Equity Research Internet April 19, 2021

TechWeiss SECTOR UPDATE

Decoding the platform economy

Internet evolution has propelled network effect-led business models, dubbed platforms. These businesses enjoy near monopolistic positions and entail high pricing power as well as switching costs. Facebook, Google, , , Naukri and IndiaMART are a few examples. They are characterised by initial cash burn phase to establish network effect to get consumers on board, followed by monetisation phase. Since they require minimal hard assets and unit economics evolve with business maturity, they need to be evaluated differently from traditional capacity businesses. Steady-state unit economics and addressable markets are key parameters for platform businesses. Network effect underpins platform business The value of network effect-led platform increases as more people use the product or service, creating natural monopolies with high entry barriers. However, in initial stages, these businesses are compelled to spend money to attract consumers to reach a critical threshold to exploit the network effect. This naturally translates into high cash burn and minuscule revenue. After establishing a strong network, companies invariably focus on monetisation of the platform and then cost optimisation. As this focus shifts, the unit economics can materially change. Platform versus capacity businesses: The valuation divergence The public market investor community is habituated to analysing capacity businesses where companies create capacities for certain products. Their success typically depends on economies of scale, efficiency of operations, distribution etc. and unit economics are fairly straightforward compared to already established peers. On the other hand, platform companies require significant initial investments to drive network effect, but focusing on cash burn is futile considering that unit economics evolve. Hence, while market share, capacity utilisation and ROIC are key parameters determining valuation of capacity businesses, in the platform business total addressable market (TAM) and long-term margins are key parameters. Get ready for the changing paradigm We see a divergence in the way public and private market investors evaluate platform businesses. We attribute this to relatively longer history of platforms in India’s private versus public markets. Not all platform businesses succeed and there can be various reasons for their failure–poor customer experience, paucity of capital, small addressable market, etc. However, we expect such platforms to capture many spheres of life going forward; the disruption is already underway in many industries–moving gradually from information-based industries to asset- heavy industries. Platforms of different size and shapes are emerging at global, national and local levels. Having said that, monopolistic nature of these businesses is bound to invite more scrutiny and we expect regulations to evolve accordingly.

Pranav Kshatriya Sandip Agarwal Pulkit Chawla +91 (22) 4040 7495 +91 (22) 6623 3474 [email protected] [email protected] [email protected]

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Rise of the platform economy

“Companies in every industry need to The internet evolution has propelled many network effect-led assume that a software revolution is business models, dubbed platforms. These are characterised by near coming.” Marc Andreessen in his famous monopolistic positions in respective markets and entail high pricing essay, Why Software Is Eating the World power as well as switching costs. While Google, Facebook, Uber, AirBnB are a few global examples, Zomato, Naukri and IndiaMART are local examples. Success of these companies has attracted a lot of money and interest from investors in their quest to discover the next big bang platform. In the initial stage, such companies need to invest to reach critical mass of the network. Hence, they are characterised by cash burn phase, followed by monetisation phase. We also note that platform businesses are different–minimal hard asset requirement, unit economics can significantly change as the company enters monetisation phase–from traditional businesses and they are evaluated differently than typical capacity businesses. Their eventual unit economics and addressable market are key valuation parameters. Network effect underpins platform business The internet evolution has led to emergence of many businesses, characterised by network effect, creating near monopoly in their respective verticals. Facebook & in social media, Google in search, Uber in cab aggregation and AirBnB in vacation rental are a few examples of such businesses. Unique feature of network effect-led businesses is that the value of the platform increases as more people use the product or service. This creates natural monopolies with high entry barriers. Network effects can either be same sided or cross sided. Social networks are examples of same-sided network where a higher number of friends on a platform will render that platform more valuable for users and will, in turn, attract more users. Uber is an example of cross-sided network effect where a higher number of riders will attract higher number of cars, which in turn will increase cab availability, which will further attract more riders. High cash burn in initial stage, followed by monetisation phase In initial stages, network effect-led companies have to focus on on-boarding consumers to reach a critical threshold to exploit the network effect. Hence, companies tend to have high customer acquisition costs with minuscule revenues. Customer experience also tends to be key focus area as engagement on platforms is of paramount importance at this juncture. This naturally translates into high cash burn in the initial stage. At times, as companies expand geographically, cash burn tends to increase, but that also brings scale to the network. Therefore, companies earn minuscule revenues in initial stages with high cash burn. After establishing a strong network, companies invariably focus on monetisation of the platform and then cost optimisation. Platform companies can have multiple ways of monetising the platform with advertising, virtual goods, membership fees, etc., being some of the options. For scaling the platform, we have seen companies expanding into new geographies or into adjacent areas to replicate their business models and backward integrating to capture higher value of the supply chain.

