Category Archives: business modeling

Netflix: You don’t bring me flowers anymore.

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Michael, a loyal customer discovered that Netflix loves it’s young new customers more.

It costs to acquire customers; you lose money to get the customers and you hope to make money over the long run. Sometimes it’s very obvious that a company trying to buy your love. Cable companies give discounts for trial periods. Cell phone companies give you free phones (that don’t work with competitor’s systems). Strangers give you free smiles.

What’s less obvious but just as common is that the vendor works extra hard to provide top quality service; particularly during that period when your likely to be suffering from buyers remorse.

So it appears that Netflix tends to dispatch DVDs faster to new as yet unproven customers. It also appears to be reluctant to ship quickly users who watch and return their movies quickly.

The pricing games that address the customer acquisition problem are analogous, but different, from those that segment a market so that customers are charged closer to the value the place on the product. But they both play into the question of scale; if a company has scale advantages – and most do – it’s fatal to not play these games when your competitors are. The scale advantages reaped by reaching a broader range of customers enables a firm to lower prices overall. For this reason some people like to presume that these games are create a social good.

The rub is you to play these games successfully demands a degree of secret keeping. If the buyers begin to understand how the game is played they begin to shop – shopping raises the cost of sales. If the buyers become extremely suspicious they don’t trust the vendor and cost of sales explodes.

It’s getting really hard to keep secrets. It’s hard to play these games when Michael’s got Perl scripts. But does he have an attorney?

Meanwhile in other news on the customer acquisition front:

The $1.5-million settlement ends a class-action suit on behalf of rubes who may have been brainwashed into buying tickets for those stinkers based on raves by David Manning of The Ridgfield Press, an imaginary critic cooked up by Sony marketers to deliver blurbs for newspaper ads. Sony defended its right to plant phony reviews under the umbrella of free speech.

Skype and eBay

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I feel I ought to have an unique opinion about this; but I don’t.

All I have are some very scattered random thoughts about it. I’m sad that what’s fundamentally a European company has been swallowed by a Valley company and I wonder how that will play out. I’m curious how a two sided network effect business will absorb an peer to peer network business. I tend to think that this says more about eBay’s fear of Google than anything else; particularly in the payment’s area. This is ought to be about the displacement of the current financial and transaction systems; the displacement of the current telecom industry is a side show. This is all climate change stuff. I don’t think they overpaid; but what do I know about that? Nothing. But at least it keeps me from making a joke about winner’s curse.

It will be very interesting to see how quickly they can merge the developer networks of the two companies. The eBay developer network is very robust; while skype’s seems very young. Skype did capture, a few months ago, a very senior Microsoft director who presumably knows a lot about developer networks; Scoble’s old boss. That Scoble reports to the Director, Platform Evangelism at Microsoft says a lot about the nature of his job. Comments about markets as conversations will be avoided.

I’m slightly curious if they know that the peer to peer architecture in Skype is dominate over the server based one in both eBay and Paypal. It’s fascinating to spin ideas about what a rich peer to peer transaction framework might look like. If you think of skype a foot hold into the IM space; and in turn a foot hold into the dash board of your trading partner relationships. It’s full of fun techno-geeky-ui scenarios. I mean how different is presence from package tracking any hows? All that falls into the Dave Winer memorial “dig we must” bucket. So it’s more about shovels in the ground than it’s ideas in the air.

Puck some numbers at random; maybe Skype currently has 5 Million users and maybe eBay paid 3 Billion; so $600/user. For what it’s worth Microsoft’s $400 Million for 9 million Hotmail users comes to $44/user. I assume Microsoft knew exactly how many of their users were using Hotmail; I wonder if eBay knew that for Skype.

Noting that you don’t buy things at an eBay auction maybe from now on we can refer to the act of answering a phone call as “winning.”

Business Plan

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I love that illustration. The canonical business plan! The article is good too. I’ve been stewing some on how the mobile phone is a coordination problem solver; and that their pricing, i.e. per minute pricing, reflects to a large degree how often the usage consists of very short calls; each one of which solves a tiny coordination problem. I.e. where are your, where shall we meet, I’m going to be late, call Harry, etc. etc.

