Indian Startups – Comparative Analysis of Unicorns Afzal Anwar and Himadri Das Great Lakes Institute of Management, Gurgaon,

Abstract

The Indian Internet space is growing at a very fast rate. Companies are attracting high investment and fetching even higher from the investors. Many companies have attained the so-called Unicorn status of valuation over USD 1 Billion in a very short time. This paper examines these unicorns focused on the consumer space from a valuation model perspective. Traditional valuation methods are ineffective in case of these companies as they fail to capture high future growth potential, high uncertainty and heavy losses in spite of growing revenues, some typical characteristics of such companies. This paper focuses on carrying out a comparative analysis across Indian unicorns in an attempt to find a relationship to explain the high valuation with respect to their customers and other growth drivers, which depend on the space the business operates in.

Keywords: India, Internet, Unicorn, Valuation, E-commerce

Introduction

We are living in times of cheap and easily accessible capital, where entrepreneurs have succeeded in quickly converting their business ideas into billion dollar valuations that defy common perceptions about earnings, profits, multiples and the short terms focus of capital markets (Driek Desmet et al, 2000). Valuing these companies as per traditional models has become a challenge, especially when these are coupled with high uncertainty and heavy losses. Most practitioners are left wondering why these Indian internet unicorns merit such high valuations. Do they have higher return on assets and lower marginal costs? Do they see faster growth? Are they more profitable? So, who are these Indian Internet Unicorns? (Libert et al, 2014)

According to a Harvard research on trends in valuation that have evolved over a period of time across different business models, there are four business models (Libert et al, 2014) :

1. Asset Builders – They create and develop assets to produce and sell other physical things. Eg. Volkswagen, Coca Cola, Nike. 2. Service Providers – They provide services to their customers through their employees. Eg. Verizon, Accenture, Goldman Sachs. 3. Technology Creators – They develop proprietary products. Eg. , Oracle, Apple. 4. Network Orchestrators- They create peer network in which participants collaborate in value creation. Eg. , , Alibaba, Trip Advisor.

Based on reports quoted in international and national publications, private equity investments into some of the largest names in the Indian internet market, India is now home to at least 7 unicorns in consumer facing internet businesses (USD 1bn+ companies) such as three of India’s biggest ecommerce players: , and ShopClues; taxi hailing app: ; wallet/mobile commerce company: ; online classifieds company: Quikr; online restaurant and food delivery platform: Zomato; and mobile advertising network: Inmobi. All of these Indian Internet Unicorn are network orchestrators. They build a network in which the participants interact and share in the value creation. They may sell products or services, build relationships, share advice, give reviews, collaborate, co-create and more. The existence of these businesses and the revenues they generate depend significantly on the use of the Internet. Libert et al, 2014)

These type of companies have achieved rapid growth in a short period of time (<10 years) by creating a unique value for their customers. The traditional valuation techniques are not able to quantify the value of these internet businesses (Gupta & Chevalier, 2002). Traditional valuation techniques are based on metrics such as profit after tax (PAT), earnings before interest (EBIT), EBITDA, or free cash flows. These methods, however, cannot be employed for internet companies as they generate massive losses. A need, therefore, exists to come up with newer and more meaningful metrics using which fair assessment of these companies can be done. These Internet unicorns are here to stay as the convenience of shopping online, home delivery, availability of service 24X7, ease of access and low prices are the real benefits that traditional business models can’t offer. Most of these companies are, however, not even close to profitability at their current scale of operations but investors are nevertheless giving them high valuations based on the expectation of strong future profitability as the scale of operations increases.

Literature Review

Characteristics and Specifics of Valuing Internet Companies

Internet firms are very different from other firms in terms of much longer time taken to turn profitable. Internet companies typically generate heavy losses for the first several years, which results from high marketing costs and network growth costs that dramatically decrease their earnings in spite of high revenues. Internet companies grow at a very fast rate from a revenue perspective, with the most successful ones increase their revenues many fold in the early phase of development. Valuing internet companies is a great challenge given the lack of validated data, heavy losses and many uncertain factors about the future.

