Week 7 – Cloud Computing

This week’s focus was on Cloud Computing, a topic of dear interest to me. The first thing we were tasked to do was discuss which business models appear appropriate for the cloud. In order to do that, we need to look at the NIST (http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf) Definition of Cloud Computing, which notes the following essential characteristics:

  • On-demand self-service
  • Broad network access
  • Resource pooling
  • Rapid elasticity
  • Measured service

In effect, the Cloud Is a great technology platform for businesses that have started at zero, and would like to scale up without incurring the costs of purchasing hardware, or the significant capital investments of a data centre. Start-ups are a great candidate to use Cloud technology platforms. Another suitable business is traditional businesses that require a platform for proof-of-concepts (POCs). Large companies can try new technologies without any consequence on existing infrastructure, and can be shut down just as easily. Another business type suitable for the Cloud is organisations that need to do batch processing of information in a timely manner. Two examples of that is Metservice, who use AWS (http://www.stuff.co.nz/technology/digital-living/8741213/Amazon-ahead-in-the-cloud) to augment their on-shore weather forecasting simulations, as well as Qantas (http://www.itnews.com.au/news/qantascom-begins-transition-to-aws-402996) who use AWS to do flight and weather forecasting.

Next, we looked at two assigned readings, the first being How CloudFlare promises SSL security – without the key (http://arstechnica.com/information-technology/2014/09/in-depth-how-cloudflares-new-web-service-promises-security-without-the-key/). This article discusses how organisations want to use Cloud computing resources, which allow large organisations, like banks, absorb denial of service attacks. However, these entities want to use Cloud computing without handing over the keys to the kingdom so to speak, or in this particular case, the SSL Private Key used to decrypt communications. Therefore, CloudFlare have created a method that allows the Private Keys to remain stored on Customer’s Servers, rather than on the CloudFlare servers. This allows organisations to take advantage of the cloud, while still controlling their own security.

The second reading was on How can we protect our information in the era of cloud computing (http://www.cam.ac.uk/research/news/how-can-we-protect-our-information-in-the-era-of-cloud-computing). The article describes how information can be protected in the cloud by creating multiple copies in a decentralised manner, also known as peer-to-peer. The article goes on to quote Professor Jon Crowcroft saying “We haven’t seen massive take-up of decentralised networks yet, but perhaps that’s just premature”. I’d argue that we do see massive peer-to-peer networks, they’re just being used to distribute movies and other pirated material. As legal authorities moved to shutdown torrent trackers, these then evolved into Magnet Links (https://en.wikipedia.org/wiki/Magnet_URI_scheme) which no longer require a torrent tracker, but instead identify content based on a hash value.

The final task was to look at the pros and cons of New Zealand Government’s Cloud First strategy (https://www.ict.govt.nz/guidance-and-resources/information-management/requirements-for-cloud-computing). The pros below are listed from the previous link:

  • Cloud computing solutions are scalable: agencies can purchase as much or as little resource as they need at any particular time. They pay for what they use.
  • Agencies do not have to make large capital outlays on computing hardware, or pay for the upkeep of that hardware.
  • Cloud computing provides economies of scale through all-of-government volume discounts. This is particularly beneficial for smaller ICT users.
  • Agencies can easily access the latest versions of common software, which deliver improved and robust functionality, and eliminating significant costs associated with version upgrades.
  • If agencies are able to access the same programmes, and up-to-date versions of those programmes, this will improve resiliency and reduce productivity losses caused when applications are incompatible across agencies.

The cons highlighted in the article is that using the Cloud isn’t a free pass to outsource risk, ultimately it’s the agency’s responsibility to use or not use the Cloud. This includes for example, ensuring that data above RESTRICTED isn’t in a public cloud.

Week 6 – Social Media Technologies

This week we looked at social media technologies, covering an overview of social media technology, social media graphs, finding network communities, and similarity of nodes.

First, we looked generically at social media technologies. For me, the interesting point was organisation’s shift away from more formal knowledge management systems (like Moodle) towards more social knowledge management systems like Wikis. This reflects in my opinion that knowledge in an organisation is held by everyone, and should be updated by everyone where appropriate.