The article points out that some businesses have noticed this and as they look for substitutes for cell phones they quickly found Wifi capable phones. Of course the family radios solve the same problem. You see those wired, Borg like, to the retail store employees of most big box stores now. The term “family” was always a diversion from the real target market for these; though I have seen some kids in malls with them in hand. The boys in the back room saw to it that family radio wasn’t regulated so it may not be used to place phone calls.

Apparently somebody dropped the ball on Wifi. The carriers have had to drop back and try defend the network by pressuring the handset manufactures and declining to certify handsets that puncture their garden walls.

It must frustrate the cellular carriers that they haven’t found pricing plans to reach these applications effectively. Maybe the pressure to introduce them hasn’t arrived yet. But much as family radio wasn’t about families I’ve presumed that mandatory location sensitivity for cell phones isn’t about emergency services; it’s about enabling pricing plans that are extremely location sensitive.

But maybe not as small in radius as a toilet bowl plunger.

Reverse Flash Flood

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In the southwestern united states they warn you about flash floods in the canyons. Storms dump a few inches of water at someplace upstream and this water is then aggregated into a giant pulse of water that sweeps down thru the canyon your standing in, killing you. The sky is clear and there is very little warning, maybe just a slight increase in the flow before the flood passes thru.

I’ve always been a fan of this realtime stream flow data network the government runs. It allows them to make accurate forecasts for the downstream flooding. The black dots on this chart show the gages pinned to their maxiumum. A number of gages aren’t reporting.

The tree like networks that draw the water out of river basins and down to the sea are made up of billions of links. Each segement of the stream another link. These networks are powerlaw distributed, the mighty rivers at their roots the hubs of thier distribution systems.

On Monday morning I filled the cars with gas, topping them up to capture the last of the gas at last weeks prices. I was actually surprised that none of the gas stations had raised their prices. Gas on the wholesale market in New York was already up and I assumed that station owners would reprice that huge expensive asset each morning. One guy I asked said “Later, we do it around midday.” Another guy said “The boss hasn’t come in yet.”

This morning we were awoken by a sound you don’t hear in the summer. The oil truck was delivering oil across the street. A few minutes ago another oil truck filled the tank of another neighbor. I don’t know if that my neighbor’s topping up, or if it’s their oil guys pushing oil out to their customers so they can, in turn, top up their tanks.

This is an interesting example of the long tail at work. The moment that supply shifts from abundant and dependable to scarce and volitile everybody along the entire distribution system changes their behavior. They address the volitility risk by adding reserves to their storage capacity, but they also shift capital into oil and gas because of the perception that their price will be higher in the future. I.e. it’s a good investment to top up my car’s gas tank or for my neighbors and their oil guy to top up their storage tanks.

This is a facinating example of the long tail at work. If the entire periphery of the distribution system tops up it’s as if the river basin suddenly starts running up hill. The calculations about risk and future values changes for each and every link in the entire distribution chain.

Ker. Ching. – using network effects to dominate tiny markets

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In growing markets new buyers lack information to select the best vendor, the one that fits their needs best. In this absence of information they grasp at straws; other measures which they can understand. Proxies for quality. The simplest model for why you get highly skew’d distributions, like those seen in market share numbers, is to have new entrants link to whom ever already has a lot of links. Market share in one time period generates market share in the next; not just a little bit, but a lot! This is why early movers can have so strong an advantage. They can then create a brand – and a brand is nothing if not a proxy for doing the real work checking if a vendor fits your needs.

GapingVoid is yet another blog written by a consultant who’s attempting to puzzle out how the Internet is reshaping his craft. He’s a marketing consultant. For example he’s currently experimenting with liqouring up gatherings of bloggers to see if that rebounds to the benefit of one of his clients, a vineyard. One of his success stories is English Cut, a high end tailor – $4K/suit. This posting muses about how successful that experiment has been. It’s kind of guilty gloating. He quotes an observer who notes how the English Cut has captured the hub for high end tailoring. It’s become the brand in high end tailoring. He also muses that this kind of tailoring isn’t a particularly scalable business; double your customers doubles your work. Which is trouble if you have to do it all by hand yourself.

