A Better Way To Price iPhone Apps (and mp3s)

Author: Marc Fawzi

License: Attribution-NonCommercial-ShareAlike 3.0

If the price of an app was demand-indexed, starting at some arbitrary price near $0 when the app is launched and then going up and down with demand, then that would have interesting consequences.

Obviously, how that is tuned varies from app to app. For example, for a given class of apps, the demand-indexed price may be e.g. $0.50 at 1,000 downloads a day and $0.99 at 10,000 downloads. Many factors would go into deciding that curve (1), but the point is that the price should change with the rate of demand just like the price of scarce goods. However, unlike scarce goods, where there is the concept of an optimal [market] price at which the most profit is generated, the demand-indexed price would be optimal within the entire range of ‘FREE to CHEAP.’

This way if a developer has a great app with a great potential they get the most adoption upfront, helped by the near-free price, and as the market for that app heats up they get to enjoy higher profits from a higher price.

I think Apple got the idea for $0.99 for music singles from the publishing business where the price of e.g. music CD or a book is fixed and does not go up and down with demand.

The assumption is that a book or music CD can be replicated infinitely at a fixed cost per unit, so why slow down sales with a higher price if the demand is shooting up? However, when we’re talking about an mp3 or an .app the cost of replication is so negligible that pricing an app or mp3 at $0.01 produces a profit (after initial sunk cost of development/creation and assuming no recurring costs like cloud usage fees or bandwidth exist other than those paid for by Apple and factored into their model) so increasing the price from $0.10 to $0.25 will NOT slow down sales with rising demand because people are willing to pay ANYTHING between FREE and CHEAP for something they think is good and the perception of how good the app is increases with demand for that app (as that leads to more chatter among connected consumers and more hype in the press) …. So all one has to do is to figure out what is “CHEAP” for the given class of app (via a user survey) and then introduce the app at e.g $0.01 and change the price daily (or even in real time) while keeping it less than or equal to CHEAP.

There are a couple of key considerations to take into account when attempting this model. It has to do with the nature of demand in the long tail market for content.

Wikipedia 3.0 (3.14 Years Later)

Author: Marc Fawzi

License: Attribution-NonCommercial-ShareAlike 3.0

I’ve just received a couple of questions from a contributor to an IT publication who is writing about the state of the semantic web.

I’m taking the liberty of posting one of the questions I received along with my response.

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[The Semantic Web has seen] several years of development. For instance, there’s been steps to create Syntax (e.g., OWL & OWL 2, etc.) and other standards. Various companies and organizations have cropped up to begin the epic work of getting ‘the sematic web’ underway.

How would you characterize where we are now with the web. Is Web 3.0 mostly hype?
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RDF, which emerged out of the work being done on the semantic web, is being used now to structure data for better presentation in the browser. It’s being used by Google and Yahoo. So you can say that the semantic web is starting to bear some fruits. But unlike OWL-DL, RDF does not have the structure to implement a logic model for the given domain of knowledge, which is required by machines to reason about the information published under that domain. However, RDF and RDFa (and other variations) are perfect for structuring the information itself (as opposed to the logic model for the given domain of knowledge) so the next step will be to use RDF to structure information for machine processing, not just for browser presentation, and that would be combined with the use of domain-specific inference engines (which, in this case, would combine logic programs and  description logic for the various knowledge domains) to build a pan-domain basic-AI-enabled “answer machine,” which is fundamental to any attempt to making machines ‘comprehend’ the information on the Web, per the full blown semantic web vision.

The “hard” problem with the semantic web is not the natural language processing, since we don’t really need it right at the start: we can always structure the information in such a way that it can be processed by machines and then comprehended using the aforementioned pan-domain AI, or, in the case of search queries, we can come up with a query language with proper and consistent rules that is easy to use by the average educated person, such that the information/query is machine-process-able and may be comprehended using domain-specific AI.

The “hard” problem is how can all the random people putting out the information on the Web agree to the same ontology per domain and same information structuring format when they do not have the training or knowledge to even understand the ontology and the information structuring format?

So both ontology creation/selection and information structuring has to be automated to remove incompatibilities/variances and human errors. But that’s not an easy task as far as the computer science involved.

However, instead of hoping to turn the whole web into a massive pan-domain knowledgebase, which would require that we conquer the aforementioned automation problem, we can base our semantic web model on expert-constructed domain-specific knowledgebases, which by definition include domain specific AI, and which have been in existence for some time now, providing a lot of value in specific domains.

The suggestion I had put forward three years ago in Wikipedia 3.0 (which remains as the most widely ready article on the semantic web with over 250,000 hits) was to take Wikipedia and its set of the experts, who are estimated at 30,000 peers as of 2006, and get those 30,000 experts to help build the ontologies for all domains currently covered by Wikipedia as well as properly format the information that is already on Wikipedia so that a pan-domain knowledgebase can be built on top of Wikipedia, which would be able to reason about information in all domains of knowledge covered by Wikipedia, resulting in the ultimate answer machine.

The Wikipedia 3.0 article and some of links there describe how that can be done at a high level as well as some implementation ideas. There is nothing ”hard” there except the leadership problem.

It’s clear that the leadership problem has not been tackled, especially having seen how Jimmy Wales started and quit a VC funded search startup after I had penned the article. Taking VC money (and being part of that model) is not the right way to do it. The right way is to carry it out with community support as a non-profit venture, not supported by VC funds. That’s partly because any such startup would require the free labor of those 30,000 Wikipedia contributors in their roles as domain-specific experts. So it just wasn’t going to work out to expect those contributors to work for free so that Jimmy Wales can make a fortune.