This is a bearish article written at the height of the Indian market bubble in August 2007. I was sure that an unprecedented and unsustainable "wall of liquidity" had pushed up asset prices for all asset classes in all markets around the world, and that a huge global crash was imminent. My convictions grew out of my experience in 2006 and early 2007 working with a large mortgage bank in Los Angeles, which gave me a unique first hand view of the emerging mortgage crisis in the U.S. - both on the "origination" side where risky loans were being approved, and on the "secondary market" where dubious loans were packaged into fancy MBS and CDO securities. By early 2007 subprime loans began defaulting and some US subprime lenders had collapsed, and by mid 2007 when I moved back from Los Angeles to Mumbai it was clear to me that the global liquidity boom was turning into a global credit crunch, certain to be followed by a recession. I was amazed to see that investors in Mumbai were living in a world of excesses, reckless lending/investing, and "bubble mentality", and I hoped that my article would contribute a tiny bit to bring some sanity in a raging bull market. Sadly, the Sensex went up by several thousand points after I wrote this article, and my credibility took a beating. I must admit I felt relieved and pleased that the crash did happen in early 2008. The mortgage bank where I worked in Los Angeles collapsed in 2008, becoming one of the largest bank failures in US history. I am pleased that I correctly anticipated the global crash, but I seriously underestimated the extent of the downturn. After sitting on cash for a long time, I bought stocks in March 2008 after the Bear Stearns crash, when I thought the market had corrected sufficiently for valuations to be reasonable again.
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The fall and rise of Indian stock markets
Partha Sarathy
August 10, 2007
There are three fundamental factors driving Indian stock markets that have not been well understood in India, and these could have a dramatic impact on investors’ returns.
First, the recent rise in stock prices and land prices in India have less to do with the strength of the Indian economy, and reflect a larger global asset price bubble, fuelled by the so-called global “wall of liquidity”. Second, we are now seeing a global credit crunch which could create a global recession and pull down stocks and property prices in all global markets, including in India. Third, the true impact of the “India shining” story has not been completely factored into stock prices, and we should expect stocks of leading Indian companies to rise by many multiples in the next 10 years. Let’s review each of these trends, and see how they could impact Indian investors.
Global asset price bubbles
Many Indian investors believe that high stock prices for Indian stocks reflect their strong corporate profits, long-term growth potential, and global recognition of the “BRICS” investment thesis. While this belief is partially correct, there is also a mistaken belief that Indian stocks will be immune to much of the troubles in global stock markets, especially the ongoing mortgage crisis in the US.
Two important points to note here. First, the rise in Indian stocks since 2004 is not a uniquely India-centric phenomenon; Indian markets have closely followed the same growth trajectory seen in virtually all stock markets around the world. Second, the US mortgage crisis is not a localized issue between some reckless borrowers and imprudent lenders; it is a part of a massive global credit crisis that will take months to unravel, is bound to cause a recession, and will pull down asset prices everywhere, including in India.
Let’s look at the rise in Indian stock prices. Since the 2001-03 recession, when the US Federal Reserve reduced interest rates from 6.5% to 1%, prices of stocks, property, commodities and all other financial assets have risen sharply in virtually all economies. The 154% rise in Sensex since 2004 is ordinary compared to the 365% rise in Shanghai, 195% in Karachi, 173% in Mexico, 101% in Buenos Aires, 90% in Spain, and 87% in Australia. A large part of this rise comes from increased corporate earnings, which is a very healthy phenomenon, but disturbingly a large chunk has come from rising P/E ratios for all global markets. For example the forward P/E ratio for India’s Sensex is 17, which is close to an all-time high, while forward P/E ratios have gone up to 36 in China, 15 in the US, 14 in Indonesia and 13 in Europe.
Similar is the story with property prices across the world. Indian property prices have grown 10%-20% per year, which is in line with property price rises in other markets. According to Knight Frank’s global annual house price index, the markets with highest price rise between Q1 2006 and Q1 2007 were: Latvia 61%, Estonia 24%, Bulgaria 23%, Lithuania 22%, Norway 16%, South Africa 14%, Singapore 14%, Canada 13%, UK 11% and Belgium 9%.
Similarly, global commodity prices have shot up to unprecedented levels since 2004. In the first 5 months of 2006 alone, zinc rose 100%, copper prices rose by 80%, silver by 60%, palladium 50%, tin 40%, gold 39%, aluminum 36% and platinum 36%.
The conclusions are clear. First, Indian stocks have closely followed the global asset price rise that has happened in virtually all markets; they are not driven by unique India-specific issues. Second, the large, simultaneous rise in asset prices across all markets across all asset categories is truly unprecedented, and should make prudent investors wary. Third, the rapid rise in P/E ratios across all global markets is clearly unsustainable, and as normality returns we should expect P/Es to fall steeply. Fourth, if global asset prices were to crash due to the credit crisis, Indian markets will follow closely, regardless of the ‘India shining’ or BRICS story.
Wall of liquidity and interest rates
Why have asset prices risen so sharply across the world? Broadly, asset prices have been boosted by abnormally low interest rates, which have been pulled down by the so-called global ‘wall of liquidity’. Global financial markets have been flooded with surplus liquidity as investment-seeking funds from around the world have poured into various asset categories.
The largest source of global liquidity is the Yen “carry trade”, where investors purchase Japanese Yen bonds at zero interest rate, convert the funds into US Dollars, invest it in US bonds (or any other bonds or stocks) that carry high returns. Since the Japanese bond carries zero interest, the entire return is a profit to the investor, and the profit could be even higher if the investor uses debt to buy the Japanese bond, or if the Japanese Yen depreciates against the Dollar. Japan has become the world’s largest creditor country, and the carry trade runs into hundreds of billions of dollars.
There are many other sources of global liquidity. Middle Eastern oil exporters earn more than $1 trillion every year due to high oil prices, and much of this has flowed into European stocks, bonds and property. In the US, pension funds and mutual funds are sitting on almost $1 trillion dollars of investments by the baby boomer generation; their investments are at a peak as they approach retirement starting 2007. Foreign central banks have invested almost $2 trillion into US Treasury bonds, mopping up their export surpluses to prevent their currencies from appreciating sharply. For example China holds foreign currency reserves of $1.3 trillion, Japan holds $500 billion, Russia $417 billion, and Hong Kong $136 billion.
With all this surplus liquidity looking for investment opportunities around the globe, one dramatic impact has been the fall in interest rates, especially in the US. Short-term interest rates are set by the US Federal Reserve, but long-term rates are determined by supply and demand for US Treasury bonds, which in turn determines interest rates for individual and business loans. Since 2004 the Fed has increased short-term interest rates 16 times, from 1% to 5.25% in order to cool down the economy. However, long-term interest rates have remained unusually low, just above 4% in much of 2004 and 2005. In his testimony to the US Congress in February 2005, US Fed Chairman Alan Greenspan commented on this unusual combination of high short-term rates and low long-term rates; this is now famous as the “Greenspan conundrum”. The low long-term rates are also surprising when you consider that Asian/European buyers of US Treasury bonds receive almost zero or negative real returns since the depreciating Dollar wipes out their nominal returns when converted into their local currencies.
Unusually low long-term rates created an “inverted yield curve” in 2006, with long-term rates lower than short-term rates. For example in February 2007, the rate on 10-year Treasury bonds was 4.81%, while 3-month Treasury bills were at 5.16%. Normally the rates on long-term bonds are higher than short-term rates to reflect additional risk involved in the longer tenure. The yield curve gets inverted when long term bond investors expect a recession and anticipate that interest rates will come down in the following years. Hence the inverted yield curve is a strong predictor of a recession, for example each of the 6 recessions in the US since 1970 was preceded by an inverted yield curve. The prolonged inversion of the yield curve in 2006 strongly indicates an imminent recession in the US.
In addition to low interest rates, the interest rate difference – called the “yield spread” – between safe government bonds and riskier bonds has also been abnormally low. Historically the spread between 30-year mortgage rates and 10-year Treasury rates has been between 1.5% and 2.5%, reflecting the higher risk involved in mortgage loans compared to safe Treasury bonds. But in 2005 and 2006, the spread fell to as low as 1.1%, reflecting unusually low risk aversion by investors. Similarly, the spread on highly risky “junk bonds” issued by private equity firms to fund leveraged buy outs was 8%-10% until a few years ago, reflecting the high risk involved, but since 2004 the spread has come down to as low as 3%.
The inverted yield curve and low spreads have created massive distortions in US financial markets, especially in the credit market. Firstly, it means that since 2004 US bond markets have substantially under-priced risk, i.e. investors of long term US Treasury bonds have been very reckless in not demanding (or at least, not receiving) returns commensurate to the long tenure of these bonds. Second, it means that all day-to-day interest rates that impact individuals and businesses have been under-priced compared to the risk involved, including mortgage rates, car loan rates, corporate bonds, junk bonds raised for leveraged buy outs, and collateralized debt obligations. Huge quantity of funds have flowed into these debt instruments that either should not have happened, or should have at much higher interest rates. Third, the availability of surplus debt funds at unnaturally low interest rates has encouraged individuals and companies to borrow more debt than they should, spend more on consumption than they should, and what is worse, make long-term investment decisions based on unnaturally low rates prevalent at that time. Fourth, once long-term interest rates and credit spreads correct themselves to fully reflect the risk involved in various loans, it will cause a far-reaching re-pricing of virtually all loans in the economy, impacting every individual and corporate borrower.
Sub-prime mortgage crisis
The sub-prime crisis in the US mortgage industry is the most visible illustration of the madness in US credit markets. As the US Fed reduced interest rates in 2001-03 from 6.5% to 1% to mitigate the dot com meltdown and the aftermath of the 9/11 crisis, the low interest rates began to fuel a mortgage boom even before other parts of the economy began to revive. As the economy rebounded, property prices began to rise, but interest rates still stayed low. The combination of rising property prices and all-time low interest rates fuelled a massive mortgage mania, with property prices in some markets rising 20%-40% annually. Not only did consumers take loans to buy new homes, they also took loans to refinance old expensive loans with new cheaper loans. One popular option was cash out refinance, where borrowers refinanced their old loans at lower rates and took out a large amount of money from the equity they had in their homes, while their monthly installments remained the same due to lower rates. This was smart when home prices were rising, but reckless if home prices had remained steady, since few borrowers would have wanted to increase their loan amount and destroy their home equity to spend on SUVs and vacations. The next to get on the bandwagon were “sub-prime” (less credit-worthy) borrowers, many of whom would not have qualified for loans. So the mortgage industry came up with a series of innovative “products”, such as option ARMs (start with a low “teaser rate” of 1%-2% that you can afford, and before the rate “resets” to a higher level in a few years you hope to sell the apartment at a big profit); negative amortization loans (initially pay small installments that do not even cover the regular interest and principal, so the loan amount keeps increasing just as it would in a credit card loan if you just pay the minimum balance); “no income no asset” loans (so-called “liar loans” where you don’t need to provide supporting documents for your income or assets if you have good credit history); and 100% LTV loans (take a regular mortgage for 80% of the loan amount, and another home equity loan for the balance 20%).
Clearly, a lot of mortgages were made to borrowers who didn’t really qualify for them, and at unnaturally and unsustainably low interest rates. Once rates rise and the higher mortgage installments kick in, millions of homes could go into foreclosure, depressing property prices for years and bankrupting many lenders. The culprit here is not reckless mortgage lenders, ignorant borrowers or lax regulation, though all these did play a part. The real culprit was the unique structure of the American mortgage industry, which contains multiple levels of intermediaries, with limited information exchange, conflicting incentives, and no accountability for ensuring credit quality. Mortgage brokers sell complicated (and often inappropriate) mortgage products to ignorant borrowers based on commissions they receive. Mortgage banks compete to fund loans originated by brokers, usually with limited understanding of the credit worthiness of borrowers. But mortgage banks do not hold the loans on their books, they make a profit by selling off loans to larger mortgage banks. Ultimately Wall Street firms securitize these loans by pooling large quantities of loans and selling “mortgage backed securities” (i.e. bonds backed by the interest and principal payments on the underlying mortgages) to investors. These bonds are rated by credit rating agencies, and purchased by large financial institutions, including pension funds, mutual funds and banks. These investors ultimately bear the risk involved in the underlying mortgage loans, but are separated from individual borrowers by so many intermediaries that they have very little understanding of the credit quality of these loans. So a pension fund that purchased seemingly safe AAA-rated mortgage backed securities for its investment portfolio may suddenly find the bond’s value eroding when the underlying loans go into foreclosure, for example when a reckless school teacher in San Diego who took a million dollar Alt-A loan is unable to make payments after the interest rate is reset higher.
The problem with the sub-prime mortgage crisis in the US is not that some reckless borrowers and imprudent lenders will go bankrupt. The real issue is that (1) almost $2 trillion dollars worth of securitized loans of dubious quality have been purchased by large global financial institutions; (2) there is no way to know which of these bonds will diminish in value and to what extent, because of the multiple levels of intermediation and limited information; and (3) if these securitized loans default, it will hurt hundreds of millions of individual investors who have invested in those pension funds, mutual funds, and banks. To a pessimist, this is the worst global financial crisis since the Great Depression.
Private equity mania
Another consequence of the wall of liquidity is the record amounts raised by US private equity firms and hedge funds in 2006-07. The combination of low interest rates and high stock prices created a perfect environment for private equity in recent years. Traditionally, private equity firms generate wealth by cutting costs. They take over a company using a lot of debt and a little equity, restructure the company, shut down unprofitable divisions, fire employees, cut advertising and discretionary expenses, sell underused assets, boost the company’s profits, increase earnings per share, and eventually sell the “new and improved” company at a profit. Private equity firms love to take on huge quantities of debt, since (1) profits from cost cutting are amplified by the leverage obtained through debt funding, (2) debt raised for the acquisition helps reduce taxes, and (3) large debt repayments force employees, suppliers and partners to cut costs.