Based on the overview of recent academic research on the valuation of internet firms Jansen and Perotti (2002) draw the following conclusions:

1. Traditional accounting data remains important for valuing internet companies, however, the link between accounting numbers and Internet valuation is very weak. 2. Web traffic is not a major value driver for valuing internet companies 3. Financial Analysts stimulated the overvaluation of internet stock 4. New valuation factors are unsustainable

Financial Value Drivers

The following is a look at valuation factors, both financial and non-financial, which have great impact on companies’ earnings and value.

Price to Sales Ratio

Demers and Lev (2000) examine the value relevance of two categories of Internet companies’ expenditures related to the acquisition of intangible assets: (i) marketing expenses and (ii) product development and R&D expenses. They state that the market will positively value both of these variables as long as it views them as positive investments. They also examine the value relevance of the income statement components cost of goods or services sold. With the exception of cost of goods sold, the income statement components were significantly value relevant in 1999.

Cash-Burn Ratio

Demers and Lev (2000), construct a proxy for internet companies’ ability to sustain their current rate of cash burn. Their proxy for cash burn is cash on hand divided by current period’s cash flows from operations. They find that this proxy is a significant value-driver through 1999- 2000. Their proxy is defined as (cash on hand)/(current period’s cash flows from operations). Kozberg (2001), Hand (2000a), Hand (2000b) used non-financial data from the Nielsen called “Internet Audience” database which carried detailed information on the web browsing habits of approximately 57,000 Internet companies. However, this database is no longer freely available.

Gross Merchandise Value (GMV)

GMV indicates total sales value of merchandise sold through their market place over a period. For a consumer facing internet unicorn, it means the listed (not discounted) price charged to the customer multiplied by the number of items sold. Companies in India are currently burning cash at an average rate of 1.35X the GMV sold, through heavy discounting, marketing, free shipping and handling, cash incentives and various other incentives that e-tailers give to attract consumers.

Characteristics:

• Does not account for the deep discounts most consumer facing internet companies offer. • Nor does it factor in product returns.

So, if a firm says its annualized GMV is $100 million, it can actually be booking discounted sales of anywhere between $20 million and $70 million. Valuation at about 2-2.5 times a firm’s GMV is considered a reasonable number, while market leaders fetch valuations of up to four times their GMV. (Livemint Report,2015)

Non - Financial Drivers

The non-financial drivers which have significant effect on valuation for internet companies are those factors that cannot be found in accounting and financial statements, though they have huge impact on the future growth potential of internet companies, especially when most companies are generating very low revenue. Demers & Lev (2001) examined and suggested that all of these performance measures are relevant to the valuation of Internet companies.

1. Web Traffic – a ranking of the website traffic. 2. Reach – the percentage of Internet users that visit a particular web property. 3. Views – the number of pages that are viewed. 4. Hold – how long visitors stay on the site 5. Loyalty – driven primarily by the average number of visits to the site per unique visitor per period a. Number of Register Customers b. Daily Active Users 6. Strategic Alliances with other brands

Most researches assume that the future gross profits to be positively and linearly related to a current period’s gross profit, operating expenses and web site usage. It is based on the assumption that current period Website usage reflects potential future demand for the company’s products and affects the rates a firm can charge for advertising on the company’s Websites.

