APIs as Personal Systems in the Networked Marketplace

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Networked Marketplace

“What gives life to and sustains the corporation resides on the ‘outside,’ not within its direct control, and the customer is the primary mover of those external realities and forces. It is the prospect of providing a customer with value that gives the corporation purpose, and it is the satisfaction of the customer’s requirements that gives it results.” 

[Peter Drucker, 1954]

We are customers

Some of us are also employees, managers, CEOs, investors, partners, and service providers to the very same organizations that produce and sell what we buy. With smartphones in our pockets, tools we can access in our browsers and personal clouds, we are also dissolving the duality of buyer and seller.

In response to a Hubspot question about how to plan for mobile in the long term I said:

THE QUESTION TO ME IS HOW DO WE THINK ABOUT THE WAY PEOPLE ACCESS INFORMATION AND BUY GOODS AND WHAT ARE THE IMPLICATIONS?

There’s a collection of Zen koans called the Gateless Gate. Among other things, koans transcend dualism. The traditional sales process is fully dualistic – there’s a buyer, and there’s a seller. We are witnessing the dissolution of the traditional sales role, as recommendation commerce evolves and store- fronts become wherever you happen to be, doing whatever you are doing. Which brings us to the Storeless Store and Saleless Sale.

Doc Searls has been working to provide tools for individuals to manage relationships with organizations for more than a dozen years. His project, Vendor Relationship Management (VRM), is about improving how buyers and sellers relate.

In a conversation we had a couple of years ago he said:

The Industrial Age is nearly two centuries old, and isn’t ending. Over that time we have become very good at selling. The flywheels in the selling machine are huge.

So far the Net has offered to sellers countless ways to improve what they’re already doing. That’s why the automation people appear to be more successful than the “conversation people.” They’ve been at it longer, and the Net gives them more ways to get better at what they already do.

The “conversation people” also have two problems. One is that they work for the sell side. This subordinates their work to sell-side systems, imperatives and defaults. The other is that their tools are inadequate.

Even as many of the tools are inadequate for us to become agents of our own experiences, there is a significant market movement in that direction. The recent popularity of ad blocking apps has fueled an already active conversation over adtech and tracking. Here’s a series of posts on debugging adtech assumptions.

Doc Searls had written about the Father of all Business Models more than five years ago:

The attention economy will crash for three reasons. First, it has always been detached from the larger economy where actual goods and services are sold to actual customers. Second, it has always been inefficient and wasteful, flaws that could be rationalized only by the absence of anything better. Third, a better system will come along in which demand drives supply at least as well as supply drives demand. In other words, when the “intention economy” outperforms the attention economy.

How did we get here?

The deeper and original meaning of agency is acting for one’s self.

Dan Pink has been thinking about this question for a while. In 2002, he found that 25 million people already worked for themselves. At the time of his research he was a free agent having left his full time job to write his own byline.

He tallied the numbers for an article he wrote for Fast Company magazine. That’s how I met Dan. The article was the prequel to his highly predictive book Free Agent Nation: The Future of Working for Yourself.

How did free agency happen? Dan says:

Four ingredients were essential:

1.) the social contract of work — in which employees traded loyalty for security — crumbled;

2.) individuals needed a large company less, because the means of production — that is, the tools necessary to create wealth — went from expensive, huge, and difficult for one person to operate to cheap, houseable, and easy for one person to operate;

3.) widespread, long-term prosperity allowed people to think of work as a way not only to make money, but also to make meaning;

4.) the half-life of organizations began shrinking, assuring that most individuals will outlive any organizations for which they work.

Dan was a recent guest on Brian Clark’s podcast where they discussed the state of free agent nation in 2015.

The four ingredients Dan wrote about have become solid trends. A trend is defined as the general direction in which something tends to move.

Anthropologist Grant McCracken has studied American culture for 25 years. One of the themes of his work has been how Americans invent and reinvent themselves. His definition of trend is that it is “an emerging consensus” that “must act like an eddy that runs through us, changing thing called ‘x’ and the activity called ‘y’.”

A macrotrend runs thorough the whole culture. In an interesting turn, this year Grant is affiliated with the Berkman Center for Internet and Society at Harvard. Which is where Doc continues to head ProjectVRM.

When network effects kick in

The image on this post is my interpretation of the night and day experience we can create as conversation agents — a network of individuals who can act each for one’s self. The concept comes from The Intention Economy: When Customers Take Charge byDoc Searls.

Adtech and in many ways what we call personalization engines as mass marketplace tools are experiencing a backlash because in this construct, relationship is demeaned. Transaction is all that matters.

That might be undesirable, but not completely off putting. Anyone who has experienced being followed online by ads for items they researched, or already bought understands the annoyance factor of getting things exactly wrong.

The other side of the coin is for all the big data crunching, context and content talk, and martech investments, customers still don’t get what they really want. Fragmented experiences come at a high cost of time and energy.

In How quickly will ads disappear from the Internet? Horace Dediu, one of the world’s most respected business analysts and renowned experts on complex data analysis, says:

I was always bemused by the notion that the Internet was able to exist solely because most users did not know they could install an ad blocker. Like removing Flash, using an Ad blocker was a rebellious act but one which paid off only for early adopters. But like all good ideas, it seemed obvious that this idea would spread.

What we never know is how quickly diffusion happens. I’ve observed “no-brainer” technologies or ideas lie unadopted for decades, languishing in perpetual indifference and suddenly, with no apparent cause, flip into ubiquity and inevitability at a vicious rate of adoption.

Watching this phenomenon for most of my life, I developed a theory of causation. This theory is that for adoption to accelerate there has to be a combination of conformability to the adopter’s manifest needs (the pull) combined with a concerted collaboration of producers to promote the solution (the push). Absent either pull or push, adoption of even the brightest and most self-evident ideas drags on.

