Postmortem Business Model Analysis of Twitter1.0 (before acquisition): a case study of a failed digital platform model

A successful digital platform has to have a business model design that generates two types of economies of scale: supply-side economies of scale and demand-side economies of scale.

•  Supply side economies of scale are all about designing a low-cost producer, thus, production efficiencies. They are driven by low marginal cost of production and information, ideally zero, simply because it leads to a decreasing average cost of production as the production volume rises.
• Demand-side economies of scale are about scaling demand through positive network-effects at the customer side. In other words, they are “driven by efficiencies in social networks, demand aggregation, app development and other phenomena that make bigger network more valuable to their users”. (Parker, Van Alst, Choudary , & Van Alstyne, 2016)

It sounds very simple and easy. In reality, however, it is very difficult to design a successful digital platform business model.

As an example, Twitter presents a case of a failed digital platform business model.

As the world already knows, Elon Musk acquired Twitter on October 27, 2022. Right after the acquisition, the firm was delisted from the New York Stock Exchange on November 8 in the same year. Today, Twitter is no longer a publicly listed company. Ever since, Elon Musk has already exerted his absolute decision-making power in conducting a series of drastic corporate overhauls to transform the firm’s operation.

Simply put, the historical Twitter before the acquisition and the new Twitter after the acquisition are 2 fundamentally different entities with 2 different business models. In order to distinguish these two business models, let’s call the former Twitter1.0 and the latter Twitter2.0.

Twitter1.0 miserably failed to attain these two desirable features of a successful digital platform business model—namely supply-side economies of scale and demand-side economies of scale. As a result, it registered negative operating net income for 2 years, 2020 & 2021 (Table A).

Ironically, even after the acquisition, Elon Musk’s approach of corporate overhaul called attentions of many skeptics and triggered a series of withdrawals of its major advertisers. (Pintado, 2022)

Overall, both Twitter1.0 and Twitter2.0 illustrate how difficult it is to design and engineer a successful digital platform business model.

This post-mortem analysis of the business model of Twitter1.0, using only the information available in the public domain, intends to identify its historical problems and contemplate their solutions.

Differentiated Brand in the Highly Segmented Ecosystem

Now, let’s look at the ecosystem of microblogging SNS, where Twitter1.0 operated.

Twitter1.0 operated in a highly segmented ecosystem. Parler (Conservative oriented SNS), Gab (US far-right oriented SNS), and Truth Social (SNS founded by Donald Trump) —when we look at these peers in the ecosystem, they are politically charged operators.

In a way, this highly segmented ecosystem shaped the de-facto value propositions of Twitter1.0. Twitter1.0 differentiated itself from others by operating as a de-facto politically moderate brand. In addition, Twitter1.0 also highly regarded the authenticity and the accountability of its contents. (Twitter, Inc., 2022)

And, in order to deliver these value propositions, Twitter1.0 extensively conducted content moderation, content curation and ML ethics monitoring—call them collectively “content controls”—to eliminate disinformation, misinformation, extreme hate speech, and incitement of violence.

On one hand, Twitter1.0’s content controls provided a safeguard for its advertisers and constituted an essential element of its value propositions. On the other hand, its de-facto brand profile has constrained its reach within the politically moderate user segment.

In addition, there have been some criticisms against Twitter1.0’s content controls as means of free-speech suppression and censorship collusion with government.

As a matter of fact, Elon Musk, one of those critics, has been exposing the history of Twitter1.0 by disclosing “Twitter Files”, internal documents of Twitter1.0, in public. Here, we have a couple of Tweet reactions on “Twitter Files”.

The moral verdict and the legality of Twitter1.0’s “free-speech suppression” and “censorship collusion with government” is beyond the scope of this analysis. In addition, it is not self-evident whether Elon Musk has an ability to establish a fair free-speech culture at Twitter2.0. For now, this analysis regards Twitter1.0 only as a profit-seeking business model rather than a social common goods.

Demand-side Economies of Scale

As aforementioned, Twitter1.0 failed to generate demand-side economies of scale. It suffered from two types of poor network effects. First, it failed to scale its user base, suffering from poor same-side network effect within the user space. Second, it failed to leverage its user base in generating a higher advertising revenue, suffering from poor cross-side network effect between its users and its advertisers.