One of the more up and coming uses of social media is a new customer service management channel. http://thenextweb.com/socialforbusiness/2014/10/21/social-media-in-unexpected-parts-of-business/ discusses some interesting opportunities for social media in organisations, specifically:

  1. Cross-team relationships
  2. Monitoring customer conversations
  3. Customer behavioural targeting

Of the three, I’ve had recent experience evaluating an SAP product called Hybris (https://www.hybris.com/en/) which is focused on monitoring customer conversations, and customer behavioural targeting. I think it’s important to note that we live in a multi-channel world where customers choose the social networks they wish to interact with an organisation with, whether that be in real time on Twitter, a Facebook group, sending an email, or using a feedback form. In this scenario, a customer posting a comment on Facebook should be treated no differently than them using a form on the website, and expects the same service. More interestingly is the use of social media for sentiment analysis, which filters all the data from sites like Twitter, and searches for both positive and negative words. This way organisations can see as news is released, or campaigns are released, whether there is positive or negative sentiment towards the brand. This allows organisations in real time to adjust their marketing to amplify positive effects, or counter negative effects.

There were three readings this week, the first being Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. CRM is Customer Relationship Management software, which is used to capture the relationship a customer has with an organisation. Social CRM augments a traditional CRM with social connectivity. I think some organisations would view this as a communication revolution. The key takeaway for me was noted by Trainor, Andzulis, Rapp, & Agnihotri (2014) as social CRM as a technology “alone may not be sufficient to gain a competitive advantage. Instead, social media technologies merely facilitate capabilities that allow firms to better meet the needs of a customer”. This wasn’t a surprising outcome for myself. I view social media as a natural collaborative extension to email, and really the modern version of bulletin boards and newsgroups. So tooling that takes that into account will really help to gain insight, and at best, amplify a conversation with a customer, but in itself won’t do too much.

The second reading was Social media? Get serious! Understanding the functional building blocks of social media. Kietzmann, Hermkens, McCarthy, & Silvestre (2011) defined seven functional blocks of social media:

  1. Presence – The extent to which users know if others are available
  2. Relationships – The extent to which users relate to each other
  3. Reputation – The extent to which users know the social standing of others and content
  4. Groups – The extent to which users are ordered or form communities
  5. Conversations – The extent to which users communicate with each other
  6. Sharing – The extent to which users exchange, distribute, and receive content
  7. Identity – The extent to which users reveal themselves

All social media sites have these aspects, just in varying degrees. My own personal belief is that Identity is the next big thing on the Internet, and unlike Mark Zuckerberg’s view that “Having two identities for yourself is an example of a lack of integrity” (http://www.michaelzimmer.org/2010/05/14/facebooks-zuckerberg-having-two-identities-for-yourself-is-an-example-of-a-lack-of-integrity/), I believe that we all have multiple identities, one for work (LinkedIn), one for family (Facebook), one for close friends (SnapChat), one for dating etc.

The final reading was Chapter 10 of Mining of Massive Datasets. This, and a few of the videos for this week covered some of the algorithms used as a foundation of grouping nodes together, and understanding their relationships. We looked at relationships between sets of clusters, or Betweenness, and then went into some detail on the Girvan-Newman algorithm, which Rajaraman, Leskovec, & Ullman notes “visits each node X once and computes the number of shortest paths from X to each of the other nodes that go through each of the edges”. While this sounds scary, really it all means how do we infer useful information from a social network? Well, like-minding topics or people like to cluster around each other. So how do you know there’s a cluster? How do you know the relationship between that cluster and other clusters, so you can decide which content to show? Algorithms exist to help us make sense of all this related information.

References:

Trainor, K., Andzulis, J., Rapp, A., & Agnihotri, R. (2014). Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. Journal of Business Research, 67(2014), 1201-1208.

Kietzmann, J., Hermkens, K., McCarthy, I., & Silverstre, B. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(2011), 241-251.

Rajaraman, A., Leskovec, J., & Ullman, J. (2010). Mining of Massive Datasets.

Week 5 – Recommender Systems

This week we looked at Recommender Systems. We all see Recommender Systems everywhere on the Internet, the big ones being Amazon.com recommending “Other products you may like”, and Netflix recommending other movies to watch.

