google.value.analysis.for.business.growth
TRANSCRIPT
WHERE BUSINESS APPS LIVE
Value Analysis of Google Inc.
By Shekhar Bhartiya ([email protected])
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
Disclaimer
Although the author publisher have made every effort to ensure that the
information in this report was correct at published time, the author do
not assume and hereby disclaim any liability to any party for any loss,
damage, or disruption caused by errors or omissions, whether such errors
or omissions result from negligence, accident, or any other cause.
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
About Author
- Shekhar is passionate and energetic professional program/project manager with demonstrated skills in sales, sales management, general
management, strategic planning, business planning, execution, P&L responsibility and management, leadership/coaching, channel development and
growth.
- Shekhar has proven track record in leading Business and IT transformation engagements. Specialist in CIO/CXO advisory and large scale
transformation initiatives ranging from diversified conglomerates to new ventures with many of the fortune 500 companies.
Delivered significant top-line and bottom-line impact at a number of clients in industries such as Manufacturing, Consumer Products, Utilities, Telco,
Travel & Logistics, Retail, Banking, Oil & Gas and Real Estate.
Shekhar has studied in Asia and Germany, grew up in India and worked in various places like US, Europe, India, ANZ, Japan, Korea and many South
Asian countries
© 2015 Chandra Shekhar Bhartiya
Contact me for any fortune 500 firm value analysis to understand the business value of adopting
and using Business Applications to drive business transformation and long lasting improvements
WHERE BUSINESS APPS LIVE
SUMMARY OF VALUE ANALYSIS OF GOOGLE INC
Industry Trends
and Business
Expectations
Google is a new generation media company with a business model that is exceptionally well positioned to capitalize on the
continuing transition from traditional print and television media to Internet-based content and advertising. With advertising
representing about 95% of its revenue, Google is one of the largest advertising companies in the world.
The GOOGLE future growth is tied to continuously optimizing its business and IT operations to better manage their
business processes, advancing technologies and innovations.
Google Outside-In
Analysis Objective
- Understand what critical Business/IT process drives Google Inc’s revenue, net and Operating Income
- Analysis of Google’s 10 years of financial data reveals for year 2015-17 these are critical focus areas will bring
maximum returns for Google
Fund Management, AR and AP optimization and integration, Sales Management, IT infrastructure integration and optimization,
Assets Management.
Why call SAP
Google’s operational IT system to run backend process is home grown and some others but data reveals that probably it is a
time to call the professionals like SAP to handle key ERP process. Data reveals that integration and optimization of top impacted
areas will improve top and bottom line but a balanced approach will be required.
Top Impact Areas
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
METHODOLOGY (OUTSIDE IN VALUE ENGINEERING IN CONJUNCTION WITH STATISTICAL ANALYSIS)
Google Peer
data Statistical
Analysis
To Establish
drivers of
business
growth from
Peer Data
User the business
drivers for further
data analysis with 10
years of Google
financial data
To Establish the
magic link between
business drivers and
Google’s business
and IT process
Conclusion on Key
Business and IT
process which will
drive Google’s
future growth
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
GOOGLE INC. AND PEERS ANALYSIS (YEAR 2014)
Source: Respective financial statements for year 2014 (normalized in Mill USD with
current market exchange value)
ROA Return on Assets %
ROE Return on Equity %
Company Market Value ($mil) Enterprise Value Revenue (mil) ROA (%) ROE (%) Operating Margin (%) Net Margin (%) Gross Margin % Operating Income Net Income
Google Inc (GOOGL) $ 455,579.00 $ 383,587.00 $ 69,611.00 11.59 14.02 24.99 21.67 62.86 17,395.79$ 15,087.00$
Facebook Inc (FB) $ 267,372.00 $ 251,702.00 $ 13,507.00 9.61 10.76 35.93 20.8 82.73 4,853.07$ 2,810.00$
Tencent Holdings Ltd (TCEHY) $ 173,545.00 $ 172,602.00 $ 13,248.00 15.09 32.06 38.73 29.4 60.89 5,130.95$ 3,933.00$
Baidu Inc (BIDU) $ 61,327.00 $ 54,794.00 $ 9,085.00 13.2 25.56 22 23.26 61.5 1,998.70$ 2,136.00$
Yahoo! Inc (YHOO) $ 35,117.00 $ 30,359.00 $ 4,711.00 17.83 26.84 0.54 153.49 71.89 25.44$ 7,231.00$
Yahoo Japan Corp (YAHOY) $ 14,242.00 $ 11,181.00 $ 3,547.00 15.12 20.22 44.54 31.22 80.05 1,579.83$ 1,267.00$
Conclusion Based on above data Target Value Driver
The ***interaction of Operating Income
And Revenue*Gross Margin
(**Predictive Strength 93%)
The interaction of ROE % and ROA% *Operating Margin
(**Predictive Strength 95%)
The interaction of Operating Income
And Revenue* ROA%
(**Predictive Strength 91%)
Drives
Drives
Drives
Above Data says Gross Margin%, Operating Margin% and ROA% is interlinked and are key to Operating Income. Now let
look at these key driver for Google using 10 years (2005-2015) of quarterly financial data**Predictive strength- Predictive strength measures how well a predictor accurately predicts a target, predictor with a predictive strength of 100%
perfectly predicts a target.
