Activity 6: Starting a Community

For this final blog, I will be looking at a hypothetical social platform, Fit Achieve, and the various strategies it would use as a start up.

The Platform

Fit Achieve would be a platform which sets daily or weekly exercise goals for a user based on their current abilities and activity amounts, as well as overall goals. These overall goals can be hours of exercise per day or week, and / or based around weight loss, muscle gain, or an increase in overall fitness. Users would use the platform to track their exercise history, see how close they are to achieving their personal fitness goals, and motivate themselves to reach their goals. Fit Achieve will also encourage users to add friends, allowing them to motivate each other, compete, or show off.

Stage One Benefits

Stage one benefits are those that are not related to social benefits that earlier users will still gain before they have a large number of friends on the platform. It is very important that all social platforms have these benefits so that users are encouraged to continue using the application until they have gained a significant social group based on that platform. Fit Achieve would do this by helping its users track what exercising they have been doing and showing what goals they have achieved. Therefore, the platform would still offer benefits to users, even if they aren’t friends with anyone on the platform.

Start Up Costs

As with any business, Fit Achieve would incur some start up costs. Obviously, one of the major ones will be server capacity. This would be reduced by outsourcing to a platform as a service provider, such as Google or Amazon. This would allow the application to scale to suit, as well as reduce initial investment costs. Another major area of costs will be gaining users. Fit Achieve would do this by avoiding using traditional advertising, and instead opt for online ads and utilising social media. Once the application has begun to gain a user base, by encouraging users to promote the application to their friends and linking to their other social platforms, network effects and word of mouth will also help the platform gain momentum and eventually reach critical mass.

Early Adopter Benefits

Early adopter benefits are put in place in many social applications to encourage users of the new platform to stay until the social effects take place and the platform reaches critical mass. Fit Achieve would encourage early adopters by displaying when a user has been a user since, and therefore giving them more authority within the community. The application would also display how many of their generated and self set goals someone has achieved. Therefore, those who have been members for longer would have had more time to complete more goals, and again, would appear to have more experience and authority within the platform’s community.

Thank you for reading my blog posts. In the comments, tell me about how you would do any of these start up stages in your own platform.

Activity 5: Activating Network Effects

Source

Last.fm is a web application whose primary focus is on recommending music to their users. This is done by using a desktop or mobile application called "The Scrobbler" which records what songs have been played on the user's computer or device. This information is then synced with the web application, which compares what the user has been playing and how often with other users who also listen to these songs, then makes recommendations. Users can also be recommended artists by using the tagging system and searching for a genre they enjoy. The application also has a range of other capabilities, such as adding and sharing music tastes with friends and sending messages to them. Users can also see gig information, share if they are planning on attending and make comments on them, as well as on the artists, albums, and tracks in general.

Network Effects

Last.fm has made great use of activating network effects. Network effects are the advantages to a platform for having a larger user base^[1]. Positive network effects encourage the growth of the network, and with more people using the application, its value increases. With a higher value, this in turn encourages more users.

Direct Network Effects

The direct network effects of a web application are that its value increases (both according to the users and other stakeholders) as the user base increases. This means that users are more likely to make an investment into the platform as the number of its users increases. In Last.fm's case, as more people use the application, more people are having their musical tastes recorded, allowing the application to make more informed recommendations to its users. It also means that more users are able to interact with each other, making the application more social and enjoyable to use.

Indirect Network Effects

Indirect network effects are those advantages gained from third party complimentary services which increase the value of the original product. Last.fm has allowed these to be created by releasing an API, allowing other developers to create services relating to the web application.

Cross Network Effects

Cross network effects are that a rise in one group of users leads to an increase of value to another group of users. For Last.fm, as with many other websites, an obvious connection would be a rise in casual users would result in a higher number of advertisers, allowing for the website to gain more funding, and in turn, support more users. An increase in users and record label employees who add artist, album, and song information also adds value to the application for those who don't, as they have a wider range of music to share and be recommended, and the information provided is likely to be more correct as more people are checking it.

Local Network Effects

Local network effects, also known as 'social network effects', is that a user's decisions are influenced by those in a typically small set of other users. Therefore the most benefits can be gained from those the user is connected to, not just the size of the user base as a whole. Last.fm encourages its users to connect with friends, providing the option to search a user's Facebook friends for their accounts. By building a network of friends in the application, users are able to get music recommendations from their friends, send them messages, and share what gigs they are going to. Therefore, although all users are able to get benefits from the applications, those with a group of friends who they base their interactions with the platform around are able to gain the most.

Future

Last.fm previously offered a music streaming service, which was available until last year. This signals that they are focusing more on their recommendations system as well as providing more accurate music information. Last.fm should also consider improving their mobile offerings, which currently, at least on Android, are severely lacking. As for extending their use of network effects, they could improve their cross network effects by making it easier for those who edit artist, album, or song information, as well as provide record companies more incentive to take control of their artists' pages.

