APML
APML is an XML file format used to store the attention profiles of users, and it's got the potential to become a revenue stream for web 2.0.
From my time at various web 2.0 start-ups, one of my gripes with the industry was that too many start-ups were focussing on advertising as their sole revenue stream, gunning to grow big and sell to GYM (Google Yahoo Microsoft), and hope that whoever bought them would figure out how to make them profitable. This strategy has worked for some, but I doubt that it will work for all. There are start-ups out there who may have to find other ways to make money, rather than just chase VCs and old media conglomerates, and shovel the cash on a bonfire.
My topic today is about an xml file format called APML, used to store attention profiles of users. The technology is not complex, and there are plenty of places where it could be implemented. What's more, the interesting thing about APML is that is has the potential to turn a lot of the data that web 2.0 startups have into valuable information that can be sold as a commodity. In other words, a revenue stream that isn't "AdBucks saves the day!"
Introducing APML
APML is an xml file format for Attention Profiles, and Attention Profiles are lists of things that you are interested in, with a value signalling how interested you are in that thing. Examples of attention profiles include:
- A list of your favourite music artists collected by Last.fm, ranked by how often you listen to them.
- A list of topics that you like to Twitter about, ranked by a count of how many times you mention each topic.
- A list of products that I looked at and purchased / bid on from Amazon or Ebay, ranked by clicks.
- A list of urls I have bookmarked via del.icio.us, ranked by the number of visits to each link.
- A list of articles I have read on BusinessWeek, ranked by the number of visits to each article.
Let's grab a real example so you can see what I mean. Here's my my last.fm profile. Notice the list of top artists, ranked by the number of times I've played songs/albums by those artists. And here is the APML file of the same data. There we go, an APML file of my favourite musicians/music categories, courtesy of SunLabs and Last.fm.
Comparing the two sources, you get a better idea of what APML does to Attention Profile data. Here's a diagram of the schema for APML:
If you like all the juicy details, here you go. In the meantime, I'll give you a quick runthrough.
The head element contains some basic descriptive data about the Attention Data, who it belongs to (the email), what application / site generated it, and when. Then in the Body element, we have 2 sub-elements; profile, and applications. Profile is where the real meat is, it's essentially a grouping of the Attention Profile data, in my example Last.fm provided a list of all the music I like, and the music I have listened to in the last week.
The Profile element contains 2 sub-elements; ExplicitData, ImplicitData. Explicit and Implicit Data are similar in their schema, meaning that they store the same kind of data, but the difference is this; Explicit data is data that the user generated and gave to the application/site (in my last.fm example, that is the list of musicians I listened to), and Implicit data is data that the application generated (the list of music categories that I liked). I tell last.fm what artists I like (Explicit), and last.fm tells me what music categories I listen to (Implicit).
The Explicit/Implicit data element contains 2 sub-elements; concepts, and sources. Concepts are a collection of elements representing the thing that you are interested in, such as a music artist, and a value representing your level of interest in that item. The value is ranked from 1 to 0, with 1 being the thing which you are most interested in, and everything else being compared against that top concept. Sources are a collection of the sources of the data stored in concepts, such as blog sites, web applications, etc.
Jumping up a couple of levels, the Application element is used to store information about the application which generated the APML file, as well as any meta-data that the application carries with it. I have to put my hands up here and admit that I don't fully understand this element, so you're best bet is to flick through the juicy details rather than take my vague description as gospel.
Why its good
All APML does essentially is add structure to information about what we as people are interested in, which for its own sake seems a rather pointless venture. However, as Orwell once said, knowledge is power, and what APML offers is a number of opportunities to turn an excess product of web 2.0 into a commodity of value.
Finding out what stuff people are interested in and organising that information has proven to be useful information to businesses, just ask Tesco why they place beer next to nappies*. Web 2.0 start-ups are collecting a massive amount of this raw data about their users, but not necessarily putting it to full use.
But who's buying the data? That's where I get creative and illustrate to you a business idea that I think could work for an APML analytics service...
This diagram might be worth 1000 words, but I'll explain it anyway (accessibility ; ) ). Various web services which collect data about their users convert that data to APML, and post it to us. We take it, store it in a big database alongside data from other providers, and then do 3 things with that data:
1 - Provide an analytics service to interested 3rd parties, advertisers and publishers to analyse a large data-set and spot interest patterns across a whole range of consumer products (nappies and beer?).
2 - Provide a recommendation engine service to interested web services looking to pool a large data-set to get highly-accuracy suggestions to users.
3 - Provide (and this is where I shoot myself in the foot) an Advertising Management service that suggests the most relevant ads based on the user's APML data across various services.
The general idea for the revenue stream here would be to charge for these 3 individual services, and apportion a percentage of that revenue to the APML data providers, based on how much of their data was used by the customer. That's really all there is to it.
Summary
APML helps to standardise data about what users are interested in, and in doing this, an opportunity might be open to build a web-based service that can sort all of that data and spit it out into a neat little analytics service for 3rd parties to analyse to bits, alongside the recommendation engine to serve to interested web services, as well as the (choke) Ad management service to suggest relevant ads. All in all, this could be a way to provide those web 2.0 services with a revenue stream where they provide a web service with APML, and get money back in return.
To explain the nappies and beer reference, Tesco's loyalty card scheme provides a wealth of information about consumer purchase patterns, one of which was that men in their 30's would be visiting their stores on a Friday night to buy nappies of all things (as my sister's partner must be finding out). They strategically placed beer next to nappies, and boosted their sales.