Valuation Methods

Many researchers and industry practitioners have mentioned merits and demerits of various valuation methods in their papers. Two of the most talked about valuation method are cash flow discounting and real options. However, the companies that we will be evaluating There two methods are widely discussed and each have their own limitations. Schwarz and Moon (2000) in their paper presented their model based on real options theory and capital budgeting techniques. The model had two sources of uncertainty: first, regarding changes in revenues and second, regarding expected rate of growth in revenues. Based on their model, they found that two sets of parameters have a significant effect on the value of a company. First and most obvious, the variable component of the cost function which is proportional to the revenues. Second, and not so obvious, set of parameters that have effect on the value of the firm are the parameters for the stochastic process of changes in the growth rate in revenues. However, they made many assumptions and made judgement calls for the parameters for which they had no data. Athanassakos (2007) work that was built upon Schwarz and Moon stated that traditional valuation methods used to valuate internet companies may lead to very low P/E ratios vis a vis observed multiples. If the observed high P/E ratios are high that will make most investors turn away from such investments. However, high P/E ratios can be justified based on the embedded options which can yield higher returns in the future and lower the risk associated with the firm. He also states in his paper that traditional valuation methods such as (discounted cash flow approach) understate value twice. Athanassakos (2007) argues that traditional method can be easily applied to stable companies. However, for internet companies this is less helpful as large part of the value is derived from embedded option (real option, discussed later in detail) Fernandez (2007) and Desmet, et al ( 2000) argue for cash flow discounting method. Zarzecki (2011) states that simplified valuation methods such as the P/E method or the P/R method are useless in cases when there are no profits or when the revenues rise dramatically fast. Instead of the above, some analysts suggest the use of such parameters as the number of customers or revenues that are three years ahead for multiple valuation. However, such an approach is incorrect as speculations on the future understood as three or five years ahead from the moment of valuation are simply not very useful in a situation when the risks continues to be very high over the next 10 or 20 years.

MAIN VALUATION METHODS BALANCE INCOME MIXED CASH FLOW VALUE OPTIONS SHEET STATEMENT GOODWILL DISCOUNTING CREATION BOOK VALUE MULTIPLES CLASSIC EQUITY CASH EVA BLACK AND ADJUSTED PER UNION OF FLOW ECONOMIC SCHOLES BOOK VALUE SALES EUROPEAN DIVIDENDS PROFIT INVESTMENT P/EBITDA ACCOUNTING FREE CASH CASH OPTION LIQUIDATION OTHER EXPERTS FLOW VALUE EXPAND THE VALUE MULTIPLES ABBREVIATED CAPITAL CASH ADDED PROJECT SUBSTANTIAL INCOME FLOW CFROI DELAY THE VALUE OTHERS APV INVESTMENT ALTERNATIVE USES Source: IESE Working Paper – Pablo Fernandez (2007)

Balance Sheet Methods

These methods seek to determine the company’s value by estimating the value of its assets that lies in its balance sheet. They determine static value and does not take into account company’s growth potential in future. They also do not consider factors outside the balance sheets such as industry performance, capital issues, market volatility etc. Some of the methods that it includes are: book value, liquidation value, substantial value, adjusted book value.

Income Statement- Based Methods

As the name suggest, these methods are based on examination of company’s income statement. These methods determine the value of the company’s value through its earnings, sales, dividends, multiples, sales multiples etc. Some of the methods that it includes are: Value of Earnings(PER) , Value of Dividends( DPS), Sales Multiples, Price/Sales, Price/Subscriber among others. (Fernandez,2007) Value of Earnings

In this method, equity’s value is obtained by multiplying the annual net income by a ratio called PER (price earnings ratio), that is:

Equity Value = PER X Earnings

Sometimes, the relative PER is also used, which is simply the company’s PER divided by the country’s PER. However, this method becomes ineffective in valuing these internet unicorns as there no reported earnings On this account, we can also rule out value of dividends as there are no earnings which can support any dividend payouts. Also, these companies are not listed and are privately held. There is no obligation to pay dividends.

Multiples

This valuation method is quite widely used in valuating companies, who are currently generating heavy losses. In reference to our paper, we will be focusing on multiples method in our analysis, which will be later explained in methodology and analysis section. There are different types of multiples one can look at:

1. Sales Multiples a. Price/Sales Ratio b. Return on Equity 2. Value of the company/ Revenue 3. Value of the company/ Earnings before interests and taxes (EBIT) 4. Value of the company/ Earnings before interests, taxes, depreciation and amortization ( EBITDA) 5. Value of the company/Operating Cash Flow

Financial Value Drivers

1. Price to Sales Ratio

Demers and Lev (2000) examine the value relevance of two categories of Internet companies’ expenditures related to the acquisition of intangible assets: (i) marketing expenses and (ii) product development and R&D expenses. They state that the market will positively value both of these variables as long as it views them as positive investments. They also examine the value relevance of the income statement components cost of goods or services sold. With the exception of cost of goods sold, the income statement components were significantly value relevant in 1999.