User Base

Chart A finds Twitter1.0 at the bottom of the user base ranking. It reveals that Twitter1.0 had a problem in scaling the size of its users. It might suggest that Twitter1.0 had never achieved its critical mass to generate the network-effect among the users.

Revenue

On its revenue, Twitter1.0 had a high dependency on its advertising revenue (Table A: 89% in 2021; 86% in 2020). In the past, Elon Musk expressed his aversiveness to advertisement and suggested to shift the revenue streams of Twitter from advertising to subscriptions. (Conger & Hsu, 2022)

Now, let’s assess if his view is viable. Box A suggests that the high dependency on advertising revenue is quite common in Alphabet (Google) and Meta. Here, Alphabet (Google) and Meta are not apple-to-apple with Twitter1.0. They symbolize a profile of diversified portfolios of digital platform businesses, while Twitter1.0 is a micro-blogging SNS. Here, I excluded Amazon intentionally, since the market-place digital platform has a significantly different revenue stream profile from Twitter: a significant portion of Amazon’s revenue comes from online store and retail third party seller services (Coppola, 2022).

Table A captures the critical profitability issue of Twitter1.0. 

Now, Box A below characterizes a general revenue profile of a digital platform business model. A high dependency of advertising revenue is a common revenue profile of digital platform business model. The flip side of the fact is that it would be rather challenging to scale the revenue only through subscriptions, although there is nothing wrong about pursuing subscription as an alternative revenue stream.

If Twitter1.0’s dependency on advertising revenue is not a fundamental problem, what else could have been the problem?

Now, Chart B below reveals its extremely poor performance in generating advertising revenue compared with its digital platform peers.

Again, in Chart B above, Google and Meta represent diversified portfolios of digital platform businesses. So, the comparison here is not apple-to-apple for Twitter1.0. Nevertheless, this chart emphasizes that the scale difference is disproportional. It suggests some issues of scalability at Twitter1.0.

In this context, we can use a more comparable scaled metric, the advertising revenue per user, to compare Twitter1.0 and Meta. The advertising revenue per user measures a firm’s ability to leverage its user base in generating advertising revenue. The metric is more comparable since it eliminates the impact of the size difference of the user base between these two firms.

More importantly, we can use the advertising revenue per user as a proxy to measure the cross-side network effect between the users and the advertisers.

Now, Meta has two operational segments: family of apps (FoA) and reality lab. FoA includes Facebook, Instagram, Messenger, and Whatsapp. And reality lab is its virtual reality (VR) related business line. Its VR’s business was still in the phase of development and did not generate revenue in 2021. Only FoA represents the primary revenue streams of Meta.

In this sense, we can compare the advertising revenue per user between Twitter1.0 and Meta’s family of apps (FoA) segment (Facebook, Instagram, Messenger, and WhatsApp). 

Box B shows the calculation of the advertising revenue per user for Meta’s FoA segment.

While Twitter1.0 has its metric at $19.57 per use, Meta’s FoA has $33.47 per user (Box B). 

It means that Meta’s FoA has an ability to leverage its user base in generating the advertising revenue 1.71 times as much as Twitter1.0 does.

The comparison underscores Twitter1.0’s inability to leverage its user base to generate a higher advertising revenue. In other words, it demonstrated a poor performance in scaling the cross-side network-effects between its users and advertisers.

In theory, if Twitter1.0 had achieved the same level of the advertising revenue per user as Facebook ($33.47 per user), the firm can generate up to $ 7.69 billion of advertising revenue (vs its current $ 4.5 billion) even at the existing user base. That would boost its operating margin up to 32.6% (pro-forma on 2021 base). Given the operating margin of Meta (36% to 43% quarterly) and Alphabet (31% annual), the hypothetical pro-forma improvement of Twitter1.0’s would make it not only profitable but also competitive.

Of course, this pro-forma operating margin is a loose hypothetical number because it ignores additional cost factors (such as extra cost required for generating a higher advertising revenue per user). It could be misleading at the operating income level. That said, at least, this hypothetical scenario tells us an upside potential for the advertising revenue even without increasing the user base, by solely improving the quality of its services (to generate a higher advertising revenue per user).

Now, a relevant question would be: what can Twitter1.0 improve to boost the advertising revenue per user?

One possibility is diversification of services. Meta FoA had a higher advertising revenue per user than Twitter1.0’s, maybe because it had a diversified portfolio of social network services. Adding more features, such as AI-driven features and video features, might attract more advertisers as well as users.

