Need for Economists in Corporate India

Corporate India has been caught on surprise on many counts recently and most of them are macro economic events.

These have been namely credit rate hikes, inflation due to oil prices (consequent demand for better salaries and attrition) , market entry of new players and above all the rupee appreciation that shave off nearly 1000 basis points off the profitability of unhedged exporters.

Add to this the uncertainty in stock markets over remote events in the sub prime mortgage market in the United States that has actually led to many corporates getting below expectation results in their listing or Initial Public Offerings despite good fundamentals.

All these point to need for better corporate planning and strategizing for economic changes and events especially in a networked world.

Table 1-Top Macro Economic Events that caught corporate India by surprise and their impact

? Credit Policy Hikes by RBI 2006-2007 leading to expensive debt.
? Rupee Appreciation and RBI steps including curbs on ECB.
? Oil Prices and Inflation.
? US Mortgage Market, Effect on Global Equity Markets including India.
? SEZ Policy and impact on communities (this is more of socio-economic topic)

The primary impact of this has been exporters like Infosys missing their earnings guidance due to rupee appreciation, corporates like WNS having lower listed prices ,rising credit costs including for banks , and considerable rework of SEZ plans for corporates like Tatas and Reliance.

These are the biggest names in India, so the impact of lack of econometric planning and forecasting on smaller players is likely to be more.

Most corporates in advanced economies have business intelligence units and economic strategy and planning units. They are used mainly for forecasting sales using scientific quantitative methods like base driver models, time series models and regression models to predict and anticipate demand and align corporate supply and demand chains accordingly.

The usual audience for them is at CXO or Board level advisory positions.

In India while many corporates have started creating these units they are yet to gain the credibility and respect that they would have got in Western Companies.

Main reasons for these are as follows –
depth of Indian academia in application oriented research and their ability to adjust to corporate demands,
skepticism regarding modeling techniques most of which are complex for end users and corporate audiences ,
lack of investment in forecasting soft wares (like SAS , SPSS and even Excel/Solver ) and human resources in these units.

Most Indian corporates would rather hire five more sales managers than invest in two economists who would help create a much better forecast to help plan the corporate strategy.

This is partly due to historic mindsets and partly due to cultural risk aversion, as corporates engage in cost cutting, sales is looked upon as revenue units and planning units are cost centers. An additional complicating factor is that many companies still believe in push based sales, rather than pull based demand targets.

Table 2
Examples of Business Intelligence Units / Planning Units in Indian Corporates.

ICICI
Reliance
Muruguppa Group
Airtel

Examples of Business Intelligence Units / Planning Units in other countries.

General Motors
British Telecom
Nestle
Citigroup

An alternative for corporates unwilling to go into full fledged economics planning units is to become subscribers for customized content providers by third party providers.

This content could be in the form of business research, market research and segmentation studies, predictive models or even economics newsletters. The chief drawback to this is that due to the outsourcing and Knowledge Process Outsourcing boom, sales margins for third party content providers is much more when catering to the global market.

However even for the outsourcing sector it would be advisable to keep a foot in the domestic market, keeping in mind long term growth plans of Indian corporates and the ability to build domain expertise much better while catering to onshore Indian clients rather than offshore global clients. In the short term, these would be lower margins but it would help in building the domain expertise necessary for them to move up the value chain.

As the Indian economy is poised for sustained growth, the size and scale of this domestic demand for economics content would likely scale up manifold. Indian corporates should actually benchmark their demand planning and economic units from international players and partners

Future Online Advertising Revenue Sharing Models

Imagine if one company had control to 60 % of all advertising in other media channels like Television ,Newspaper or Radio throughout the globe.

Given Google’s current dominance of the online Online Advertising revenue, there are likely to face significant anti trust operational risk within the next three years.Especially if they continue to play hardball on Uncle Bill from RedMond like the one they did with botched Yahoo -Microsoft deal.

The current model of pay per click for Adwords and earn per click for Adsense is unfair to both content generators and online advertisers leaving them vulnerable to  Google’s algorithms trying to cope with increasing click fraud perpetuated systematically.

Future  Online Advertising Revenue Sharing Models could include –

1) Pay per impression or time spent on site for content generators getting a higher weight age for  content generators and Pay per actual purchase for Adwords/online sales.

This removes the cost per milli (C.P.M) model to cost per customer model for advertisers which is only fair.

2) Enhanced social network and Instant Messenger advertising- If blog owners can make money from popular blogs,emails can contain ads , why can’t social network users on myspace and Facebook and orkut make some money atleast from people visiting their pages/profiles.This may involve some discreet ads below posts /messages.

This can only boost Google’s revenue in the long run and be good for the whole industry also.

3) Text Ads to Banner Ads – Banner Ads /Flashier Ads to actually increase appeal on online plain vanilla text ads. Also include some flash ads in all You Tube or Video content. This content /ads will be priced much differently and distinctly than treating it as just glorified text ads like it is treated currently. It could also create a new wave of new media advertising creative professionals savvy in Silverlight and Flash.