Instead of this traditional method, since 2006-07 private equity firms have had 3 methods to generate wealth: (1) cost cutting, which is the old method, (2) rising stock prices, which help the private equity firm make a profit even without cost cutting, and (3) low interest rates, which encourages excessive debt since everyone wants to take on as much debt as possible while rates are low. With interest rates and yield spreads at historical lows since 2004, private equity firms have exploited this once-in-a-lifetime opportunity by raising record sums from investors and making incredibly large deals, often at large premiums over prevailing share prices. These deals have helped to push up stock prices further, generating even more profits to private equity firms, and causing even more funds to flow to private equity firms.
Not only have private equity firms been reckless with debt, so have lenders. Faced with surplus liquidity, private equity firms have been able to raise funds from bond investors very cheaply. The yield spreads on “junk bonds” issued by private equity firms to fund leveraged buy outs has come down from 8%-10% a few years ago to as low as 3% since 2004, indicating how reckless borrowers have become. Other contractual terms that private equity firms have demanded, and usually achieved, from bond investors include "covenant-lite" deals for leveraged buy outs, and "toggles" that allow borrowers to pay loans with fresh bonds.
The parallels between the sub-prime mortgage boom and the private equity are ominous – low interest rates, reckless borrowers, imprudent lenders, insufficient collateral, excessive debt, speculative investments, dilution in under-writing standards, poor due diligence, complex and risky loans, and minimal regulatory oversight. The net effect is that too many loans were made that wouldn’t have been made under saner circumstances. In both mortgages and private equity, these errors pushed up asset prices so much that investors overlooked the shortcomings and poured more money into these, setting the stage for a bubble.
Over-leveraged hedge funds
If private equity firms have handled debt badly, hedge funds have been worse. Hedge funds normally make large “hedged” investments (i.e. similar bets in opposite directions) to exploit minor imperfections in market prices, so that they do not lose their capital regardless of whether the market moves up or down. To make money from such small bets, hedge funds employ large debt funding and complicated derivative instruments so that the “leverage” multiplies their profits. In theory, this is a sensible and sophisticated way to invest, and smart hedge fund managers have earned fees of hundreds of millions of dollars for investing other people’s money.
However, two factors have spoilt this neat theory and made hedge funds far more risky than ever. First, in recent years stock prices and all other asset prices have moved consistently upwards, so some hedge funds have stopped prudently hedging their bets in both directions, and sometimes make huge risky leveraged bets in one direction. If that bet fails, the entire firm collapses; this is roughly what happened with Amaranth Advisors in September 2006 when it made large concentrated bets on petroleum prices. Second, low interest rates have encouraged hedge funds to use huge and often incredible amounts of borrowings. A Financial Times article (January 19, 2007) mentions a hedge fund that is two times levered, backed by fund of funds' money which is three times levered, and investing in subordinated tranches of collateralized debt obligations which are nine times levered. Every $1 of investors' capital is leveraged to own $54 of bonds, so a 2% price decline in the bonds could wipe out the firm’s entire capital.
Bubble, bubble, everywhere
Liquidity-driven asset bubbles are not restricted to the US market; this is truly a global phenomenon. Consider Spain, which joined the EU in the 1990s and faced unusually low interest rates set by the European Central Bank. Combined with Spain’s favorable demographics, and access to the European market, the low interest rates have caused Spanish property prices to soar by 270% between 1996 and 2006, which is very high by European standards. The Spanish property boom has been so spectacular that it ignited a passion among North Europeans staying in cold, wet and gloomy locales to buy their “home under the sun” in Spain.
Liquidity-driven bubbles seem to be at work in China, India and other emerging markets as well. Strong economic growth and rising exports have pushed up corporate earnings in India, and these fundamental reasons partially explain the sharp rise in stock markets. However a substantial part of the rise in stock prices comes from rising P/E multiples, which can only be explained by excess liquidity inflows, mostly from low global interest rates and large overseas fund inflows. For example, external commercial borrowings of $20 billion have helped Indian companies to cut interest expense and fund investments, especially since global interest rates are low, and the Rupee is appreciating against the Dollar. Then there is the Yen carry trade, where hedge funds raise cheap Yen funds to invest in Indian stocks and property, and benefit from both the price rise as well as Rupee appreciation.
One peculiar instance of the bubble is the abnormally strong currencies in Asia. Everyone expects Chinese and Indian currencies to appreciate due to the inflow of export earnings, remittances, and foreign investments. The parallel is with the Japanese Yen, which doubled in the 1980s on the back of strong exports. However, Japan had been running large export surpluses since the 1970s, and the Yen appreciated as late as in 1985 after the export surplus began to account for a large chunk of the total currency transactions on exports and imports. In India’s case the export surplus is negative, and even if we include services and remittances the surplus is tiny compared to total exports and imports. Clearly, these fundamentals alone cannot explain the appreciation of the Rupee from 48 to 40 against the Dollar. A large proportion of this appreciation must be due to inflows of hot money, especially the Yen carry trade, which could reverse quickly. This is difficult to prove, but one guess is that the correct exchange rate for the Rupee is closer to 44 to the Dollar, and the rise to 40 in early 2007 is a temporary liquidity-induced phenomenon.
Greenspan conundrum in Asia
A common theme across emerging markets is the inability of central banks to cool down their overheating economies. Rapid growth in Asian economies is creating supply bottlenecks and inflation in input prices. Consider the rise in commodity prices due to massive requirements from Chinese factories, high staff attrition rates and salary hikes due to the rapid growth of Indian IT and BPO outsourcing companies, and shortage of commercial rental space across Asian economies as companies race to set up offices and factories.
Asian central banks have repeatedly hiked interest rates to try to cool down the economy, but given the integration of financial markets, that strategy has not worked. Companies can easily overcome the central banks’ restrictions by raising low-cost funds from developed markets through such means as the Yen carry trade, suitcase transfers from Hong Kong to mainland China, and complicated funding instruments in India that could be selectively interpreted as debt or equity depending upon which government agency is asking. Clearly, this is the Asian equivalent of the Greenspan conundrum where interest rate tightening by the central bank fails to cool the economy since interest rates and liquidity remain high.
Clearly, the combination of high growth and excess liquidity is creating massive asset bubbles across China, India and other emerging markets, as Alan Greenspan recently commented about China’s overheated stock markets.
Minsky asset bubbles and credit cycles
A remarkable aspect of this asset price bubble is the extent to which it is predicted by American economist Hyman Minsky’s financial instability theorem. According to Minsky, asset bubbles are a consequence of the credit cycle, which is the periodic expansion and contraction of debt funding available to companies and individuals. Depending upon their credit worthiness, borrowers fall into one of 3 types: (1) hedge investors who can repay the principal and interest from their cash flows (these are credit worthy investors), (2) speculative investors who can repay the interest but do not have funds to repay principal (unless they reschedule the loan or sell the asset at a profit), and (3) Ponzi investors who can repay neither the interest nor the principal (and must take a new loan or sell the asset to repay the first loan).
In the early part of a credit cycle, when the first type of investors dominates the market, the market is stable and asset prices are reasonable. In the peak of a credit cycle, the third and second types of investors dominate the market, and this creates an asset bubble that is bound to burst. Minsky’s financial instability theorem also states that over periods of prolonged prosperity, the credit cycle moves to a peak, excess credit attracts too many Ponzi investors into the market, and this creates an asset bubble. This phenomenon seems to be playing out in stocks and property markets in all global markets.
The US mortgage crisis is a text book case on how Minsky asset bubbles are created. The boom began with loans made to credit worthy “prime” borrowers in 2003-04, reached mania levels as speculative borrowers entered the market for “Alt-A” loans, and became a crisis when “sub-prime” borrowers flooded the market in 2005 and 2006. Similarly, the Indian mortgage boom began with credit worthy borrowers who bought apartments cheaply in 2003, then attracted speculative borrowers who bought second homes for investment in 2005, and the bubble peaked in 2006 and 2007 as less credit worthy borrowers bought over-priced homes without the capability or intention to repay the full loan, eager to flip the home for a quick profit.
Perhaps the same phenomenon is happening is happening in private equity and hedge funds as well. The good buyout deals in the past usually involved strong companies bought at low valuations and with prudent levels of debt, often at high interest rates. Surplus liquidity in recent years has encouraged private equity players to do risky deals at high valuations with excess debt that they cannot repay if either valuations come down, or interest rates go up.
The bubble has burst
All indications are that we have climbed the peak of the liquidity-driven asset bubble, the bubble has already burst, and we are now seeing the beginning of a global credit crunch that will persist for 1-2 more years. Since the middle of 2007 there is a significant contraction in the global credit markets, which is both pushing up interest rates and reducing the availability of debt funding. Bond issuers are finding it difficult to attract buyers for any kind of bonds, including corporate bonds, junk bonds issued by private equity firms for buyout deals, mortgage loans, and mortgage backed securities.
Evidence of the credit crunch is seen everywhere, and even large private equity deals are running into trouble. When private equity firm Cerberus tried to sell $7.5 billion of bonds to fund the acquisition of Chrysler, the bonds had to be sold at a discount to pacify nervous investors. Many borrowers have dropped or postponed plans to raise more debt, including Carlyle, Arcelor Mittal, MISC, and US Food Services.
The most glaring illustration of the credit crunch is the difficulty faced by mortgage banks in selling securitized AAA-rated bonds to investors. Till the end of 2006, these mortgage backed securities were seen as safe investments, comparable to AAA-rated corporate bonds and priced just a little higher than safe Treasury bonds. However, in recent weeks bond investors have become so nervous that they refuse to buy any mortgage backed security, even AAA-rated securities consisting of high quality “prime” loans. This has thrown the US mortgage industry into a new crisis, one that is distinct from the problem of sub-prime lending. If bond investors refuse to buy mortgage backed securities, mortgage banks must either hold the loans on their balance sheets (as mortgage lenders do in India, but unthinkable given the American industry structure), or shrink their business and sell only “conforming” prime loans that they can sell to Fannie Mae and Feddie Mac, which is what most mortgage banks are doing now.
Following Minsky’s theorem, we have just crossed the Minsky moment, where the credit cycle reverses and a period of surplus credit gives way to a credit squeeze, and the asset bubble bursts. What we now face is a long and painful period of economic realignment and contraction.
High interest rates and global recession
The first consequence of the credit squeeze is rising interest rates. US long-term Treasury rates have jumped an unprecedented 0.55% since June 2007 as bond investors suddenly grew nervous and decided that they needed higher returns to compensate for the risks involved. In the last 3 months, the spread between Treasury bonds and mortgage loans has also suddenly retreated from its all-time low of 1.15% and moved to the historic average of 2%, as bond investors become more concerned with the mortgage crisis. These increases in interest rates are large and unusual given that bond markets usually move a few basis points each day. A sudden 0.55% hike in rates is the financial equivalent of an earthquake, and the aftershocks and tidal waves will continue for some time.
Three factors are pushing up global interest rates. First, many investors have become nervous about credit quality and under-pricing of risk as they understand more about the US mortgage crisis, and they are either reducing their exposure to US bonds, or asking for higher interest rates for the bonds they buy. Second, Asian central banks are reducing (or threatening to reduce) their exposure to US bonds, drying up liquidity further. Third, rapid growth in Asia is overheating global commodity markets and oil prices, prompting central banks to push up short-term interest rates (or at least, retain high rates) to cool down the economy. The US Fed decided on August 6, 2007, to keep interest rates steady at 5.25%, in spite of demands that interest rates need to be reduced to prevent the mortgage crisis from causing a recession.
The combination of higher inflation and rising interest rates is creating a double whammy. While inflation is causing central banks to push up short-term rates, the global credit crunch is pushing up spreads higher and causing long-term rates to move even higher. The inflationary impact of Asian growth and high oil prices is an old story, and has never really impacted the US financial markets. What is new this time is that excess global liquidity, which in previous years helped to push down interest rates abnormally, has dried up causing rates to move up sharply.
A sharp rise in long-term rates could cause a global recession. Corporate profits will decline mildly, investment spending will come down sharply, consumer confidence will vanish, and speculative investments will get wiped out. Consider the US mortgage industry. Default rates are already at very high levels, but could reach crisis levels if long-term Treasury rates climb to 6% and sub-prime adjustable rate mortgages get reset to 15% and higher. Even without high interest rates, the sub-prime mortgage loans are facing very high levels of delinquency and foreclosure, and the inventory of unsold homes is rising. Once interest rates rise, the crisis could spread to Alt-A loans and even prime loans, foreclosures could shoot up, and real estate prices could fall sharply. These disturbances could also ripple into mortgage backed securities and “CDO squared” securities created out of mortgage backed securities, causing billions of dollars of losses to pension funds and hedge funds who have invested in these securitized loans.
Rising rates and widening spreads could create a similar crisis in private equity and hedge funds. At least a few of the recent buy out deals are likely to collapse, reeling under high interest rates, excessive debt, and excessive acquisition prices. Two hedge funds managed by Bear Stearns have already collapsed under the weight of reckless investments and excess debt. They had large exposure to mortgage backed securities and were highly leveraged, so a reduction in the bonds’ values wiped out the entire funds. Boston-based $3 billion hedge fund Sowood Capital Management lost $1.5 billion in July 2007 when its losses in corporate bonds were amplified by its high debt levels. Among its investors were Harvard University’s endowment fund ($500 million) and the Massachusetts state pension system ($30 million).
