeper insight into the actual issue. For
E.g.: If the sentiment analysis yields the word Favoritism as a frequently flagged keyword in the
managerial effectiveness area. This points to the root of the issue.
Also each of the key hotspots identified can be assigned rating points depending on the firm’s
discretion.
The engine overlays the performance information of all the employees with the survey insights to
gain deeper understanding of the performing cluster’s concerns and predict skilled professional
attrition in advance to control the same. Hence the engine would now equip the firm with the
ability to understand the sentiments of their performing cluster which is major motif of this module.

Retention Module Model:
The hotspots should be periodically monitored to see if the firm’s strategy is quelling the
employees’ concerns. The engine would provide comparison across periods and the onus is on the
firm to minimize the overall incidence of the hotspots.

The overall hotspot score needs to be evaluated as this is representative of the overall concerns of
all the employees.
The weighted average is the per capita employee concern and the per capita in this case needs to
be as minimum as possible.
The Retention module’s output the Quality Retention Index (Ri) is calculated as follows:

Project Delivery Module:
The previous aspects dealt with exclusively the workforce, although workforce is an important
aspect of growth, in the primary motive of a business is to make Profit, at least this holds true for
all non-charitable organizations.
Typical Approach:
The firms usually report their revenue periodically and compare/contrast it with the previous
periods.
This approach normalizes all forms of work delivery to a common revenue level (into monetary
units) and compares it across periods to present the revenue performance of a firm.
The calculation is fairly simple:
Thus with all the individual indices calculated on the following assumptions:

∝- Proportional to
Thus the final equation of the BizGrow Engine is:
BizGrow Engine Model:

BizGrow Engine

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