Alphabet (Google) Business Model Teardown

Jen van der MeerBusiness Model Practice, Tech Titans

Alphabet Flywheel Timeline

Business Model Tear Down

We know the company as Google, but they have officially organized to call themselves Alphabet. The “bet” for Google goes beyond its well known search and cloud services to include life sciences, connected devices, and big “moonshots.” Compared is most compared Tech Titan, Facebook, Alphabet has done a better job to diversify its revenue streams. Let’s take a look.   Business model innovation can be understood as a factor of three physical forces: waves, flywheels, and business models themselves. 

Google Pre Revenue > Flywheel 1

1999-2000

Pre Business Model

search engine indexes through queries

Flywheel 1

Create relevancy-based ads

Google Pre-Business Model to Flywheel 1

Google’s founding story is now well known, when it was started as a research project called “backrub” by Larry Page and Sergey Brin when they were both PhD students at Stanford University. The company was incorporated in 1998 and with early funding from angel investors (including Jeff Bezos, founder of Amazon) the company was encouraged to develop their PhD thesis into a product. 

The business model early story has been murkier, but now chronicled by Harvard Professor Shoshana Zuboff in the book The Age of Surveillance Capitalism. The way Google was set up from the start based the core product value on the “data exhaust” generated in every search – the behavioral data around queries like the way the query is phrased, the spelling, punctuation, click patterns, location. 

Google’s starting flywheel fed off the data exhaust to generate a feedback loop and process of continuous learning and improvement. More searchers searching lead to more people to learn from, enabling the algorithms “to learn and produce ever-more relevant and comprehensive search results.” Professor Zuboff explains we are not the product, nor are we unpaid workers. We are the sources of raw material supply of data into this loop.

Google was able to raise $25 MM on the promise of this superior search engine, during the upswing of the dot com boom. When Google started to monetize, the founders frowned on  ads as a revenue source, instead providing web services to other portals. Despite the founders’ opposition to advertising, the team had started experiments by selling ads through the classic model at the time: text based ads sold on a CPM basis or cost per thousand views.  

When the dot com bubble crashed in 2000, investors pushed the team to accelerate and demonstrate exponential revenue streams. The ad offering was changed: Google would tell advertisers what queries to buy, and they would use the data exhaust and algorithms to provide “relevancy” – ads targeted to a specific user. When Google borrowed competitor Overture’s auction-based pricing model but then added a prediction element: pricing would be multiplied by the likelihood of a user clicking o the ad. 

For Professor Zuboff, this moment initiated the “laws of motion” built on behavioral surplus, or data exhaust, ad initiating a new form of capitalism, Surveillance Capitalism. For business model designers of the next form of capitalism, understanding the sequence for how Google turned its first flywheel is invaluable for understanding the logic behind all tech giants. 

 

Pre business model

Search queries feed learning system 

Flywheel 1

Search queries deliver relevant ads

Business Model 1

Advertising

Flywheel 2

2012 > onward

Flywheel 2

Add youtube,network, display

Wave Rider

drive the shift to internet ads

Flywheel 2: YouTube, Network, and Display

The business model innovation that drove the entire shift in advertising was a performance-based offering: ricing was based on a “click-through rate” – the likelihood that a user would follow through on an ad. 

After Google started to extend its reach outside of the core search offering as early as 2003 in their Adsense program, the sheer power of the pay-what-works model massively accelerated adoption and Google’s growth rate took off. 

Gmail and Gdocs where offered at first for free under the same premise: the ability to collect behavioral data around the use of all of Google’s products. 

After its IPO in 2004, Google continued to feed the learning loop with more acquisitions and properties and ad types, acquiring YouTube in 2006, and then DoubleClick, an ad network, in 2008. 

Flywheel 2 operated under the same continuous learning loop as Flywheel 1, but now with exponentially increasing types of behavioral data used in the company’s offerings. 