2. Cash-Burn Ratio

Demers and Lev (2000), construct a proxy for internet companies’ ability to sustain their current rate of cash burn. Their proxy for cash burn is cash on hand divided by current period’s cash flows from operations. They find that this proxy is a significant value-driver through 1999- 2000. Their proxy is defined as (cash on hand)/(current period’s cash flows from operations). Kozberg (2001), Hand (2000a), Hand (2000b) used non-financial data from the Nielsen called “Internet Audience” database which carried detailed information on the web browsing habits of approximately 57,000 Internet companies. However, this database is no longer freely available.

Most researches assume that the future gross profits to be positively and linearly related to a current period’s gross profit, operating expenses, customer life time value (CLTV) and web site usage. It is based on the assumption that current period website usage reflects potential future demand for the company’s products and affects the rates a firm can charge for advertising on the company’s Websites.

Cash Flow Discounting -Based Methods

These methods determine the company’s value by estimating the cash flows it will generate in the future and then discounting them at a rate basis calculated future risk. Nowadays, it is the most relevant method as in this method company is viewed as a cash flow generator. It unambiguously separates real investment expenses and investment costs accounted for in time according to accepted (usually arbitrary) accounting principles. Cash flow discounting does not eliminate the need to make some difficult predictions about the future, nevertheless it treats the issues of extremely high growth rates and uncertainty consistently (Zarzecki,2011). This method solely relies on forecasting of performance.

Economic Value Added (EVA)

EVA is a good indicator of the sustainability of the company’s business model. Accounting measures often need to be adjusted for valuation. A primary adjustment to earnings is to subtract the cost of committed capital. This requires a precise measure of capital. Economic Value Added (EVA 3) considers R&D, marketing and advertising expenses as investment rather than expenses. Using EVA, a greater percentage of the value appears in the earlier years, where forecasting is easier. When initial costs (R&D, marketing and advertising) are capitalized as investments, this creates higher earnings in the earlier years, where forecasting is more practical (Jansen & Perotti, 2002). The trouble with EVA, is that, it simply assesses the past performance of the company, rather than the future opportunities. It is therefore not suitable for the valuation of internet companies, which still have everything to prove in terms of economic performance (Gupta, J. & Chevalier, A,2002).

Methodology

As mentioned, for the purpose of this paper only seven internet unicorn businesses were considered for the analysis. Detailed analysis for only three of the seven was carried out due to lack of credible data (Refer Appendix A)

Data Sources

1. Venture Intelligence Database 2. VC Circle Publication 3. Newspaper Reports 4. Official Websites Data Attributes Collection

Given the different nature of businesses, each business was assessed at different parameter (Refer to Table 1). For our analysis, we did not consider Quikr due to unavailability of credible data. Below is the table with attribute details for each company. In the analysis section, both tabulated and compiled data for Flipkart.com, Snapdeal.com and Ola is available as part of the analysis.

Attributes Independe Dependent Independent Independent nt Variable Variable (Y) Variable (X) Variable (X) (X) Valuation Number of Registered Flipkart.com (USD NA NA Users Million) Valuation Number of Registered Snapdeal.com (USD NA NA Users Million) Valuation Number of Registered ShopClues.com (USD NA NA Users Million) Valuation Ola Cabs (USD Number of Cabs NA NA Million) Valuation Number of Number of Zomato Media (USD Monthly NA Restaurants Million) Visitors Valuation Number of Registered Paytm (USD NA NA Users Million) Valuation Quikr (USD NA NA NA Million) Sources: Venture Intelligence, VC Circle Reports, Newspaper Reports. NA: Not Available

Approach

After the data collection, graphical and regression analysis were done for Flipkart, Snapdeal, Paytm and OlaCabs to understand if there exists a meaningful relationship between each of theirs valuation and the common denominators of their unique businesses.

Regression was done to test the hypotheses that states that the independent variable (i.e., registered users, number of cabs) for each of the business has a significant relationship with the dependent variable (i.e., post money valuation).

Simple graphical plot was done to analyze the correlation between the dependent and the independent variable. This helped us establish the basis for regression and further understand the extent of correlation.

