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Leveraging Analytics for Businesses
February 2015
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PwC
Agenda
Introduction
Analytics: Future of Business Consulting
Business Cases: Examples from PwC US Advisory
How to enhance your analytical skills
How to pitch your analytical skills
Questions
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February 2015
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PwC
Analytics: Future of Business Consulting
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February 2015
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PwC
The Top 6 Tech Skills You Need in 2015
The article appeared in the business magazine Inc. on 27th
Jan
Coding
Big Data
Cloud Computing
Mobile
Data Visualization
UX Design Skills
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The need of analytics
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February 2015
Method 1: India is a developing country
Method 2: India is a developing country
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PwC
Theoretical Analytics vs. Application Analytics Theoretical
Analytics: Research in fields of topics like machine learning,
optimization, text mining, data visualization, regression, PCA,
regression,
decision trees, linear programming etc.
Application Analytics: Solving problems of clients
Finding how many people are going to attrite from an
organization in next 1 year: regression modeling
Clustering products from an inventory to optimize their
transportation on basis of size, volume, dimensions
Finding sentiments of users about a particular bank from social
platforms like Facebook: text mining and sentiment analysis
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February 2015
Analyst: Theoretical Analytics World: Application Analytics
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PwC
Knowledge Transfer b/w The Two Domains
People in Theoretical Analytics
Professors
Researchers
IEOR
Institutes (MIT, Stanford)
People in Application Analytics
PwC Diamond
Fractal Analytics
Google Analytics
McKinsey & Co.
Opera Solutions
EXL Inductis
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February 2015
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Business Cases Case 1: HR Analytics Predicting Attrition for FY
14-15
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Outcomes:
Project plan with deadlines and responsibilities
Hypotheses inventory and mapped data elements
Area Hypotheses
Current and Past Managers Current/past manager/coach attrition
will lead to attrition
Current and Past Managers Time under the same manager may affect
attrition
Current and Past Managers Number of employees churned under the
manager will affect
attrition
Demographics Tenure will have a U curve for attrition
Demographics International employees have higher attrition
Education/ Credentials Part time education/MBA/credentials in
last cycle will lead to
attrition
Education/ Credentials Specific academic background /institutes
will have higher
attrition
Engagement Levels Low engagement score lead to attrition
Engagement Levels Employee engaged in an industry vertical
having higher growth
will have higher attrition propensity (more job
availability)
Filters Attrition is higher for some particular vertical/
business unit
Filters Some offices will have higher attrition
Filters Smaller offices have higher attrition
Hours worked and vacation time High one-off vacations compared
to past may lead to churn
Hours worked and vacation time Overall number of working hours
would have higher attrition
Hours worked and vacation time Change in working hours compared
to past year would have
impact on attrition
Hours worked and vacation time Change in working hours compared
to peers would have impact
on attrition
Hours worked and vacation time Change in health condition will
lead to attribution
Hours worked and vacation time Sabbatical will lead to higher
attrition
Hours worked and vacation time Leave utilization will affect
attrition
Area Hypotheses
Current and Past Managers Current/past manager/coach attrition
will lead to attrition
Current and Past Managers Time under the same manager may affect
attrition
Current and Past Managers Number of employees churned under the
manager will affect
attrition
Demographics Tenure will have a U curve for attrition
Demographics International employees have higher attrition
Education/ Credentials Part time education/MBA/credentials in
last cycle will lead to
attrition
Education/ Credentials Specific academic background /institutes
will have higher
attrition
Engagement Levels Low engagement score lead to attrition
Engagement Levels Employee engaged in an industry vertical
having higher growth
will have higher attrition propensity (more job
availability)
Filters Attrition is higher for some particular vertical/
business unit
Filters Some offices will have higher attrition