***An interaction effect is the combined effect of two inputs. If the interaction effect is not zero, the combined influence of two inputs is greater than
the simple addition of their independent main effects. In other words, combining the two fields simultaneously explains more of the variance in the target
field than analyzing the fields separately© 2015 Chandra Shekhar Bhartiya
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
WHERE BUSINESS APPS LIVE
MAGIC LINK?
Improving Return on
Assets % (ROA),
Operating Margin %
(OM), Gross Margin %
(GM) will have huge
impact on Google's
Operating Income
How GOOGLE
internal IT operations
and business
transformation are
related to ROA,OM,
GM
Magic Link?
Lets select OM %, ROA % and GM % as key indicators for driving Google Operating Income
Now lets establish the Magic Link
The Conclusion
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
GOOGLE PEER COMPARISON (OM %, ROA % AND GM % )
KPI 1 Gross Margin % - A positive Gross Profit is only the first step for a company to make a net profit. The gross profit needs to be big enough to also cover related labor, equipment,
rental, marketing/advertising, research and development and a lot of other costs in selling the products
Google Inc had a gross margin of 62.86% for the quarter that ended in Jun. 2015, it has been in long term decline. The average rate of decline per year is -1.2%.
KPI 2 Operating Margin % - It is important to see a company maintains its operating margin over time. Among the same industry, a company with higher operating margin is more
efficient in its operation. Google Inc operating margin has been in 5-year decline. The average rate of decline per year is -7.3%.
KPI 3 Return on Assets % - ROA% measures a firm's efficiency at generating profits from shareholders' equity plus its liabilities. ROA shows how well a company uses what it has to
generate earnings.
0.1 % increment of operation margin % will
generates 703 Mil USD of operating income0.1 % increment of ROA % will generates 63
Mil USD of net income
1 % increment of gross margin % will
generates 176 Mil USD of Gross Profit
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
WHAT INFLUENCES GOOGLE GROSS MARGIN %
(15 YEARS OF QUARTERLY GOOGLE FINANCIAL DATA SAYS)
Key Business Parameter Secondary Target Value Driver
The ***interaction of Property, Plant and Equipment
and COGS to Revenue*Google Gross Margin %
(**Predictive Strength 97%)
The ***interaction of COGS to Revenue
and Accounts Payable/Accounts Receivable
*Google Gross Margin %
(**Predictive Strength 96% for AP,
95% for AR)
The ***interaction of COGS to Revenue
and Days Sales Outstanding*Google Gross Margin %
(**Predictive Strength 96%)
The ***interaction of Cash and Cash Equivalents
and COGS to Revenue *Google Gross Margin %
(**Predictive Strength 96%)
The ***interaction of COGS to Revenue and Total Assets *Google Gross Margin %
(**Predictive Strength 100%)
Drive
Drive
Drive
Drive
Drive**Predictive strength- Predictive strength measures how well a predictor accurately predicts a target, predictor with
a predictive strength of 100% perfectly predicts a target.
***An interaction effect is the combined effect of two inputs. If the interaction effect is not zero, the combined influence of two
inputs is greater than the simple addition of their independent main effects. In other words, combining the two fields
simultaneously explains more of the variance in the target field than analyzing the fields separately © 2015 Chandra Shekhar Bhartiya
Analysis of
data in next
slides
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
WHERE BUSINESS APPS LIVE
ISOLATED CONCLUSION ON GROSS MARGIN %
Magic Link?
Improvement Areas Related Process Related IT Areas
Cash and Cash Equivalents Cash (Fund Management) Fund Management System
COGS Sales Operations Sales Management
Account Payable (AP) AP Process ERP System (AP )
Account Receivables, (AR), DSO AR Process (DSO) ERP System (AR)
Property, Plat and Equipment Assets Management Assets Management System
Total Assets Assets Management Assets Management System
A magic link is established
These related process and IT Areas will impact Gross Margin
Slight increment (1 %) of gross margin will generates 176 Mil USD of Gross
Profit
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
WHAT INFLUENCES GOOGLE ROA %
(LAST 15 YEARS OF QUARTERLY GOOGLE FINANCIAL DATA SAYS)
Key Business Parameter Secondary Target Value Driver
The ***interaction COGS to Revenue
and Operating Margin %*Google ROA%
(**Predictive Strength 91%)
The ***interaction of Building and Improvements
and Accumulated Depreciations*Google ROA%
(**Predictive Strength 89%)
The ***interaction of Operating Margin%
and Gross Margin%*Google ROA%
(**Predictive Strength 85%)
The ***interaction of Accounts Receivable
and Building and Improvements *Google ROA%
(**Predictive Strength 84%)
The ***interaction of Accounts Payable and
COGS to Revenue *Google ROA%
(**Predictive Strength 81%)
Drive
Drive
Drive
Drive
Drive
© 2015 Chandra Shekhar Bhartiya
**Predictive strength- Predictive strength measures how well a predictor accurately predicts a target, predictor with
a predictive strength of 100% perfectly predicts a target.