This is just the tip of the iceberg. When the 2 hedge funds run by Bear Stearns ran into trouble, creditors led by Merrill Lynch forced a distress sale of the hedge funds’ assets, and they found that the securities were worth far less than previously believed. Even A-rated securities were worth just 85% of the face value, and B-rated securities were almost worthless. The banks halted the sale before the distress sale set off a spiral of lower valuations and further distress selling by other hedge funds. Some analysts believe that this was a cover up to protect investors. If the sale had proceeded and the low valuations published, investors across the world would have had to mark down the value of their holdings. That means almost $2 trillion worth of mortgage backed securities could be re-priced, resulting in large losses and panics across the global markets.
If a credit crunch looms, recession is not far away. The scenario of high inflation, high interest rates, and low or even negative GDP growth would be the opposite of the benign global economic environment that we have had for the last 3-4 years. Once long-term interest rates rise, it pushes up the rates for all kinds of loans, including credit card debt, car loans and personal loans. With oil prices at very high levels and American consumers routinely spending more than they earn, these rate hikes could cause high levels of bankruptcy among individual borrowers, distress sale of cars and homes, and sharp reduction in consumer spending. High rates would also hurt corporate profitability, and force companies to cut investments and lay off workers. The combination of rising interest rates, reducing private consumption, lower business investments and rising unemployment could push the economy into a recession.
Once a recession kicks in, stock markets and property prices will fall steeply, perhaps by as much as 20%-30%. The resulting chaos would trigger a massive flight to quality, as investors move to blue chip stocks, safer Treasury bonds, and so on. The flight to quality could hurt exactly those sectors that have seen the sharpest rallies in recent years – private equity, emerging markets, sub-prime mortgages, hedge funds, and so on.
It’s impossible to predict when the correction will happen. When a market is unhealthy, the crash could be delayed for months and years even after everyone knows that a crash is imminent. During the dot com mania stocks fell 2 years after Alan Greenspan warned about irrational exuberance. The US mortgage bubble broke in 2006 though economists had warned about it since 2004. Some commentators have compared stock market crashes to the laws of cartoon physics, where a character runs off a cliff and continues horizontally, until it looks down and discovers that there is no ground under its feet, and only then does it fall down. One could speculate that the global economy has already run off a cliff, but does not realize it yet, and will crash once the realization sinks in.
Fall and rise of Indian stock markets
The sharp rally in Indian stocks since 2004 was driven by excess liquidity from overseas investors who pushed up P/E levels to new highs, with modest participation from domestic investors. Once liquidity dries up and rising rates trigger a global flight to quality, there will be a massive sell-off by overseas investors from the Indian markets. The Yen carry trade will vanish and create its own ripples as hundreds of billions of dollars worth of bonds are sold and investors scramble to buy Yens to cover their trades. Hedge funds and private equity firms that face redemptions from their investors in the US will sell Indian stocks and property at any price they can get. Inevitably, the withdrawal of liquidity will cause the Sensex and property prices to fall sharply.
This crash could happen in spite of everything that is positive about the Indian economy, including strong corporate profits, potential for long-term growth, favourable demographics, and the success of the outsourcing sector. All stocks will get beaten down, including sound companies with excellent fundamentals. The Sensex and property prices could remain depressed for 1-2 years.
However, there are 3 positive factors that could play out. First, the distress sale by foreign investors will give Indian investors a chance to buy undervalued stocks and property. Worse the panic, better the buying opportunity. Second, as foreign investors pull out of emerging markets the Rupee will temporarily depreciate and help Indian exporters. The Rupee could perhaps move to 45 to the Dollar, which seems to be the right value, and help IT and BPO exporters. Third, the recession could give a great opportunity for some leading Indian firms to expend their operations, acquire weak global competitors, and develop into true global giants in the next 5-10 years.
Among the global giants to emerge from this crisis could be Tata Motors, Infosys, L&T, ICICI Bank, and Dr. Reddy’s Labs. Their comparables would be Sony and Toyota, which overcame Japanese economic crises in 1985 and 1991 to become global leaders. As investors begin to differentiate between these global giants and firms that would remain as domestic giants, the stock prices and P/E for the global giants could overtake those of their Indian peers. That differentiation would parallel the divergence in P/E that we saw for Indian software firms after the dot com meltdown, when tier-1 IT firms raced ahead of tier-2 IT firms in revenues, growth rates, profit margins, P/E multiples and stock prices.
So the message for Indian investors is clear. The rise in Indian stocks is less due to the “India shining” story than due to a wall of liquidity that has flooded the global financial markets. This liquidity has dried up, and we are now facing a global recession that could be worst since the Great Depression. This crash will also pull down Indian stocks and property prices, regardless of the strength of the Indian economy. Investors should use the downturn to buy into rising global giants from India, whose shares could multiply many times over the next 5-10 years.
Saturday, February 28, 2009
KPO Trends, September 2007
This article talks about evolving trends in the Knowledge Process Outsourcing (KPO) industry - which includes business research, financial analysis, market research, statistical analysis and other research/analysis processes delivered from India to global consulting firms, investment banks (not many left these days!), market research firms, banks/financial institutions, and other large corporations. I had a unique opportunity to join the nascent KPO practice at one of India's largest BPO firms in 2003, and helped grow the KPO business to over 1,500 analysts when I left in 2008. It has been a great journey and an enriching experience, and helped me appreciate all the dimensions of KPO - its possibilities, challenges, headaches, excitement, and potential.
PDF copy:
http://f1.grp.yahoofs.com/v1/gCGrScLnI-5gW_lyjCsJJkBUb-ZS8l4EGMDxP-Ah0D5nRwWAUWDUSuMwGczYnZctFqjditXsLlkD00uKOG5e4A/KPO%20Trends%2C%20September%202007.pdf
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KPO Trends for 2010
U. Partha Sarathy, September 2007
The Knowledge Process Outsourcing (KPO) industry has been around for less than 10 years, but has already achieved significant milestones in terms of revenues, headcount and client impact. I believe the next 5-10 years of the KPO industry’s growth will be even more dramatic, as firms build scale, develop new capabilities, and follow new business models. Below are some trends that I see becoming more important in the coming years in the KPO industry.
Trend 1: Demand set to rise exponentially
The increasing acceptance of the KPO model suggests that the coming years will see exponential rise in demand for KPO, and substantial mismatch between supply and demand. Two factors are driving demand. First, while the KPO industry has historically targeted the professional services segment (investment banks, market research firms, consulting firms, etc.), I see increasing demand from the “corporate” segment, i.e. large companies in a wide variety of industries. Professional services firms were natural candidates for KPO since research and analysis is their core activity, and these industries had experience in working globally. The corporate segment is a less obvious target for KPO, but demand from these new verticals could be many multiples of the demand from professional services. Second, the KPO model is still nascent and demand is likely to grow substantially as the KPO model gets established. KPO is perhaps the first instance where offshore outsourcing is creating a new industry that did not exist in the developed economies; unlike IT outsourcing and BPO where offshore vendors are largely replicating what onsite vendors have always provided. The KPO industry is really the creation of India-based providers, so as the model gets more established, I expect demand to grow 50+% per year for the foreseeable future. The demand-supply mismatch could lead to rising billing rates, increased employee attrition, rising salaries, pressure on service delivery, and rising profit margins.
Trend 2: Customers interested in buying a suite of intelligence services
While the early KPO deals focused on specific research/analysis services, I see customers increasingly moving to a suite of intelligence services. For example a client may need an offshore solution that includes specific horizontal services (industry research, MIS/reporting, statistical modeling, competition research) at different levels of complexity and sophistication (basic research, detailed analysis, modeling, etc.). This model helps address a complex business problem by dis-aggregating it into different components that can be solved using specialized techniques/skills. Two factors seem to be driving this trend. On the demand side, some clients are building market intelligence cells that integrate multiple components – such as market research surveys, industry research, competition research, marketing MIS/performance metrics, and statistical modeling – and they need the KPO provider to support them with a suite of intelligence services. Offshoring makes it easier for the client to achieve centralization of various research/analysis functions at the KPO firm’s offshore location, even though these functions may be fragmented across their onsite divisions/units. On the supply side, KPO providers have demonstrated synergy in combining multiple components of intelligence together. For example Marketics Technologies, the analytics division of WNS, has developed the “M-Scan” methodology for building scalable “assembly line” analytics teams using centers of excellence for various analytics disciplines. Providing a suite of services also allows the KPO firm to provide a career path for their analysts to move up the value chain
Trend 3: Need for verticalization
The KPO industry today specializes in a few key “horizontal” offerings – business research, financial research, analytics and support services – targeted mainly at the professional services segment. I believe that over the next few years KPO firms will develop verticalized offerings tailored for specific industries – such as pharmaceuticals (e.g. competition analysis, sales force optimization, product pipeline analysis), insurance (e.g. direct marketing analytics, actuarial analysis), mortgage banking (pricing of mortgage-backed securities, pre-payment risk modeling), and so on. Instead of telling clients “we offer these 4 horizontal services and we would like you to buy these”, the pitch needs to become “we offer these specific services that your industry requires”. KPO firms will also need to develop deep industry-specific skills in pre-sales, diagnostics, solution design, transition and service delivery.
Trend 4: Rise of the “factory model”
One of the big trends in the KPO industry is the move from an effectiveness focus (“can you do this work from India?”) to an efficiency focus (“can you do it with high quality, low costs, and continuous improvement?”). To cater to this demand, KPO firms are moving away from the “offshore body shopping” model in some early KPO projects (small groups of analysts working on relatively unstructured projects) to what is called the “factory model”, which involves large teams of analysts with similar skills, following a structured process, and being managed in a BPO-like environment. For example, a large financial research process for an investment bank would utilize analysts with commerce/accounting degrees who have received necessary training, rather than depend exclusively on hiring CAs and MBAs. This allows the KPO firm to build and manage large teams to meet the requirements of large clients, and keep costs under control. Key elements of the factory model are:
“De-skills” the research process by breaking up a business problem into structured, less complex components that can be addressed through specialized techniques/skills.
Enables the KPO firm to hire from a larger and less expensive pool of talent, including from Tier-II cities.
Reduced impact of attrition, since analysts are easier to hire, and inter-changeable.
KPO providers are able to use the factory model to build new capabilities to meet more client requirements, and at the same time they will also have to turn down fragmented and sub-scale KPO work that does not fit in with the factory model.
Trend 5: Consolidation in the industry
I expect that much of the incremental growth in the KPO industry will be captured by large “hybrid” (blended BPO and KPO) offshoring providers. Other firms – niche KPO firms, pure-play KPO firms and KPO units of captive BPO firms – will grow, but may be unable to maintain their market share, due to their inability to build “vertical” competencies and deliver using the factory model. I expect the large “hybrid” (BPO/KPO) firms to undertake three kinds of mergers and acquisitions:
Horizontal India-based KPO firms (offering specific services such as analytics, supply chain services, market research), in order to build new capabilities, reduce SG&A costs, and expand to tier-II cities to source talent
Vertical US-based firms to build industry-specific offerings and acquire clients, e.g. boutique research/consulting firm focused on a particular industry sector
KPO units of captive BPO firms, which may face pressure due to rapidly rising employee costs, high attrition and stagnating career paths for employees
Trend 6: Knowledge management to drive service delivery
I believe that knowledge management will increasingly become a critical success factor for KPO industry in the years ahead. As KPO firms try to rapidly scale up, hire from a “down skilled” talent pool, and keep costs low, they need to develop knowledge management systems and processes that can help them document, share and build upon the knowledge that belongs to their employees. Our experience suggests that good KM systems in a KPO firm can make a substantial experience in improving productivity, reducing errors, and reducing training time. I believe that KM is to KPO what six sigma quality is to BPO – a mechanism to achieve dramatic improvements in quality, productivity and cost effectiveness.
Trend 7: Multiple pricing models
With the growth of multiple kinds of KPO work, I believe the industry will move away from “per FTE” pricing to a range of pricing models. At the top end of the spectrum, consulting-type of work (e.g. risk management analytics, equity research) will follow a value-based pricing; the idea is that the KPO helps the client achieve substantial revenue growth or cost reductions, and the KPO is entitled to a share of that value. At the next rung, research/analysis work (involving MBA, CA) will continue to be priced on a “per FTE” or “per hour” basis. The “factory model” work will clearly move to a “per transaction” pricing, for example per company profile for a research firm, or per comparable company for an investment banking study.
Trend 8: “White labeled” services
With India-based KPO firms offshoring a wide range of share of services to professional services firms, there is an attractive opportunity for India-based KPO firms to offer “white label” services. For example, some KPO firms provide end-to-end outsourced services for global market research firms, including research design, survey execution (telephonic or online), data processing, statistical analysis, and PowerPoint presentations. This makes it possible for Indian KPO firms to handle almost 100% of the market research work in the US (except client service and commercials). This could happen in 3 ways: (1) Indian KPO partners with a small US-based market research agency which handles the front-end work, while the Indian KPO does the back-end work; (2) Indian KPO acquires US-based MR agency in order to “carve out” more business for the back-end in India; or (3) Indian KPO offers end-to-end MR services as a full-fledged MR agency, competing with its existing MR clients.
PDF copy:
http://f1.grp.yahoofs.com/v1/gCGrScLnI-5gW_lyjCsJJkBUb-ZS8l4EGMDxP-Ah0D5nRwWAUWDUSuMwGczYnZctFqjditXsLlkD00uKOG5e4A/KPO%20Trends%2C%20September%202007.pdf
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KPO Trends for 2010
U. Partha Sarathy, September 2007
The Knowledge Process Outsourcing (KPO) industry has been around for less than 10 years, but has already achieved significant milestones in terms of revenues, headcount and client impact. I believe the next 5-10 years of the KPO industry’s growth will be even more dramatic, as firms build scale, develop new capabilities, and follow new business models. Below are some trends that I see becoming more important in the coming years in the KPO industry.