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Fault-lines for platform businesses Not all platform businesses succeed and there can be various reasons for their failure–poor customer experience versus competition (Orkut versus Facebook), lack of capital versus competition (Shopclues and Snapdeal versus Amazon and Flipkart), small addressable market, etc. However, we do expect many network effect-led businesses to emerge in various verticals as the market matures. What initially started as information-based networks are incrementally disrupting hard asset- based industries as well. Initial players, such as search engines, messaging platforms and social networks were pure information-based networks. However, next generation businesses such as ride hailing, hotel room aggregators and are multi-faced networks and are impacting industries which have underlying hard assets. Some of these networks are local, national as well as global. Platform versus capacity business: The valuation divergence The investor community is used to analysing capacity businesses where companies create capacities for certain products or services and there are multiple players offering similar products. Success of such companies typically depends on economies of scale, efficiency of operations, distribution reach, etc. and unit economics are fairly straightforward compared to already established peers. In the platform economy, however, successful companies typically have dominant market share, significant pricing power, strong unit economics and high direct or indirect switching costs for users. Platform companies do require significant initial investments to drive network effect, but focusing on cash burn is futile considering the unit economics evolve and can be significantly different in a mature stage. Hence, while for capacity businesses market share, capacity utilisation and ROIC are key parameters determining valuation, in the platform business total addressable market (TAM) and long-term margins are key parameters. What to track in platform business?

Platform business metrics can be classified In capacity businesses, investors track parameters like revenue, fixed & variable into four--acquisition related, engagement costs, capital employed, margins, etc. For a platform business, measurement related, economic related and marketplace parameters can be broadly classified into four categories--acquisition related, and competitor metrics engagement related, economic related and marketplace & competitor metrics. In acquisition-related metrics, total traffic and composition of free versus paid traffic are important parameters to track. Engagement metric typically tracks how users are engaging with the platform which determines strength of the network as well. Daily/Monthly Active Users (DAU/MAU), time spent by users, retention ratios are key engagement metrics. The quality of engagement, the strengthening of network with user addition are key things to track in initial stages. Lifetime value of the customer (LTV) and customer acquisition costs (CAC) are the most important financial parameters to track.

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Network effect and platform businesses

 The internet evolution has led to emergence of many businesses, characterised by network effect creating near monopoly in their respective verticals. Facebook & Twitter in social media, Google in search, Uber in cab aggregation and AirBnB in vacation rental are some examples of such businesses.  Unique feature of network effect-led businesses is that the value of the platform increases as more people use the product or service. This creates natural monopolies with high entry barriers.  Network effects can either be same sided (direct) or cross sided (indirect). While social media and messaging platforms are examples of same-sided networks, transaction platforms, marketplace etc., are examples of cross-sided networks.

Internet evolution and the network effect Internet evolution has led to democratisation of information and knowledge. Over the years, it has had profound impact on various businesses–creative destruction leading to transformation in many industries. One key feature of many internet- based platforms has been their ability to add users at an astronomical pace and by virtue of higher number of users they can offer significantly better services than any competition. This network effect acts as an entry barrier for other companies, leading to near monopolies of such platforms. Facebook & Twitter in social media, Google in search, Uber in cab aggregation and AirBnB in vacation rental are some examples of such businesses in the global arena. IndiaMART InterMESH’s (IndiaMART) B2B product discovery platform and Info Edge’s recruitment platform Naukri are examples from India.

Network effect is a phenomenon whereby Simplistically, network effect is nothing but “Demand Side Economies of Scale,” rising number of participants on a platform implying that the value of the network, service or product increases as the number improve its value/attractiveness to of people using it increases. For example, the product provides more value to user participants n+1 than user n. For most industry platforms, addition of a single user on the platform increases the value in a non-linear fashion as more number of users join the platform. The importance of network effect lies in the strong moat it creates around the business. Ebay network effect example

Source: Harvard Business School

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Types of Network Effects

Social media networks, search engines etc. Network effects can be either same sided or cross sided. Same-side networks are are examples of same-sided networks direct network effects that are created by the impact of users on one side of the market on other users on the same side. A classic example of same-sided network effects is Facebook. The more friends one has in their network, more likely is he/she able to attract more connections. Another example is Google, wherein higher number of users helps improve the Google search algorithm, which further attracts more users. Social media networks, communication platforms and messaging apps are examples of direct or same-side networks. Same-side and cross side network effect