***An interaction effect is the combined effect of two inputs. If the interaction effect is not zero, the combined influence of two
inputs is greater than the simple addition of their independent main effects. In other words, combining the two fields
simultaneously explains more of the variance in the target field than analyzing the fields separatelyc
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
WHERE BUSINESS APPS LIVE
ISOLATED CONCLUSION ON ROA %
Magic Link?
Improvement Areas Related Process Related IT Areas
COGS Sales Operations Sales Management
Building and Improvements Real Estate Management Real Estate Management System
Account Payable (AP) AP Process ERP System (AP )
Account Receivables, (AR) AR Process (DSO) ERP System (AR)
A magic link is established
These related process and IT Areas will impact ROA%
Slight increment (0.1 %) in ROA will generates 63 Mil USD of incremental
net income
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
WHAT INFLUENCES GOOGLE OPERATING MARGIN
(LAST 15 YEARS OF QUARTERLY GOOGLE FINANCIAL DATA SAYS)
Key Business Parameter Secondary Target Value Driver
The ***interaction of Net Margin %
and Accumulated Depreciation %*Google Operating Margin %
(**Predictive Strength 94%)
Net Margin % and Property, Plant and Equipment *Google Operating Margin %
(**Predictive Strength 90%)
The ***interaction of Accounts Receivable
and Net Margin%*Google Operating Margin %
(**Predictive Strength 89%)
The ***interaction of Net Margin%
and Goodwill*Google Operating Margin %
(**Predictive Strength 89%)
The ***interaction of Net Margin%
and Total assets*Google Operating Margin %
(**Predictive Strength 89%)
Drive
Drive
Drive
Drive
Drive
© 2015 Chandra Shekhar Bhartiya
**Predictive strength- Predictive strength measures how well a predictor accurately predicts a target, predictor with
a predictive strength of 100% perfectly predicts a target.
***An interaction effect is the combined effect of two inputs. If the interaction effect is not zero, the combined influence of two
inputs is greater than the simple addition of their independent main effects. In other words, combining the two fields
simultaneously explains more of the variance in the target field than analyzing the fields separately
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
Proof of
analysis in
Appendix 1
WHERE BUSINESS APPS LIVE
ISOLATED CONCLUSION ON OPERATING MARGIN %
Magic Link?
Improvement Areas Related Process Related IT Areas
Accumulated Depreciation Depreciation Methods Depreciation Rule Management
Property Plan and Equipment's Asset Management Asset Management System
Account Receivables AR Process (DSO) ERP System (AR )
Goodwill Acquisition and merger process IT consolidation and optimization
A magic link is established
These related process and IT Areas will impact Operating Margin
Slight increment (0.1 %) of operating margin will generates 703 Mil USD of
operation income
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
GOOGLE IT AND BUSINESS TRANSFORMATION AREAS
IT Areas This will drive Primary target
• Fund Management
(End to end Effective Cash and Fund management
is crucial for Google, it greatly affects the key growth drivers)
GM%, OM% and ROA %
Will increase the gross, operating and net Income of Google
• CRM and Sales System
(COGS is very important for Google, a world class pricing engine
and sales management will benefit the key growth drivers)
GM % and ROA %
Will increase the gross, operating and net Income of Google
• Account Receivables (Integration and optimization, ERP)
• Account Payables (Integration and optimization, ERP)
(End to end integrated AR and AP will drive Google’s Growth)
GM%, OM% and ROA %
Will increase the gross, operating and net Income of Google
• Assets Management system
(Integrated and optimized assets management system) GM%, OM% and ROA %
Will increase the gross, operating and net Income of Google
• IT infrastructure (Integration and Optimization)
(Google might be better than others in this areas
but a internal IT optimization will drive Google’s Growth)
GM% and OM%
Will increase the gross, operating and net Income of Google
Relevance
100%
100%
90%
90%
90%
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
NEXT STEP – WHAT TO DO IN THESE AREAS?
DEEP DIVE USING GOOGLE INTERNAL DATA
Business Transformation
- Include these process in a more deeper study with internal data along side with external
Internal IT Transformation
- Along side of process look how the process is defined with regards to IT operations and then see how it can be improved?