Trend 1: Demand set to rise exponentially
The increasing acceptance of the KPO model suggests that the coming years will see exponential rise in demand for KPO, and substantial mismatch between supply and demand. Two factors are driving demand. First, while the KPO industry has historically targeted the professional services segment (investment banks, market research firms, consulting firms, etc.), I see increasing demand from the “corporate” segment, i.e. large companies in a wide variety of industries. Professional services firms were natural candidates for KPO since research and analysis is their core activity, and these industries had experience in working globally. The corporate segment is a less obvious target for KPO, but demand from these new verticals could be many multiples of the demand from professional services. Second, the KPO model is still nascent and demand is likely to grow substantially as the KPO model gets established. KPO is perhaps the first instance where offshore outsourcing is creating a new industry that did not exist in the developed economies; unlike IT outsourcing and BPO where offshore vendors are largely replicating what onsite vendors have always provided. The KPO industry is really the creation of India-based providers, so as the model gets more established, I expect demand to grow 50+% per year for the foreseeable future. The demand-supply mismatch could lead to rising billing rates, increased employee attrition, rising salaries, pressure on service delivery, and rising profit margins.
Trend 2: Customers interested in buying a suite of intelligence services
While the early KPO deals focused on specific research/analysis services, I see customers increasingly moving to a suite of intelligence services. For example a client may need an offshore solution that includes specific horizontal services (industry research, MIS/reporting, statistical modeling, competition research) at different levels of complexity and sophistication (basic research, detailed analysis, modeling, etc.). This model helps address a complex business problem by dis-aggregating it into different components that can be solved using specialized techniques/skills. Two factors seem to be driving this trend. On the demand side, some clients are building market intelligence cells that integrate multiple components – such as market research surveys, industry research, competition research, marketing MIS/performance metrics, and statistical modeling – and they need the KPO provider to support them with a suite of intelligence services. Offshoring makes it easier for the client to achieve centralization of various research/analysis functions at the KPO firm’s offshore location, even though these functions may be fragmented across their onsite divisions/units. On the supply side, KPO providers have demonstrated synergy in combining multiple components of intelligence together. For example Marketics Technologies, the analytics division of WNS, has developed the “M-Scan” methodology for building scalable “assembly line” analytics teams using centers of excellence for various analytics disciplines. Providing a suite of services also allows the KPO firm to provide a career path for their analysts to move up the value chain
Trend 3: Need for verticalization
The KPO industry today specializes in a few key “horizontal” offerings – business research, financial research, analytics and support services – targeted mainly at the professional services segment. I believe that over the next few years KPO firms will develop verticalized offerings tailored for specific industries – such as pharmaceuticals (e.g. competition analysis, sales force optimization, product pipeline analysis), insurance (e.g. direct marketing analytics, actuarial analysis), mortgage banking (pricing of mortgage-backed securities, pre-payment risk modeling), and so on. Instead of telling clients “we offer these 4 horizontal services and we would like you to buy these”, the pitch needs to become “we offer these specific services that your industry requires”. KPO firms will also need to develop deep industry-specific skills in pre-sales, diagnostics, solution design, transition and service delivery.
Trend 4: Rise of the “factory model”
One of the big trends in the KPO industry is the move from an effectiveness focus (“can you do this work from India?”) to an efficiency focus (“can you do it with high quality, low costs, and continuous improvement?”). To cater to this demand, KPO firms are moving away from the “offshore body shopping” model in some early KPO projects (small groups of analysts working on relatively unstructured projects) to what is called the “factory model”, which involves large teams of analysts with similar skills, following a structured process, and being managed in a BPO-like environment. For example, a large financial research process for an investment bank would utilize analysts with commerce/accounting degrees who have received necessary training, rather than depend exclusively on hiring CAs and MBAs. This allows the KPO firm to build and manage large teams to meet the requirements of large clients, and keep costs under control. Key elements of the factory model are:
“De-skills” the research process by breaking up a business problem into structured, less complex components that can be addressed through specialized techniques/skills.
Enables the KPO firm to hire from a larger and less expensive pool of talent, including from Tier-II cities.
Reduced impact of attrition, since analysts are easier to hire, and inter-changeable.
KPO providers are able to use the factory model to build new capabilities to meet more client requirements, and at the same time they will also have to turn down fragmented and sub-scale KPO work that does not fit in with the factory model.
Trend 5: Consolidation in the industry
I expect that much of the incremental growth in the KPO industry will be captured by large “hybrid” (blended BPO and KPO) offshoring providers. Other firms – niche KPO firms, pure-play KPO firms and KPO units of captive BPO firms – will grow, but may be unable to maintain their market share, due to their inability to build “vertical” competencies and deliver using the factory model. I expect the large “hybrid” (BPO/KPO) firms to undertake three kinds of mergers and acquisitions:
Horizontal India-based KPO firms (offering specific services such as analytics, supply chain services, market research), in order to build new capabilities, reduce SG&A costs, and expand to tier-II cities to source talent
Vertical US-based firms to build industry-specific offerings and acquire clients, e.g. boutique research/consulting firm focused on a particular industry sector
KPO units of captive BPO firms, which may face pressure due to rapidly rising employee costs, high attrition and stagnating career paths for employees
Trend 6: Knowledge management to drive service delivery
I believe that knowledge management will increasingly become a critical success factor for KPO industry in the years ahead. As KPO firms try to rapidly scale up, hire from a “down skilled” talent pool, and keep costs low, they need to develop knowledge management systems and processes that can help them document, share and build upon the knowledge that belongs to their employees. Our experience suggests that good KM systems in a KPO firm can make a substantial experience in improving productivity, reducing errors, and reducing training time. I believe that KM is to KPO what six sigma quality is to BPO – a mechanism to achieve dramatic improvements in quality, productivity and cost effectiveness.
Trend 7: Multiple pricing models
With the growth of multiple kinds of KPO work, I believe the industry will move away from “per FTE” pricing to a range of pricing models. At the top end of the spectrum, consulting-type of work (e.g. risk management analytics, equity research) will follow a value-based pricing; the idea is that the KPO helps the client achieve substantial revenue growth or cost reductions, and the KPO is entitled to a share of that value. At the next rung, research/analysis work (involving MBA, CA) will continue to be priced on a “per FTE” or “per hour” basis. The “factory model” work will clearly move to a “per transaction” pricing, for example per company profile for a research firm, or per comparable company for an investment banking study.
Trend 8: “White labeled” services
With India-based KPO firms offshoring a wide range of share of services to professional services firms, there is an attractive opportunity for India-based KPO firms to offer “white label” services. For example, some KPO firms provide end-to-end outsourced services for global market research firms, including research design, survey execution (telephonic or online), data processing, statistical analysis, and PowerPoint presentations. This makes it possible for Indian KPO firms to handle almost 100% of the market research work in the US (except client service and commercials). This could happen in 3 ways: (1) Indian KPO partners with a small US-based market research agency which handles the front-end work, while the Indian KPO does the back-end work; (2) Indian KPO acquires US-based MR agency in order to “carve out” more business for the back-end in India; or (3) Indian KPO offers end-to-end MR services as a full-fledged MR agency, competing with its existing MR clients.
IT in the US health industry, March 2003
This article was based on my experience in the healthcare sector - initially as a management consultant and later as an analyst with a venture capital firm where I closely worked with an investee company to evaluate BPO opportunities in the US healthcare area.
PDF copy:
http://f1.grp.yahoofs.com/v1/gCGrSVLlL5BgW_lyLtl74nMLWh5QK-JU_YZv4ZPRs-glYH4Aq3c3DUq_fIbcTAEsNW4BEcN5rrPDQP68_X2IIA/IT%20in%20the%20US%20health%20industry%2C%20March%202003.pdf
PDF copy:
http://f1.grp.yahoofs.com/v1/gCGrSVLlL5BgW_lyLtl74nMLWh5QK-JU_YZv4ZPRs-glYH4Aq3c3DUq_fIbcTAEsNW4BEcN5rrPDQP68_X2IIA/IT%20in%20the%20US%20health%20industry%2C%20March%202003.pdf
Bluffing your way through research, May 2005
I wrote this when I was with a large BPO firm, managing a team of "knowledge process outsourcing" (KPO) professionals performing business research, financial analysis, statistical analysis, and so on for global clients. The article also draws upon my experience in management consulting, where research and analysis is an essential part of the consulting process.
PDF copy:
http://f1.grp.yahoofs.com/v1/gCGrSZMft71gW_lyKRKva8nl1AMajMUPO-Id5IPIe4iDmDNK71smUaO7qRz04PQ1hgdK6M_v9iVCTAJOJEHlQw/Bluffing%20your%20way%20through%20research%2C%20May%202005.pdf
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Bluffing your way through research
Partha Sarathy
So you decided to take up a career in business research, against the best advice of your friends and well wishers. Your life has been turned upside down, trying to grapple with vague client requirements, lack of data, insane deadlines and despotic bosses. Sounds familiar? Don’t worry, help is at hand. We have just completed our exhaustive study on what makes some researchers highly successful. Here are the nine techniques used by highly successful researchers that you can follow to impress the boss, overwhelm clients, get a raise and build a great reputation.
1. power of derived variables
When you are at your wit’s end trying to make sense of your data, derived variables can help you salvage the report. Say you have data on sales, market cap, number of branches and number of employees, but there is no discernible pattern or relationship. Then use derived variables like (employees/branch) or (market cap/employee) to get results that are intriguing and plausible but cannot be easily dismissed. Make it more exciting by comparing one derived variable versus another. If you are desperate, use compounded derived variables. Who can argue when you say that strategy 1 is superior to strategy 2 because the former yields higher (sales)*(P/E)/(employees/branch)?
2. Magic of matrices
A matrix has the power of a thousand words, so use a matrix if you are confused beyond words. Beginners usually draw simple ‘X versus Y’ matrices that anyone can understand (and challenge). Skilled practitioners know how to use abstract variables that infuse the matrix with authority, power and mystery – such as innovation quotient, revenue distance, and degree of verticalization. A good matrix is like modern art – you can admire and interpret it any way you want, but you can’t dismiss it. Here are some best practices on how to make use of matrices:
use 4x4 or 3x3 matrices instead of 2x2 matrices
assign derogatory names to one quadrant (dogs, laggards, mother-in-law)
manipulate the axis scales to ensure that no quadrant is empty
compare important variables with irrelevant/unheard of variables
At the hands of a great master, the matrix is elevated to truly sublime levels of truth, life, beauty and meaning. If only the almighty had an opportunity to consult with MCK or BCG before framing the ten commandments!
3. powerpoint for combat
PowerPoint is definitely the greatest human invention since the wheel and toothbrush. Used well, it becomes a weapon of mass destruction, creating shock and awe among the audience. The power of PowerPoint is its ability to present anything any way you want regardless of the content. You can create a 1-slide overview of a 1,000-page report, or (more frequently) create a powerful 20-slide presentation out of a 1-sentence idea. This is much like the separation of the presentation layer and application layer in software development – and unlike that other curious IT phrase, what you see is not what you get. Naturally, PowerPoint is very popular with VPs and top management. After extensive research, we have been able to identify the reasons why VPs love PowerPoint so much:
VPs inhabit very high altitudes in organization charts, and their bodies have evolved to use PowerPoint summaries to cope with oxygen deprivation
Given their rarified altitudes, VPs need to get a 30,000 feet view of what is happening in the company and the market
Viewed from 30,000 feet, the world looks like a PowerPoint slide
4. Box charts
Helps you represent and illuminate relationships between variables in a clear manner, regardless of how well you understand the alleged relationships. Classics include the 5-forces model of industry structure or the diamond model of competitive advantage of nations. Moral of the story: if you and I draw 5 boxes and throw in a few arrows the client would take us to court, but if a Harvard professor does that it’s called a ‘model’. The way you connect the boxes in your chart reflects what is really going on in your mind – for example:
A few boxes with minimal arrows (means “I have no idea what the heck is going on”)
Profusion of arrows connecting every box with every other box (“I am overwhelmed, I give up”)
Random arrows between some of the boxes (“I know the answer, but I want to preserve the mystery”)
Complex chain of boxes and arrows, like the structure of a particularly obnoxious chemical compound (“I am suffering from indigestion”)
5. Circular reference
This is a powerful technique that gives you the license to use concepts/phrases in a deliciously vague manner by defining them in terms of other equally vague variables, at least some of which are defined in terms of the original one. For example your report on reverse engineering may define it as a byproduct of something called process deconstruction, which is defined in a footnote on page 256 as a special category of reverse engineering. Circular reference in software will cause computers to hang, but management is happily immune to this malady. A variation of this technique is called non-existent reference, also called the Emperor’s New Clothes reference. You make wild claims in the first part of the report by saying “as we will prove in chapter 5, ---”, but chapter 5 only contains a few oblique references to the original concept, and later chapters breezily assert “as we demonstrated in chapter 5, ---“. Your chances of being able to get away with this trick depends upon:
length of your report: anything below 100 pages is risky
readers’ senility: Vice Presidents and above are safe
readers’ gullibility: MBAs are fine, but never try this with accountants and lawyers
6. Torture your data until it confesses
If you have lots of numbers that don’t seem to fit into any pattern, there are 2 things you should do so that your results agree with what you want to prove: (1) keep on trying different operating variables, and (2) use different analysis techniques. For example, if you compare net margins and get no clear results, try other variables like EBIT, EBITDA, operating margins, gross margins, contribution margins, and so on until something clicks. (Keep this in mind next time you see a report comparing Earnings Before Taxes and Depreciation, After Interest and Advertising Expenditure.) Also try analysing the data in various ways: averages, ratios, correlations, square roots, logarithms, exponentials, conditional variables, anything else you can think of. With these 2 techniques, you can prove anything using any data.