Source: Hagiu and Wright

Marketplace, ride hailing, exchanges are On the other hand, cross-side network effects occur when users on one side of the examples of cross-sided networks market attract users on the other side of the market. A marketplace is a classic cross- side network or indirect network where more buyers attract more sellers, which in turn attract more buyers. Uber is perhaps one of the best examples of the cross-side network effect, leading to maximisation of value for users on the platform. A higher number of users attracted more drivers, which in turn led to better service levels (e.g. faster service) and lower cost (economies of scale). This attracts more drivers and so on. After over a decade of its inception, growth in the user base (93mn monthly users currently) as well as drivers (~3.5mn) is remarkable. Uber network effect

Source: medium.com

A similar effect has been seen in Doordash–largest food delivery company in the US. In its two-way network effect, more merchants on the platform started attracting more consumers, which in turn attracted more dashers (delivery agents) to the platform. The logistics platform benefits from three areas--local network effects, economies of scale and rising brand affinity. These three factors combined lead to more stakeholders on the platform.

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Doordash network effect

Source: Company

Compared to Facebook and Google, which already have a dominant share in their respective markets, Doordash is a relatively new company having started its operations in 2013. Consequently, it is still investing heavily in growth to acquire new customers, merchants and riders and expanding into new geographies, resulting in higher losses. Having said that, Doordash has generated profits (contribution margin) for cities that it earlier entered in. Doordash- Components of a marketplace order

Source: Company Network effect leads to near monopolies

Network effect improvement leads to The network effect naturally drives concentration of users to one platform, creating herding of users, creating near monopolies near monopolies in respective verticals. Hence, although creation of another platform is technologically and financially easy, attracting users to trigger network effect is extremely difficult, which results in dominant market share for such platforms. Facebook’s domination in the social media space is an example of such network effect.

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Facebook- Market share

100

80

60

(%) 40

20

0

Jul-13 Jul-20

Jan-10 Jan-17

Jun-09 Jun-16

Oct-11 Oct-18

Apr-15

Feb-14 Sep-14 Feb-21

Dec-12 Dec-19

Aug-10 Aug-17

Nov-15

Mar-11 Mar-18

May-12 May-19 Facebook Pinterest Twitter StumbleUpon YouTube Tumblr reddit Instagram LinkedIn Digg MySpace

Source: StatCounter

Google also demonstrates strong network effect, reflected in over 90% market share in the search industry.

Google- Market Share

7.5 93.5

6.0 91.8

4.5 90.1

(%) (%) 3.0 88.4

1.5 86.7

0.0 85.0

Jan-21 Jan-09 Jan-11 Jan-13 Jan-15 Jan-17 Jan-19

Sep-09 Sep-11 Sep-13 Sep-15 Sep-17 Sep-19

May-12 May-10 May-14 May-16 May-18 May-20 Bing Yahoo! Other Google (RHS)

Source: StatCounter

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Cash burn, followed by monetisation

 In initial stages, network effect-led companies have to focus on on-boarding consumers to reach a critical threshold to exploit the network effect. Hence, companies tend to have high customer acquisition costs with minuscule revenues.  Customer experience is also key focus area as engagement on platforms is of paramount importance at this stage. This naturally translates into high cash burn in the initial stage. At times, as companies expand geographically, cash burn tends to increase, but that also brings scale to the network.

 After establishing a strong network, companies invariably focus on monetisation of the platform and then cost optimisation. Platform companies can have multiple ways of monetising the platform with advertising, virtual goods, membership fees etc., being a few options.

 For scaling the platform, we have seen companies expanding into new geographies or into adjacent areas to replicate their business models and backward integrating to capture higher value of the supply chain.

Revenue follows engagement

Platform businesses focus on user Considering the value of a platform in initial stages is negligible as the number of engagement over revenue in initial stages users on the platform is minimal and hence the focus of the business is on gaining users. Most companies don’t earn much revenue during this phase as they chase active users. Revenue follows engagement

Source: Edelweiss Research

Facebook is a classic example of how revenues in the early stage were muted as the company’s primary target was to increase the number of users on its platform. While revenues were still increasing, albeit on a smaller base, they took off once the user base raced past 1bn. Twitter also had similar run in terms of revenue. Revenues were off to a very slow start and the sole focus was on growing the user base for the first five years since inception. It is now able to parlay a significant user base into USD51bn company with healthy revenue.