On the basis of regression, R square and adjusted R square for the each of the companies was analyzed to understand the variability in the data and extent of correlation between dependent and independent variable (Refer Table 1). R square helps us explain how well the model predicts the observation. Whereas, adjusted R square helps in understanding the proportion of variance in the dependent variable that can be predictable from the independent variable.

In the following section, the data and analysis for each of the three unicorns has been presented as part of the paper, for the readers, to understand the correlation between valuation and the non-financial drivers.

Analysis

In the following analysis section, we have tried develop a common model using different quantitative techniques, which can help us in understanding their relationship pattern for each of the unicorns. However, due to lack of data availability, we have only considered four of the existing seven unicorns. We have not considered the deal rounds for these unicorns which were participated only by individual investor (Angel Investor), for example, Ratan Tata’s investment of USD 2.5 million in Paytm.

Based on the methodology adopted in the previous section, following were the key findings:

1. Simple graphical plot between post money valuation and number of registered users/number of cabs showed a positive strong correlation.

a. In case of Flipkart, for each round of funding, there was an average increase of 128.7% in post valuation corresponding to an average jump of 68% in number of registered users. b. In case of Snapdeal, for each round of funding, there was an average increase of 81.68% in post valuation corresponding to an average jump of 27.14% in number of registered users. c. In case of OlaCabs, for each round of funding, there was an average increase of 215.08% in post valuation corresponding to an average jump of 170.40% in number of registered cabs. d. In case of Paytm for each round of funding, there was an average increase of 40% in post valuation corresponding to an average jump of 308% in number of registered users.

2. Regression analysis was used to analyze the extent of correlation and the variability in the data model (Refer to below mentioned. Table 2 and Appendix A for scatter charts.)

Table 2: Findings based on Regression Analysis Flipkart.com Snapdeal.com OlaCabs Paytm R Square 0.92 0.76 0.92 0.86 Adjusted R 0.9 0.71 0.91 0.8 Square

As per Table 2, we can conclude that for all the three unicorn, there is a high degree of correlation between the dependent variable (post money valuation, Y) and independent variable (number of registered users/cabs, X). Also, the data model explains more than 70% of the variations.

3. Through graphical plot, we found that out of the four unicorns, Paytm’s post money valuation relationship with number of registered users is modest and has gradually increased over period of time compared to other businesses. Detailed Analysis Unicorn I: Flipkart.com Table 3 : Tabulated Flipkart.com data through various data sources

Round Date Money Post Money Reported Revenue No of Raised Valuation Revenue Multiplier (Post registered (US$M) (US$M) (US$M) Money/Revenue) users (in Millions) 8 Jul- 700.00 15,200.00 472.17 32.19 45.00 15 7 Dec- 700.00 11,000.00 474.53 23.18 26.00 14 6 Jul- 1000.00 6,975.59 491.37 14.20 22.00 14 5 May- 210.00 2,636.09 491.37 5.36 18.00 14 4-B Oct- 160.00 1,780.44 491.37 3.62 10.00 13 4-A Jul- 200.00 1,665.66 NA NA 10.00 13 3 Feb- 150.00 1,013.07 NA NA 3.00 12 2 Mar- 20.00 163.06 11.05 14.76 2.00 11 1 Dec- 9.00 65.85 NA NA NA 09 Seed Oct- 1.00 14.75 NA NA NA 09 Source: Venture Intelligence, VC Circle Reports, Newspaper Articles. NA - Not Available. The highlighted rows were not part of the analysis Graphical Plot

Table 4 : Compiled Flipkart.com data for graphical plot

Post Money Registered Deal Time % Increase in % Increase in Valuation Users Period Valuation Registered Users (US$M)) (Millions) March 163.06 2.00 2011 February 1,013.07 3.00 521.30% 50.00% 2012 July 2013 1,665.66 10.00 64.42% 233.33% October 1,780.44 10.00 6.89% 0.00% 2013 May 2014 2,636.09 18.00 48.06% 80.00% July 2014 6,975.59 22.00 164.62% 22.22% "December 11,000.00 26.00 57.69% 18.18% 2014 July 2015 15,200.00 45.00 38.18% 73.08% Source: Venture Intelligence, VC Circle Reports, Newspaper Articles. NA - Not Available. The highlighted rows were not part of the analysis