In what other ways do you think that Last.fm is using network effects well? Tell me about it in the comments.

Reference

1. Shuen, A. (2008). Web 2.0: A strategy guide: Business thinking and strategies behind successful web 2.0 implementations. Sebastopol, CA: O'Reilly Media, Inc. 

Activity 4: Lightweight Models and Cost Effective Scalability

Lightweight models and cost effective scalability is another O'Reilly pattern of web 2.0 applications. This is essentially the pattern of innovation in assembly for business models. It therefore involves getting products to market faster, reducing costs and risks, as well as encouraging simplicity within the application's assembly.

Pinterest

Pinterest is a service operating both web and mobile applications. It allows users pin images or videos to themed boards which they can share with friends or keep private. Users can also follow users or boards which interest them and like or share their content to their own boards.

Best Practices

The first best practice which Pinterest is implementing really effectively is outsourcing wherever practical or possible. The company has utilised Amazon Web Services, namely Amazon Simple Storage Service for its data storage and Amazon Elastic Compute Cloud for its data analysis. This means that the company is not responsible for providing and caring for large data banks which it can use for storage and therefore can divert the resources, such as money and manpower, which it would have devoted to this to other areas of its operation.

Another best practice which Pinterest is performing particularly well in is that it has been designed for scaling. The application experienced rapid growth across a nine month period, going from 50 thousand users to 11 million. This could potentially have been disastrous if the application hadn't been designed in this manner, as the company by itself probably couldn't have kept up with the demand being placed on its servers, and therefore would have had constant outage problems, causing users to give up on their services and turn to others. However, it avoided this by scaling with demand. By outsourcing their data storage and processing to Amazon, they are can (and have) enabled autoscaling on their data storage and load balancing on their processing, allowing for them to have the optimum setup for their current data requirements.

Future

Obviously, Pinterest has a very bright future ahead of it should it continue to implement its strategies as well as it is now. It may begin to look into other areas of this web 2.0 pattern, such as outsourcing its own functionality and expertise to other services, or changing its revenue model, however it doesn't appear to need to make any major changes any time soon.

Do you know of any other companies which have a data strategy which is as light weight and cost effective as Pinterest? Tell me about it in the comments.

Activity 3: Perpetual Beta

Another one of O'Reilly's web 2.0 application patterns is the perpetual beta of these applications. This involves the constant updating current and adding of new features to an application, meaning that there has been a shift in the way that these programs are delivered, moving away from being a product and becoming a service.

Tumblr

Tumblr is a microblogging platform which allows its users to create their own posts or share others by reblogging them. They can also follow these blogs and have their posts appear on their dashboard, or like posts if they want to show their appreciation without the content appearing on their own blog.

Best Practices

One of the best practices that Tumblr is a great example of is that it releases early and often. This means that it is constantly adding new features to its product as well as updating and improving its existing ones. Therefore, any bugs within their system are also being found quickly and rectified. This means that users are constantly getting improvements and maintain their interest in the platform, whilst the developers are continuously getting feedback from users ensuring that they are in fact creating what the users want.

Another best practice that Tumblr is utilising is engaging its users. The staff of the website maintain major two blogs relating to this, one a general purpose one, and another dedicated to updates from their software engineers. This lets users know what updates have been made to the website, both front and backend, and how these will affect the user's experience and interactions with the website.

Comparison

Blogger is a competing blogging platform owned by Google. However, this service has its users focus on creating their own content rather than sharing the content of others, but unlike Tumblr, it does allow for commenting on posts. However, compared to Tumblr, Blogger has a lot of work to do on its perpetual beta strategy. Like Tumblr, its staff members maintain a blog on what updates are being made to the platform, but it is infrequently updated. There appears to only very rarely have new features added or updated, with 5 functionalities added or updated in the past two years, compared to Tumblr's many more.

However, Blogger does have a major focus on the best practice of making operations a core competency. Being owned by Google, its uptime can be monitored through their apps status dashboard. This releases information on whenever there has been a service disruption or outage. Through this service, Google also tells its users how they will be affected, when they expect the issue/s to be solved and if it is possible for it to be recurring. This is a great initiative by Google, as it shows their trust in their users and they make themselves publicly responsible for any problems users encounter.

Future

Tumblr has many options and paths for the future, and since it is in perpetual beta, its developers are easily able to trial new features and updates. Tumblr could improve its services by embracing making operations a core competency by releasing information about its uptime, like Blogger, and increase user trust in the platform. Another area which Tumblr could improve its operations is by engaging its users more in co-development, either by opening itself up more to user suggestions for improvements or by creating an option where users can become testers for functions that haven't been generally released yet.