Filters Smaller offices have higher attrition
Hours worked and vacation time High one-off vacations compared
to past may lead to churn
Hours worked and vacation time Overall number of working hours
would have higher attrition
Hours worked and vacation time Change in working hours compared
to past year would have
impact on attrition
Hours worked and vacation time Change in working hours compared
to peers would have impact
on attrition
Hours worked and vacation time Change in health condition will
lead to attribution
Hours worked and vacation time Sabbatical will lead to higher
attrition
Hours worked and vacation time Leave utilization will affect
attrition
Area Hypotheses
Current and Past Managers Current/past manager/coach attrition
will lead to attrition
Current and Past Managers Time under the same manager may affect
attrition
Current and Past Managers Number of employees churned under the
manager will affect
attrition
Demographics Tenure will have a U curve for attrition
Demographics International employees have higher attrition
Education/ Credentials Part time education/MBA/credentials in
last cycle will lead to
attrition
Education/ Credentials Specific academic background /institutes
will have higher
attrition
Engagement Levels Low engagement score lead to attrition
Engagement Levels Employee engaged in an industry vertical
having higher growth
will have higher attrition propensity (more job
availability)
Filters Attrition is higher for some particular vertical/
business unit
Filters Some offices will have higher attrition
Filters Smaller offices have higher attrition
Hours worked and vacation time High one-off vacations compared
to past may lead to churn
Hours worked and vacation time Overall number of working hours
would have higher attrition
Hours worked and vacation time Change in working hours compared
to past year would have
impact on attrition
Hours worked and vacation time Change in working hours compared
to peers would have impact
on attrition
Hours worked and vacation time Change in health condition will
lead to attribution
Hours worked and vacation time Sabbatical will lead to higher
attrition
Hours worked and vacation time Leave utilization will affect
attrition
44 All onboarding survey questions can be separate data file
45 Date survey taken
46 Company Company joined after leaving Client
47 Company Location
48 Company Industry
49 Role
50 Salary
51 Level
52 Function
53 Left for competition? Yes/no
54 Continued to work as a contractor? Yes/no
55 Left for job in Federal sector? Yes/no
56 Filters Role To be customized as per requirements
57 Sub Role
58 Line of Service
59 Sub Level of Service
60 Job Code
61 Level
62 Level Descr
63 Role
64 Role Descr
65 Geo Market
66 Region
67 Country
68 Identified as HIPO (yes/no)
69 Identified as Pivotal employee (yes/no)
70 Identified as HIPO previous years (yes/no) can be separate
data file
71 Identified as Pivotal employee previous years (yes/no) can be
separate data file
72 Total hours worked current year
73 Total hours worked in previous years can be separate data
file
74 Client hours worked current year
75 Client hours worked in previous years can be separate data
file
76 Biz development hours worked current year
77 Biz development hours worked in previous years can be
separate data file
78 Vacation hours current year
79 Vacation hours in previous years can be separate data
file
80 Parental Leave Hours current year
81 Parental Leave Hours in previous years can be separate data
file
82 Sick Leave hours current year
83 Sick leave hours in previous years can be separate data
file
84 Current Vacation Balance
85 Vacation balance in previous years can be separate data
file
86 Hours over capacity (Month by Month) current year
87 Hours over capacity (Month by Month) previous years
88 Increase/decrease from previous years
89 Current work zipcode
90 Past work zipcodes can be separate data file
91 Home zipcode
92 Old home zipcodes can be separate data file
93 Miles commute (if available)
94 Miles commute past (if available) can be separate data
file
HIPO/ Pivotal
identification
can be separate data file
Hours worked and
vacation time
Onboarding survey
Commute distance
Company joined
Post Client status
44 All onboarding survey questions can be separate data file
45 Date survey taken
46 Company Company joined after leaving Client
47 Company Location
48 Company Industry
49 Role
50 Salary
51 Level
52 Function
53 Left for competition? Yes/no
54 Continued to work as a contractor? Yes/no
55 Left for job in Federal sector? Yes/no
56 Filters Role To be customized as per requirements
57 Sub Role
58 Line of Service
59 Sub Level of Service
60 Job Code
61 Level
62 Level Descr
63 Role
64 Role Descr
65 Geo Market
66 Region
67 Country
68 Identified as HIPO (yes/no)
69 Identified as Pivotal employee (yes/no)
70 Identified as HIPO previous years (yes/no) can be separate
data file
71 Identified as Pivotal employee previous years (yes/no) can be
