In 2007 I wrote some introductory Thoughts on a Socio-Economic Environment based on Nothing. This article continues to explore the value of things in a highly intangible, knowledge-based economy. It wanders through internet-based payment systems, economic structure, role of government, organisation of information, community, and society, before disappearing into the realms of philosophy. It contains no answers, but may prove thought-provoking. Continue reading “Valuing Nothing”
Something happened at the start of July 2008 that only happens once every 2 years. For a brief period, everything about the world was not public knowledge. A handful of people worked day and night to fill this chasm of information. To document everything that was suddenly new and uncertain. Meanwhile the world filled up with hardened veterans, many of whom seem to struggle with, well, everything:
“How do I get to Northrend?” – Well, perhaps that new harbour or zeppelin tower that’s been built might give you a clue?
“Where’s Dalaran?” – Did you try riding to the end of the road and then looking up to see what’s blocking out the sun? (Dalaran is pictured right.)
The world is, of course, the World of Warcraft. And the 2-yearly occasion is the start of public testing of the latest expansion, Wrath of the Lich King: The only time a significant proportion of the game world changes.
What’s alarming is that these questions are not from new, inexperienced players. These are from people that have already played the existing game for months or years. They clearly want to know, but seem to have lost the basic ability to explore the game world themselves.
This article explores the concept of “exploration”, and tries to explain how one of the most complex virtual worlds ever created has become popular among players that are not natural explorers. Continue reading “Exploration is Dead. Long Live Exploration!”
The UK‘s local public transport data is effectively a closed dataset. The situation in the US seems similar: In spite of the benefits only a handful of agencies have released raw data freely (such as BART and TriMet on the west coast of America).
That hasn’t stopped “screen-scraping” of data or simply typing in paper timetables (from Urban Mapping to many listed here). Unfortunately, the legal basis for scraping is complex, which creates significant risks for anyone building a business. For example, earlier this year, airline Ryanair requested the removal of all their data from Skyscanner, a flight price comparison site that gathers data by scraping airlines’ websites. How many airlines would need to object to their data being scraped before a “price comparison” service becomes unusable?
Micro-blogging, primarily through Twitter, has started to show the potential of individual travellers to report information about their journeys: Ron Whitman‘s Commuter Feed is a good example. Tom Morris has also experimented with London Twitter feeds.
This article outlines why the “social web”/tech-entrepreneur sector may wish to stop trying to use official sources of data, and instead apply the technology it understands best: People. Continue reading “Social Reconstruction of Public Transportation Information”
This article explores why the best information in World of Warcraft (WoW) is not available from within the game. It considers how to better bring information into the game environment.
Above is a World of Warcraft screenshot, showing an in-game browser. This is not a feature of the game. The “Knowledge Base” is technically a support database written exclusively by the game’s developer and operator, Blizzard. However, an enterprising hacked called Vladinator noticed that this in-game database took its information from a specific webserver. The Knowledge Base could therefore be re-directed to a different webserver: In this case a server that shows information from Wowhead, a third-party site that contains reference material on almost every item, quest, and thing in the game.
Blizzard was quick to block the hack.
This article attempts to explain the utterly illogical structure behind these events. It builds on some of my earlier comments about the use of micro-transactions for in-game education (“Learn2Play”). Continue reading “Platform Azeroth: Why Information is Broken”
Dave McClure addressed a Edinburgh Entrepreneurship Club/Edinburgh-Stanford Link event on 29 January 2008. He outlined some of the advantages of “Web 2.0”, talked extensively on the use of real-time metrics to evolve web services, developed a history of social networking websites, and highlighted the interesting aspects of Facebook. This article summarises Dave’s talk, with some additional commentary from myself.
Advantages of Web 2.0
Web 2.0 is characterised by the:
- low cost of acquiring large numbers of users,
- ability to generate revenue through advertising/e-commerce,
- use of online metrics as feedback loops in product development,
- sustainable long term profitability (at least for some).
Dave McClure did not actually try and define the term, which was probably wise. Generally the term is applied to websites and services where users collaborate or share content.
Web 2.0 has a number of advantages (although it could be argued that some of these apply to earlier iterations of the internet too):
- APIs – the ability to act as a web-based service, rather than just a “website”.
- PC-like interface, albeit still 5 years behind contemporary PC interfaces.
- RSS feeds (for data sharing) and widgets (user interfaces embedded elsewhere).
- Use of email mailing lists for retaining traffic. While email certainly isn’t a “web 2.0” technology, his argument is that email is increasingly overlooked as a means of retaining website visitors.
- Groups of people acting as a trusted filter for information over the internet.
- Tags (to give information structure) and ratings (to make better content stand out).
- Real-time measurement systems rapidly giving feedback. Key is the immediacy of the information, and the ability to evolve the web service to reflect that.
- Ability to make money from advertising, leads and e-commerce. While true since about 1995, the web user-base is now far larger, so the potential to leverage revenue also greater.