4) The 100 $ limit for adsense – Google really should disclose to investors how much money it owes to people for the adsense revenue below 100 $ as the long tail on the Internet can be very very long. Why have a limit on the internet anyways especially if the adsense customer is willing to provide electronic transfer details or Paypal equivalent payment transaction details, then those limits should be much lower as transaction costs per unit transaction would be lower.

What prevents Microsoft from launching a lower priced alternative to Adsense/ Adwords really beats me !!

5) Offline advertising/Microsoft moves – Imagine ads on your windows desktop like any other software supported by ads. Lets say Office without discreet ads on right hand side comes for 250 $ and Office with ads comes for 100 $ lower .(Assuming lifetime value of a customer to be  100 $ here). Tying ads to sell more Vista ?!!!Might just work.. 🙂

The online ad world is  ready for price wars —-as economies slow down, advertisers demand better bang for the buck from media partners and competition ready to heat up in the lucrative online ad world.

Butterfly Effect and Butterflies in my stomach


Chaos in the Markets – How a home loan default in America hurts investors in India

The recent crisis in global equity markets as a result of the sub prime mortgage meltdown in the United States has caught many investors by surprise. Economic fundamentals of most global markets had no major changes yet the disturbances were severe enough to erode and nullify recent rallies in the market. The subsequent panic caused in world currency and equity markets were severe enough to raise multiple levels of concerns in central banks, governments and the investors. What analysts worldwide called a re-rating of risk appetite has led to severe loss of appetite for many investors.

While on one hand it points to the increasingly close knit network of the world financial economy with all its subsequent gains in efficiency and capital investment, it also points to a new area in evaluating equity valuations in varying times of global risk. It is thus necessary for the Indian investor to understand not only those markets in the world are now strongly co related but also the macro economic drivers that affect various markets including hedge funds , sub prime markets, and the US federal bank’s unparallel influence beyond its borders.

Introducing the Butterfly Effect- The butterfly effect is a concept in chaos theory. It refers to the idea that a butterfly’s wings might create tiny changes in the atmosphere which could lead to a tornado appearing somewhere else. Thus changes which apparently seem random are actually caused by small changes in the initial system. The flapping of wings represents a small change in the initial condition of the systems.

Similarly the loosening of credit in the United States sub-prime mortgage system partly due to the increased liquidity in the system and collateralization of loan based collaterals to financial intermediaries has caused the widespread chaos in the worldwide financial systems. Simply put, there was too much money in the market that mortgage companies could easily borrow, and transferring risk was also easy due to securitization of these loans. As a result, while financial discipline was lost while lending to a booming real estate market, the risk was systematically transferred and in fact hidden during the collateralization process to the whole financial system.

Quite notably, the about turn by the United States Federal Bank also caught the market by surprise. By cutting the inter –bank lending rateand the later the key Fed rate, the Fed ,led by Bernanke in it’s first crisis since the post Alan Greenspan era, effectively did a volte-face on it’s previous stand of waiting and watching. Indeed markets in Alan Greenspan’s time used to talk of the Greenspan put – basically a concept that is asset prices tumbled too far the Federal bank would step in to take control.

Note: This has important lessons for even Indian lender. In the United States median house prices almost doubled from 1993 to 2005, while incomes rose just 49 percent. In India, the real estate boom has been even faster and thus the increased danger of the bubble bursting.

The recent crisis also pointed to the fact that Indian Markets continue to be sensitive to downward global cues, to the point of ignoring value investing. The concept of value investing was created by Graham –Dodd and most successfully used by Warren Buffet. It said that it was better to concentrate on the fundamentals of the company and expected cash flows rather than try and anticipate its future price. The metric used was the long term rice to earnings ration of a particular stock. Thus the ups and downs of the market have actually taught both the retail and the institutional investor the fundamentals of investing on actual value rather than be caught up in bearish or bullish swings.

At this point, the recent record fund raising by mutual funds is also an increased area of concern. The regulators led by AMFI, SEBI and the RBI should focus that mutual funds are sold transparently with easy to understand tables of disclosures, and adequate amounts of effort is spent on educating the common man, who is the worst affected in any stock market bust.

A Poetic MBA

Here is a link to my poetry book.

Read and Enjoy, Poetic stories of an eccentric MBA.

http://www.lulu.com/content/1028221

An example-

A poem to a MBA batchmate  shot dead

Manjunath

Dear departed Manju,
This is all I have to say
See you in heaven, man
We’ll be batchmates there someday.

We’ll play some football,
You would shout aloud once more.
To pass the ball, to pass it to you
And we’ll have tea afterwards, in the heavenly dhaba store.

You will sing again,
And we’ll drink again.
Goodbye Macha,
Goodbye till we meet again.

Dear Mother

Dear Mother

Mother, I was your beloved child.

Then I got married and left you behind.

But in my heart you always stay,

Your example helps me be a mother today.

 

You cared for us, with loving hands and patient ears

You protected us for so many years.

Now when I try and am a mother to my son.

I cannot help but think of us as one.

yashoda_krishna_rb05.jpg

So even though we now live apart.

We are together, in soul and heart.

I love you just as my child loves his mother

Though far away, in thoughts together.

May you always be,

Healthy and well.