Recommender systems recommend things we should concentrate our attention on. Recommender systems have been around for ages, when we look at the articles in the newspaper, do we ever stop to wonder, why has the newspaper recommended this article for me? Well, obviously the newspaper can’t make a perfect newspaper for everybody personally, so it uses editorial recommendations, or hand selected recommendations.

However, now that we have the Internet, mass-recommendation is possible, and preferable in the age of The Long Tail.

The basic concepts of recommendation engines is:

  • Users want recommendations
  • Sites have a list of items for recommendation (movies, books, people)
  • We take some inputs like ratings, demographics, and content data
  • We output a prediction of what people might like, the top choice being the recommendation

We could recommend what people are likely to hate, but there’s not much point in that, so recommender engines are tuned towards positive recommendations.

There’s two types of recommender systems:

  • Content-based, which looks at content (like movies), and recommends similar content to that user. So if you’ve watched a cowboy movie and rated it highly on Netflix, Netflix will recommend other cowboy movies that are similar (same actors, same time period, same genre etc).
  • Collaborative filtering, which looks at customers or items, and says, if you like the same book as other users who purchased this book, then here are the items that those customers purchased as well. For example, if you purchased a camera, and other people who purchased that camera also bought a case, Amazon will recommend that case to you.

Sites that recommend similar to Netflix include:

  • Hulu.com (popular TV shows)
  • Pandora.com (music)
  • Crunchyroll.com (Japanese Anime shows)

Sites that recommend differently to Netflix include:

  • iPredict. iPredict recommends outcomes based on the concept of shares. A share pays out $1 if an outcome becomes true. Therefore, $1 = 100% likelihood that an outcome is true. So if a share is trading at $0.04, then there is a 4% likelihood according to the free market than that outcome is true. This is a bit like having 100,000 people bet on recommendations for what movie you would enjoy based on you liking Harry Potter, and then taking the average of their bets, and saying “that’s what the market thinks you’ll enjoy”.

We also were to look at item profiles for the various items, and describe attributes used to describe those items:

  • Ant Man Movie – Year of production, age classification, length of movie, genre, release date, critic scores, director, writers, actors, award nominations,tropes contained, etc.
  • A document (like the Wikipedia page on Recommender systems) – Links to other Wikipedia pages, categories of the page, Links to External pages, number of words in the article, bounce rate of people visiting the page who then left the site, number of people who arrive at Wikipedia at this article first.

Finally, we were to combine all the previous topics we discussed (search engines, advertisers, and recommenders) into a start up business model that delivered groceries to the home.

For me, the model would be shipping American candies and snacks automatically to people’s home, as a Snack Subscription.

First, explaining the business model – the value proposition is delivering delicious American candies and snacks regularly to people who enjoy and value them. The revenue model is Subscription based, where every month people exchange $30 to get a box of snacks delivered to them. The target customer is anyone in the world who values American snacks so much they’re willing to pay $30 a month every month ($360 a year) on American snacks. The distribution channel is via the web for the ordering service, and physical courier for the delivery service.

The second step would be to create a website and get that crawled by a search engine like Google. It would be important to know the keywords that customers are searching for using something like Google Trends for Snack Subscription. Google knows that people searching for Snack Box also want to search for Snack Subscription. We’d have to get links to our websites from other popular websites, to increase our rankings with the Pagerank algorithm.

Of course, it’s not always easy to get links on popular websites, so instead we can advertise instead. Advertising is all getting attention to our links, so we can take our above research for Snack Subscriptions, look at our competitors, and start bidding on keywords. To get better value for money, we can increase the information quality of our advertising by having a site optimised for mobile, including video, and describing our site in a format that the Google Knowledge Graph understands. This in turn will decrease the amount of money we have to pay per click, and increase the likelihood of our ad being displayed on the limited number of advertising slots available on a search query.

Finally, once we have customers arriving at our site, we can present a range of snack subscriptions, and give them the choice of ranking which types of snacks they prefer. These explicit rankings will provide a motivated customer with a more accurate selection of products they’d enjoy. But if they didn’t want to rank anything, we could provide options based on what other customers on the site prefer.

Week 4 – Advertising

This week we covered Advertising. We looked at some of the older types of digital advertising, such as banners, and how to determine when to place them. Banner advertising relies on users randomly browsing websites, and then the advertiser determining what ad to display to the user. There’s only a small amount of information available used to decide what ad to display, such as previous search terms, or previous pages visited. This means there’s a relatively low information value for an ad to a visitor, leading to a low click rate, and a low ROI.