The following remark suggests another possibility, an improvement in the quality of user targeting for advertisers.

“Twitter’s advertising business has long been smaller than that of rivals like Facebook, in part because it didn’t offer the same level of user targeting.” (Duffy, Elon Musk has upended Twitter’s business. Here’s how he could fix it, 2022)

That pretty much covers the revenue side problems of Twitter1.0.

Overall, these observations collectively reveal that the primary revenue side problem of Twitter1.0 is not its high dependency on advertising revenues, but rather the following two issues:

• its inability to scale its user base.
• its inability to scale the cross-side network-effects between its users and advertisers.

Simply put, Twitter1.0 had poor demand-side economies of scale. More specifically, it failed to drive the positive network effects.

Overall, Elon Musk’s aversiveness toward the advertisement might face some impediments for the growth of Twitter2.0.

Supply-side Economies of Scale

Twitter1.0 also suffered from its poor supply-side economies of scale because of its high marginal cost of production relative to the total revenue (Table A) for the following reasons.

• partly due to its inefficient and obsolete system architecture (data architecture) and
• partly due to the required operations to deliver its value propositions.

The age of Big Data means that a digital platform business has to operate through high volume and high velocity of data. And processing and analyzing high volume and high velocity data cost a lot. In this sense, it is important to capture a digital platform business model from the perspective of data architecture (system architecture).

Without fail, Twitter's motto in the image below implies that its operation needs to deal with high volume and high velocity of data.

Twitter1.0, as a microblogging service, had to process an enormous number of events in real time (approximately 400 billion events per day as of 2021) in a petabyte volume scale (Zhang & Malife, 2021). Twitter1.0 had to be able to perform high throughput data processing in a high scale with a low latency to cope with high velocity of data flow.

Simply put, in the context of large volume of data, Twitter1.0 had to optimize the data architecture in order to prevent “data loss and inaccuracy for real-time pipelines” and “reduce system latency,” (Zhang & Malife, 2021).

Since the Tweet traffic has increased in the past years, the old system (Box C) could not respond to ever-increasing volume and velocity of data.

In 2021 the firm just introduced more efficient data/system architecture (Box D) using Cloud to streamline and reduce the workload of daily operation, thus operational cost. 

Nevertheless, despite the modernization of its data/system architecture, Twitter1.0 forecasted a further increase in its operation cost toward the foreseeable future across its spectrums: cost of revenue, research and development, sales and marketing, and general administration (Twitter, Inc., 2022, pp. 48-49).

That obviously indicated that there were some serious issues about its operation cost somewhere else at Twitter1.0 than the efficiency of data/system architecture. 

Historical Business Model CANVAS of Twittwe1.0

To visually capture the historical business model of Twitter1.0, I present a Business Model CANVAS of Twitter1.0 in the middle of this section.

Business Model CANVAS is designed to answer 9 important questions in order to capture the business model of a firm. The next image captures the structure of the CANVAS.

Those 4 components highlighted in light blue at the upper right try to assess the “desirability” of the business model for the customers, asking the following questions.

1.    Customer Segments:

• Who are the firm’s target customers?

2.    Value Proposition:

• What kind of values does the firm want to propose to the customers?
• More specifically, what sort of customers’ problems can the firm solve?
• What kind of customers’ needs your firm wants to satisfy?

3.    Channels:

• What kind of channels can your firm use to deliver the value propositions?

4.    Customer Relationships:

• How does the firm manage customer relationships with each Customer Segment?

Those 3 sections highlighted in light green at the upper left try to assess the “feasibility” of the business model with the following questions of the ability of the firm to deliver its “desirability”:

5.    Key Activities:

• What key activities would the firm perform to deliver the value propositions?

6.    Key Resources:

• What assets are required to deliver the value propositions?
• And does the firm own the required assets to deliver the value propositions?

7.    Key Partnerships:

• Which partners does the firm rely external resources that it does not possess?

And the 2 sections highlighted in light red at the bottom focus on the “viability” of the business model. And it asks the simple questions about the financial aspects of the business model: revenue streams and cost structure.

8.    Revenue Streams:

• What are the sources of revenues arising from delivering its value propositions to the customer segments?

9.    Cost Structure:

• What is the cost structure for the firm to deliver the value propositions?

The creators of Business Model CANVAS articulated that a successful business model needs to satisfy all of “desirability”, “feasibility”, and “viability”.