All this is very mysterious. Clearly blogs inject more information into the market. Since lack of data aids early movers blogs would appear to tempering early the mover advantage of the mindless linking to what ever is popular. But just as clearly blogs don’t do that. One reason they don’t: the blog gets caught up in it’s own early mover effect. Observe any class of blogs and you’ll find few hubs that arrived late to the game. Worse yet the late arriving hubs often appear to be explained by their association with older or larger hub. That skewed distribution means that if your a winner then it’s Ker. Ching. and if your not then your a dead body.

This seems to it implies that the globalization implicit in blogging is very damaging for small players. If the market doesn’t expand tremendously then blogging is just another tool for consolidating an industry. That blogging, in this case, is just another distribution channel – a distribution channel of marcom. Distribution channels define the links, The links lead to the skewed distribution. The skewed distribution creates a pile of dead bodies. Traditionally some industries, like high-end tailoring, because they don’t scale the participants don’t threaten each other and the members can be quite collegial. Enter the internet where the network effects rule and that collegiality at risk. It’s winner take all.

Open Source Office

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I see that Sun has set up an Open Source Office in a further attempt to bring some coherence to their strategy and tactics for relating to the open source phenomenon.

This kind of activity can be viewed from different frames. I, for example, haven’t the qualifications to view it thru the Java frame. But let me comment on it from two frames I think I understand pretty well.

Sun has done some reasonably clever standards moves over the years. As a technology/platform vendor the right way to play the standards game is to use it as a means to bring large risk adverse buyers to the table. Once you got them there you then work cooperatively with them to lower thier risks and increase your ablity to sell them solutions. Since one risk the buyers care about is vendor lock-in (and the anti-trust laws are always in the background) the standards worked out by these groups are tend to be reasonably open. Standards shape and create markets. Open enables vendor competition.

This process is used to create new markets, and from the point of view of the technology vendor that requires solving two problems. First and foremost it creates a design that meets the needs of the deep pocket risk adverse buyers. Secondly it creates a market inside of which the competition is reasonably collegial. The new market to emerges when you get the risk percieved by all parties below some threshold.

Open source created a new venue, another table, where standards could be negotiated. Who shows up at this table has tended to be different folsk with different concerns. That’s good and bad.

The open source model works if what comes out of the process is highly attractive to developers (i.e. it creates oportunities for them) and the work creates a sufficently exciting platform that a broad spectrum of users show up to work collegially in common cause to nurture it.

The goals of the two techniques are sufficently different that both approachs can use the word open while meaning very different things. It has been very difficult for Sun to get that. For example the large buyer, risk reducing, collegial market creating standards approach talks about a thing called “the reference implementation” and is entirely comfortable if that’s written in Lisp. The small innovator, option creating, collegial common cause creating standards approach talks about the code base and is only interested in how useful as feedstock for the product they are deploying yesterday.

It’s nice to see that Sun has created an Open Source Office; it’s a further step in coming to terms with this shift in how standards are written and the terms that define the market are negotiated. But, my immediate reaction was: “Where’s the C?” as in CTO, or CIO, etc.

What does the future hold. Will firms come to have a Chief level officer who’s responsible for managing the complex liason relationships that are implicit in both those models of how standards are negotiated? I think so. This seems likely to become as key a class of strategic problems as buisness development, marketing, technology, information systems, etc.

Open source changes the relationship between software buyers and sellers. It has moved some of the power from firm owners and managers down and toward the software’s makers and users. But far more interestingly it has changed the complexity of the relationship. The relationship is less at arms length, less contractual, and more social, collaborative, and tedious.

This role hasn’t found a home in most organizations. On the buyer side it tends to be situated as a minor subplot of the CTO’s job; while of course the CIO ought to be doing some as well. On the seller side it’s sometimes part of business development or marketing even. That this role doesn’t even exist in most organizations is a significant barrier to tapping into the value that comes of creating higher bandwidth relationships on the links in the supply chain.