separate data file
72 Total hours worked current year
73 Total hours worked in previous years can be separate data
file
74 Client hours worked current year
75 Client hours worked in previous years can be separate data
file
76 Biz development hours worked current year
77 Biz development hours worked in previous years can be
separate data file
78 Vacation hours current year
79 Vacation hours in previous years can be separate data
file
80 Parental Leave Hours current year
81 Parental Leave Hours in previous years can be separate data
file
82 Sick Leave hours current year
83 Sick leave hours in previous years can be separate data
file
84 Current Vacation Balance
85 Vacation balance in previous years can be separate data
file
86 Hours over capacity (Month by Month) current year
87 Hours over capacity (Month by Month) previous years
88 Increase/decrease from previous years
89 Current work zipcode
90 Past work zipcodes can be separate data file
91 Home zipcode
92 Old home zipcodes can be separate data file
93 Miles commute (if available)
94 Miles commute past (if available) can be separate data
file
HIPO/ Pivotal
identification
can be separate data file
Hours worked and
vacation time
Onboarding survey
Commute distance
Company joined
Post Client status
List of variables from Datawarehouse/ HRIS
Category Data Elements Comments
1 ID EmplID
2 ID Scrambled ID
3 Job Title
4 Sex
5 Race
6 Country of origin
7 Age/Date of Birth
8 Full/Part Time
9 Function
10 Function Descr
11 Client facing role Yes/no
12 Hire Date
13 Status (active/departed)
14 Termination Date
15 Termination reason code
16 Interviewer name
17 Interviewer level
18 Interviewer rating
19 Starting pay
20 Sign-on bonus
21 Rehire (yes/no)
22 Intern (yes/no)
23 Hiring source Referral, campus, application, etc
24 Previous Company Company where s/he worked before
25 Direct from college (yes/no)
26 Direct from grad school (yes/no)
27 Worked as a contract employee for Client (yes/no)
28 Years of experience in prior company
29 Total years of experience prior to joining
30 Previous company salary
31 Previous company location
32 Previous company Industry
33 Previous company role
34 Previous company function
35 Referer
36 Current Status of referrer
37 If referer no longer with firm, reason for departure
38 If referer no longer with firm, date of departure
39 Hiring manager
40 Years worked under hiring manager
41 Current Status of hiring manager
42 If hiring manager no longer with firm, reason for
departure
43 If hiring manager no longer with firm, date of departure
Client to scramble original IDs
Demographics
Hiring/Termination
information
Interview
Performance
can be a separate file if multiple interviews
per employee
Previous company
Referer
Hiring Manager
Hypotheses inventory Mapped data elements
HR Analytics| Phase 1 - Program design
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PwC
Outcomes:
Core analytic dataset containing essential data elements
Data dictionary, data audit and hypotheses feasibility
documents
44 All onboarding survey questions can be separate data file
45 Date survey taken
46 Company Company joined after leaving Client
47 Company Location
48 Company Industry
49 Role
50 Salary
51 Level
52 Function
53 Left for competition? Yes/no
54 Continued to work as a contractor? Yes/no
55 Left for job in Federal sector? Yes/no
56 Filters Role To be customized as per requirements
57 Sub Role
58 Line of Service
59 Sub Level of Service
60 Job Code
61 Level
62 Level Descr
63 Role
64 Role Descr
65 Geo Market
66 Region
67 Country
68 Identified as HIPO (yes/no)
69 Identified as Pivotal employee (yes/no)
70 Identified as HIPO previous years (yes/no) can be separate
data file
71 Identified as Pivotal employee previous years (yes/no) can be
separate data file
72 Total hours worked current year
73 Total hours worked in previous years can be separate data
file
74 Client hours worked current year
75 Client hours worked in previous years can be separate data
file
76 Biz development hours worked current year
77 Biz development hours worked in previous years can be
separate data file
78 Vacation hours current year
79 Vacation hours in previous years can be separate data
file
80 Parental Leave Hours current year
81 Parental Leave Hours in previous years can be separate data
file
82 Sick Leave hours current year
83 Sick leave hours in previous years can be separate data
file
84 Current Vacation Balance
85 Vacation balance in previous years can be separate data
file
86 Hours over capacity (Month by Month) current year
87 Hours over capacity (Month by Month) previous years
88 Increase/decrease from previous years
89 Current work zipcode
90 Past work zipcodes can be separate data file
91 Home zipcode
92 Old home zipcodes can be separate data file
93 Miles commute (if available)
94 Miles commute past (if available) can be separate data
file
HIPO/ Pivotal
identification
can be separate data file