7. Research methodology
Great researchers do not waste time in pointless research, they are extremely results-oriented and client-centric. Their formula for success is the research methodology they use, which goes like this:
step 1: identify what the client wants to hear
step 2: decide what results you want to show
step 3: list down the hypotheses that could yield those results
step 4: identify what data you need for this
step 5: collect data
step 6: analyze data
step 7: write the report
Unfortunately, novice researchers spend too much time and effort on steps 5, 6 and 7, without thinking through the client-centric steps 1, 2 and 3. Gandhi must have had researchers in mind when he said: ‘have purpose, and means will follow’.
8. chinese numbers
When you estimate market potential for a new/unfamiliar product, always use at least 3 different methods that give wildly different results. This helps you to (1) choose the answer the client really wants to hear, (2) cover your posterior when years later the market size data is published (e.g. if you projected a market size between $1 billion and $100 billion, chances are you won’t be wrong). At least one of your methods should be based on the funnel principle, which starts like this:
country’s population is X million
number of households is Y million
spending on existing product A is $P per year
assumed proportion of households interested in new product B is Q%, and so on.
9. Know what the client does not know
When you present a report on an unfamiliar industry/topic to people who have been working in that area for 20 years, you have to focus on something your audience would never have heard about. Here are some examples that great researchers use:
case study of company X which faced a similar problem in Italy (if client is in Europe, use Mexico instead of Italy)
market size projections in China (the advantage of China is that you can talk of billions, trillions or quadrillions, and the audience won’t/can’t refute you)
(PEG ratio/Net margin) ratio for companies in that industry
impact of proposed EU regulation (if client is in Europe, mention NAFTA), and so on
PDF copy:
http://f1.grp.yahoofs.com/v1/gCGrSZMft71gW_lyKRKva8nl1AMajMUPO-Id5IPIe4iDmDNK71smUaO7qRz04PQ1hgdK6M_v9iVCTAJOJEHlQw/Bluffing%20your%20way%20through%20research%2C%20May%202005.pdf
---------------------------
Bluffing your way through research
Partha Sarathy
So you decided to take up a career in business research, against the best advice of your friends and well wishers. Your life has been turned upside down, trying to grapple with vague client requirements, lack of data, insane deadlines and despotic bosses. Sounds familiar? Don’t worry, help is at hand. We have just completed our exhaustive study on what makes some researchers highly successful. Here are the nine techniques used by highly successful researchers that you can follow to impress the boss, overwhelm clients, get a raise and build a great reputation.
1. power of derived variables
When you are at your wit’s end trying to make sense of your data, derived variables can help you salvage the report. Say you have data on sales, market cap, number of branches and number of employees, but there is no discernible pattern or relationship. Then use derived variables like (employees/branch) or (market cap/employee) to get results that are intriguing and plausible but cannot be easily dismissed. Make it more exciting by comparing one derived variable versus another. If you are desperate, use compounded derived variables. Who can argue when you say that strategy 1 is superior to strategy 2 because the former yields higher (sales)*(P/E)/(employees/branch)?
2. Magic of matrices
A matrix has the power of a thousand words, so use a matrix if you are confused beyond words. Beginners usually draw simple ‘X versus Y’ matrices that anyone can understand (and challenge). Skilled practitioners know how to use abstract variables that infuse the matrix with authority, power and mystery – such as innovation quotient, revenue distance, and degree of verticalization. A good matrix is like modern art – you can admire and interpret it any way you want, but you can’t dismiss it. Here are some best practices on how to make use of matrices:
use 4x4 or 3x3 matrices instead of 2x2 matrices
assign derogatory names to one quadrant (dogs, laggards, mother-in-law)
manipulate the axis scales to ensure that no quadrant is empty
compare important variables with irrelevant/unheard of variables
At the hands of a great master, the matrix is elevated to truly sublime levels of truth, life, beauty and meaning. If only the almighty had an opportunity to consult with MCK or BCG before framing the ten commandments!
3. powerpoint for combat
PowerPoint is definitely the greatest human invention since the wheel and toothbrush. Used well, it becomes a weapon of mass destruction, creating shock and awe among the audience. The power of PowerPoint is its ability to present anything any way you want regardless of the content. You can create a 1-slide overview of a 1,000-page report, or (more frequently) create a powerful 20-slide presentation out of a 1-sentence idea. This is much like the separation of the presentation layer and application layer in software development – and unlike that other curious IT phrase, what you see is not what you get. Naturally, PowerPoint is very popular with VPs and top management. After extensive research, we have been able to identify the reasons why VPs love PowerPoint so much:
VPs inhabit very high altitudes in organization charts, and their bodies have evolved to use PowerPoint summaries to cope with oxygen deprivation
Given their rarified altitudes, VPs need to get a 30,000 feet view of what is happening in the company and the market
Viewed from 30,000 feet, the world looks like a PowerPoint slide
4. Box charts
Helps you represent and illuminate relationships between variables in a clear manner, regardless of how well you understand the alleged relationships. Classics include the 5-forces model of industry structure or the diamond model of competitive advantage of nations. Moral of the story: if you and I draw 5 boxes and throw in a few arrows the client would take us to court, but if a Harvard professor does that it’s called a ‘model’. The way you connect the boxes in your chart reflects what is really going on in your mind – for example:
A few boxes with minimal arrows (means “I have no idea what the heck is going on”)
Profusion of arrows connecting every box with every other box (“I am overwhelmed, I give up”)
Random arrows between some of the boxes (“I know the answer, but I want to preserve the mystery”)
Complex chain of boxes and arrows, like the structure of a particularly obnoxious chemical compound (“I am suffering from indigestion”)
5. Circular reference
This is a powerful technique that gives you the license to use concepts/phrases in a deliciously vague manner by defining them in terms of other equally vague variables, at least some of which are defined in terms of the original one. For example your report on reverse engineering may define it as a byproduct of something called process deconstruction, which is defined in a footnote on page 256 as a special category of reverse engineering. Circular reference in software will cause computers to hang, but management is happily immune to this malady. A variation of this technique is called non-existent reference, also called the Emperor’s New Clothes reference. You make wild claims in the first part of the report by saying “as we will prove in chapter 5, ---”, but chapter 5 only contains a few oblique references to the original concept, and later chapters breezily assert “as we demonstrated in chapter 5, ---“. Your chances of being able to get away with this trick depends upon:
length of your report: anything below 100 pages is risky
readers’ senility: Vice Presidents and above are safe
readers’ gullibility: MBAs are fine, but never try this with accountants and lawyers
6. Torture your data until it confesses
If you have lots of numbers that don’t seem to fit into any pattern, there are 2 things you should do so that your results agree with what you want to prove: (1) keep on trying different operating variables, and (2) use different analysis techniques. For example, if you compare net margins and get no clear results, try other variables like EBIT, EBITDA, operating margins, gross margins, contribution margins, and so on until something clicks. (Keep this in mind next time you see a report comparing Earnings Before Taxes and Depreciation, After Interest and Advertising Expenditure.) Also try analysing the data in various ways: averages, ratios, correlations, square roots, logarithms, exponentials, conditional variables, anything else you can think of. With these 2 techniques, you can prove anything using any data.
7. Research methodology
Great researchers do not waste time in pointless research, they are extremely results-oriented and client-centric. Their formula for success is the research methodology they use, which goes like this:
step 1: identify what the client wants to hear
step 2: decide what results you want to show
step 3: list down the hypotheses that could yield those results
step 4: identify what data you need for this
step 5: collect data
step 6: analyze data
step 7: write the report
Unfortunately, novice researchers spend too much time and effort on steps 5, 6 and 7, without thinking through the client-centric steps 1, 2 and 3. Gandhi must have had researchers in mind when he said: ‘have purpose, and means will follow’.
8. chinese numbers
When you estimate market potential for a new/unfamiliar product, always use at least 3 different methods that give wildly different results. This helps you to (1) choose the answer the client really wants to hear, (2) cover your posterior when years later the market size data is published (e.g. if you projected a market size between $1 billion and $100 billion, chances are you won’t be wrong). At least one of your methods should be based on the funnel principle, which starts like this:
country’s population is X million
number of households is Y million
spending on existing product A is $P per year
assumed proportion of households interested in new product B is Q%, and so on.
9. Know what the client does not know
When you present a report on an unfamiliar industry/topic to people who have been working in that area for 20 years, you have to focus on something your audience would never have heard about. Here are some examples that great researchers use:
case study of company X which faced a similar problem in Italy (if client is in Europe, use Mexico instead of Italy)
market size projections in China (the advantage of China is that you can talk of billions, trillions or quadrillions, and the audience won’t/can’t refute you)
(PEG ratio/Net margin) ratio for companies in that industry
impact of proposed EU regulation (if client is in Europe, mention NAFTA), and so on
Economics of distribution chain for consumer durables, IIM Lucknow working paper, 1999
This article was co-authored with Prof. Janakiraman at my alma mater, IIM Lucknow, and was published as part of IIM Lucknow's working paper series in 1999. The article was based on my experience as a sales executive with Philips, where I was intrigued by the relationship between volume discounts offered to authorized dealers and the prevalence of wholesaling between authorized dealers and sub-dealers. It was great fun to create a theoretical model to estimate the "optimum" volume discounts in various scenarios, though I am pretty sure this article does not have any practical utility at all.
PDF copy:
http://f1.grp.yahoofs.com/v1/gCGrSQJblVVgW_lyQFgo5ENmpgjw_DYFd_5JeOFjQ-zHc4ePy5NBWWNSH8NzeQ7u9DQPYMuoKQ6kRkm-wzCDKg/IIML%20working%20paper%2C%20economics%20of%20distribution%20chain.pdf
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A STUDY OF COSTS & PROFITABILITY
IN A DISTRIBUTION CHANNEL FOR DURABLES
Contents
INTRODUCTION
Case 1. all brands have equal margin, but no volume discounts
Case 2. brands have unequal margins, but no volume discounts
Case 3. volume discount, but no quantity slab
Case 4. volume discount with slab, but no wholesaling
Case 5. volume discount with slab and with wholesaling
Figures 1 - 25
INTRODUCTION
In managing its distribution channel, a durables brand in the Indian market has to control the key channel variables :
(a) number of dealers / wholesalers / distributor's retailers to be serviced,
(b) the margin to be given to the dealer / wholesaler / distributor's retailer,
(c) the value of the volume discount to be given to the dealer for bulk purchase,
(d) the minimum quantity to be purchased by the dealer to qualify for the volume discount
Durables brands in India usually decide on these variables on the basis of experience and judgement. There is at present no theory to explain how these channel variables impact the profitability of the brand and the dealer, and to help the brand determine the optimum margins, number of dealers to be serviced, etc. This paper attempts to build up a theory of the channel dynamics for durable goods, by analysing the channel in terms of its costs and profitability.
In doing so, our theory must take into account that distribution channels in India have several complicating factors :
(a) brands may use more than one channel structure ( direct dealers / wholesaling / distributor channels ) at the same time,
(b) dealers usually try to 'convert' customers from low-margin brands to those which give them high dealer-margins,
(c) large dealers often buy in bulk from brands offering volume discounts, and sell ( 'wholesaling' ) to smaller dealers, sharing the volume discount among them,
(d) customers check out prices across several showrooms before buying, thereby causing the dealers' selling prices to converge to a 'market-ruling price',
(e) when brands offer credit to dealers for their purchases, and when customers pay for their purchases in cash, dealers stand to make huge profits even if they give large discounts to customers to clinch the sale.
To analyse the impact of all these variables, we first consider the case of a simple dealer channel where these complications do not exist, and then factor in these complications one by one, to arrive at a more comprehensive model that captures the dynamics of our real-life markets.
The simple model we first consider, in Chapter -1, assumes that :
(a) all brands have equal dealer margins
(b) dealers do not hold excess stock
(c) dealers do not engage in wholesaling among themselves
(d) consumers' dealer-preferences are independent of their brand preferences
(e) brands do not give credit to dealers for their purchases
(f) brands do not offer volume discounts to dealers for bulk purchases
Note : All figures are shown at the end.
1. all brands have equal margin, but no volume discounts
1.1 Consider a simple dealer channel for durables in the Indian context. Assume that there are a large number of dealers in the market, and a small number of brands. Assume that all dealers sell all brands, and assume that brands sell directly ( i.e., not wholesaling or distributor channel ) to the dealer. Assuming there is no wholesaling or carrying of excess stock, the dealer buys from the brands whatever quantity is purchased by the customer. If there is no difference between the margins or other terms offered by the different brands, the dealer will have no reason to prefer one brand over another. Thus the brand-wise sales made from his shop will mirror exactly the brand-wise purchase preferences expressed by the customer. The brand-wise purchase preferences expressed by the customers will be a function of the relative perceived costs and benefits of the various brands. Assuming that whatever customer segments and brand preferences exist in one area will also exist in other areas in the given market ( city / state ), the demand for each brand can be expressed as a certain fixed fraction of the total market. For example, the cost / benefits for a brand 'A' might be so perceived by the various customer segments that 15 % of total customers in the given market ( city / state / etc. ) prefer brand 'A'. Similarly 20 % of all customers in the market might prefer brand 'B', 5 % for brand 'C', and so on. This proportion of demand is assumed to hold good for the entire market, so the demand for the product as experienced by each dealer will also be in the same proportion. If 100 customers walk into dealer 1's showroom in a month, they will buy 15 pieces of brand 'A', 20 of brand 'B', 5 of brand 'C', and so on. This assumes that customers' preference for a particular dealer will be related only to attributes like distance from place of residence, accessibility of dealer's location, decor and service, reputation, etc., and not to the brand preference. Essentially, we assume that a customer's brand-preferences are independent of his dealer-preferences. Thus the sales volumes in each dealer's showroom may vary, but the brand-wise preference proportions will remain the same.
