The Future Business Model of Payroll

 ADP Paypal
Money Movement  $1.7 trillion  $354 billion
Revenue  $12.21 billion  $11.27 billion
Profit  $1.75 billion  $1.42 billion
Market Cap  $43.3 billion  $59.7 billion
  • ADP revenue includes full HCM services besides payroll.

Notice something here? ADP moves a lot of money than Paypal, but makes less revenue on money movement (less the revenue from other HCM services). It has a smaller market cap too. Why? Well, ADP is in the business of solution shop and value add process while Paypal is a facilitated network.

There are three general types of business models: solution shops, value-adding process businesses, and facilitated networks. Solution shops are institutions structured to diagnose and recommend solutions to unstructured problems. Almost always, solutions shops charge their clients on a fee-for-service basis. The value-adding process transform inputs of resources into outputs of higher value. Because value-adding process organization tend to do their work in repetitive ways, the capability to deliver value tends to be embedded in processes and equipment. The facilitated networks operate systems in which customers buy and sell, and deliver and receive things from other participants. Much of consumer banking is a network business in which customers will make deposits and withdrawals from a collective pool.

When on boarding clients, ADP is in the mode of solution shops where a heavy team executes a time-consuming and highly customized process for each major client. Once a client is on board, ADP performs the repetitive payroll service with computers in every pay cycle. In return, clients pay ADP service fees. No matter how big or small the paycheck is, for CEO or for average Joe, the service fee is the same. In contrast, Paypal is a facilitated network and the service fee is proportional to the transaction size just like credit card services.

Given its dominance in payroll business, it is very challenging for ADP to achieve high growth in this area by grabbing more market share. But high growth is still possible by changing the business model with the above analysis. That is, ADP should become a facilitated network, more specifically a bank!

It sounds ridiculous but ADP has a unique advantage to be a great bank by managing the risk well. The open secret is its massive payroll and HR data. By knowing the incomes in advance, work history, performance metrics, time management data, etc., ADP can reduce the risk a lot with data science. Another great news is that there is a huge market. Many households try to make a go of it week to week, paycheck to paycheck, expense to expense. In fact, 63% Of Americans don’t have enough savings to cover a $500 emergency. Often they have to pay a very high borrow rate to meet a small financial need. With the good risk management based on its data, ADP can potentially help us with much lower rate. It is a win-win for everyone.

To learn more how this works, please check out Payroll: An Overlooked Area in Fintech.

 

Choosing The Best Tools

India is one of few nations that can buy military equipment from both western world and Russia. When building their destroyers, India does take this advantage to install best sensors from multiple countries to their ships. However, this choosing-best-tools-for-each-problem approach is an engineering nightmare. It is extremely challenging to make sensors from Russia, Italy, France, India, etc. work smoothly together due to various compatibility issues.

The issues are not in each module itself. Essentially every large engineering project is an integration work. We can easily lose the big picture when we focus on the performance attributes of each module. So be careful next time when your architect shows you a system architecture like the below.

Disruptive Innovation: When and Where?

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In the theory of disruptive innovation, Clayton Christensen argues that the incumbent companies introduce new and improved products year-by-year with the sustaining innovations, which eventually overshoot the performance that some customers can use because companies innovate faster than customers’ lives change. Overshooting creates opportunities for firms to change the basis of competition in order to earn above-average profits. After functionality and reliability have become goo enough, for example, the next competition dimensions could be convenience, customization, and price, etc.

This theory has achieve tremendous success with strong supports in many business cases. Few academic management theories have had as much influence in the business world as the theory of disruptive innovation. However, the tricky part is how to find out when the overshoot happens and where the new competition dimension is. Even Christensen himself and masters like Andy Grove made mistakes on them.

As an early adopter and supporter, Andy Grove credits the theory of disruptive innovation as having been the main impetus for Intel introducing the Celeron processor in 1998. However, overshooting didn’t really happen in desktop computing in 1990s and early 2000s. AMD never posted strong challenges to Intel with cheaper and lower-performance CPUs. On the other hand, AMD really threaten Intel’s dominance in 2003 with their Opteron processor, which has superior performance to Intel’s Pentium 4 and Xeon. AMD missed the chance of overtaking Intel then because of their limited manufacture capability.

In the book Seeing What’s Next (2004), Christensen argued that customization and convenience would be the new competition frontier in semiconductor industry. He took Tensilica as an example. Tensilica allows engineers to customize their own systems-on-a-chip on a website.  Xilinx is another example that lets users to decide what specific functionality they need. However, convenience and customization have not become the decisive factor for customers to choose processors.

The true threat to Intel is mobile ARM processors. With the introduction of iPhone, ARM has become the king of personal/mobile computing due to its energy efficiency. More than 95 billion ARM-based chips have been shipped to date. Recently, ARM-based server chips are introduced by industry giant Qualcomm and several startups, which may significantly lower the utility bill of data centers. If ARM finally gets into data center successfully, Intel will lose its last hold.