Metrics for Startups
I believe the ability to very accurately analyse website usage, implement changes, and then analyse the results, is a key advantage of web-based services. It is an advantage often overlooked by information technology professionals and programmers. I’m not sure why – possibly because web service developers:
- don’t appreciate how hard/expensive gathering equivalent information is in other sectors of the economy, or
- are scared to make changes in case they loose business, and/or believe their initial perception of what “works” to be optimum, or
- just lack the pre-requite analytical curiosity to investigate?
Or perhaps Web 2.0 just isn’t mature enough yet for developers to have to worry too much about optimisation: A new concept for a site will probably either fail horribly or generate super-normal profits. The sector isn’t yet competing on very tight margins, where subtle optimisation can make or break profitability. Of course, optimisation of websites can deliver substantial changes in user behaviour. For example, I have found that a relatively subtle change to the position of an advert can alter the revenue generated by over 20%.
Dave McClure developed the AARRR model. AARRR segments the five stages of building a profitable user-base for a website:
- Acquisition – gaining new users from channels such as search or advertising.
- Activation – users’ first experience of the site: do they progress beyond the “landing page” they first see?
- Retention – do users come back?
- Referral – do users invite their friends to visit?
- Revenue – do all those users create a revenue stream?
For each stage, the site operator should analyse at least one metric. The table below gives some possible metrics for each stage, with a sample target conversion ratio (the proportion that reach that stage).
|Category||User Status (Test)||Conversion Target %|
|Acquisition||Visit Site – or landing page or external widget||100%|
|Doesn’t Abandon: Views 2+ pages, stays 10+ seconds, 2+ clicks||70%|
|Activation||Happy 1st Visit: Views x pages, stays y seconds, z clicks||30%|
|Email/Blog/RSS/Widget Signup – anything that could lead to a repeat visit||5%|
|Account Signup – includes profile data||2%|
|Retention||Email or RSS leading to clickthrough||3%|
|Repeat Visitor: 3+ visits in first 30 days||2%|
|Referral||Refer 1+ users who visit the site||2%|
|Refer 1+ users who activate||1%|
|Revenue||User generates minimum revenue||2%|
|User generates break-even revenue||1%|
These metrics become critical to the design of the product. Poor activation conversion ratio? Work on the landing page(s): Guess at an improvement, test it out on the site, analyse the feedback, and iterate improvements. Gradually you’ll optimise performance of the site.
I find this attempt to structure analysis and relate it back to core business performance, very interesting. However, the sample metrics can be improved on a lot, depending on the nature of the site. For example, to track virality (referral), I might watch the monthly number of del.icio.us adds, or monitor the number of new links posted on forums (Google’s Webmaster tools allow that). Tracking users all the way through the tree from arrival to revenue generation needs to done pragmatically where revenue is generated from very infrequent “big-ticket” sales: With minimal day-to-day data, it can take a long time to determine whether a change genuinely has improved long-term revenue, or whether natural fluctuations in day-to-day earnings just contrived to make it a “good day/week/month”.
Now I know this approach works, but why it works is less clear. We might like to think that we are genuinely improving the user experience, and maybe we are. However, it could be argued that merely the act of change is perceived by users as an improvement – a variation of the Hawthorne effect. The counter argument to the Hawthorne effect can be seen on sites with low proportions of repeat visitors: The majority of those experiencing the improvement will not know what was implemented before.
History of Social Networking
Dave McClure’s interpretation of the timeline of the development of social networking sites is as interesting for what it includes, as for what it omits: No Geocities; no usenet; no forums; no MUDs… The following timeline shows key services in chronological order, except without dates – all the services shown were created within the last ten years:
- Email lists (Yahoo Groups)
- 1.0 Social Networks (Friendster) – these early network established the importance of up-time (service reliability) and the ability of users to manipulate pages.
- Blogs – links between weblogs acting as networks.
- Photos and video (Flickr, YouTube) – created a sense of community, and allowed tagging/grouping of content.
- 2.0 Social Networks (LinkedIn)
- Feeds and shared social information (Upcoming.com event planner)
- Applications and widgets – the ability to embed data about a user’s friends in applications is probably “the most powerful change on the internet in the last ten years”.
- Hosted platforms (OpenSocial, Facebook) – most services are likely to allow 3rd-party developers to provide applications on their platforms.
- Vertical communities (Ning) – ultimately this may develop such that a service like Facebook acts as a repository for a user’s online identity, while specific groups of people gather on other networks.
- Availability of information – a single sign-on, with automatic data transfer between services.
The future may be “Social Prediction Networks”. This is a variation on the theme of using trusted networks to filter content: Instead of Blogging meets Search, I characterise Social Prediction Networks as Digg meets Facebook. Shrewd observers will note Facebook has already implemented Digg-like features, while simultaneously topic-specific, community-orientated Digg-clones are being launched. People gather into interest groups around a topic, and then through use of tagging and rating, the community filters content. The system effectively predicts what other people in the group will find useful. This may be an optimum approach for groups above the Dunbar number (or an equivalent number representing the maximum number of people a person can form stable relationships with).