1.2 The proportion of customers in a market who express a purchase preference for a particular brand can be termed the 'PREFERENCE SHARE' for that brand in the given market. The 'preference share' is unique for each brand, depending on its perceived costs and benefits. If the dealer does not prefer one brand to another ( which happens if brands are identical in the terms they offer to the dealer ), the brand-wise proportion of sales from his shop will be identical to the preference-shares for the different brands, which is what the customers would have demanded on their own. For the market as a whole, the market-shares of the different brands will be equal to their preference-shares.
1.3 Thus if M is the size of the given market (city or town) in sets per year, and if X'A, X'B, X'C are the preference-shares for brands A, B and C, then their sales (in sets per year) in the given market, SA, SB, SC, can be expressed by Equation - 1 :
SA = X 'A. M ..... ( Eq. 1 )
SB = X 'B. M
SC = X 'C. M, and so on.
1.4 A dealer's sales level can be expressed in terms of his counter-size, which gives the monthly sales ( sets per month ) of the dealer. If CS1, CS2, CS3 are the counter-sales of dealers 1, 2 and 3, then
M = CS1 + CS2 + CS3+ - - - ..... ( Eq. 2 )
What is true for the market must be true for each dealer, so we have
sA1 = X 'A . CS1 ...... ( Eq. 3 )
sB1 = X 'B . CS1
and so on, where sA1 is the sales for brand A from dealer 1, sB1 is the sales for brand B from dealer 1, etc.
Assuming all dealers stock and sell all brands, we have
CS1 = sA1 + sB1 + - - - ...... ( Eq. 4 )
CS2 = sA2 + sB2 + - - -
For brand A, its sales in the market, ( SA ) will be composed of the counter-wise sales it gets from all dealers, as shown below
SA = sA1 + sA2 + sA3 + - - - ...... ( Eq. 5 )
= X 'A . CS1 + X 'A . CS2 + X 'A . CS3 + - - -
= X 'A . ( CS1 + CS2 + CS3 + - - - )
= X 'A . M
which is the same as Eq. 1.
1.5 Counter-size varies from dealer to dealer, depending on their location, stock-carrying capacity, decor, display, demos given, advertisements, discounts given, and so on. While dealers situated in important locations and with large showrooms get a lot of customers, those in more remote locations and with smaller showrooms get fewer customers. The top few dealers often sell several times what the medium size dealers sell, and they in turn sell many times what the smaller dealers sell. If we were to rank dealers in the order of their counter-sales, and if we were to plot their counter-sales in a chart, we would get the chart shown in Figure 1 (all figures are shown at the end).
1.6 Assuming all dealers sell all brands, the counter-sales curve in Figure 1 can be broken down into a series of brand-wise dealer-wise sales curves shown in Figure 2. For a particular brand, the dealer-wise sales will be as shown in Figure 3.
1.7 The cumulative sales of a given set of dealers in a market is described by its 'market reach'. For a given set of dealers, market reach is defined as the ratio of the total sales of the set of dealers to the sales of all dealers in the market. This is shown by the 'Market Reach' curve in Figure 4. All dealers put together - i.e., 100 % of the dealers - account for 100 % of the market size, M. Just 10 % of the dealers ( in decreasing order of counter-size ) may account for as much as 50 % of the total market, 20 % of the dealers may account for 70 % of M, 30 % of the dealers may account for 80 % of M, and so on. Thus the market reach 'MR' of the top 10 % of the dealers would be 0.5, and that of the top 20 % of the dealers would be 0.7, and so on.
1.8 If the brand A sells to 100 % of the dealers in the market, it will have a sales level of SA . Consider a case where the brand sells only to a limited set of dealers - upto N dealers, say. Assuming that customers are not band-loyal enough to search for a showroom that stocks their preferred brand, its sales will be
sales = X 'A . ( CS1 + CS2 + CS3 + - - + CSN )
The market reach of this set of N dealers, MR1, is given by
MR1 = ( CS1 + CS2 + CS3 + - - + CSN ) / M .... ( Eq. 6 )
Thus brand A's sales will be
sales = X 'A . MR1 . M ...... ( Eq. 7 )
From dealers 1, 2, 3 - - - upto N, brand A will continue to get the same level of sales as before, but for dealers after N, it will get no sales at all. .
1.9 Thus if the brand sells to just 10 % of the dealers (in decreasing order), it will have a sales level of 0.5 SA, if it sells to 20 % of the dealers it will have a sales level of 0.7 SA, and so on. If a brand A sells to a limited number of dealers, such that the market reach of the given set of dealers is MR1 (say for example MR1= 0.7 corresponding to 50 % of dealers being covered), then its sales will be
sales = MR1 . ( X 'A . M )
and its market share will be
market share = MR1 . X 'A ....... ( Eq. 8 )
Unless the brand sells to all dealers in the market, its market-share will be less than its preference-share. The larger the proportion of dealers it sells to ( i.e., the higher its market reach ), the closer its market-share approaches its preference-share.
1.10 To maximise sales - upto its maximum potential level of ( X 'A . M ) - the brand should maximise its market reach. Other things being equal, a brand will try to sell to all dealers in the market, so that its market reach becomes 100%, and so that its sales reaches ( X 'A . M ). But as a firm sells to more and more dealers, other things don't remain equal. Its costs rise disproportionately with the number of dealers it has to service. By increasing the number of dealers it sells to, a brand benefits through (a) larger contribution from sales, as the fixed contribution margin is applied over a larger sales volume, and (b) the effect of economies of scale, due to which the contribution margin itself rises as the sales volume rises ( beyond a point, however, diseconomies of scale set in, and the contribution margin falls as sales volume rises ). The costs associated with increasing the number of dealers is the incremental 'transaction cost' ( both fixed and variable ) in servicing the extra dealers. When a brand has a small dealer base, its incremental market reach ( and hence incremental sales ) for every single new dealer being added is higher than it is for a brand already having a large number of dealers. As the brand adds more dealers, its incremental market reach diminishes continuously. This follows from the diminishing counter-sales curve of Figure 1 : while the new dealers added initially are big dealers, ultimately the brand will have to add smaller and smaller dealers.
1.11 To service each set of new dealers, the brand will have to appoint new salesmen, and bear the costs of salary, order-booking, transportation, accounting and so on. For the most part, these costs are directly proportional to the number of dealers added by the brand, and the incremental cost ( say Rs. 50,000 per annum per new dealer added ) is fairly constant over a large range. The incremental contribution from adding new dealers, we have seen, falls steeply as the number of dealers increases. To decide the appropriate number of dealers it must service, the brand has to compare the incremental contribution gain from adding new dealers to the incremental transaction cost involved. The net incremental profit in adding new dealers ( i.e., the difference of the incremental contribution over the incremental transaction cost ) normally falls as the firm adds more and more new dealers, owing to the behaviour of the two costs over different sales levels. There could be rare instances where this net incremental profit is always positive for the brand, for any number of new dealers it adds ( i.e., the brand must ideally sell to all dealers in the market ). There could also be rare instances where this net incremental profit is always negative for the brand, for any number of new dealers it adds ( i.e., it must not add any new dealers, and it must ideally reduce the number of dealers it has ). But in most cases, we would expect that the net incremental profit ( which is continuously diminishing at every stage ) is positive upto a point, and then turns negative as more new dealers are added. This point, where the net incremental profit turns negative, represents the optimum number of dealers for the brand.
1.12 The incremental contribution and the incremental transaction cost for a brand can be studied in terms of two variables, 'effective Contribution Margin' and 'Transaction Cost per set'. The 'effective Contribution Margin' is the contribution margin per set retained by the brand after the fixed selling expenses have been taken care of. For example, if the transfer price from the factory to the selling division is 70 %, the contribution margin is 30 %, or Rs. 3000 per set if the selling price is Rs. 10,000 per set. If the fixed selling expenses ( the per annum cost of office space, furniture, computers, support staff, etc. ) is Rs. 50 lacs per year, then the 'per set fixed expenses' come to Rs. 1,000 per set for a sales volume of 5,000 sets per year. The difference between the two, i.e., Rs. 2,000 per set, is the 'effective Contribution Margin' ( or 'effective CM' ) faced by the selling division, at a volume of 5,000 sets per year. The 'effective CM' can be viewed as a kind of transfer price facing the selling division, after the production costs and the fixed selling costs are met. The 'fixed cost per set' varies with the sales volume ( and hence with the number of dealers), and so does the 'effective CM'. So long as the transaction costs for new dealers is less than this 'effective contribution margin', the brand will appoint new dealers. In Figure 5, the 'effective contribution margin' curve is shown as the difference between the 'contribution margin' curve and the 'fixed cost per set' curve, for different sales volumes.
1.13 The 'Transaction Cost per set' ( or 'TC per set' ) for a brand is the variable cost incurred by it in selling one additional set - it includes the salary and travel costs of the salesman, transportation cost, etc. The 'TC per set' can be obtained from dividing the 'Transaction Cost per dealer' by the 'sales per dealer'. The cost of servicing a dealer varies according to the size of a dealer. Larger dealers need to be visited more often, need more frequent deliveries, and need more management time than small dealers, hence the transaction cost per dealer is greater for larger dealers than for smaller dealers. Firms usually classify dealers into A - B - C categories depending on their sales potential ( which, we assume, is directly related to their counter-sales volume ), and stipulate different dealer-contact norms for various categories. Thus for an "average" dealer if the 'TC per dealer' is Rs. 50,000 per annum, and if all dealers have equal dealer contact norms, the 'transaction cost per dealer' would be Rs. 50,000 per dealer for all dealers. Since dealer contact norms are unequal, a large dealer might have a 'TC per dealer' of Rs. 200,000 per annum, a medium dealer might have a 'TC per dealer' of Rs. 50,000 per annum, and a small dealer might have a 'TC per dealer' of Rs. 25,000 per annum, for instance. The 'TC per dealer' curve is thus a steadily falling curve, as shown in Figure 6. The 'sales per dealer' curve, as shown in Figure 1, is also a steadily falling curve, but falls more steeply ( for reasons we needn't go into here ) than the 'TC per dealer' curve. As seen from Figure 7, 'TC per dealer' divided by 'sales per dealer' is smaller for small dealer-strengths than it is for higher dealer-strengths, since the 'sales per dealer' curve falls more steeply than the 'TC per dealer' curve. Since 'TC per set' is simply 'TC per dealer' divided by 'sales per dealer', we conclude that 'TC per set' is a steadily rising curve, as shown in Figure 8.
1.14 The net incremental profit for the brand is calculated as the difference between 'CM effective' and 'TC per set', for various levels of dealer-strength. This is graphically shown in Figure 9. The 'TC per set' curve cuts the 'CM effective' curve at two levels of dealer strength, indicated by D1 and D2. For dealer strengths less than D1 and for dealer strengths greater than D2, the net incremental profit margin is negative. Below D1, the fixed selling-cost component is so high that the net incremental profit is negative. Above D2, the TC / set becomes so high that it exceeds the CM effective. Thus the feasible dealer strength for a brand lies in the range D1 to D2.
2. brands have unequal margins, but no volume discounts
2.1 When brands have unequal margins, dealers have an incentive to sell more of the high-margin brands and less of the low-margin brands. If customers do not have sufficient information to make their brand choice, or if their brand-preferences can be over-ridden by the dealer's recommendation, the dealer will be able to 'convert' customers from the low-margin brand to the high-margin brand. If the customers' brand-preferences are very fixed and they cannot be 'converted', the dealer will be forced to sell brands in the same proportion as is demanded by the customer by their natural preferences. The dealer incurs a 'conversion cost' in pushing his favourite brand - this includes the extra time spent by the sales staff in giving demonstrations to the customer, making a sales pitch, countering the customer's objections, offering discounts to close the sale, cost of keeping larger stock and better display, and so on. While some customers can be converted easily, others can be converted only with great difficulty, and other customers fall somewhere in the middle of this continuum. Thus the conversion cost faced by the dealer varies from customer to customer. For a given margin differential between brands, the dealer will find it worthwhile to convert customers only upto a point. Beyond this the dealer will have to convert increasingly 'tougher' customers, and his conversion cost could exceed the margin differential. Thus the degree of brand conversion done by the dealer is directly proportional to the magnitude of the margin differential. If dealer margins are unequal, the dealer will try to convert customers from other brands to the one offering the highest margins, but it can rarely happen that the conversion cost for the dealer for all his customers ( even the most loyal customers of other brands ) is more than the fixed margin differential he gets. Due to diminishing returns, the brand will not find it economically viable to offer the huge margin differentials required to help the dealer get 100 % conversions. Moreover, the brand which focuses on the 'dealer push' strategy will have much less customer pull than another which focuses on the 'customer pull' strategy, hence the dealer will find it more difficult to convert. This is why, even with the wide margin differentials prevalent in the market, rarely do we find dealers focusing exclusively on one brand.
2.2 Consider brand A whose margin mA is higher than the margin m'A given by the all other brands, such that
mA = m'A + ( d m'A )
where ( d m'A ) is the margin differential offered by brand A
Since the dealer will now try to convert customers of other brands to brand A, the proportion of brand A in his showroom sales will go up from the previous X 'A. Let the new proportion be X A = X 'A + ( d X 'A ). Thus brand A's new sales in the market will be
SA = X A . CS1 + X A . CS2 + X A . CS3 + - - -
= X A . M
The old and new dealer-wise sales curve for brand A is shown in Figure 10.