What do you think of Tumblr's perpetual beta strategy? Tell me about it in the comments.

Activity 2: Rich User Experiences

Last week, I blogged about one O’Reilly’s web 2.0 patterns, data is the next Intel Inside. This week, I’ll look at another one, rich user experience. This involves delivering a PC style and level of interactivity via web browser applications.

One web application which is notable for its rich user experience is Defringe, an online art gallery. Pictures and a short description are tiled across the page and users are able to click to open an article, containing more information on and pictures of the piece.

Best Practices

Defringe exhibits many of the recommended best practices for providing a great experience for its users. Firstly, it puts usability and simplicity first. Users are instantly able to work out how to operate the website, but just as importantly, the designers found a way to make this interactivity fun. Small details, such as the way the navigation icons change or page numbers scroll as a mouse rolls over them, give the application a sense of personality, and this in turn gives the user a more enjoyable experience.

The website also puts a focus on search over structure. Content is easily sourced through typing key words into a search bar found in the navigation on each page. Although structure is provided by allowing users to sort by category or publishing date, this definitely isn’t the application’s focus as individual articles don’t disclose this information, and therefore would make it harder for a user to look for an article they had previously read based on this.

Despite having a seamless transition between various interfaces, Defringe also preserves its content’s addressability by changing the URL for each page. This is clearly a very important ability as it allows users to share their content, and hence increase their site traffic and general brand awareness.

However, one best practice which Defringe is seriously lacking in is deep, adaptive personalisation. The application doesn’t allow for users to create accounts (although due to the application’s purpose there really is no need for this functionality), nor does it make article recommendations based on past browsing activity. This could be an ability which site developers could implement later.

Comparison

In contrast, another platform based on a similar idea but with a very different execution is Instagram. This is a web and mobile application which allows users to upload pictures with a description and tags, which help others to find the post. However, this platform operates very differently to Defringe. Firstly, and most importantly, it relies on user uploads instead of Defringe’s model of having only moderators post (albeit with giving users the ability to make a post suggestion). Secondly, its web application has no focus on search. It is impossible for users to search for content using the web application unless they click on the tag they want within a post they are looking at, although third parties have filled this gap. This complete lack of functionality makes it the antithesis of Defringe, which has a huge focus on searchability. Also unlike Defringe, Instagram is actually a very good example of having deep, adaptive personalisation. Based on a user’s activity, the web application is able to recommend other users for someone to follow.

Do you find Defringe's user experience as enjoyable as I do? Let me know in the comments.

Activity 1: Data is the Next Intel Inside

All web applications rely on data, and its management is becoming a core part of a business’ strategy in an aim to get the most out of one of their vital assets. Hence, O’Reilly has made “data is the next Intel Inside” one of its web 2.0 patterns. Many companies, such as Spotify, are learning to balance having a good control over their data, whilst giving users the access and rights to their own data and the platform's, so together they can explore the Spotify's full potential.

The Platform

Spotify is a web, desktop, and mobile application which allows users to stream millions of music tracks; follow artists, playlists, radios, and friends, as well as share libraries and playlists with friends. Based on a user’s activities, the application is able to suggest other music the user may enjoy listening to. The company is meeting one of O’Reilly’s best practices by designed their data for reuse. They have released an API, allowing outside developers to fetch Spotify’s data relating to artists, albums, and single tracks and users’ personal playlists and libraries (with their permission).

The Competition

Compared to other common music streaming platforms, Spotify is a leader in API offerings. YouTube is one of their main competitors, however it is focused on playing a single video (or much less often, a playlist), instead of Spotify’s more specialised constant streaming of audio. Both platforms allow the retrieval and streaming of tracks/videos or playlists, however Spotify does fall behind when compared to YouTube’s Analytics API, which allows developers to access viewing statistics, viewer demographics, and popularity ratings.

Spotify’s other major competitor is iTunes, which also has released an API. However, the iTunes API has a lot fewer capabilities. Like Spotify, it allows for developers to search the store and display a song or album’s information, but unlike Spotify, it only allows for a small preview of a song to be played. Most crippling though, is that Apple (the owners of iTunes) don’t allow the API to be used for any purpose except to promote the store (even going as far as requiring an iTunes icon displayed wherever it’s used), and explicitly banning entertainment usage. This means that Apple is missing out on many opportunities for its data to be used and exposed to developers with ideas, allowing for Spotify to swoop in and gain a huge market share.

The Future

One best practice Spotify must work on is allowing users to control their own data. For example, there is no way for a playlist to be exported to another application such as iTunes or Windows Media Player, even if it consists entirely of the user’s own locally stored music files. Another possible future direction for the platform is focusing on another best practice, enhancing their core data, by creating opportunities for users to make more explicit inputs, such as ratings and reviews.

What are your opinions on Spotify's use of its data? Let me know in the comments.