Dave McClure on Social Networking and Web 2.0
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.