That said, that is a necessary condition, but not sufficient condition.

Business Model CANVAS has its own limitations. For example, it does not capture the financial results of a firm numerically. Nevertheless, it is a handy tool to visualize a conceptual overview of the business model of a firm. Therefore, it should be used together with other analytical tools to analyze the business model more wholistically.

And here is my version of Business Model CANVAS of Twitter1.0. The details of its 9 components follow after the image.

If you agree with all the descriptions in Twitter1.0’s Business Model Canvas above, you can skip this tedious section to move on to the last section of Overview.

Otherwise, here is my explanation for my entries. Let’s go over these 9 components one by one.

Those 4 components of “desirability” are pretty much straight-forward. So, let’s run through them in a quick and simple manner.

1.    Customer Segments: There are 4 target customer segments for Twitter1.0.

• Personal Users
• Businesses users
• Advertisers
• Developers

2.    Value Proposition: Twitter1.0 offered the following value propositions for each customer segment.

For Users:

• Free entry to services
• Authenticity and accountability of Contents without misinformation, disinformation, extreme hate speeches and incitation to violence
• Politically moderate micro-blogging experience
• Monetization opportunity for Content Creators (Tips, Super Follows, Ticketed Spaces)

For Advertisers

• Politically moderate contents platform as a safeguard for advertisement
• Customizable advertisement platform
• Analytics and Data Access

For Developers

• Twitter Developer Platform enables developers access Twitter’s public API to build tools

3.    Channels: Twitter1.0 relied on the two types of channels to base its digital platform.

• Web Site Platform
• Mobile Apps Platform

4.    Customer Relationships:

In order to serve its target customer segments, Twitter1.0 intensively conducted content moderation and content curation & ML ethics monitoring as a part of its customer relationships.

• content moderation and content curation & ML ethics monitoring
• Advertiser relationship
• User relationship

Now, let’s move to 3 components of “feasibility”. These are about requirements for Twitter1.0 to serve “desirability” for its customer segments.

5.    Key Activities: In order to deliver its value propositions, Twitter1.0 performed the following key activities.

• Maintenance and operation of web and mobile apps for micro-blogging service
• Content Curation, Content Moderation, and ML ethic monitoring to filter unethical users and remove misinformation, disinformation, extreme hate speech, and incitements of violence.
• Provision of an ecosystem of marketing APIs for advertisers and developers.

6.    Key Resources: Twitter1.0 relied on the following primary resources to deliver its value propositions.

• De-facto Brand: Politically Moderate SNS platform
• Users = Content Creators & follower audiences
• Platform applications distributed on the mobile devices and on the web.
• Engineers
• Physical Assets

7.    Key Partnerships: This component requires some explanations.

Since Twitter1.0 was a listed company, it had multiple shareholders.

• Shareholders (listed before Elon Musk’s acquisition)

Twitter1.0 depended on its users for content creation.

•  Target Users (Content Creators and Followers)

And it relied on its advertisers for financing its operations.

•  Advertisers

In addition, there are many other third-party services to orchestrate the operation of the platform: such as payments processing, tokenization, vaulting, currency conversion, fraud prevention and cybersecurity audits.

• Third parties (payments processing, tokenization, vaulting, currency conversion, fraud prevention and cybersecurity audits.)

At last, needless to say, Twitter1.0 relied on the app store ecosystem which was dictated by tech giants such as Google, Apple, Microsoft, and Amazon for the distribution of its application platform, simply because Twitter1.0 does not have any distribution channel.

• Application Distributors (e.g. App Store, Android Apps, Google Play)

This point was reemphasized when Apple attempted to remove Twitter1.0 from its app distribution (Zakrzewski, Siddiqui, & Merril, 2022).

The last 2 components—revenue streams and cost structure—are regarding “viability” of the business model. We already analyzed these components. So, here they are.

8. Revenue Streams:

• Advertising Revenue: approx. 90% of the total revenue
• Data Licensing including subscription revenue. (Twitter, Inc., 2022, p. 47) : approx. 10% of the total revenue

9. Cost Structure:

As we observed earlier, the marginal cost of operation for Twitter1.0 was very high. Here is its cost structure numerically measured against the total revenue.