Hours worked and
vacation time
Onboarding survey
Commute distance
Company joined
Post Client status
44 All onboarding survey questions can be separate data file
45 Date survey taken
46 Company Company joined after leaving Client
47 Company Location
48 Company Industry
49 Role
50 Salary
51 Level
52 Function
53 Left for competition? Yes/no
54 Continued to work as a contractor? Yes/no
55 Left for job in Federal sector? Yes/no
56 Filters Role To be customized as per requirements
57 Sub Role
58 Line of Service
59 Sub Level of Service
60 Job Code
61 Level
62 Level Descr
63 Role
64 Role Descr
65 Geo Market
66 Region
67 Country
68 Identified as HIPO (yes/no)
69 Identified as Pivotal employee (yes/no)
70 Identified as HIPO previous years (yes/no) can be separate
data file
71 Identified as Pivotal employee previous years (yes/no) can be
separate data file
72 Total hours worked current year
73 Total hours worked in previous years can be separate data
file
74 Client hours worked current year
75 Client hours worked in previous years can be separate data
file
76 Biz development hours worked current year
77 Biz development hours worked in previous years can be
separate data file
78 Vacation hours current year
79 Vacation hours in previous years can be separate data
file
80 Parental Leave Hours current year
81 Parental Leave Hours in previous years can be separate data
file
82 Sick Leave hours current year
83 Sick leave hours in previous years can be separate data
file
84 Current Vacation Balance
85 Vacation balance in previous years can be separate data
file
86 Hours over capacity (Month by Month) current year
87 Hours over capacity (Month by Month) previous years
88 Increase/decrease from previous years
89 Current work zipcode
90 Past work zipcodes can be separate data file
91 Home zipcode
92 Old home zipcodes can be separate data file
93 Miles commute (if available)
94 Miles commute past (if available) can be separate data
file
HIPO/ Pivotal
identification
can be separate data file
Hours worked and
vacation time
Onboarding survey
Commute distance
Company joined
Post Client status
List of variables from Datawarehouse/ HRIS
Category Data Elements Comments
1 ID EmplID
2 ID Scrambled ID
3 Job Title
4 Sex
5 Race
6 Country of origin
7 Age/Date of Birth
8 Full/Part Time
9 Function
10 Function Descr
11 Client facing role Yes/no
12 Hire Date
13 Status (active/departed)
14 Termination Date
15 Termination reason code
16 Interviewer name
17 Interviewer level
18 Interviewer rating
19 Starting pay
20 Sign-on bonus
21 Rehire (yes/no)
22 Intern (yes/no)
23 Hiring source Referral, campus, application, etc
24 Previous Company Company where s/he worked before
25 Direct from college (yes/no)
26 Direct from grad school (yes/no)
27 Worked as a contract employee for Client (yes/no)
28 Years of experience in prior company
29 Total years of experience prior to joining
30 Previous company salary
31 Previous company location
32 Previous company Industry
33 Previous company role
34 Previous company function
35 Referer
36 Current Status of referrer
37 If referer no longer with firm, reason for departure
38 If referer no longer with firm, date of departure
39 Hiring manager
40 Years worked under hiring manager
41 Current Status of hiring manager
42 If hiring manager no longer with firm, reason for
departure
43 If hiring manager no longer with firm, date of departure
Client to scramble original IDs
Demographics
Hiring/Termination
information
Interview
Performance
can be a separate file if multiple interviews
per employee
Previous company
Referer
Hiring Manager
Computed Predictors
Macroeconomic/Industry data
Raw Analytic Dataset
Core Analytic Dataset
Recruitment System
Learning Management System
HR Information System- PeopleSoft
ERP Finance & Operations
910 data fields
Historic Data from 2006
Data extracted from different databases/surveys
71 individual data files
652 total predictors
Including 246 derived predictors
Core analytic dataset created
Employee
Survey
Data
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HR Analytics| Phase II Data extraction
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Methodology Phase III Model development
1. Core analytics dataset 2. Variable Treatments
Missing value
imputation Capping
Flooring
3. Modeling dataset
Modeling dataset
4. Data segmentation analysis
Outcomes:
Predictors identified, refined and validated
Final predictive model
Core Analytic Dataset
5. Visualization and transformation
6. Variables clustering 7. Iterative multivariate analysis
8. Model fine-tuning 9. Final model
10. Model validation
22.2%20.0%
5.6% 5.2% 4.4% 4.8%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Att
riti
on ra
te
Attrition pattern by performance based award in the current
year
Multivariate visualization
Variable interpretation
Multicollinearity checks
Predictor Category Attrition Predictors
Demographics Age at the time of hiring
Tier Tier at the end of the year