This leads to learning about the difference between knowing a full set of inputs to calculate the best outputs, versus only knowing a subset of inputs, and trying to calculate the best-available output. This can retrospectively be compared to the absolute best output, and the ratio between best-available output and absolute-best output can be used as a metric to understand the value of a particular algorithm.

My reflection is that advertising is really the business of attracting attention of people. There is 300 hours of new YouTube videos uploaded per minute. So can a person gain the attention of others to view their video? There’s some choices:

  1. Improve the discoverability of the video (get people to the video);
  2. Improve the informational value of the video (get people to stay on the video).

People can tell others that a video is good, and that is social media. The system can tell others that a video is good, and that is the platform. Advertisers can tell others that a video is good, and that is advertising. Therefore if we think of advertising as the business of attracting attention, it’s also in competition with platforms and social media. This probably explains why Google created a social media platform (Google Plus), owns a platform (YouTube), and why Facebook has invested heavily in video sharing.

Anyways, back to the topic of advertising. As advertising matured, the more recent innovation is advertising auctions, where advertisers bid on keywords, which they then pay for as clicks are received. Google’s platform is known as Adwords.

When it comes to displaying ads on a query, the problem boils down to:

  • How many ad slots are available on a query page
  • What are the bids that advertisers have for a query
  • How effective is the ad for that advertiser (since Google only gets paid on a click, not just an impression)
  • What budget is available (since advertisers don’t have unlimited budgets).

Google addresses the Adwords problem by:

  • Looking at the bid per advertiser;
  • Looking at the quality of the ad (based on previous click rate);
  • Looking at the attractiveness of the ad (described as format impact);

All of the above values calculates an ad’s Ad Rank. An interesting component is that they use a Vickery Auction, where the winning bid is equal to the bid of the second highest bidder. This determines who should win (the highest bidder), and what the market price should be (the second highest bid).

Of note is that there is the possibility of gaming the system, known as Click Fraud. Because advertisers pay for clicks as a proxy for attention, automated systems can generate clicks on ads, which appear legitimate, but because it’s not actually a person, the advertising doesn’t have any benefit.

That’s why other metrics of engagement, such as Facebook Likes may be better measures of attention than just clicks.

I think that clicks as a proxy for attention as a business model is vulnerable to a different business model that more accurately measures attention. If we define attention as someone listening to a message, understanding it, and actioning it, then perhaps we could:

  • Ask people if they believed the link was valuable to them (whether that be an ad or a social media interaction);
  • Measure understanding (perhaps using product engagement online);
  • Measure actions (perhaps by measuring if further activity around that ad, i.e. searching on Amazon for alternatives).

Week 3 – Search Engines

This week we covered search engines, the obvious example being Google.

Interesting side note – I used to sell search engines to companies around New Zealand, specifically the french EXALEAD CloudView from Dassault.

Google Search these days is founded around the idea of the Knowledge Graph. A good video that shows how people use the knowledge graph is at YouTube,. (2015) and is shown below:

The Knowledge Graph is explained at YouTube,. (2015) and is shown below:

The Knowledge Graph in effect means that Google understands more of the meaning behind your search terms, and their relationship to other things. This in turn allows Google to synthesise answers to your queries quicker, and sometimes by not even leaving the site at all.

The first unconventional way to use Google is to define attributes relating to the main thing you’re search for. https://www.google.com/search?as_st=y&tbm=isch&as_q=cats&as_epq=&as_oq=&as_eq=&cr=&as_sitesearch=.edu&safe=images&tbs=sur:f&gws_rd=ssl is a search for “cats” that is restricted to the .edu domain, and has been labeled for noncommercial reuse. Rather than searching for “cats edu creative commons”, Google has more accurate filters than a straight keyword search.

The second unconventional search is queries about attributes of a search topic. https://www.google.co.nz/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#safe=active&q=nz475 searches for the flight NZ475:

nz475

 

 

 

 

Google has interpreted that NZ475 is a flight, and therefore has shown me flight related information in a card. After that are search results which are pages other than Google.