Interesting Aspects of Facebook
Three were discussed:
- Social graph (friend list) – email and SMS (mobile phone) service providers have rich data on the frequency of communication between people, yet aren’t using this information to form social networks. Dave noted that two major email service providers, Yahoo and AOL, are currently struggling to thrive – this could be an avenue for their future development.
- Shared social activity streams – knowledge of what your friends think is important. Friends are more likely to influence you than people you do not know.
- API/Platform – dynamic behaviour and links across your social network.
Will growth in social networks continue? Yes – the friend list adds value to the content.
Will others compete? Probably, as a “long-tail” of networks, likely topic-specific.
Can social networks be monetarized better? Currently social networking services generate far less revenue than search services. The challenge for social networking sites is to move towards the wealthy territory of search services. At the same time, search services are moving towards becoming more like social networking sites.
How can traditional companies engage with social networking sites? Social networking sites work best for sales where a product has a strong aspect of peer pressure in the decision to buy. The most important advice is not to create a copy of a website: Instead provide less complex content that uses social networks to draw users to a website.
Applications for social networks tend to be over-complicated, normally because programmers attempt to implement functions found in software they have previously written for other platforms or websites. Generally the successful applications are very simple. Some developers have opted to break complex applications into a series of smaller applications, and use the virality of social networking sites to build traffic for one application from another.
Social network applications are exceptionally viral. They can gain users very rapidly, yet also loose users just as fast. Much of this virality comes from feeds, which typically alert friends when a user installs an application. Within a few years the feed is likely to be based on actual usage of an application.
Facebook now allows applications to be added to “fan pages” (or product pages) – so individual users need not now be forced to install an application to use it.
Those using email lists for retention are best to focus on the title of the email, and not the content. Merely make it easy to find a URL in the content. The key decision for the reader is whether to open the email. What the email says is almost irrelevant – they’ve already decided to visit the site based on the title.
These are notes from a talk given by Mike Masnick, CEO of Techdirt, a “technology information company”. Mike addressed a small Edinburgh Entrepreneurship Club/Edinburgh-Stanford Link gathering on 22 January 2008. He outlined the company’s history and philosophy – “use what’s abundant to solve what’s scarce” – and outlined an interesting approach to the delivery of expert/consultancy business services. Continue reading “Mike Masnick on Techdirt, Information and Consultancy”
In an earlier article, I mused on the role of “thought leaders” in indirectly influencing the popularity of websites. These are further rough thoughts on the topic. Caveat: this text is not well researched.
My basic premise is this: ‘Web authors and ‘bloggers are creating trust-based filters for information. Many online writers are looking to evoke discussion and change. But most readers, most of the time, are just trying to get through the day, and aren’t too interested in discussion and change. For them, the author “sounds like they know what they’re talking about”. That creates a sense of trust, and validates the author as a reliable filter for information on that topic. Even the most objective and discerning people don’t have time to review everything themselves. They merely spend more time determining which source to trust. Likely, others will trust them, which makes the source they trust a very important actor indeed.
So we create a network of trust for information. If I want to know something about topic x I might follow the recommendation of author y, because I trust their depth of reading on that topic.
Of course, that doesn’t mean that all webmasters and bloggers are automatically trusted. Far from it. The internet or “blogosphere” is so easy to publish to, it fills up with low-grade content faster than any other media in history. Authors have to earn trust, at least from their early readers. Subsequent readers may be more prepared to trust because others are already trusting (a herd or celebrity mentality).
Why is this happening? Take Herbert Simon‘s statement that, “the rapid growth of information causes scarcity of attention.” The sentiment is repeated in Davenport and Beck’s “The Attention Economy“. We simply can’t manage all the available information any more.
Is that really a new problem? It probably hasn’t been possible to know everything there is to know since the early Victorian era. In some cases there are now technical barriers to knowledge: Simply being well educated isn’t enough to allow one to understand most cutting edge scientific developments in depth. In most cases the prime problem is volume of information: In our World of Warcraft example, more information is written than is possible for a human to read. Finding the important or useful information within can be immensely time-consuming.
Trusting people one barely knows to filter information does not automatically turn these authors into celebrities. In a few cases it may do – some readers will feel the need to trust only those who appeal to many. However, if there is a trend towards writing in narrow niches with in-depth content, rather than content with mass-appeal, an individual author may never be known to millions of people, because the topics they write about aren’t sufficiently mainstream.
Those narrow niches will similarly prevent most authors from emulating the role of pre-internet mass media, notably newspapers. They do, none the less, retain the same duty to their readers: Their readers may be inclined to trust them, but that trust will be eroded if abused. Of course, much like modern mass media, readers can still be subtly manipulated…
As I noted, Google’s biasing of sources by the number and strength links to the source. This automated approach fails to value who is creating links, so has become less valuable as the internet has become more mainstream and prone to abuse. There does not yet seem to be an effective automated equivalent of personalised networks of trust – perhaps because emulating humans is hard to do?