Awards Performance award during the current year
Training Average hours per training
% Cumulative Attrite
% Cumulative Non Attrite
Lift from the model (ROC Curve) KS value
% Population
% Cumulative Percentage
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 20% 40% 60% 80% 100%
%Culmulative Attrite %Culmulative Non-Attrite
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% Cumulative Attrite % Cumulative Non-Attrite
Maximum KS of
41.55% indicate that the model is able to
discriminate between attrites and
non-attrites
Area under the curve
is 72.5%
X12 X7 X14 X1 X6 X9 X3 X2
X5 X16 X4 X8 X13 X11 X10 X15
Set of all
variables
Cluster 1
Cluster 2
Cluster 3
Param eter DF Estim ate Standard Error Wald Chi-Square Pr >
ChiSq
Intercept 1 -2.17 85 0.3284 44.0166
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Outcomes:
Final summary report of key insights
Heat maps depicting attrition risk
Individual employee level scoring
22,474 employees scored*
Employee profiles created using departure probability
deciles
Current Employee Dataset
Final Predictive Model
Where
Three logistic regression models for the three different
segments of the population
Though model formulation is same across the three models,
coefficients and predictors are different
*We have scored all active firm employees with tenure greater
than 1 year
HR Analytics| Phase IV Model deployment
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PwC
Business Cases Case 2: Trade Area Mapping and Retail Store
Positioning
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February 2015
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Retail Store Positioning
Define the trade area map
Identify the impact of retail store on digital
Identify the target customer profile
1
2
3
Identify the recommended locations and the associated trade area
for given customer profiles
Define the future state of retail-network and the expected
market-coverage / revenue impact
Analyze and quantify the relationship between retail locations
and online transactions
Identify potential areas of overlap with the trade-area map
Identify key attributes of the customers that are part of the
target market for respective client products sold via retail
channel
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PwC
Defining the trade area is dependent on target customer market
Target Customer footprint around a store Category A[1] Market
Low High
Traditional PC Users Family Fortunes Heavy buyers of PC/
Laptop/ Software Online channel
preferred Power Couples More preference
towards PC/Laptop High preference for
tablets
Heavy Gamers Family Sprawl Large family
size(4+) High PC preference
as well School Daze High propensity for
game systems Medium Online
preference Demand + Sales Index
client Store (current)
25 mile radius: no incremental
benefit from trend fit curve
Core market area Low competition
effect and high
incremental benefit (540 customers/mi)
[1] Category A : High Demand, High Competition 1+ client stores
[2] Refer ence : There is a slight decrease in trade area radius
due to competition
18 mi
Incremental benefit : 100 customers / mi
Slide 15
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0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 10 20 30 40 50
# C
us
tom
er
s
Sales vs distance
Gaming
Tablets
Software
PC/Laptops/Tablets
Sales
Poly. (Sales)
How do we optimize the trade area..
Absolute distance threshold is around 13 miles after which
the
increment is marginal
Force fitting gives an optimum trade area radius of 25 miles
around the store
Boundaries of trade area are defined based on the projected
revenue / customer growth per incremental zip code from the stores
location
Data used : sanitized sales data (mocked up from a different
project) by zipcode
Slide 16
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0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 10 20 30 40 50 60
# C
us
tom
er
s
Distance from store
Competition Analysis
Sales
Capturing the impact of competition and x-channels Comparison
analysis of scenarios with and without competition (including
alternative retail channels), to quantify the projected impact of
competition on the total revenue attributable to the store
Data used : sanitized sales data (mocked up from a different
project) by zip code
Slight decrease in the trade area radius due to competition
Without competition
With competition
Sales volume decreases due to competition
Slide 17
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Low High
Demand + Sales Index
client Store
Within the defined trade area measure the baseline
for online sales and channel preferences
18 mi
Incremental benefit : 100 customers / mi
Quantifying the impact of stores location on customers
propensity to buy online
Segments defined by propensity to use digital channel but wander
stores for checking out new products are the ones whose buying
behavior is affected by presence of a store.