2.3 While the margin differential will cause the brand-wise sales proportions from the dealers to change, we assume that it will not cause any changes in the counter-sales of the dealers. For the brand that has hiked its margins, its 'sales per dealer' will go up, but its 'TC per dealer' will not change. Its 'CM effective' will go down, as the extra margin reduces the selling price, as shown in Figure 11. As 'sales per dealer' goes up, and 'TC per dealer' remains the same, the 'TC per set' also comes down, as shown in Figure 12. The drop in 'TC per set' is substantial initially, but diminishes as we go towards the smaller dealers.
2.4 As both the 'CM effective' and the 'TC per set' decrease, the net incremental profit also changes. The change in the net incremental profit depends on whether the increased sales due to the extra margin is sufficient to decrease the 'TC per set' more than the reduction in 'CM effective'. This is shown in Figure 13.
2.5 Before the hike in the margin from ( m'A ) to ( m'A + d m'A ), the sales of the brand A in the market was
SA = X 'A . M
assuming all dealers sell all brands. With the hike in the margin, the new sales level for the brand is
SA = ( X 'A + d X 'A ) . M
The incremental sales for the brand, in sets per annum, is
( d SA ) = M. ( d X 'A )
The incremental contribution generated by the hike in the margin is
= M. ( d X 'A ) . P. c ..... ( Eq. 9 )
where P is the selling price of the set in Rupees, and c ' is the contribution margin for the brand.
The incremental cost incurred by the brand in getting this extra volume of sales is
= ( d m'A ) . ( X 'A + d X 'A ) . M ...... ( Eq. 10 )
For the brand to break-even in giving this extra margin, this extra cost ( Eq. 10 ) must be matched by the extra contribution ( Eq. 9 ). Thus at break-even, we have
M. ( d X 'A ) . P. c = ( d m'A ) . ( X 'A + d X 'A ) . M
Thus
( d X 'A ) / [ ( X 'A + d X 'A ) ] = ( d m'A ) / [ P. c ]
Since ( d X 'A ) is far less than ( X 'A ), we have
( d X 'A ) / ( X 'A ) = ( d m'A ) / ( P. c ) .... ( Eq. 11 )
2.6 Given P, c and ( X 'A ) for a brand, ( Eq. 11 ) gives the minimum degree of conversion ( d X 'A ) required for a dealer margin hike ( d m'A ) to be feasible. If the brand is not able to get this minimum degree of conversion, the hike in the margin will not make sense.
3. volume discount, but no quantity slab
3.1 Here we consider the case of a brand which offers a volume discount, but does not keep a minimum offtake restriction. All dealers, irrespective of their offtake, can avail of this volume discount. Thus a volume discount without a minimum slab has the same effect as an increase in the normal dealer margin. The impact of such a volume discount on the brand's net incremental profit is exactly the same as in the previous case of the increase in the dealer margin.
3.2 Due to the volume discount, the 'sales per dealer' goes up for the brand, while the 'TC per dealer' remains the same. Hence the brand's 'TC per set' goes down. The 'CM effective' for the brand comes down, since effectively the selling price has been reduced. From the 'TC per set' and the 'CM effective' curves, we can arrive at the 'net incremental profit' curve, as in the previous case ( shown in Figure 13 ).
3.3 If ( d yA ) is the amount per set being offered by brand A as the volume discount, then the extra sales generated by the volume discount is, as before,
d SA = M. ( d X 'A )
The incremental contribution obtained by the brand is M. ( d X 'A ) . P. c.
The incremental cost borne by the brand in getting this extra sales is
= ( d yA ) . ( X 'A + d X 'A ) . M
For break-even, we have
M. ( d X 'A ) . P. c = ( d yA ) . ( X 'A + d X 'A ) . M
Thus
( d X 'A ) / [ ( X 'A + d X 'A ) ] = ( d yA ) / [ P. c ]
As before, for ( X 'A ) > > ( d X 'A ), we have
( d X 'A ) / ( X 'A ) = ( d yA ) / [ P. c ] ...... ( Eq. 12 )
3.4 If the brand cannot get its dealers to generate at least this minimum level of conversion ( i.e., d X 'A ), it is not viable for the brand to launch this volume discount. Given P, c and ( X 'A ) , our ( Eq. 12 ) gives the minimum levels of conversion ( d X 'A ) required for break-even, for various levels of volume discount ( d yA ) that the brand can offer. From experience or from limited-scale experimentation, a brand can estimate the different levels of conversion it can expect for different levels of volume discount that it might offer. For any level of volume discount, if the level of conversion is less than the break-even required, the volume discount is not feasible for the brand.
4. volume discount with slab, but no wholesaling
4.1 We now consider a channel where a brand offers a volume discount with a minimum slab ( we still assume there is no wholesaling in the market ). Figure 14 shows the dealer-wise sales for brand A in relation to its minimum offtake slab, ( SS ). We examine in detail the case of four types of dealers - shown in the Figure 14 as G, K, M and N.
Let ( d yA ) be the amount per set being offered by brand A as the volume discount, and ( SS ) be the minimum offtake slab required for the dealer to qualify for the volume discount. Large dealers like G, whose normal offtake for brand A is more than ( SS ), will easily qualify for the volume discount. For these large dealers, brand A has effectively increased its dealer margin by ( d yA ). Brand A's sales from these showrooms will rise marginally due to the conversion effect, since the dealer's preference for brand A will increase. For these large dealers, the normal offtake without a volume discount is
X 'A . CS G > ( SS )
Due to the volume discount-induced conversion, we have
( X 'A + d X 'A ) . CS G > > ( SS )
For very small dealers ( like dealer N in Figure 14 ), their counter-size is itself smaller than the minimum slab, i.e.,
CS N < ( SS )
Assuming these dealers will not hold excess stock and will not engage in wholesaling, they will clearly not be able to qualify for the volume discount.
4.2 Dealers like ( K ) whose normal offtake from brand A is marginally less than ( SS ) will be able to avail of the volume discount if through the conversion effect they can sell more, i.e., if
( X 'A + d X 'A ) . CS K = ( SS )
If this dealer does not take the volume discount offered by brand A, his sales would be
( X 'A ) . CS K , and his margin would be mA .
If he does take the volume discount, his effective margin would be ( mA + d yA ) due to the volume discount, and his sales would be ( X 'A + d X 'A ) . CS K .
The incremental profit he gets due to the scheme is
= ( SS ) . ( d yA ) ....... ( Eq. 13 )
and the incremental sales he gets is
= ( d X 'A ) . CS M ....... ( Eq. 14 )
The incremental benefit the dealer gets by availing of the volume discount, or his 'marginal volume discount' ( MVD ), is given by
MVD = ( incremental profit ) / ( incremental sales )
From ( Eq. 13 ) and ( Eq. 14 ), we have
MVD = [ ( SS ) . ( d yA ) ] / [ ( d X 'A ) . CS M ]
= [ ( CS M ). ( X 'A + d X 'A ) . ( d yA ) ] / [ ( d X 'A ) . CS M ]
= ( d yA ) . [ ( X 'A + d X 'A ) / ( d X 'A ) ]
For ( X 'A ) > > ( d X 'A ) , we have
MVD = ( d yA ) . ( X 'A ) / ( d X 'A ) ...... ( Eq. 15 )
Consider a case where a dealer ( M ) has ( CS M ) = 200, and brand A has a normal preference share of 20 %. Thus the dealer normally sells 40 sets of brand A every month. Let the volume discount be for a minimum slab of 50 sets. The dealer will not normally be able to avail of the volume discount, since he has a shortfall of 10 sets. If the volume discount helps the dealer get an additional 5 % conversion for brand A ( i.e., 10 extra sets ), he would be able to qualify for the volume discount. Thus we have
MVD = ( d yA ) . ( 20 ) / ( 5 )
= 4 . ( d yA )
While previously the dealer would have got only his normal dealer margin for his 40 sets, he now gets his normal dealer margin and the volume discount for all the 50 sets. However, for each of the 10 extra sets he buys, he gets the normal dealer margin and 4 times the volume discount. The reason the MVD is so high is that the dealer not only gets the volume discount on the extra 10 sets, he also gets it for all the 40 sets he would have anyway bought from A even without the volume discount. For these 40 sets, he would have made a 'normal profit' with his normal margin itself, hence the volume discount for these 40 sets constitutes his 'abnormal profit'. The above example shows how profitable it is for the dealer to avail of a volume discount, even if it means 'stretching' his sales through conversion from other brands. The dealer can obtain a part of his conversion from other brands by offering the customer an additional discount for purchasing brand A's sets. In the above example, for the 10 extra sets which the dealer has to buy from brand A, he can even offer his customers a discount upto 4 times his volume discount. However, this is true only for those dealers who (a) would not have qualified for the scheme without this conversion, and (b) with the conversion, can reach the minimum offtake slab. For dealers like G who would have qualified for the minimum slab anyway, the MVD is ( d yA ) - he only gets his normal dealer margin and the volume discount. For dealers like N, there is no MVD.
4.3 Now let us consider those dealers like ( M ) whose counter-sales is such that even with the conversion effect, their monthly sales will not equal the minimum monthly offtake slab. For these dealers,
( X 'A + d X 'A ) . CS M < ( SS )
These dealers can still avail of the volume discount if they are prepared to carry over the excess stock to the subsequent month(s). Thus if the relation between their expected monthly sales and the minimum slab is such that
( X 'A + d X 'A ) . CS M . n = ( SS ) ...... ( Eq. 16 )
they should be prepared to hold stock for ( n ) months.
Assuming brands do not give dealers any credit for their purchases, if ( P ) is the price at which the dealer buys the set from brand A, if ( i ) is the monthly rate for the dealer's cost of capital, and if the dealer needs ( n ) months to completely clear out the stock ( SS ) he had taken from brand A to qualify for the volume discount ( d yA ), his incremental stock-holding cost is
= ( P / 2 ) . ( i ) . ( n ) ........... ( Eq. 17 )
since the average value of the stock held by the dealer, as it diminishes from ( P ) to zero, is ( P / 2 ).
The dealer's MVD is now
MVD = [ ( SS ) . ( d yA ) ] / [ ( SS ) - CS M . ( X 'A ) ]
= [( X'A + d X'A ) . CSM . n] . ( d yA ) / [( X'A + d X'A ) . CSM . n] - [CSM . ( X'A )]
= ( d yA ) . [ n. ( X 'A + d X 'A ) ] / [ n. ( X 'A + d X 'A ) - ( X 'A ) ]
= ( d yA ) . [ ( X 'A + d X 'A ) ] / [ d X 'A + ( X 'A ) . ( n - 1 ) / n ]
For ( d X 'A ) < < ( X 'A ), we have
MVD = ( d yA ) . ( X 'A ) / [ d X 'A + ( X 'A ) . ( n - 1 ) / n ] ...... ( Eq. 18 )
For ( n ) = 1, we have
MVD = ( d yA ) . ( X 'A ) / ( d X 'A ) ....... ( Eq. 19 )
which is same as ( Eq . 15 ).
For ( n ) > > 1, and for ( d X 'A ) < < ( X 'A ), we have
MVD = ( d yA ) . ( X 'A ) / [ ( d X 'A ) + ( X 'A ) ]
= ( d yA ) ..... ( Eq. 20 )
4.4 If the dealer M has to hold stock only for a short duration ( i.e., small values of (n) ), his MVD is enormous ( as given by ( Eq. 19 ) ), and his stock holding cost is low. But if he has to hold stock for a long duration ( i.e., large values of (n) ), his MVD is very small ( as given by ( Eq. 20 ) ) and his large interest cost will exceed the MVD. The maximum duration of time ( nmax ) for which the dealer can hold excess stock without losing out on holding cost is at that point when his holding costs just equal his MVD. Thus from ( Eq. 17 ) and ( Eq. 18 ) we have :
( d yA ) . ( X 'A ) / [ d X 'A + ( X 'A ) . ( nmax- 1 ) / nmax ] =
( P / 2 ) . ( i ) . ( nmax )
( P / 2 ) . ( i ) = ( d yA ) . ( X 'A ) / [ ( d X 'A + X 'A ) . ( nmax) - ( X 'A) ]
For ( d X 'A ) < < ( X 'A ), we have
( X 'A ) . ( nmax) - ( X 'A) = ( d yA ) . ( X 'A ) . 2 / ( P . i )
( nmax - 1 ) = 2 . ( d yA ) / [ ( P ) . ( i ) ]
( nmax ) = 1 + 2 . ( d yA ) / [ ( P ) . ( i ) ] ....... ( Eq. 21 )
Given ( d yA ), ( P ) and ( i ), the above equation gives ( nmax ), the maximum duration for which it is worthwhile for the dealer to hold stock while accepting the volume discount. If he has to hold stock for more than this duration, his MVD will be wiped out by the holding cost.
4.5 The above value of ( nmax ) also places a limit on the quantity offtake that the dealer M can take for accepting the volume discount. We have, from ( Eq. 16 ),
( X 'A + d X 'A ) . CS M . n = ( SS )
The maximum feasible value of ( n ) as obtained from (Eq. 21) when substituted in (Eq. 16) gives us the maximum value of the volume discount, (SS), that the dealer can achieve, given his counter-size (CSM) and brand A's new preference-share, (X 'A + d X 'A). Thus the maximum value of the volume discount, (SSmax), that the dealer can achieve is given by (Eq. 21) and (Eq. 16) as :
( SS max ) = ( X 'A + d X 'A ) . CS M . [ 1 + 2 . ( d yA ) / ( P ) . ( i ) ]
For ( d X 'A ) < < ( X 'A ), we have
( SS max ) / CS M = ( X 'A ) . [ 1 + 2 . ( d yA ) / ( P ) . ( i ) ] ..... ( Eq. 22 )
4.6 Given (d yA), (P), (X 'A) and (i), (Eq. 22) gives a maximum value of the volume discount, ( SS max ), that the dealer with a counter-size ( CS M ) can achieve. Beyond this value, his MVD will be wiped out by his holding cost. Alternatively, ( Eq. 22 ) gives the minimum counter-size ( CS M min ) that the dealer must have in order to be able to qualify for a given offtake slab ( SS ). Dealers whose counter-size is below this will not be able to qualify for the volume discount.