The third unconventional search is Google’s ability to crawl sensitive information. https://www.google.co.nz/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#safe=active&q=password+filetype:xls+site:.edu is a link restricted to Excel files “xls” that contain the word “password” and are on an “.edu” domain. Not all of these files are sensitive, however, this goes to show that “security by obscurity” is not an acceptable practice, now that it is easy to use Google to surface sensitive information.

The foundation of the Google Search Engine is Pagerank, a page ranking algorithm. This is described in the paper
The PageRank Citation Ranking: Bringing Order to the Web (Page, Brin, Motwani, & Winograd, 1999). To summarise, links to pages are treated as endorsements of that page. Links themselves are weighted depending on if that webpage is trusted. Trust is defined by the number of links to a website. That way a link from the BBC carries more weighting than a link from this blog.

Finally, we briefly touched on spam. Google,. (2015) defines spam as:

irrelevant or unsolicited messages sent over the Internet, typically to large numbers of users, for the purposes of advertising, phishing, spreading malware, etc.

My definition of spam is:

Something tricking you into giving it undesired attention.

Whether that be email spam trying to get you to buy shares, Facebook spam trying to get you to click on pointless videos, link spam trying to escalate the importance of fake websites, or ads on websites trying get you to visit their clickbait articles, spam is trying to steal your attention in a dishonest way.

References:

YouTube,. (2015). Explore lists and collections with Google search. Retrieved 29 July 2015, from https://www.youtube.com/watch?v=mg91_trV4hY

YouTube,. (2015). Introducing the Knowledge Graph. Retrieved 29 July 2015, from https://www.youtube.com/watch?v=mmQl6VGvX-c

Google.com,. (2015). cats site:.edu – Google Search. Retrieved 29 July 2015, from https://www.google.com/search?as_st=y&tbm=isch&as_q=cats&as_epq=&as_oq=&as_eq=&cr=&as_sitesearch=.edu&safe=images&tbs=sur:f&gws_rd=ssl

Google.co.nz,. (2015). Google. Retrieved 29 July 2015, from https://www.google.co.nz/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#safe=active&q=nz475

Google.co.nz,. (2015). Google. Retrieved 29 July 2015, from https://www.google.co.nz/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#safe=active&q=password+filetype:xls+site:.edu

Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web. Retrieved 29 July 2015, from http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf

Google.co.nz,. (2015). Google. Retrieved 29 July 2015, from https://www.google.co.nz/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#safe=active&q=spam%20definition

Week Two – Business Models

This week we covered a formal description of business models, noting their specific components as defined by Michael Rappa. These components are:

  • Value proposition
  • Revenue model
  • Target customer
  • Distribution channels

We were then tasked to look at organisations with similar business models to Google, Amazon and Netflix. I selected Facebook, Fishpond, and Github.

Facebook

  • Value proposition – Connecting the world socially
  • Revenue model – Advertising
  • Target customer – Anyone over 13 years’ old
  • Distribution channel – web, apps

Fishpond

  • Value proposition – New Zealand’s biggest online store
  • Revenue model – commission
  • Target customer – people in New Zealand
  • Distribution channels – web

Github

  • Value proposition – Online collaborative code repository
  • Revenue Model subscription
  • Target customer – developers who want a code repository
  • Distribution channels – web and apps

We then were tasked to discuss differences between B2B and B2C business models, where B2B focuses on the supply chain of raw materials, to manufacturers, to distributors, to retailers, versus the B2C supply chain of Retailers to Consumers.

The Internet makes it possible to disintermediate companies from the supply chain. A good example is Amazon.com which plays both the role of the Distributor and Retailer. As Amazon gained experience in distribution, they then expanded themselves to offer that as a service to other organisations notes (Services.amazon.com, 2015).

The most interesting change I believe the move from Manufacturer to Customer, bypassing Distributors and Retailers, a good example of which is AliExpress (http://www.aliexpress.com/). (Aliexpress, 2015) states the purpose is the find wholesale products from China Wholesalers, allowing customers to purchase at far lower costs, but with less confidence about quality, than through a retailer.