Your 100 more birthdays,

Celebrate we shall.

You were sometimes strict

But always for our good.

May you live long.

God bless and touch wood.

Predictive Forecasting in Commercial Applications

Most organizations tend to have a sales plan or forecast for the next 1 year.This is done for internal planning as well as give guidance to financial investment analysts covering the listed company.

However a lot of organizations use simplistic linear models of

1) either growth based on previous history (Last year Sales * Factor of forecast (e.g 10 % growth in sales) -TIME SERIES APPROACH

OR

2) growth based on macro economic causal factors (e.g economy is in recession hence sales will grow by 3 %) REGRESSION BASED APPROACH and

3) A consensus of industrial factors (We have spare capacity of 10 % so we will likely slash prices and have sales growth of 2 % but profit growth of -3%) DELPHI BASED APPROACH (this is also based on bottoms up market feedback and top down sales pressure).

A better approach is to combine all these approaches in one or different models .

This can help build a much more robust forecasting model for organizations using nothing more than simple combination of excel cells.

The following model assumes only seven factors and tries to build a stable and relatively easy to understand forecast model.

Forecasted Sales for this quarter =

Historic Sales for this quarter last year *A1

+ Historic Average Sales for this quarter for past three -five years (based on industry cycle ups -downs)*A2

+ Historic Sales for this quarter/Actual Sales of Last Quarter( for seasonal factors )*A3

+Causal Factor 1 ( Eg. Outsourcing is likely to grow by 15 % in this year) *A4

+Causal Factor 2 (Foreign Exchange Movement.Dollar is likely to depreciate by 10 %)*A5

+ Causal Factor 3 (Our bench strength is likely to grow by 3 % in this quarter)*A6

+ Percentage Error Factor *A7 (There will always be +-5 to15 % error in forecasts.Capturing this error also helps provide a feedback loop for planning).

Here A1- A7 are constants

In order to get actual values of A1-A7 , run this a regression (use the add-in and tools menu in excel) on actual data for past three years quarters (keeping last six months seperate)

Then run the actual equation on last two quarters and check for actual error. If error exceeds the comfort level (+-3 % for critical industries and +-15 % for harder to predict industries) . Iterate the last two steps till you get a good equation.

Then substitute in the 7 factor predictive model to build your simple and robust sales plan for this quarter.

Happy forecasting !!!

Virtual Networking- Linkedin as a Case Study

I am Ajay Ohri,from Delhi ,India and I have been on Linkedin for almost a year now. I got an invite from an American client of mine who became friends with me. LinkedIn seemed a great way to keep in touch. As a data mining and analytics specialist , I was part of the SAS , SPSS and R groups (technical email groups in data mining). But something was missing. Thats right..the interactive touch that Linkedin increasingly provided. I have now 2580 contacts, get atleast 8 invitations every day , have 11 recommendations from professional collegues and have best answers in 2 questions .( ok, I am bad at the questions). These are some of the things I found help me grow my network and gain a lot by devoting 15 minutes of my time to online networking everyday.

1) Put up a nice professional photo – It helps people who have stumbled on your profile with a good impression.

2) Put all the companies and schools you have been to. Complete your profile. Then use the search feature to build up initial contacts.

3)  Ask and get atleast 1 recommendation per company. That should not be hard if you are a good  worker whom people remember.People with more recommendations are trusted and get more invites.Give honest and credible recommendations yourself.

4) Answer atleast one question a day belonging to your domain. Or Atleast ask 1 question. Ask atleast 1 question a week. That helps build up your noticeability.

5) Be an open networker- If you are open to sharing your email address (which is any ways available with a lot of people) you can be an open networker and join the Open Networkers and Linked In Open Networkers (LIONs)

6) Publicise your profile using the public profile feature. Use the link as an email signature.

7) Try and learn more about your area of work by going through questions. It helps build up skills. Accept all recruiters invites. They and your enhanced skills will help you in a rainy day in a tough economy.

8) Be humourous in your profile message (eg Ajay Shrugged …or Ajay is changing the world one mega byte a time). People love to network with humourous people.

9)Be sensitive to cultural differences when answering questions. Eastern and Western cultures think differently about what is private, public and what is rude.

10) Have fun doing this. Otherwise if it becomes a chore , like mowing the grass, it will remain unattented to and you will miss out on the free benefits of Linkedin.

11) Pass on forwards to help other people get jobs or get in contact. This will establish you as a good person to stay linked in too.

12) Join as many groups and associations that you find interesting. Start from your school alumni groups, then with groups of your friends and so on.Found a group. I founded two Decision Stats for data mining and statisticians and Creative Destruction for lateral thinkers.

Linked In help me get a contract for my firm, numerous inetrviews,domain specialists for technical help, and friends , and fortunately Linkedin allowed me access to fabulous people who did that.It helped me learn a language called R , which is open source and helped me give ideas to make two websites www.iwannacrib.com and www.decisionstats.com. Above all it gave me 2580 acquaintances whom I can fall back on in tougher times. Collectively they do help make a difference.

All the best for your online social networking. If Bill Gates can be on Linked In after retirement, then why can’t you ?