As seen from Figure 15, the volume discount with a minimum slab has two main effects :
(a) for all dealers to the left of D1', the sales per dealer increases due to the conversion effect, [ ( X 'A + d X 'A ) . CS ]
(b) for dealers in the range D1' to D2', whose counter-size is greater than ( CS M min ), the sales per dealer increases to ( SS ).
For dealers beyond D2', the sales per dealer remains unchanged.
4.7 By offering the volume discount, the brand's 'TC per dealer' does not change, only its 'sales per dealer' and 'CM effective' change. From the 'TC per dealer' curve ( unchanged, as in Figure 6 ) and the new 'sales per dealer' curve in Figure 15, we arrive at the new 'TC per set' curve in Figure 16. For dealers to the left of D1', the TC per set is reduced since the sales per dealer has gone up and the TC per dealer has not changed. For dealers to the right of D2', the TC per set is unchanged, since the sales per dealer is also unchanged. For dealers in the range D1' to D2', the TC per set curve goes down, following the 'TC per dealer' curve ( since 'TC/set' is 'TC/dealer' / 'sales/dealer', and the 'sales/dealer' curve remains flat ). Thus the real impact of the volume discount is that dealers in the range D1' to D2' have a much lower TC per set than they had before the volume discount, while it is only marginally lower for dealers before D1'. The reduction in TC per set at D2' due to the volume discount, compared to its previous value without the volume discount, is shown as ( d TC ).
4.8 The 'CM effective' curve is lowered due to the additional discount pay-outs, as it was in Figure 11. Combining the TC per set curve in Figure 16 and the CM effective curve in Figure 11, we get the new net incremental profit curve of Figure 17. In the region D1' to D2', the net incremental profit shoots up, since in this range the CM effective keeps rising and the TC per set actually keeps falling. Compared to the values before the volume discount, the net incremental profit at D2' has risen by an amount ( d NIP ). Until D1', the net incremental profit changes only marginally, depending on which falls more - CM effective or the TC per set. Beyond D2', the net incremental profit remains unchanged. The extra NIP gained by the brand at D2', i.e., ( 'd NIP' in Figure 17 ), is equal to the reduction in the transaction cost per set ( d TC ) shown in Figure 16. Essentially the brand's NIP increases because many dealers whose normal offtake is less than the slab will stretch themselves and buy more sets to qualify for the volume discount, at no additional transaction cost to the brand. Since the sales per dealer increases ( by d S ) without any increase in the TC per dealer, the brand's TC per set will decrease, and its NIP will increase.
4.9 For dealers to the left of D1', the MVD is only ( d yA ). Dealers beyond D2' do not qualify for the volume discount, and hence have no MVD. The MVD that dealers in the range D1' to D2' get is given by ( Eq. 18 ) as
MVD = ( d yA ) . ( X 'A ) / [ d X 'A + ( X 'A ) . ( n - 1 ) / n ]
Thus we have
MVD / ( d yA ) = ( X 'A ) / [ d X 'A + ( X 'A ) . ( n - 1 ) / n ] ..... ( Eq. 23 )
Depending upon duration ( n ) for which they hold the stock, the MVD / ( d yA ) varies as follows :
for n = 1, MVD / ( d yA ) = ( X 'A ) / ( d X 'A )
assuming ( d X 'A ) < < ( X 'A ), we have
MVD / ( d yA ) = ( X 'A ) / [ ( X 'A ) . ( n - 1 ) / n ] ........ ( Eq. 24 )
Thus we have
for n = 2, MVD / ( d yA ) = 2
for n = 3, MVD / ( d yA ) = 1.5
for n = 4, MVD / ( d yA ) = 1.33
for n = 10, MVD / ( d yA ) = 1.11
and so on, till for
( n ) > > 1, MVD / ( d yA ) = 1
This is shown in the dealers' MVD curve in Figure 18.
4.10 The extra sales generated by the brand as a result of the volume discount can be calculated from Figure 19. As seen from the figure, the brand gets a sales of ( SS ) from all dealers between D1' and D2', which is more than what it would have got without the volume discount. The extra sales at D2' is given by ( dS ), while the extra sales at D1' is equal to the conversion difference, i.e., [ ( d X 'A ) . CS ]. In the range D1' to D2', the total extra sales got by the brand is the area above the old sales curve in the figure. This area is approximately equal to the area of the triangle formed by the points Q, R and S in Figure 19. The area of such a triangle, and hence the extra sales, is given by the equation
extra sales = ( 1 / 2 ) . ( d S ) . ( D2' - D1' )
Here ( d S ) = ( SS ) - ( S2 )
We have ( S1 ) = ( CS D1' ) . ( X 'A )
and . ( SS ) = ( CS D1' ) . ( X 'A + d X 'A )
hence ( S1 ) = ( SS ) . [ ( X 'A ) / ( X 'A + d X 'A ) ]
The sales curve can be approximated by the equation
S = k / D
where ( k ) is a constant and D is the dealer strength. For the old sales per dealer curve, the sales per dealer value at D1' is
( S1 ) = k / D1'
Thus k = ( S1 ) . D1'
and at D2', we have
( S2 ) = k / D2'
Substituting the value of ( k ) from above, we have
( S2 ) = ( S1 ) . D1' / D2'
Substituting here the value of ( S1 ), we have
( S2 ) = ( D1' / D2' ) . ( SS ) . [ ( X 'A ) / ( X 'A + d X 'A ) ]
Thus ( dS ) = ( SS ) - ( S2 )
= ( SS ) - ( D1' / D2' ) . ( SS ) . [ ( X 'A ) / ( X 'A + d X 'A ) ]
= ( SS ) . [ 1 - ( D1' / D2' ) . ( X 'A ) / ( X 'A + d X 'A ) ]
For ( d X 'A ) < < ( X 'A ), we have
( dS ) = ( SS ) . [ 1 - ( D1' / D2' ) ]
= ( SS ) . [ ( D2' - D1' ) / ( D2' ) ]
Thus
extra sales = ( 1 / 2 ) . ( D2' - D1' ) . ( SS ) . [ ( D2' - D1' ) / ( D2' ) ]
= ( SS / 2 ) . [ ( D2' - D1' )2 / ( D2' ) ] ...... ( Eq. 25 )
4.11 If the brand has a volume discount with two slabs, the sales, transaction cost and NIP are as shown in Figures 20, 21, 22. As seen, the brand can increase its sales and the NIP by having a large number of slabs for the volume discount. The larger the number of slabs, the more the brand's sales and NIP increases.
5. volume discount with slab and with wholesaling
5.1 When wholesaling happens, large dealers sell part of their offtake to small dealers who cannot qualify for the slab, and share the volume discount among themselves. We assume that the small dealers' sourcing decision ( buying from the brand directly vs. from a big dealer ) depends solely on the margin he gets. Then small dealers who cannot qualify for the brand's slab will prefer to buy from a bigger dealer ( and get part of the slab ) than to buy from the brand ( and get no slab at all ). This is shown in Figure 23, where we have considered a single-slab case :
- small dealers to the right of D2' stop buying from the brand and instead buy from bigger dealers to the left of D2'.
- for dealers to the left of D1' whose normal offtake from brand A is greater than the minimum slab, the MVD is only ( d yA ), and wholesaling is not abnormally profitable.
- for dealers in the range D1' to D2', wholesaling represents an 'abnormal profit'. These dealers would normally have to hold excess stock for a finite duration, incurring certain holding costs. For these dealers the normal offtake from brand A is less than ( SS ), i.e.,
( SS ) < ( CS D2 ' ) . ( X 'A + d X 'A )
They will prefer to sell the excess offtake to smaller dealers beyond D2', so that they will not have to incur the holding cost. Thus for these dealers in the range D1' to D2', we have
( SS ) = ( CS D2 ' ) . ( X 'A + d X 'A + wA ) ....... ( Eq. 26 )
where ( wA ) is the 'wholesaling share', or the fraction of the counter-sales that the dealer buys from brand A to sell to smaller dealers. Since the brand will normally try to keep the slab ( SS ) as high as possible, ( d X 'A + wA ) must be very high, and ( wA ) must be much higher than ( d X 'A ). Thus we can modify ( Eq. 26 ) to get
( SS ) = ( CS D2 ' ) . ( X 'A + wA ) ....... ( Eq. 27 )
5.2 For the brand, the incremental sales due to the volume discount is ( CS D2 ' ).( wA ), and the incremental contribution is ( CS D2 ' ) . ( wA ) . ( P. c ).
The incremental cost borne by the brand in getting this level of sales is given by
( SS ) . ( d yA ) = ( CS D2 ' ) . ( X 'A + wA ) . ( d yA )
For break-even for the brand, we have
( CS D2 ' ) . ( wA ) . ( P. c ) = ( CS D2 ' ) . ( X 'A + wA ) . ( d yA )
Thus ( d yA ) / ( P. c ) = ( wA ) / ( X 'A + wA ) ...... ( Eq. 28 )
Given ( P ), ( c ) and ( X 'A ), the above equation gives the maximum volume discount that the brand can offer for an expected level of wholesaling. From experience or from limited experimentation, the brand can estimate the level of wholesaling for different levels of volume discount, and decide whether the wholesaling will be economically worthwhile.
5.3 For the dealer, the MVD is the ratio of the extra cash he gets, to the extra sets he has bought from brand A. This is given by
MVD = ( SS ) . ( d yA ) / [ ( CS D2 ' ) . ( wA ) ]
= ( CS D2 ' ) . ( X 'A + wA ) . ( d yA ) / [ ( CS D2 ' ) . ( wA ) ]
Thus MVD = ( d yA ) . [ ( X 'A + wA ) / ( wA ) ] ..... ( Eq. 29 )
If we were to denote [ ( X 'A + wA ) / ( wA ) ] by the term 'wholesaling fraction' for brand A, ( WFA ), we have
MVD = ( d yA ) . ( WFA )
The MVD value for different levels of the 'wholesaling fraction' is shown in Figure 24.
Similarly, the maximum volume discount the brand can offer ( from Eq. 28 ) is
( d yA ) = ( WFA ) . ( P. c )
5.4 When the big dealer in the range D1' to D2' sells his excess stock to smaller dealers beyond D2', he must share part of the volume discount with the smaller dealer. Let (d yW) be the part of the volume discount that the big dealer gives the smaller dealer. For the quantity he sells to the small dealer, the big dealer retains an amount equal to his MVD minus the shared volume discount ( d yW ). Thus his retention is
= MVD - ( d yW )
= [ ( d yA ) . ( X 'A + wA ) / ( wA ) ] - ( d yW ) ...... ( Eq. 30 )
As the big dealer tries to sell his excess stock to the smaller dealer, he needs to give a larger and larger chunk of the volume discount to the smaller dealer. The shared portion of the volume discount can be approximated by a straight line rising from zero at D1'. The maximum discount that the dealer can part with is his MVD. Thus the dealer will continue to engage in wholesaling until a maximum value of wholesaling share, ( w A max ) where the maximum discount shared with the smaller dealer is
( d yW max ) = ( d yA ) . [ ( X 'A + w A max ) / ( w A max ) ] .... ( Eq. 31 )
For large levels of wholesaling, we have ( X 'A ) < < ( w A max ), hence
( d yW A max ) = ( d yA ) = MVD
5.5 As seen from Figure 23, dealers beyond D2' do not buy anything from the brand, preferring instead to buy from the bigger dealers. For dealers to the left of D1', the brand gets increased sales, but the brand loses all the sales it previously got from dealers beyond D2' ( upto D2, which was the maximum dealer strength of brand A before wholesaling, as seen from Figure 9 ). The difference between the two will determine whether the brand makes a net gain or loss in sales. This net sales gain for the brand due to wholesaling can be found from Figure 25, which also plots the market reach for the brand at various levels of dealer strength. To simplify the analysis, we have assumed that with wholesaling, the sales for the brand in the range D1' to D2' is not ( SS ) but simply ( CS D2 ' ) . ( X 'A + wA ) , where ( wA ) is the wholesaling share for all dealers to the left of D2'.
Thus before wholesaling, the sales for brand A would have been given by
SA = X 'A . MR1 . M
and after wholesaling, the brand's sales is given by
SA = ( X 'A + wA ) . MR2 . M
The extra sales for the brand to the left of D2' is given by [ MR2 . ( wA ) ].
The sales lost in the range D2' to D2 is given by [ ( MR1 - MR2 ) . ( X 'A ) ]
The brand's net gain in sales is given by
d S = [ MR2 . ( wA ) ] - [ ( MR1 - MR2 ) . ( X 'A ) ]
= [ MR2 . ( wA ) + X 'A ) ] - [ ( MR1 ) . ( X 'A ) ]
The brand has a net gain in sales when
[ MR2 . ( wA ) + X 'A ) ] > [ ( MR1 ) . ( X 'A ) ]
or when
[ MR2 / MR1 ] > [ X 'A / ( wA ) + X 'A ) ] ..... ( Eq. 32 )
Figures 1 - 25 :
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