The final focus for this week was to focus on a business model that wasn’t covered in the lectures. The business model I selected was disintermediation, where airlines sell directly to customers via their online booking websites, rather than going through a travel agent. The most well known airline in New Zealand with online booking is Air New Zealand at http://www.airnewzealand.co.nz. Interestingly, the experience gained by Air New Zealand in online bookings has allowed them to expand their offering into booking hotels states (Airnewzealand.co.nz, 2015), which is reintermediation in a different associated industry to airlines. The net consequence is that travel agents are starting to move to a Pay as you go model, where each booking is paid via a service fee directly to the consumer notes (Houseoftravel.co.nz, 2015), rather than being subsidised through rebates by the airline.

Another example of disintermediation is (Apple.com, 2015), who sell directly to consumers through their stores or online. However, they haven’t fully embraced disintermediation, and still wholesale to other retailers.

Finally, we needed to create a business model for a startup idea. I choose Eneropp, a startup I’ve created which is a subscription service for energy utilities which allows them to quickly provide a mobile online services website and app to their customers (Eneropp, 2015). The business canvas I created is:

businesscanvas

at https://canvanizer.com/canvas/DDbgBJ-KhRQ

The business model the start-up uses is Lock-in & Subscription in combination.

References:

Services.amazon.com,. (2015). Boost your sales with Amazon’s world-class fulfillment.. Retrieved 24 July 2015, from http://services.amazon.com/content/fulfillment-by-amazon.htm

Aliexpress,. (2015). Find Quality Wholesalers, Suppliers, Manufacturers, Buyers and Products from Our Award-Winning International Trade Site. Wholesale Products from China Wholesalers at Aliexpress.com.. Retrieved 24 July 2015, from http://www.aliexpress.com/

Airnewzealand.co.nz,. (2015). Hotels – Air New Zealand. Retrieved 24 July 2015, from http://www.airnewzealand.co.nz/hotels

Houseoftravel.co.nz,. (2015). Fees – House of Travel. Retrieved 24 July 2015, from http://houseoftravel.co.nz/popups/fees.htm

Apple.com,. (2015). Mac – Shop Mac Notebooks & Desktops – Apple (NZ). Retrieved 24 July 2015, from http://www.apple.com/nz/shop/mac

Eneropp,. (2015). Eneropp. Retrieved 24 July 2015, from http://eneropp.com/

Week One – e-Business Cases – Netflix

This week covered e-Business Cases, around three particular companies, Google, Amazon, and Netflix.

For Netflix, we looked at the products offered, which are stated at (Ir.netflix.com, 2015) include:

  • Streaming Internet TV for movies and TV series;
  • DVD rental;
  • Original content licensing to TV networks.

(Ir.netflix.com, 2015) states that Netflix’s Business Model is:

Netflix is a global Internet TV network offering movies and TV series commercial-free, with unlimited viewing on any Internet-connected screen for an affordable, no-commitment monthly fee.

Netflix is quite social, with recommendations being at the heart of social activity. (Netflix.com, 2015) notes that social features include connecting to Facebook, and sending recommendations to friends.

(Netflix.com, 2015) states that the information that Netflix collects includes:

  • Information you provide to Netflix to operate the platform, such as your name, and credit card information;
  • Rating information and viewing preferences;
  • Information collected automatically about how you interact with the platform, such as what devices, when, where, and what network speeds;
  • Social networking information if enabled;
  • Demographic information and browsing behaviour where possible.

(Techblog.netflix.com, 2012) highlights some of what recommendations can be based on, including:

  • Household viewing interests, not just individual viewing interests;
  • Context;
  • Title popularity;
  • Evidence;
  • Novelty;
  • Diversity;
  • Freshness.

 

References:

Ir.netflix.com,. (2015). Netflix : Netflix’s View: Internet TV is replacing linear TV. Retrieved 16 July 2015, from http://ir.netflix.com/long-term-view.cfmpl

Netflix.com,. (2015). Netflix – Watch TV Shows Online, Watch Movies Online. Retrieved 16 July 2015, from https://www.netflix.com/SocialTerms?locale=en-US

Netflix.com,. (2015). Netflix – Watch TV Shows Online, Watch Movies Online. Retrieved 16 July 2015, from https://www.netflix.com/PrivacyPolicy

Techblog.netflix.com,. (2012). The Netflix Tech Blog: Netflix Recommendations: Beyond the 5 stars (Part 1). Retrieved 16 July 2015, from http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html

Week One – e-Business Cases – Amazon

This week covered e-Business Cases, around three particular companies, Google, Amazon, and Netflix.