•  Cost of Revenue consumed 35% of its Total Revenue in 2021 according to Twitter’s 2021 FORM 10-K Filing. It includes costs of physical assets amortization of intangible assets, facility costs, and traffic acquisition costs (TAC). (Twitter, Inc., 2022, p. 48)
• Research and Development (R&D) consumed 25% of the Total Revenue in 2021. It includes salaries for in-house engineers and administration staffs. (Twitter, Inc., 2022, p. 48) I presume that some portion of this item was spent for operation of content moderation, content curation, and ML-ethic monitoring.
• Sales and Marketing consumed 23% of the Total Revenue in 2021. This item consists primarily of personnel-related costs for its employees engaged in sales and marketing and amortization of acquired intangible assets, allocated facilities costs, and other supporting overhead costs.
• General and administrative: 12% of the total revenue
• Litigation settlement, net: 15% of the total revenue

The comparison of the cost structure against the total revenue suggests a couple of issues.

First, the high sales and marketing cost is not contributing to the revenue generation.

Second, heavy litigation settlement exposed a poor management in regulatory compliance and a poor conduct of data ethics. That suggests that the extensive content controls—content moderation, content curation, and ML-ethics monitoring—are not contributing to the regulatory compliance and the conduct of data ethics as well as the revenue generation.

Overall, the cost structure suggests some inefficiencies in the workforce.

That pretty much sketched an overview of the business model of Twitter1.0.

Overview

Elon Musk expressed his intent to shift away from advertisement and focus on subscriptions for its revenue streams. It is true that advertising revenue is very cyclical to macro-economy. When the economy declines, advertising revenue would also decline.

Nevertheless, a high dependency on advertising revenue is common among two digital platform giants, Meta and Google; and subscription revenue is rather small portion of their total revenue. This implies two things for non-marketplace digital platform business (excluding marketplace digital platforms such as Amazon): the importance of the advertising revenue and the limited upside potential of subscription revenue. In addition, subscriptions create entry-friction and could raise the entry barrier and might drive negative network-effects to reduce the user base.

In other words, Elon Musk’s willingness to distance away from the advertisement might impose a significant constraint on the future revenue prospect of Twitter2.0.

Good news was that there was an ample upside potential for the advertising revenue for Twitter1.0 even at the existing size of its user base, according to the comparison of the advertising revenue per user between Twitter1.0 and Meta. Therefore, it would have been productive for Twitter1.0 to explore the upside potential of its advertising revenue: e.g. to boost the cross-side positive network-effect among its users and advertisers by improving its user targeting or/and diversifying its services.

Another possibility to boost its revenue is to scale its user base by transforming its de-facto brand value, a politically moderate SNS platform, to reach a wider political spectrum of users. Elon Musk is apparently attempting this strategy by promoting “free-speech” for all. Nevertheless, here is a catch. The ecosystem of micro-blogging is highly segmented according to political propensities. Elon Musk’s attempt to consolidate users of all political clusters into one single platform might be a challenging experiment, partly because users might be already satisfied with their current platforms which are politically segmented. There might be many users who would choose to stay within their tribal clusters to only share their political values. Therefore, it is not self-evident whether dumping its de-facto brand value could boost its user base or not.

On its cost, we questioned whether there are a room for reducing the operational cost of Twitter1.0. Since its data/system architecture was recently modernized and streamlined, the new data/system architecture was supposed to reduce the workload, thus, the operational cost. On the contrary, Twitter1.0 forecasted a further rise in its operation cost. Its cost structure suggests inefficiencies in HR management and inefficiencies in its content controls. Potentially, there might be an ample room for streamlining the workforce and its content control protocols.

To make the situation worse, today, Elon Musk has brought Twitter2.0 another layer of cost burden, the interest payment arising from the acquisition funding, that raised the interest expense to the order of $ 1 billion (Hirsch, 2022).

The legacy high operational cost structure inherited from Twitter1.0 together with the new financing cost that Elon Musk brought to Twitter2.0 led to a series of his panic lay-off campaigns (Duffy, Twitter layoffs continue under Elon Musk, 2022) .

Whether his lay-off campaigns would lead to a higher operational efficiency or a deterioration in the quality of its services remains not self-evident and an open question as of the time of writing.

There seem to be great challenges ahead for Twitter2.0.

Overall, Twitter1.0 presents a case of failed digital platform business model. The way Elon Musk is going to re-design and re-engineer its business model to make Twitter2.0 profitable, if successful, might present us a great insight for turnaround of a failed digital platform business model.

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