© 2015 Chandra Shekhar Bhartiya
WHERE BUSINESS APPS LIVE
About Author
- Shekhar is passionate and energetic professional program/project manager with demonstrated skills in sales, sales management, general
management, strategic planning, business planning, execution, P&L responsibility and management, leadership/coaching, channel development and
growth.
- Shekhar has proven track record in leading Business and IT transformation engagements. Specialist in CIO/CXO advisory and large scale
transformation initiatives ranging from diversified conglomerates to new ventures with many of the fortune 500 companies.
Delivered significant top-line and bottom-line impact at a number of clients in industries such as Manufacturing, Consumer Products, Utilities, Telco,
Travel & Logistics, Retail, Banking, Oil & Gas and Real Estate.
Shekhar has studied in Asia and Germany, grew up in India and worked in various places like US, Europe, India, ANZ, Japan, Korea and many South
Asian countries
© 2015 Chandra Shekhar Bhartiya
Contact me for any fortune 500 firm value analysis to understand the business value of adopting
and using Business Applications to drive business transformation and long lasting improvements
WHERE BUSINESS APPS LIVE
© 2015 Chandra Shekhar Bhartiya
Appendix 1 (Statistical Data Analysis details)
WHERE BUSINESS APPS LIVE
© 2015 Chandra Shekhar Bhartiya
Google’s peer data analysis using Statistics
Objective – Gross Margin%, Operating Margin% and ROA % with regards to other
parameters in whole data set
Technique – Since these all are continuous target, so a Linear Regression (ANOVA)
based approach is used
Company Market Value ($mil) Enterprise Value Revenue (mil) ROA (%) ROE (%) Operating Margin (%) Net Margin (%) Gross Margin % Operating Income Net Income
Google Inc (GOOGL) $ 455,579.00 $ 383,587.00 $ 69,611.00 11.59 14.02 24.99 21.67 62.86 17,395.79$ 15,087.00$
Facebook Inc (FB) $ 267,372.00 $ 251,702.00 $ 13,507.00 9.61 10.76 35.93 20.8 82.73 4,853.07$ 2,810.00$
Tencent Holdings Ltd (TCEHY) $ 173,545.00 $ 172,602.00 $ 13,248.00 15.09 32.06 38.73 29.4 60.89 5,130.95$ 3,933.00$
Baidu Inc (BIDU) $ 61,327.00 $ 54,794.00 $ 9,085.00 13.2 25.56 22 23.26 61.5 1,998.70$ 2,136.00$
Yahoo! Inc (YHOO) $ 35,117.00 $ 30,359.00 $ 4,711.00 17.83 26.84 0.54 153.49 71.89 25.44$ 7,231.00$
Yahoo Japan Corp (YAHOY) $ 14,242.00 $ 11,181.00 $ 3,547.00 15.12 20.22 44.54 31.22 80.05 1,579.83$ 1,267.00$
WHERE BUSINESS APPS LIVE
© 2015 Chandra Shekhar Bhartiya
Google’s 10 years of financial data analysis using Statistics
Objective – Gross Margin % with regards to other parameters in whole data set.
Technique – Since these all are continuous target, so a Linear Regression (ANOVA)
based approach is used
WHERE BUSINESS APPS LIVE
© 2015 Chandra Shekhar Bhartiya
Google’s 10 years of financial data analysis using Statistics
Objective – Return on Assets % with regards to other parameters
Technique – Since these all are continuous target, so a Linear Regression (ANOVA)
based approach is used
WHERE BUSINESS APPS LIVE
© 2015 Chandra Shekhar Bhartiya
Google’s 10 years of financial data analysis using Statistics
Objective – Operation Margin% with regards to other parameters
Technique – Since these all are continuous target, so a Linear Regression (ANOVA)
based approach is used
WHERE BUSINESS APPS LIVE
About Author
- Shekhar is passionate and energetic professional program/project manager with demonstrated skills in sales, sales management, general
management, strategic planning, business planning, execution, P&L responsibility and management, leadership/coaching, channel development and
growth.
- Shekhar has proven track record in leading Business and IT transformation engagements. Specialist in CIO/CXO advisory and large scale
transformation initiatives ranging from diversified conglomerates to new ventures with many of the fortune 500 companies.
Delivered significant top-line and bottom-line impact at a number of clients in industries such as Manufacturing, Consumer Products, Utilities, Telco,
Travel & Logistics, Retail, Banking, Oil & Gas and Real Estate.
Shekhar has studied in Asia and Germany, grew up in India and worked in various places like US, Europe, India, ANZ, Japan, Korea and many South
Asian countries
© 2015 Chandra Shekhar Bhartiya
Contact me for any fortune 500 firm value analysis to understand the business value of adopting
and using Business Applications to drive business transformation and long lasting improvements