For Amazon, we looked at the products offered, which are stated at (Services.amazon.com, 2015) include:

  • A marketplace for selling products;
  • A marketplace for selling services;
  • A B2B marketplace;
  • Advertising;
  • Payment Gateway;
  • Order fulfillment;
  • Customer-service-as-a-Service;
  • Cloud platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS).

A former employee of Amazon, (Wei, 2015) notes that Amazon’s Business Model is:

Amazon is a classic fixed cost business model, it uses the internet to get maximum leverage out of its fixed assets, and once it achieves enough volume of sales, the sum total of profits from all those sales exceed its fixed cost base, and it turns a profit.

Amazon’s Retail business has social throughout the technology, specifically recommendations based on what others have purchased, as well as product reviews. Amazon is investing in social, with the recent purchase of Goodreads notes (Eha, 2013).

(Amazon.com, 2015) states that Amazon.com captures information about you including:

  • Information you give them, such as your shipping address;
  • Information gathered through the use of their services such as items placed in your shopping cart;
  • Information from mobile devices, if you’ve used their mobile applications or devices;
  • E-mail information such as whether you’ve opened an e-mail from Amazon.com.

Amazon.com has made a play on monitising The Long Tail. (Wunker, 2015) defines The Long Tail as:

Long tail business models sell small quantities of a very large number of items.  They are the antithesis of blockbuster business models, which sell large quantities of a few items.

People have niche interests that when scaled over the whole world, provide enough volume to make selling products in that niche profitable. Amazon.com has the scale and the discoverability on the Internet to make The Long Tail profitable.

References:

Services.amazon.com,. (2015). Sell on Amazon: Amazon business services. Retrieved 16 July 2015, from http://services.amazon.com/content/amazon-seller-services-products.htm?ld=NSGoogleAS

Wei, E. (2015). Amazon and the “profitless business model” fallacy. Remains of the Day. Retrieved 16 July 2015, from http://www.eugenewei.com/blog/2013/10/25/amazon-and-the-profitless-business-model-narrative

Eha, B. (2013). Amazon Gets Social, Buys Book Recommendation Site Goodreads. Entrepreneur. Retrieved 16 July 2015, from http://www.entrepreneur.com/article/226243

Amazon.com,. (2015). Amazon.com Help: Amazon.com Privacy Notice. Retrieved 16 July 2015, from http://www.amazon.com/gp/help/customer/display.html?nodeId=468496#GUID-A2C397AB-68FE-4592-B4A2-7550D73EEFD2__SECTION_467C686A137847768F44B619694D3F7C

Wunker, S. (2015). Long tail business models — Amazon on offense and defense. Newmarketsadvisors.com. Retrieved 16 July 2015, from http://www.newmarketsadvisors.com/blog/bid/36296/Long-tail-business-models-Amazon-on-offense-and-defense

Week One – e-Business Cases – Google

This week covered e-Business Cases, around three particular companies, Google, Amazon, and Netflix.

For Google, we looked at the products offered, which are stated at (Google.co.nz, 2015) include:

  • Search
  • Social
  • Mobile
  • Analytics
  • Cloud platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS).

(Google.com, 2015) states that Google’s mission is to:

Google’s mission is to organize the world’s information and make it universally accessible and useful.

Which is reflected in their broad product offering.

Google’s Business Model is primarily advertising, with (Investor.google.com, 2015) stating that for the first nine months of 2014, 90% of revenue came from advertising, and 10% coming from other sources such as the Google Cloud Platform.

Google had a big bet on social, with the creation of their Google Plus platform, which hasn’t been very successful notes (Denning, 2015), with the following quote:

The number of truly active users on Google+ is significantly less than 1% of the total 2.2 billion Google users

And has led to the fragmentation of Google Plus into discrete products such as Google Photos.

(Google.co.nz, 2015) states that Google captures information about you including:

  • Information you give them, such as profile information for Google Plus;
  • Information gathered through the use of their services like YouTube statistics and interactions with advertising;
  • Device information from the device you use to access their services;
  • Location information;
  • Cookies they’ve stored.