This isn’t an arguement about what the right answer is because the answer is obvious some of both models. Some software will be sold in tight alignment with carefully crafted specifications and CIOs will labor tirelessly to supress any deviance from those specs. Some will be passed around in always moving piles of code where developers and users will both customize and refactor platforms in a continous dialog about what is effective. The argument here is about how firms are going to evolve to manage the stuff in the second catagory. That’s not about managing risk, that’s about creating, tapping, collaboratively nurturing opportunities.

Vectors

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Evolution of Infectious DiseasePaul Ewald’s book is a rant against conventional wisdom. It opens with a flat out denial: parasites and diseases do not tend to evolve toward more benign relationships with their hosts. The conventional wisdom is based a series of just so stories, an optomism that would do Pangloss proud and a Kansas school board model of evolution.

This stuff has consequences. It’s actionable. Bacterial, viruses, etc. evolve very quickly. When we change their environment they are almost always find ways to leverage those changes. Decisions about public health can leverage that in positive ways. Or, they can blindly ignore it and creating horrible unintended consquences.

I read, rather than skimmed, the whole book because the stories are rich in analogies to the stories I’m most interested in: those about middlemen, platforms, and networks. For example mosquitos act as a intermediary for malaria. In this domain they are called the “vector.” Like the postal system, UPS, or the rail roads they provide transportation services. Understanding many of the stories in this book demands picking apart how how the infecious agent evolves in the face of pressure from both the distribution channel and the host. If the nature of one changes the bacteria (virus, tape worm, etc.) changes to strike a different balance.

There is a facinating insight here: severity of the disease is tied to the nature of the distribution channel. For example diseases which use mosquitos for as their vector, like malaria, are usually more severe than airborne diseases. A mosquito borne disease tends to be severe so it can immobilize it’s host and assure that the mosquito has an easy time feeding and when it does it’s meal contains carries the infection. If we can arrange for the patients to be moved into well screened houses where they can’t be reached by the mosquitos then this scheme falls apart. In which case it’s preferable for the affliction to evolve to keep their hosts mobile – i.e. a less severe varient of the disease emerges. This kind of modeling suggests why the common cold is relatively benign (it needs to keep the host mobile). It is very suggestive about why the trenches and field hospitals of the first world war may have generated the 1918 influensia epidemic; where the army provided continual supply of fresh hosts and mixed them intimately with those infected.

Key to many of the interesting scenarios around networks, standard, and businesses are the situations where the links are made between two different groups and these stories with the disease is the leveraging services of two distince parties seem quite analagous. A middleman, in a business context, covers his expenses by charging the parties on either side, usually differing amounts. So the dating service will charge men more than women while eBay will charge sellers while it advertises to buyers.

The same pattern happens here. Malaria is reasonably benign from the point of view of the mosquito. Analagously the fraud around eBay, the broken hearts around dating services, the viruses on Windows, the spam in your mail box are all reasonably benign from the point of view the intermediaries.

Reading this book you begin to think that any time you see mixing between two classes of actors you need only look and you’ll find a parasite that’s discovered a way to play the middleman. The stories I found the most disturbing are the ones where caretakers become the vector. There is a disease of coconut palms that uses the machette’s of the plantation workers as it’s vector. That story has the horrible plot twist that there are two ethnic groups and only one of these group’s plantations were infected. It had nothing to do with how they ran the plantations, only that the disease agent was issolated because the two groups never exchanged machettes.

He believes, but doesn’t quite have the research to prove, that many of the horribly virilent diseases that have emerged in hospitals over the last few decades can be explained, and then controled, by using these ideas. That these deseases have evolved so they can use the doctors and nurses as vectors and the patients as hosts. The key to pulling that off is to evolve to be benign in the vector and virilent in the host. Any difference between patient and caretaker is an opportunity waiting for a mutation to leverage. Newborns are particularly rich in these differences. So are patients taking antibiotics because they have suppressed their entire spectrum of bacteria. There is an ugly story about an outbreak of murderous diarrhea in Chicago. All cases were traced back to 27 hospitals; but how did they spread between the hospitals?