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Facebook- Revenue follows engagement Twitter- Revenue follows engagement

3000 100 350 3,500

2400 80 280 2,800

1800 60 210 2,100

(mn) (mn)

1200 40 bn) (USD 140 1,400 mn) (USD

600 20 70 700

0 0 0 0

FY09 FY10 FY11 FY12 FY13 FY14 FY15 FY16 FY17 FY18 FY19 FY20

FY10 FY11 FY12 FY13 FY14 FY15 FY16 FY17 FY18

MAU Revenue (RHS) User Base Revenue

Source: Bloomberg, Edelweiss Research Source: Bloomberg, Edelweiss Research

Initial losses are imperative in network businesses

Critical mass is essential for network effect As explained in the previous sections, for the network effect to kick in, a minimal to kick in number of users on the platform, called critical mass, is imperative. The biggest challenge lies in getting these minimal users aboard. To build critical mass, companies could dole out benefits such as free services for a limited period and/or resort to growth hacking tools such as referral bonus, subsidising a service fee, etc.

Once critical mass is gained, the value obtained from the product or service is greater than or equal to the cost of the product or service. As the value increases owing to the rising number of users of a product, more users would want to latch on. As more users join, it further increases the value of the product or service, thereby creating a self-fuelling machine.

Gaining critical mass requires investments

Source: Ray Stern

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Twitter witnessed similar (gross) profit curves… …so did Snapchat

2950 1600

2350 1200

1750 800 (USD mn) (USD

1150 mn) (USD 400

550 0

-50 -400

FY10 FY11 FY12 FY13 FY14 FY15 FY16 FY17 FY18 FY19 FY20

FY17 FY16 FY18 FY19 FY20 FY15 Source: Bloomberg, Edelweiss Research Source: Bloomberg, Edelweiss Research

Considering this, initial unit economics with respect to revenue per customer and Unit economics might be incorrect metric to track in early days of a business cost per customer may not be the correct metrics to track. As customers get used to the convenience offered by the platform, they tend to pay more for the services over a period of time. Also, companies do have pricing power in terms of cost of supply which tends to fall as there is enough scale and efficiency benefits kick in. Companies like Zomato, Ola, Uber and did spend significantly on creating supply of the platforms, in terms of drivers or delivery partners, but they cut down fees paid to partners over a period of time.

Customer acquisition remains key focus in In many cases, losses incurred by platforms tend to increase as they expand their early years scale. Hence, generally platforms focus on user acquisition in initial years, which is followed by revenue optimisation, which is then followed by cost optimisation. Considering these factors, we note that many early stage businesses could be making losses at the unit level itself, but they can still create long-term value considering the moat created by the network effect.

We have observed such transition in Zomato’s food delivery business. While the company used to lose as much as INR47 on every order in Q1FY20, the improvement in unit economics due to higher number of orders per rider and higher charges resulted in increase in contribution profit per order to INR27 in Q1FY21. While the pandemic did accelerate Zomato’s revenue per order, the trend in reduction in discounts and rising revenue per order was prevailing before that.

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Zomato- Food delivery unit economics

Source: Company

IndiaMART invested heavily in customer In case of IndiaMART, the company made sustained investments in employees, acquisition and stronger content in initial content development, buyer engagement and customer support even at an stages insufficient profit as it chased critical mass. This was the investment phase which lasted till FY16 where its cost was increasing at a faster pace than revenue. However, as the company achieved critical mass, it could take the foot off the cost paddle as the network effect started playing out. Hence, revenue grew much faster, while cost growth sobered.

In FY16, IndiaMART moved from investment to monetization phase

Source: Company, Edelweiss Research High quality data enables multiple monetisation opportunities While most transaction platforms differ in the type of customers they charge, they generally generate revenue either singularly or via a combination of advertising, matchmaking, offering technology or other complementary services. High engagement on the platform and ability to track each step by users on the app provides great insights on user behaviour. This is extremely useful not only for optimising the platform, but also for understanding the customer. Hence, advertising-based models were the earliest businesses on internet and they remains a core part of some of the established platforms. For consumer engagement-based platforms, such as Google, other social media applications, advertising is the largest source of revenue. These companies have been focusing on garnering more user data which can help them better target advertisements.

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Interestingly, other transaction-based platforms, such as Amazon and Zomato, also derive significant portion of revenue from advertising. Amazon’s advertising profit pool for 2020 is estimated to be bigger than all other segments. Zomato and Swiggy, in their food delivery platforms, put advertisements of different , which is a source of revenue. Importantly, since costs associated with advertisement are minimal, they tend to drive high profits.