“Ad blocking offers a real-time example of this phenomenon.” In the post he talks about network effects. The very same effects organizations have been trying to engage to achieve virality for their videos and scale for their programs to grow transaction volume.

Both sellers and buyers want the same thing to happen — provide and acquire a service or product. But there is no connection point where a matching of what is on offer and true intent wrapped into preferences and real time data can take place.

To benefit from network effects, businesses need to participate to the network marketplace where relationships are what matters.

We need systems that talk to each other. This is where APIs come in.

APIs as personal systems

Phil Windley says, “personal means that I own and control the system, the processing, and the data.” As opposed to personalized where someone else controls what it shows me based on the data it has.

In The Intention Economy, Doc provides a few examples of how that might work. One of them is fairly complex, but worth thinking about because it creates a vivid picture of how personal systems would work with organization’s system to make it a win-win for everyone.

Here’s the example(emphasis mine):

Now for the complex scenario, involving a salesman we’ll call Bob, who works for a company we’ll call BigCo. Bob lives in Denver and is going on an overnight business trip to see a client in San Francisco.

Bob’s personal cloud has these apps on the inside:

  • TripEase (which doesn’t exist yet, but something like it will)
  • Calendar
  • Expensify
  • OpenTable
  • TomTom
  • Quickbooks
  • Singly

The APIs on the outside are the exposed competencies of:

  • Marriott
  • United Airlines
  • Avis
  • Visa
  • Salesforce

We start as Bob confirms an onsite visit with his San Francisco client.

When Bob schedules the meeting in his calendar, an appointment event is sent to his personal cloud. Evented applications and services Bob uses are listening for these events and respond by taking action on Bob’s behalf. In this case, Salesforce fills out appointment details in his calendar, such as Bob’s client’s office location and directions for parking nearby.

Meanwhile, rules in Bob’s TripEase app react to the same event, recognize the appointment is in San Francisco and that Bob will need travel arranged. TripEase knows Bob’s travel preferences (e.g., a single room at a Marriott hotel, a compact car from Avis, an aisle seat on United, and a request to United for an upgrade to business class if a seat is available) and marks available choices from each on his calendar. TripEase also knows that Bob has memberships in loyalty programs with each of those companies and also with other companies, in case Bob’s first-choice preferences aren’t available. Bob sees the choices, makes them, and TripEase puts those in the Calendar.

In the background, TripEase also raises a new business trip event, causing Expensify to bring up an expense journal, so an accounting of expenses can be made at the end of the trip. So, when Bob gets to the airport and buys a sandwich with his Visa card, Expensify automatically adds that purchase to this trip’s expense journal. And, because Expensify and TripEase are cooperating, Expensify has more context for purchases and can make better categorization decisions without Bob’s involvement.

After landing in San Francisco, Bob turns on his smartphone, and its location function raises an event indicating Bob is now at SFO. Avis hears and responds to Bob’s location event by preparing Bob’s preferred car and paperwork.

To help with that, TripEase also lets Avis know that Bob will decline Avis’s insurance offer and return the car with a full tank of gas. If Avis does not already have this information, TripEase fills Avis in on the facts, and auto-signs Avis’s agreement, so Bob doesn’t have to bother with that. TripEase also puts Bob’s appointment destination in the TomTom app on Bob’s smartphone.

After meeting with his client, Bob drives to the Marriott and parks there. When he arrives at the reception desk, he is greeted by name and given his preferred room (on the north side of a high floor) and a key to his room and the exercise facility. He also receives a notification from Open Table that two of his favorite restaurants have reservations available. He decides on one and proceeds to his room. Open Table also sends an e-mail and a text to his client with reservation information.

All these connections are made in the background, on Bob’s behalf, by apps and services that he or his fourth party have already programmed, using KRL#.

After checking out of the hotel (automatically, of course), driving his car back to Avis at SFO, flying back to Denver, and driving back to his house, TomTom tells TripEase that Bob has completed the trip and raises an end-of-trip event.

Expensify sees the event and moves Bob’s journaled expenses—air fare, hotel, rental car, gas purchases, dinner, personal driving mileage (to and from the airport), and parking fees—from his trip journal to his expense report. After reviewing and approving the report, Bob tells Expensify to send it to BigCo’s Salesforce system, which is in the cloud Salesforce keeps for BigCo.

Expensify has also sent copies of expenses to Bob’s own personal cloud, which comprises these:

  1. His personal data store (PDS), which is where he keeps his personal data, fed from many sources, including all the apps and services mentioned. While Bob could keep his PDS on a self-hosted server, his preference is to use Singly, a fourth-party he pays to keep his data and relationships sorted out, secure, and up to date. Singly also has a rules engine he can use, but he isn’t limited to that one alone.
  2. His personal API. This can live anywhere, but would most likely live with his PDS.
  3. His rules, written in KRL.
  4. His memorandum book.
  5. His journal and ledger, kept by QuickBooks.

His memorandum book is the modern version of what for centuries was the first step in double-entry bookkeeping: the place where everything that happens is first written down. This helps Bob (and his tax preparer) remember what happened when, as well as what it cost. From there, it goes to his journal and then to his ledger, which can generate the usual reports. Meanwhile, he has an accountable and auditable trail of records.

Would we not want to have a personal system like this? Where our settings combined with our actions to trigger the delivery of a service and we get to understand our own patterns, preferences, and improvements based on data. A better version of the quantified self, a two-way version of the much touted Uber experience.

This is all built on relationships where we decide what we share and with whom. Richer experiences reduce the role of luck for organizations as well, imagine doing forecasts on actual bookings through APIs — and take care of bribery programs and of the expensive guesswork in the process.

 


Conversation Agent – Valeria Maltoni

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