Top Segments Midlife Highlife Power Couples Online Living Family
Fortunes
Slide 18
Online Sales analysis for Category A market
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0
2000
4000
6000
8000
10000
0 10 20 30 40 50 60
# C
us
tom
er
s
Distance
Sales vs Distance
Sales
Competition Presence
Poly. (Sales)
Poly. (Competition Presence)
Effect of store on online sales
Quantifying the channel preferences and influence of store
wandering on transaction volume and value will help us quantify the
impact of retail store on digital-transactions
Data used : sanitized sales data (mocked up from a different
project) by zip code
Online sales+ Retail PoS
Retail PoS
Sales volume increased due to presence of store
Effectively, marginal increase in trade area observed due to
online sales
Slide 19
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Business Cases Case 3: Exploring consumer demand and preference
for bundled services
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February 2015
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Market Overview : High Speed Internet Demand Penetration of
Client
*Product mix is calculated using Mock Data
**Demand & Penetration is derived from Claritas survey data
21
November 2014
Demand
Product A* Product B* Product C*
Low High
Downtown and northern suburb of Dallas have higher penetration
and product mix along with having a high demand
These regions have high demand for only one product type
Suburbs of Dallas show a higher demand for High speed Internet
as compared to regions around Downtown
Size : Penetration
Market Overview - Dallas
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Market Overview : Competition Analysis
22
November 2014
Low High
Demand
Low Medium High
This region has high demand and low competition with decent
population base
Size : Population
This region has high demand and low competition with decent
population base
These regions have high demand and low competition with a decent
population base which suggest Client should focus on entering these
zips
Majority of the zips show low competition in the high speed
internet space. However, these regions are dominated by two of the
competitors
**Demand & Competition is derived from Claritas survey
data
Market Overview - Dallas
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PwC
Selected Zips based on certain profile attributes
23
November 2014
Market Segmentation can be done by choosing from the different
scenarios. Similar markets can be focused with similar strategies.
After selecting markets with such filters , strategies should be
created on the basis of customer profile and preferences.
Customer Profiles Selection - Dallas
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PwC
Customer Profile
24
The following segment Economizers dominates the Dallas area.
This segment consists of the poorest financial groups. Consists of
racially mixed singles and single-parent families, watching
wresting and listening to gospel radio
Customer Profile Details - Dallas
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PwC
How to enhance your analytical skills
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February 2015
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PwC
Gain expertise or working knowledge of theoretical analytics
Coursera: Data Scientist Toolbox
MIT Courseware (courses offered under Sloan School of
Management):
http://ocw.mit.edu/courses/sloan-school-of-management/
MA 106: Linear Algebra
IC 102: Probability and Statistics for Engineers, Sheldon
Ross
Kaggle: For problems in the domain of business analytics
Visualization super awesome website: d3js.org
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February 2015
http://ocw.mit.edu/courses/sloan-school-of-management/http://ocw.mit.edu/courses/sloan-school-of-management/http://ocw.mit.edu/courses/sloan-school-of-management/http://ocw.mit.edu/courses/sloan-school-of-management/http://ocw.mit.edu/courses/sloan-school-of-management/http://ocw.mit.edu/courses/sloan-school-of-management/http://ocw.mit.edu/courses/sloan-school-of-management/http://ocw.mit.edu/courses/sloan-school-of-management/
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PwC
How to pitch your analytical skills
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February 2015
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Process flow for a good problem solving
Step 1: Understand the problem (without the bias that you have
to solve it analytically)
Step 2: Put yourself in the client shoes and realize what you
would have wanted as a solution if you would have been the client.
Nobody is asking you to achieve a correlation coefficient of 0.999
but rather a probably lower value of 0.7 and an implementable
solution.
Step 3: Give the first try to solving the problem imagining that
you know nothing about analytics, putting business sense to it
Step 4: Attack! Attack! Attack! Put all your analytical skills,
techniques you have learned into solving the problem. Go in pure
analyst mode
Step 5: Now rephrase the analytical solution found out by you in
a layman interpretable form: graphs, charts, numbers,
comparisons
28
February 2015
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Questions...
This publication has been prepared for general guidance on
matters of interest only, and does
not constitute professional advice. You should not act upon the
information contained in this
publication without obtaining specific professional advice. No
representation or warranty
(express or implied) is given as to the accuracy or completeness
of the information contained
in this publication, and, to the extent permitted by law,
PricewaterhouseCoopers LLP, its
members, employees and agents do not accept or assume any
liability, responsibility or duty of
care for any consequences of you or anyone else acting, or
refraining to act, in reliance on the
information contained in this publication or for any decision
based on it.
2015 PricewaterhouseCoopers LLP. All rights reserved. In this
document, PwC refers to
PricewaterhouseCoopers LLP which is a member firm of
PricewaterhouseCoopers
International Limited, each member firm of which is a separate
legal entity.