The competition dimension of processors did change as Christensen predicted. However, it is not because of overshooting but because of the shift of computing paradigm. Since processors are not used by the end users directly but are only a module of computing devices, we should not try to find the new competition dimension by only looking at their attributes but have to see the big picture of ecosystem.

Risk Aversion and Sunk Cost Fallacy

In his book Misbehaving, Richard H. Thaler tells an interesting story. In a class on decision-making to a group of executives from a company in the print media industry, Thaler puts the executives to a scenario: Suppose you were offered an investment opportunity for your division that will yield one of two payoffs. After the investment is made, there is a 50% chance it will make a profit of $2 million, and a 50% chance it will lose $1 million. When Thaler asked who would take on this project, only three of twenty-three executives would do it. Then he asked the CEO how many of the projects would he want to undertake (suppose all projects were independent, that is the success of one was unrelated to others), the answer is all of them! Continue reading

Agile Software Development: China Navy

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The best demonstration of agile software development is probably the modernization of China Navy. Following a “Run Swiftly in Small Steps” strategy, China Navy has undergone a stunning modernization push that puts it near parity with the US. Look below how China Navy has steadily improved each class of their destroyers in gradually shorter and shorter time. They are the grand master of agile development. Continue reading

Payroll: An Overlooked Area in FinTech

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payroll-image

A lot of brain power and money have been poured into FinTech, especially lending and payment areas. These are indeed exciting areas with new business models and technologies. On the other hand, people rarely associate the sexy FinTech with payroll services. Although it may sound boring, payroll is actually an overlooked gold mine for innovators. Traditionally, payroll service companies make money by service fees. New HCM service companies such as Zenefits work as insurance brokers while providing free payroll and HR services. But if we lean under the hood and look at the process, there is an interesting opportunity. Continue reading

Smile 1.2.0 Released!

Dear Smilers,

We are proud to announce the release of Smile 1.2.0.

The key features of the 1.2.0 release are:

  • Headless plot. Smile’s plot functions depends on Java Swing. In server applications, it is needed to generate plots without creating Swing windows. With headless plot (enabled by -Djava.awt.headless=true JVM options), we can create plots as follows:
    val canvas = ScatterPlot.plot(x, '.')
    
    val headless = new Headless(canvas);
    headless.pack();
    headless.setVisible(true);
    
    canvas.save(new java.io.File("zone.png"))
    
  • All classification and regression models can be serialized by
    write(model) // Java serialization
    

    or

    write.xstream(model) // XStream serialization
    
  • Refactor of smile.io Scala API.
    • Parsers are in smile.read object.
    • Parse JDBC ResultSet to AttributeDataset.
    • Model serialization methods in smile.write object.
  • Platt scaling for SVM
  • Smile NLP tokenizers are unicode-aware.
  • Least squares can handle rank deficient now.
  • Various code improvements.

Unicorn 2.0 is Released!

unicorn

There are a lot of NoSQL databases out there. We have used or tried out many of them. We love a lot of cool features they offer. However, we also face many unique challenges in a highly regulated HCM SaaS business. So we have kept looking for the unicorn database to meet our requirements. Unfortunately, none of existing solutions fully address all of our challenges. So we asked ourselves two years ago if we can build our own solution. It was how Unicorn database was born. Unicorn is built on top of BigTable-like storage engines such as Cassandra, HBase, or Accumulo. With different storage engine, we can achieve different strategies on consistency, replication, etc. Beyond the plain abstraction of BigTable data model, Unicorn provides the easy-to-use document data model and MongoDB-like API. Moreover, Unicorn supports directed property multigraphs and documents can just be vertices in a graph. With the built-in document and graph data models, developers can focus on the business logic rather than work with tedious key-value pair manipulations. Of course, developers are still free to use key-value pairs for flexibility in some special cases.

During the past two years, we have learned a lot and made a lot of improvements, which resulted in Unicorn 2.0, which we are excited to open source to the community. Continue reading

In Memory of Andy Grove

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andy-grove

Legendary former Intel CEO Andy Grove left us recently. Wearing many hats, he is an entrepreneur, a teacher, a writer, a philanthropist, etc. As Marc Andreessen says on Twitter, he is “the best company builder Silicon Valley has ever seen, and likely will ever see“. Even after more than thirty years, his book “High Output Management” is still a must-read for all middle level managers.

In his another best-seller book, “Only the Paranoid Survive“, he introduced the concepts such as “strategic inflection point” and “strategic dissonance”, which have become part of the lexicon both in academia and in practice. A strategic inflection point is a time in the life of business when its fundamentals are about to change. That change can mean an opportunity to rise to new heights. But it may just as likely signal the beginning of the end.

inflection-point

Andy Grove steered Intel through several strategic inflection points, for example, the shift from the memory business to microprocessors when they realized they couldn’t keep up with Japanese competition. Soon Intel will face another inflection point. Actually this new inflection point already started. Unfortunately, Intel doesn’t have Andy Grove any longer. Continue reading