Graph1 for Flipkart.com

Flipkart Graphical Plot 16,000.00 50.00 14,000.00 45.00 40.00 12,000.00 35.00 10,000.00 30.00 8,000.00 25.00 6,000.00 20.00 15.00 4,000.00 10.00 2,000.00 5.00 0.00 0.00 March 2011 Februaruy July 2013 October May 2014 July 2014 "December July 2015 2012 2013 2014

Registered Users (Millions) Post Money Valuation (US$M))

Results from Regression:

Post Money Valuation= -1231.691+369.761*(Number of Registered Users)

The Regression Equation for flipkart.com Regression Statistics conveys that though there is a weak initial correlation between post money valuation Multiple R 0.957477648 and number of registered users. The R Square 0.916763447 correlation becomes stronger as the number Adjusted R Square 0.902890688 of registered users increase on the platform. Standard Error 1707.980745 The same was deduced and established using the graphical plot, hence, explaining Observations 8 the high valuation.

Unicorn II : Snapdeal.com Table 5: Tabulated Snapdeal.com data through various data sources

No of Money Post Money Reported Revenue registered Round Date Raised Valuation Revenue Multiplier (Post users (in (US$M) (US$M) (US$M) Money/Revenue) Millions) 10 Feb-16 200 6,483.5 137.5 47.1 87 9 Aug-15 500 5,066.1 25.8 196.0 60 8 Apr-15 250 5,012.6 150.4 33.3 30 7 Oct-14 637 1,774.2 27.6 64.3 25 6-A Aug-14 1.64 957.3 27.6 34.7 20 6 May-14 100 1,012.1 101.2 10.0 20 5 Feb-14 134 682.9 97.0 7.0 20 4 Apr-13 50 205.6 6.2 33.2 20 3 Jul-11 40 210.6 1.7 125.3 NA 2 Jan-11 7.5 21.9 1.7 13.1 NA 1 Sep-09 2.25 5.2 0.5 9.7 NA Source: Venture Intelligence, VC Circle Reports, Newspaper Articles. NA - Not Available. The highlighted rows were not part of the analysis

Graphical Plot

Table 6: Compiled Snapdeal.com data for graphical plot Post No. of registered Percentage Percentage Increase in Deal Time Money (In users (In Increase in number of registered Period USD millions) valuation users million) April 2013 205.65 20.00 February 682.94 20.00 232.09% 0.00% 2014 May 2014 1,012.06 20.00 48.19% 0.00% August 957.32 20.00 -5.41% 0.00% 2014 October 1,774.20 25.00 85.33% 25.00% 2014 April 2015 5,012.59 30.00 182.53% 20.00% August 5,066.11 60.00 1.07% 100.00% 2015 February 6,483.55 87.00 27.98% 45.00% 2016 Source: Venture Intelligence, VC Circle Reports, Newspaper Articles. NA - Not Available. The data for highlighted rows are skewed due to unavailablity of data points.

Snapdeal Graphical Plot 7,000.00 100.00 6,000.00 80.00 5,000.00 4,000.00 60.00 3,000.00 40.00 2,000.00 20.00 1,000.00 0.00 0.00 April 2013 February May 2014 August 2014 October 2014 April 2015 August 2015 February 2014 2016

No. of registered users (In millions) Post Money (In USD million)

Results from Regression

Post Money Valuation= -369.43+85.64*(Number of Registered Users)

Regression Statistics The Regression Equation for Snapdeal.com Multiple R 0.869049399 conveys that though there is a weak initial R Square 0.755246857 correlation between post money valuation Adjusted R Square 0.714454667 and number of registered users. The Standard Error 1313.193813 correlation becomes stronger as the number Observations 8 of registered users increase on the platform. The same was deduced and established using the graphical plot, hence, explaining the high valuation. Unicorn III : Ola Cabs Table 7: Tabulated Ola Cabs data through various data sources