Companies can also gain customer insights to backward integrate in the product developments itself. Amazon has been building a strong private label portfolio based on its consumer insights on various products. Similarly, Swiggy has started operating its own cloud kitchens.

Advertisements on Amazon Advertisements on Swiggy

Source: Company Source: Company Virtual goods are also major revenue contributors, particularly on gaming platforms, apart from the fees charged for premium versions of games. Players value virtual goods, which boosts their ability to win games, particularly in a multi-player setting. Virtual goods have become significant contributors to the revenue in the gaming business and many developers are only charging for virtual goods while keeping the gaming platform free to use.

Virtual goods on Candy Crush Virtual goods on Roblox

Source: Company Source: Company

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Expansion in adjacent areas can add tremendous value

Expansion into adjacent areas can help Platform companies are inherently valuable considering near monopolistic presence companies better understand customer in markets, high switching costs for users and pricing power. However, expanding requirements, helping them improve their into adjacent areas drastically improves their value as they expand their sphere of overall product influence. First, expanding in adjacent areas gives significantly superior customer insights; Google expanded in many areas to acquire better customer insights– Android, YouTube, Maps, News, etc. Similarly, Facebook acquired Instagram and then WhatsApp. Both these acquisitions helped Facebook better understand customers across platforms, which helps in its core advertising business across all platforms.

Essentially, adding functionality or access to these adjacent areas makes the platform more valuable as more users take advantage of these functions. By moving to these adjacent areas, users contribute more personal data, which is used to further increase the quality of product/ service via the use of analytics.

Further, as a company expands into more adjacent areas, it becomes increasingly difficult for new firms to enter by building a competing ecosystem. Companies like Uber also diversified into adjacent transportation services space leveraging its existing drivers and logistics network delivering food () and other packages (Uber Rush). Why most platform businesses fail?

Most businesses are unable to reach the One of the primary reasons why most platform businesses fail is that they do not stage of attaining critical mass of users for reach critical mass of users. This may be due to several reasons--poor technology network effects kick in compared to competitors (Friendster compared to Facebook) or poor user experience (Internet Explorer compared to Mozilla Firefox and Google Chrome), lack of capital (Sidecar compared to Uber and Lyft) or not adapting to local conditions (EBay in China). Pages on Friendster sometimes took several seconds to load, leading to an opening for Facebook to capitalise on the opportunity. Microsoft’s Internet Explorer had captured a market share in excess of 95% by 2004 when Mozilla burst on to the scene. It has been widely debated that Microsoft was unable to introduce any new path-breaking features. Newer browsers like Mozilla Firefox, Opera and Navigator were superior to Microsoft IE in terms of performance, user experience and security which ultimately led to the downfall of Internet Explorer. Sidecar was the pioneer in the ride-sharing industry with its founder Sunil Paul receiving the patent for using wireless network for ride hailing as early as 2002. However, it was unable to match the kind of money that its peers were managing to burn in order to acquire to acquire customers. It is estimated that by the time Sidecar had failed, it had managed to raise a meagre USD35mn compared to USD1.26bn by Lyft and USD6.61bn by Uber. Amongst the three, Uber was aggressively investing to achieve a higher level of liquidity and it spent heavily not only to acquire drivers by guaranteeing drivers hourly rates (USD25), but also by incentivizing referrals and subsidizing fares. Sidecar’s failure was despite the fact that it was the first to come up with several of today’s ubiquitous features. Apart from this, there might be several other reasons why platform fail to become successful: (i) subsidizing the wrong side of the market; (ii) entering the market at the wrong time; (iii) users switching between multiple platforms (multi- homing/multi-tenanting); (iv) unable to breach the entry barrier created by strong incumbent companies; and (v) niche companies taking away market share. We have also seen a few horizontal platforms getting decimated by emergence of vertical specific players, which provide superior experience in each of the verticals.

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One such example could be disruption of Justdial–a horizontal platform offering suppliers of various products and services–by the emergence of verticals in respective platforms. Zomato emerged as a platform of choice in the business, Ola & Uber emerged for cabs, MakeMyTrip became the platform of choice in the travel vertical and so on.

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The valuation divergence

 The investor community is used to analysing capacity businesses where companies create capacities for certain products or services and where there are multiple players offering similar products.

 Success, typically, depends on economies of scale, efficiency of operations, distribution reach etc. and unit economics are fairly straightforward compared with already established products.