Wave

Shift to Pay-Per-Click Ads

Flywheel

Expand types of behavioral data

Business Model

Even more relevant ads

Google Flywheel 3

2008 - Present

Flywheel 3

Cloud services, apps

Google Cloud

Services and apps

Flywheel 3: Cloud Services

Google’s playbook for providing free services and then using the behavioral data to feed the continuous learning loop began with Google docs and Gmail. But it wasn’t until Google followed Amazon’s move into cloud storage services that the company began to generate substantial revenues and profit from these services. 

Cloud services was launched In April as App Engine, a platform for developing and hosting web applications in Google-managed data centers. Now offered directly to people and businesses as  Google Cloud Platform (GCP), the company offers cloud computing services that run on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail and YouTube. 

G suite, the offering that includes professional Gmail, Drive, and other Google Apps, is now included within the Cloud Service business.

Google’s Cloud offering, however, has trailed competitor offerings Amazon AWS and Microsoft Azure in terms of market share. 

 

Wave

Shift to Cloud Services

Flywheel

Built off of core algorithm, compute and storage capabilities

Business Model

Pay-per-use apps and data services

Google Flywheel 4

2010 - Present

Flywheel 4

devices that collect data

Google Data Totems

not just devices, but totems of data

Flywheel 4: Devices

Keep Professor Zuboff’s critical perspective in mind when viewing Google’s device strategy. The company acquires and builds devices as part of the original flywheel: collecting more and more behavioral data and continuously improving the overall product experience. 

Devices are data totems: handheld objects built into our everyday lives that generate voluminous amounts of behavioral data. 

Google’s first foray into devices began with its partnership with HTC to launch the Nexus in order to take advantage of its Android operating system.

Google has had a moderately successful cell phone strategy partnering with OEMs (other equipment manufacturers) through the launch of multiple rounds of Nexus and Pixel phones.

The company has also used its massive cash pile to make large scale device acquisitions, including the Nest smart home sensor system, and most recently the Fitbit wearable products. 

Google has also successfully launched Google Home to rival Amazon’s Echo and other smart home devices. 

In totality, however, devices, cloud services, and other revenue streams are still massively dwarfed by Alphabet’s primary business model: Advertising.

 

Wave

Smart phone smart home

Flywheel

Devices priced for data acquisition

Business Model

Device sales 

Not a Flywheel: Other Bets

2015-Present

Other Bets

not yet a big flywheel contributor

Moonshots and Others

kites, drones, and self driving cars

Other Bets

In 2015, Google made a radical decision to rename and reorganize the company. Newly appointed CFO Ruth Porat was the senior architect of the plan. 

 

Alphabet is the ultimate homage to pension managers and hedge funders. “We also like that it means alpha-bet (Alpha is investment return above benchmark), which we strive for!” Larry Page wrote on the Google Blog on August 10.

Since then the company broke out their capital investments and profit and loss for these “Other Bets” separately from the rest of the advertising revenue, in order for investors to correctly price the company. Google was so successful telling big stories of moonshot bets that investors had previously imagined that Google was wasting money pursuing these dreams. In the financial details, however, Other Bets has barely started to contribute to the top line. 

The bets within Other Bets regularly get pruned, started with the famed and failed Google Glasses to more recently shifting Nest back to the core of the business. Belt tightening this year is expected to reduce the burn at many of these bets, and many of these subsidiaries have reached out to alternate investors for substantial amounts of funding – including Verily, Sidewalk Labs, and others. 

 

 

Alphabet’s business model story needs to be understood compared to Facebook. Both companies disrupted, transformed, and dominated the advertising industry, but have not yet transformed other industries. 

Alphabet has been more successful than Facebook in diversifying revenue streams into device sales, Cloud Services, and other offerings, all of which serve to feed behavioral data exhaust and the continuous learning loop.  

The founders were originally disdainful of advertising and sought to pursue new technical futures through their Moonshot Bets, but none of these bets has paid off enough to drive the company away from its core, which is fundamentally an advertising business model.

Alphabet has not experienced the extend of the scandals that Facebook has weathered, but new regulation from the EU or a new election cycle result in the US may limit the company’s outlook. 

Anything to add?

This teardown is our best guess following Alphabet as a public company and listening to stories from employees. Let us know if you agree, disagree, or want to tell us a story about Alphabet’s moves. 

 

This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services.