Post No of Money Reported Revenue Money registered Round Date Raised Revenue Multiplier (Post Valuation Cabs(in (US$M) (US$M) Money/Revenue) (US$M) Millions) 6-A Sep-15 500 4,740.9 7.7 616.4 0.25 5-B Jul-15 0.15 2,416.5 8.3 290.4 0.2 5-A Apr-15 400 2,424.7 8.3 291.4 0.115 4 Oct-14 210 656.5 8.3 78.9 0.033 3 Jul-14 41.6 174.4 2.7 64.0 0.011 2 Jul-13 20 29.3 2.7 10.7 0.004 1 Apr-12 4 11.5 0.1 79.4 0.001 Source: Venture Intelligence, VC Circle Reports, Newspaper Articles.

Graphical Plot

Table 8: Compiled Ola Cabs Data for graphical plot

Post Money Registered Deal Time % Increase in % Increase Registered Valuation Number of Period Valuation Cabs (US$M)) Cabs(Millions) April 2012 11.54 0.001 NA NA July 2013 29.32 0.004 154.17% 300.00% July 2014 174.35 0.011 494.58% 175.00% October 656.49 0.033 276.53% 200.00% 2014 April 2015 2,424.75 0.115 269.35% 248.48% July 2015 2,416.49 0.200 -0.34% 73.91% September 4,740.94 0.250 96.19% 25.00% 2015 Source: Venture Intelligence, VC Circle Reports, Newspaper Articles. NA - Not Available. The highlighted rows were not part of the analysis

Ola Cabs Graphical Plot 5,000.00 0.300

4,500.00 0.250 4,000.00

3,500.00 0.200 3,000.00

2,500.00 0.150

2,000.00 0.100 1,500.00

1,000.00 0.050 500.00

0.00 0.000 April 2012 July 2013 July 2014 October 2014 April 2015 July 2015 September 2015

Registered Number of Cabs (Millions) Post Money Valuation (US$M))

Results from Regression:

Post Money Valuation= 27.97 +0.0167*(Number of Registered Cabs)

The Regression Equation for Ola Cabs Regression Statistics establishes a strong correlation between the number of registered cabs and the post money Multiple R 0.96160801 valuation that the company has been able to R Square 0.924689964 command. The correlation becomes stronger Adjusted R Square 0.909627957 and the adjusted R square is also high. Standard Error 535.6000629 However, it is important to note that the for the Observations 7 initial years the number of registered cabs in the data model is constant. This is because of lack of available data. The correlation could have been far stronger or weaker. Unicorn IV: Paytm.com Table 9 : Tabulated Paytm.com data through various data sources

Post No of Money Reported Revenue Money registered Round Date Raised Revenue Multiplier (Post Valuation users (in (US$M) (US$M) Money/Revenue) (US$M) Millions) 1 April 2007 5.86 20.4 20.4 1.0 NA December 2 2.5 30.3 30.3 1.0 NA 2007 December 3 10 93.7 17.0 5.5 NA 2008 September 4 10 252.4 48.3 5.2 20 2011 November 5 60 373.7 32.0 11.7 15 2014 February 6 404 614.9 33.7 18.3 50 2015 March 7 0.16 638.4 31.4 20.4 100 2015 Source: Venture Intelligence, VC Circle Reports, Newspaper Articles. NA - Not Available. The highlighted rows were not part of the analysis

Graphical Plot

Table 10: Compiled Paytm.com Data for graphical plot

No. of Percentage Post Money Percentage Deal Time registered Increase in Valuation (In Increase in Period users (In number of USD million) valuation millions) registered users September 2011 252.42 20.00 November 2014 373.70 15.00 48% -25%

February 2015 614.90 50.00 65% 233%

March 2015 638.36 100.00 4% 100% Source: Venture Intelligence, VC Circle Reports, Newspaper Articles. NA - Not Available. The data for highlighted rows are skewed due to unavailability of data points. Paytm Graphical Plot 700.00

600.00

500.00

400.00

300.00

200.00

100.00

0.00 September 2011 November 2014 February 2015 March 2015

Post Money Valuation (In USD million) No. of registered users (In millions)

Results from Regression:

Post Money Valuation= 157.596 + 9.25 * (Number of Registered Users)

The Regression Equation for Paytm establishes a strong correlation between the number of Regression Statistics registered users (in Million) and the post Multiple R 0.929180705 money valuation that the company has been R Square 0.863376782 able to command. The correlation becomes Adjusted R Square 0.795065172 stronger and the adjusted R square is also high. It is important to note that the number of Standard Error 85.07719221 observation is limited due to the available data. Observations 4 However, regression has been used consistently to establish a correlation between Paytm’s post money valuation and non- financial driver such as number of registered user.