 In the platform economy, however, successful companies typically decide the market size, have significant pricing power, entail strong unit economics and involve high direct or indirect switching costs for users.

 For capacity businesses, market share, capacity utilisation and ROIC are key parameters determining valuation. In the platform business, total addressable market (TAM) and long-term margins are key parameters.

ROIC serves as anchor for evaluating capacity businesses Internet is a relatively recent phenomenon and there are only a handful listed internet businesses in India. Hence, although investors understand that the network effect-driven internet businesses are fundamentally different than typical capacity businesses, we are laying the framework on what are the differences and what points investors should focus on while evaluating such businesses.

We start with what we mean by capacity business. In a typical capacity business, production capacity is set up for providing specific output such as cement, telecommunication services, hotel rooms, automobiles, etc. There tends to be a proportional cost for setting up capacity and other fixed and variable costs. Hence ROIC can give a reasonably good understanding of the quality of investments and serves as an anchor for evaluating the company. Capacity business- Production process

Production Input Material Output Capacity

Source: Edelweiss Research

While value of a capacity business can be In a capacity business, the value of the network is directly proportional to the size of more accurately measured by the number of the network (utility α n, where n= number of nodes in the network). This is also users in the network (Sarnoff’s law), value of known as Sarnoff’s Law, which predicts the utility of capacity businesses. The utility a platform business is more accurately for platform businesses is more accurately defined by Metcalfe’s Law which states defined by Metcalfe’s law that value/utility of the business is proportional to the square of number of nodes in the network (utility α n2). This implies that the growth potential of such businesses is not linear, enabling them to expand at a much faster pace.

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Sarnoff's law and Metcalfe's law

Source: Guides.co Role of capital in platform business versus capacity business While capital is required in both the businesses, due to different characteristics of evolution, role of capital is different in both. In capacity businesses, since the capacity is directly proportional to the capital employed, amount of capital available and cost of capital play a big role in determining the eventual winner. Hence, in case of a platform business, established players can win against much better capitalised players provided they have a sufficiently strong network.

Platform businesses create high entry On many occasions, we have seen incumbent players with strong network effect barrier which cannot be overcome merely by winning over well capitalised players. Google wanted to succeed in social media higher capital availability business with Google+. Despite its strength in search business and strong balance sheet, it could not replicate the consumer engagement of Facebook and had to eventually shelve the platform. Closer home, 99acres and MagicBricks could hold onto their leadership despite entry of well capitalised players like Housing.com, Commonfloor, among others, in the real estate classified space.

Role of capital in platform business Role of capital in capacity business

Source: Edelweiss Research Source: Edelweiss Research

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TAM and eventual unit economics are important metrics to evaluate Typical capacity businesses also tend to have competitors with respective capacities and unit economics. Economies of scale, positioning of products, efficiencies of the process, etc., tend to create variations. But one can still broadly compare these parameters to understand the drivers of ROIC and its movement. Growth rate of the business, reinvestment requirements and cost of capital are the key determinants in valuing a company. Considering longer historical track record and multiple similar companies across geographies, estimating them is relatively easy.

On the other hand, platform businesses tend to have much less number of players and investments towards setting-up capacity tend to be minuscule. Companies do need to invest in getting critical mass of customers to get the network going, but there is no proportionality of the cost with respect to the capacity of the business. These businesses are driven by the network effect in which getting initial customers is expensive, but the incremental cost of acquiring customers tends to decline. Unit economics tend to evolve materially across cycles

Eventual profitability is a function of Considering the platform business goes through three phases: 1) acquiring customer acquisition costs (CAC) and customers to reach critical mass; 2) monetisation of platform; and 3) cost Lifetime Value of Customer (LTV) rationalisation to achieve you desired profitability, key thing for valuing a business is to understand what will be the eventual profitability of the business and what could be the addressable market (TAM). Eventual profitability is a function of two key factors–customer acquisition costs (CAC) and lifetime value of the customer (LTV). CAC tends to be high in the initial period as the network is smaller, but tends to dip as the utility of the network increases with higher number of customers on the network. LTV is nothing but average revenue per customers over average duration of customer. Average duration of customer is determined by the churn of the customer. Revenue and churn of customers also tend to change over a period of time. Customers spend more money on the platform due to convenience offered by it; this also leads to lowering of the churn. Hence LTV/CAC metric can be different in initial stages of the network versus later stages.