One of the key technologies of Google is the Search Engine. Google has a function called the Knowledge Graph, which (Google.co.nz, 2015) explains:

With the Knowledge Graph, Google can understand the difference, helping you more precisely express what you mean as you enter your search.

The Knowledge Graph is how Google can determine that when you search for weather, that when combined with your location, you want the results for the weather in Wellington for the next few days.

Of course, all sites look much the same on the Internet. So in order to provide trust to results, a social component is added, Recommendations through Google Plus, or other platforms like Yelp! for restaurant reviews. These Social recommendations allow users to determine if a site is trustworthy, with the number of users recommendations used a proxy about whether to trust a recommendation.

In the past, search engines competed with directories, including physical world equivalents such as the Yellow Pages. But directories only ever provide a static view towards looking up information at another site. Google with the Knowledge Graph has started to interpret queries, and return results directly without pointing to other websites. For example, searching for “convert 100 usd to nzd” returns back an actual conversion based on a conversion rate, rather than a directory, which would only direct you to a currency conversion website.

As (Investor.google.com, 2015) states, advertising is the big revenue earner at Google. Google has the attention of the world, and as (Solveforinteresting.com, 2015) notes:

Converting money to attention is simply advertising.

References:

Google.co.nz,. (2015). About Google – Products. Retrieved 16 July 2015, from https://www.google.co.nz/about/products/

Google.com,. (2015). About Google. Retrieved 16 July 2015, from http://www.google.com/about/

Investor.google.com,. (2015). Retrieved 16 July 2015, from https://investor.google.com/earnings/2014/Q3_google_earnings_tab6.html

Denning, S. (2015). Has Google+ Really Died?. Forbes. Retrieved 16 July 2015, from http://www.forbes.com/sites/stevedenning/2015/04/23/has-google-really-died/

Google.co.nz,. (2015). Privacy Policy – Privacy & Terms – Google. Retrieved 16 July 2015, from http://www.google.co.nz/policies/privacy/#infocollect

Google.co.nz,. (2015). Knowledge – Inside Search – Google. Retrieved 16 July 2015, from http://www.google.co.nz/search/about/insidesearch/features/search/knowledge.html

Solveforinteresting.com,. (2015). The three currencies of the online economy – Solve for Interesting. Retrieved 16 July 2015, from http://solveforinteresting.com/the-three-currencies-of-the-online-economy/

MSYS559-15B(NET) Blog for Waylon Kenning

As part of my Masters in Electronic Commerce, I’m taking a paper called MSYS559 – E-Business Technologies. Over the next 12 weeks I’ll be posting regular updates on this blog about my findings throughout the course.

IMG_3768But first, about myself. My name is Waylon Kenning, and I completed a Bachelor of Electronic Commerce from the University of Waikato in 2008. Seven years later (!), I’ve decided to take up the online Masters of Electronic Commerce. The reason for doing this masters is twofold:

  1. I study all these technologies in my day job, so why not consider them from an academic perspective, and gain a masters degree?
  2. Having a masters degree would make it easier to gain a Danish Working Visa, or to move to Germany and find a job there.

For this course I hope to gain a more critical understanding of some of the technologies that I’m exposed to day-to-day. I also look forward to bringing to the class my practical experiences with these technologies, and being able to have a critical discussion about them.

In my day job, I’m an Enterprise Architect at Contact Energy, a listed Energy Generator and Retailer (or Gentailer) in New Zealand. Things I’m considering in my day to day job are:

  • How we can use Cloud platforms such as Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service to either reduce our Cost-to-Serve (or Opex), and gain agility in our ability to deploy new technologies to meet the needs of the organisation;
  • How we can deploy mobile technologies to deliver better customer experiences, or to increase the safety of our employees, using tools such as Google Glass;
  • How we can start to process large amounts of customer information using big-data technologies such as in-memory databases to create insights into customer behaviour;
  • How we can take advantage of social channels like Twitter and Facebook, and integrate those into our Customer Relationship Management system, so we have a unified way of responding to customer interactions through multiple channels;
  • Understanding the cultural impacts of technology and their vendors, as we gain a better understanding of our SAP ERP and CRM platform.

I’ll be doing the course mostly from the privacy of my own apartment in Wellington. Occasionally I’ll be doing it from my desk at work throughout my lunch break, but typically I reserve the 9pm-12am slot of time to course work throughout the week.