Discussion Section

The primary purpose of this study was to examine the seven consumer internet unicorns in India in order to understand effective valuation metrics and methods that are employed or can be employed. The methodology adopted in this paper was limited by the available data. As these companies are unlisted, financial data to a large extent is inaccessible and the numbers frequently quoted in the media are highly questionable. Given the nature of Indian internet companies, traditional metrics for valuation such as earnings, free cash flow etc., do not work. In this paper, we have tried to find a relationship between the valuation of these companies with respect to their potential indicators such as number of registered customers for e-commerce players and number of cabs for ride hailing app, Ola Cabs. Through our analysis using graphical and regression techniques, we have been able to establish a strong positive correlation between valuation and un-conventional metrics as specified in the analysis section. However, the analysis is not complete and the results can be further improved with help of more accurate and reliable data. One of the major challenges in valuing these companies is lack of relevant data to define metrics and benchmarks which can be uniformly applied across these highly valued startups.

The results generated from the valuation model suggest two possible explanations: (1) meaningful relationship between post money valuation and non-financial drivers (2) there is a hint of exaggerated valuation of the company by the investors, which can be fueled by unrealistic expectations towards future earnings. Critics of financial analysts have argued that the intangibles of such companies are not adequately valued in quantitative models. Number of customers/assets are important metrics which can provide key insights towards customer’s equity. However, our results show that customer equity and other non-financial driver such as registered users, number of cabs, monthly visits, daily transactions, etc., have a significant relationship. Also, through our analysis we can deduce that the non-financial drives in itself cannot explain the valuation of these Indian unicorns. If the data is made available for various unicorns, one can build upon the methodology mentioned in the paper. With lack of data and data sources, it is still not clear what is the metric used by venture capitalists and PE firms for valuation and whether these are justified. We can definitely expand the current model further but much work is required on the data gathering which remains a challenge.

Acknowledgement

We would like to acknowledge support of Kapildeep Singh Chauhan, a student at Great Lakes Institute of Management, Gurgaon, for providing his expertise in data collection and analysis during the course of the research. We would like to sincerely thank him for his continued support. His key insights and feedback greatly improved the research paper.

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APPENDIX A – Scatter Plots with Trend Line: Regression Analysis

Based on the methodology explained in earlier sections, following are the regression analysis explained using scatter plots. It will be meaningful to review the paper and the analysis section with the below plots for better understanding. Flipkart Snapdeal

Post Money (In USD million) Linear (Post Money (In USD million)) Post Money (In USD million) Linear (Post Money (In USD million))

18,000.00 8,000.00 16,000.00 7,000.00 14,000.00 6,000.00 12,000.00 5,000.00 10,000.00 4,000.00 8,000.00 3,000.00 6,000.00 2,000.00 4,000.00 1,000.00 2,000.00 Post Money Valuation (USMD) 0.00 Post Money Valuation (USMD) 0.00 0.00 20.00 40.00 60.00 80.00 100.00 0.00 10.00 20.00 30.00 40.00 50.00 -2,000.00 Number of Registered Customer Number of Registered Customers ( In Million)

Ola Paytm Post Money (In USD million) Linear (Post Money (In USD million)) 60.00

5,000.00 50.00

4,000.00 40.00 30.00 3,000.00 20.00 2,000.00 10.00 1,000.00 0.00 0.00 0.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00 Post Money Valuation (USMD) 0.000 0.050 0.100 0.150 0.200 0.250 0.300 Number of Registered Users (In millions) Number of Cabs ( in MIllion) Post MoneyValuation (In USD MIllions) Linear (Number of Registered Users (In millions))