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Key measurement parameters

 Investors track parameters like revenue, fixed & variable costs, capital employed, margins, etc., for a capacity business. For a platform business, evaluation parameters can be broadly classified into four--acquisition related, engagement related, economic related and marketplace & competitor metrics.  In acquisition-related metrics, total traffic and composition of free versus paid traffic are important parameters to track. Engagement metric typically tracks how users are engaging with the platform, which determines strength of the network as well. Daily/Monthly Active Users (DAU/MAU), time spent by the users, retention ratios are key engagement metrics.

 Quality of engagement and strengthening of network with user addition are key things to track in the initial stage. Lifetime value of customer (LTV) and customer acquisition costs (CAC) are the most vital financial parameters to track.

While network effects are dynamic in nature, changing rapidly in response to both internal strategic decisions and external competition, it is generally possible to measure the strength of these effects using a combination of different metrics. Network effects are not binary in nature. Furthermore, the strength of network effect varies from industry to industry and industries with stronger network effects tend to grow faster. Unlike metrics like pricing power, which tend to be lag indicators after the network effects have already matured, below mentioned metrics generally capture the strength of networks while they are developing. Acquisition-related metrics Organic versus paid users

Organic users should grow as a successful In any platform business, the start is often slow, and the first few users need to be platform business scales incentivised into using the platform. This is simply due to the fact that the platform does not offer any advantages in the initial stages. For platforms like Facebook with users on the same side, there is no use of logging in to the system if your friends aren’t on it. For a platform business with multiple sided business like Uber, drivers/ customers wouldn’t use the platform if there is not sufficient demand/ supply on the other side. Consequently, paid users are always high in the initial stage. As the adoption rate of businesses improves and network effects kick in, the platform starts to become more valuable, the number of organic users start increasing and platforms are not forced to spend heavily in acquiring customers. User traffic Like the trend in organic users, an increase in direct traffic also indicates rising preference for the platform. This also decreases the dependence on external sources for traffic boosting its standalone value.

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Sources of traffic

Source: Online-metrics.com

While a growing rate of higher users does not guarantee a successful platform, lack of increasing users generally indicates lesser adoption. Further drill down needs to be done using other metrics to gauge the overall response to the product. Time series of paid customer acquisition costs (CAC) Ideally, platform businesses where network effects take place, should see a decline in customer acquisitions costs over a period. However, the decline is contingent on other external strategic decisions and factors like competitive intensity, availability of substitute products/ services etc. as well. For instance, acquisition costs in the car hailing industry remained elevated for long as it had to offer incentives to drivers as well as customers to use their platform. On the driver side, these companies incurred massive costs just to find and replace drivers. On the other side, it also had to offer high discounts to customers. This resulted in lowly funded businesses like Sidecar dropping out of the race despite introducing more innovative features on its platform. Engagement-related metrics Daily Active Users/ Monthly Active Users Most sites/ applications keep a track of Daily Active Users and Monthly Active Users on their sites. The number of active users who use the application on a daily basis are daily active users. However, DAUs does not tend to reveal the number for users who use the platform only once and not henceforth. Hence, platforms also keep a track of the MAU which indicates users who use it on a monthly basis. Social media sites like Facebook and Twitter actively track and share both these metrics. The ratio of DAU/MAU is also used by several companies to measure stickiness of users. It is also used by start-ups to evaluate potential growth of revenue. A drawback of this metric is that it does not help in detailed analysis of which set of users has been actively using the platform and which users are moving out. Time spent/ Time spent by new users

A successful platform business tends to Platforms typically track the time spent by users, both on micro and macro levels. At engage users for a longer period of time the micro level, they generally measure the time spent on a single page, whereas at the macro level, it is measured by the time spent on the site. This metric is more relevant for platforms like social media which aim to keep users hooked on for longer periods of time increasing its overall value.

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Retention by location/ geography For a business with local network effects (limited to geographies) like Uber/Airbnb/ Doordash, retention needs to be measured for that particular geography. Typically, retention is expected to rise as platform adoption increases.

However, this metric does not take into account early adopters who generally tend to be stickier in nature. For instance, tech savvy people are more likely to adopt a new launched social networking site and stick around for longer if the quality is superior. So, retention of early users might be higher and a dip in later users might incorrectly indicate lack of success of the product. Power user curves

Power user curves measure a user’s Power user curves are histograms of a user’s engagement categorised by the engagement over a long period number of days they were active from either one to all days of the month. Power user curves offer a distinct advantage over plain DAU/MAU metrics:

1. Display variability amongst users--how many are deeply engaged and how many are occasional users.

2. Coupled with cohorts, power user curves can be used to track progressive engagement levels to gauge the level of response for a new product or a service.