Escaping the Long Tail – an infectious disease example

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Imagine the plight of the poor bacterium. It want’s to be a big player, but it’s just one of a huge number of bacterium and it’s tough climbing up the rankings. First off it needs to get past that huge barrier to entry, the stomach. Very occationally it manages that. But now it discovers that the ecology it’s entered, the intestine, is crowded with vast numbers of natives. These guys aren’t very welcoming. Worst yet they are well adapted to local market conditions. Early adopters I suspect. What to do?

Evolution of Infectious DiseaseCholera’s solution to this problem is to: appeal to the Government, manipulate the platform vendor, trick the host body. It delivers a swift kick to the digestive track wall, via a toxin, and the host body flushes the entire digestive track. This empties out the ecology of all those pesky competitors. The end result is that Cholera’s ranking isn’t so far down the tail anymore.

I wonder if there is an example of this pattern in business. The direct analogy would require a platform vendor who regularly deals with bad actors by flushing out the entire ecology. Sort of like the police clearing out a marketplace when ever fights start breaking out among some of the market participants. Democratic governments with regular elections might be an example. A varient of the classic problem of regulatory capture. Which then reminds me of the phrase “A new broom sweeps clean.”

In point of fact Cholera’s not particularly interested in capturing the host’s regulatory system. The goal is actually to achieve the maxiumum reproduction and then to spread successfully thru it’s distribution vector; i.e. the water supply.

Some of the management cult memes exhibit that pattern. They aren’t particularly focused on enhancing the operations of the firm but instead infect the minds of the employees who then leave carrying the memes to other firms. Their success is then enhanced by triggering the exodus of existing employees.

Giving it all away, just to get rich.

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Paul Kedrosky writes about the source of superior investment returns; which states that the key is to find the actionable investments where you disagree with the consensus. Certainty, say that gas prices will move in a direction different from the consensus estimate, is one thing but then what action do you take?

In my noodle this got mixed up with a model of group forming I’ve been meaning to write up. It’s from a paper that Karim passed along. In this model the group has a consensus model of the world, while it’s members have their personal models. Call these vectors in belief space. Over time the group’s model shifts, if you are optimistic about the wisdom of crowds it shifts toward the truth. The member models also shift toward the group’s model via socialization, indocrination, etc.

It’s fun to draw analogies between that model and some of the other models (1, 2, 3) of groups.

More interestingly though. One of the canonical dozen questions about open source is why members freely reveal: relinquishing ownership what are presumably valuable bits of knowledge to the commons. You can ask that same qustion in an investment context.

Let’s say you are confident that we are about to transition over Hubbard’s peak and the price of oil will sky rocket. Do you reveal this knowledge to others? To first order the investment answer is no. Instead you go find actionable investments (say long haul railroads on the upside and trucking companies on the downside) and then you just stand by and wait to get rich.

But investing is a funny thing. You get your prize when the consensus model comes into alignment with your investment. Predicting the movements of the consensus is what counts, that’s only somewhat aligned with accurate predictions about reality. When calculating when you’ll be able to cash out the question isn’t “when will I be proven correct” it’s “when will the crowd believe I’m correct.” The sooner that happens the better the investment oportunity.

Investments based on any number of certain events (peak oil, global warming, China’s currency, the US balance of trade, Iraq’s oil reserves, etc. etc.) are most highly leveraged just at the moment before the crowd begins to take them seriously.

For that reason you might freely reveal your more accurate model in the hope of accellerating the crowd’s phase transition out of it’s delusions.

One driver of free revealing can be an entirely selfish attempt to increase the net present value of your investments.

No, later.

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So, are the end to end principle (pdf) and worse is better really the exact same idea? Both are kind of negative in tone. Both have MIT appearing as a character in their stories.

End to end isn’t a paper about the risks of agency, or middlement, but those issues are clearly in the background. Worse is better is more conscous of the social engineering that is going on as the designer makes choices about the shape of his system. Neither is particularly clear that the designer is crafting an option space, a search paridigm, a platform, or a standard. To my mind both have become deeply assocated, over time, with our culture’s romatic notions about the little guy, the rural, the entrepenur. Ideas that are currently appear in the role of “long tail.”