3. Provide different avenues of detailed analysis unlike DAU/MAU which provide a single number.

For a platform that is seeing high rate of adoption with its target audience, the shape of the power user curve would be right leaning, indicating that most of the MAUs are logging in on a regular basis. For platforms with weak adoption, the curve would be left leaning with most customers accessing the site infrequently. Ideally, users should be moving to the right side of the curve with time. Power user curves

Source: Andreessen Horowitz

Bounce rate

Bounce rate is measured as the percentage of users that leave the page without taking any action. Apart from content, factors that affect bounce rate include type of audience, landing page designs, load time of pages, search engine ranking, etc.

Bounce rate is typically relevant for transactional platforms like ride sharing or purchase of any product or services. Higher bounce rates might indicate an unclear or complex landing page or no call to action button.

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Conversion rate

All valuable platform businesses normally see higher conversion as they mature and increasing supplier density. This is also a natural outcome of network effects that start to build up once users start coming on the platform.

Conversion Rate

Source: thenetwortheffect.com

Conversion rates are typically relevant for transaction platforms looking to match buyers/sellers or service providers/customers. For instance, for a ride sharing industry, as more drivers sign up on the application, wait time reduces for customers, leading to higher conversion and consequently more rides. Similarly, in the food delivery industry, more restaurants on the platform (particularly popular ones) will lead to higher orders by customers, leading to higher conversions. Economic-related metrics LTV/CAC Lifetime Value (LTV)/ Customer Acquisition Cost (CAC) is an important indicator for the unit economics of a business. LTV is the average revenue that a single customer is expected to generate over the duration of usage, while customer acquisition is the average cost for gaining a single customer. The ratio can be used to determine how much money the company is generating for each rupee that it spends. The ratio indicates whether the company is spending too much money to acquire customers.

For a successful platform, LTV/CAC should be For a transactional model, the LTV can be calculated by the gross monetary value generally trending upward done on the platform adjusted for the cost of goods sold as well as other costs. The CAC can be calculated on a cost per click basis adjusted for the conversion rate. It may also include any customer support personnel or promotion/discounts for referrals etc. Ideally, steady-state ratio should be greater than 1, which would allow the platform to scale effectively.

Unit economics= LTV/CAC

Where,

LTV= (Average transactions*Average revenue per transaction*Average gross margin*Average customer lifetime)/number of customers in the period

CAC= Sales and marketing costs / number of customers acquired

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Marketplace and competitor related metrics Match rate

Every multi-sided platform aims to connect users on different sides in the most frictionless manner. Each industry may have a different parameter to measure the match rate of its users. For instance, the ride sharing industry can measure the utilization for drivers by measuring how much time they are riding with a passenger versus the time they are riding without passengers. Another related parameter for the industry could be the waiting time for drivers and passengers. Yet another related parameter could be the conversion rate for passengers who log on to the application. Similarly, there are other industry-specific parameters like look-to-book ratio for the hospitality industry which measures the number of people who actually make a booking once they have looked at the options.

Option of multi-homing

Most users seek multiple platforms searching for the best available option before making final selection of a product/service if there is no major differentiation offered. While dominant platforms generally make such alternative offerings difficult, this concept of multi-homing continues to exist in several industries.

Multi-homing also reduces the strength of network effects. The case of multi- homing in the past has been clearly visible in the messaging industry, social networks and gaming industry. In the gaming industry, Microsoft, Nintendo and Sony kept their prices low for a long time, hoping that their products would ultimately the larger market share. However, low prices ensured multi-homing by serious gamers, resulting in no single company becoming dominant with the market share fluctuating, depending upon the release of a product.

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Conclusion

Platform businesses are inherently different from capacity businesses in several ways, right from their evolution to their evaluation metrics. While capacity businesses see steady growth as they keep adding production capacity, platform businesses clock exponential growth once they attain critical mass of users and network effects kick in. However, most platform businesses falter at the early stage and are unable to gain that critical mass of users to dominate the segment. There have also been instances where incumbent players with dominant positions have lost out to newer companies with better products/ services. Platform businesses with strong network effects act as a strong moat, preventing other businesses from entering their own territory.

The usual metrics that are used to measure typical capacity businesses do not apply while measuring platform businesses. Even pricing power typically appears once the business has scaled and occupies a dominant position in the industry. Indicators like traffic, paid versus organic users, power user curves and retention can be used as lead indicators or measured concurrently to gauge progress of a platform business.

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