#week10blog

Last Monday, we had our pitching for DropBy. Aljoe was the one to pitch for the team. He wasn’t that prepared in pitching, sir Mabula said he needs improvement on pitching but at least he did his best to explain about DropBy. We also presented Muznah’s finished low fidelity work for DropBy. By the end of our pitch, sir Mabulay reminded us to reach at least 100 customers to be validated by the end of the semester. 

 On Tuesday, on our scheduled class, we didn’t meet because it was a holiday but he told us that we will have a make-up class on Saturday instead. We didn’t do much on Wednesday and Thursday for the reason of having our proposal defense on those said dates. 

 By Friday afternoon, there was a seminar by Impact Hub Manila held on the Performing Arts Theatre (PAT). Sir encouraged us to attend this event. The Impact Hub Manila introduced two startups to us, Cropital and Eight Media. Cropital was founded by Ruel Amparo. It was a crowdfunding platform for farmers. He presented his pitch deck very well. He shared to us his inspiring story on how he started his startup. He said that he was once like us. So, he started by making the problem clear that there is a problem on about 11 million farmers in the Philippines. There was a lack of funding and low productivity. These two thing kept on going on a cycle. As a result, the farmer will be in debt. Then, he presented on how cropital works. Cropital works by 1. Assess risks 2. Crowdfund 3. Support and 4. Grow. He said that they made and an impact by empowering farmers, increase in productivity and income. Lastly, he told us about the lessons he learned throughout his journey. The only thing you know is that your plan is flawed. Find the flow and plan for failure is what he advised. We should also know how to define the meaning of mistake, stupidity, failure and losing.  He also said that we should toughen up. We have to know the inevitable and gain control by then. The next startup that was presented was Eight Media. Eight Media was founded by Gerald Castillo. He started by saying that he made a promise back in high school to never get employed. He shared to us the lessons he learned. He said that you should do what you love. What Gerald loves was tech and love for writing. He reminded us that business is about making money. Ideas are cheap, execution is the key. He stated a quote from Pablo Picasso, “Good artists copy, great artists steal.” He found a way and put his ideas into action. Then, the last lesson for him was to create your own luck. I didn’t quite understand of how eight media works but the important thing for me is that I learned his lessons on his journey of how he started. 

 On Saturday, I was a bit late for the class but Sir Mabulay talked about customer segments and different market types. He also discussed of the one case study on his validation board. It started first as electronic bicycle company that worked for 3 months selling the e-bike to students but only one e-bike was sold. There are four market types. These are existing market, re-segmented market, new market and clone market. The market that would most likely succeed is and existing market because the customers are known, the only problem you will be dealing is with your competitors. Among the four the least one to succeed is the new market because the customers are not known. It will take time to educate them about your product because you it is not easy for them to adapt fast. I learned that in making the validation board, you can’t always make it right. If you made a false assumption about the customer’s needs then you should consider pivoting. 

A week after our midterm pitching activity, since the slide decks we made and use has some correction to be change, we need to revise and finalize it. The first thing we did is we separated the problem encountered by the parents or students in looking for tutors and the problem of those individual who find it hard to look for part-time job for an additional income. The separation of the problems is due to the two-sided users of our mobile application. Another thing is how our application works is made into two parts: for the tutee and for tutors. Then for our business model, we provided the precise information so that people can understand it easily. Lastly for our competitive landscape that needs to be more comprehensive and it should give a clear comparison of our product to our competitions.

Before the boom of online engagement and Web 2.0, the natural method of data collection was through human input into records or computers. These days, any individual that participates online essentially creates their own data imprint; automatically recorded whenever they click on an ad, ‘like’ a Facebook page or enter a website. The ever-present role of the Internet in our everyday lives means data now grows by 2.5 quintillion bytes per day (Siegel, 2013, 2). The sum of this huge, growing resource can only properly be measured by what Siegal (2013, 3) describes as “machine learning”: the use of computers to quantify and process this data to develop new knowledge and capabilities, such as predictions. "Predictive Analytics" describes the method of using data mining to determine likely outcomes, which organisations use determine the likely outcome of their products and services. Examples include the prediction of mouse clicks for websites with pay per click advertisements (Siegal, 3), dating predictions for online sites such as Match.com; and the ability of companies such as Hewlett Packard (HP) to predict the chance of an employee’s 'flight risk’ (whether they will quit their job).

Our willingness to contribute data to companies like Facebook allow them to monitor personal usage and on-sell information to interested parties such as advertising agencies. Algorithms developed by Cambridge University and Microsoft allow Facebook to make predictions based on characteristics such as demoraphics, page likes, Facebook usage, location and language and purchasing activity (Cohen, 2014). This following video provides a really good simple explanation of the type of data Facebook mines and how.

Facebook advertisements and increasingly their ‘page suggestions’ (subtly weaved into News Feeds), are based on individual user characteristics. For example, both my housemates are females in their late twenties – their Facebook pages constantly include ads for weight loss products, bridal advertisements and dating websites with ‘single, faithful policemen’ in their local area: all products for which they would be classified as the target audience. I recently went kayaking and posted a status about it along with photos and ever since, I have received ads for kite surfing on the side of my home page.

Similarly, I recently changed by mobile data plan and ever since, there has been an advertisement for Vodaphone’s new data plan on the side of my page. It seems impossible Facebook could know the details of my phone contract, but perhaps the mere fact that I am on Facebook regularly means I am a good target audience for affordable data products.

Below is a snapshot of Facebook’s Data Use Policy. Whilst it is extremely simple and easy to read, it raises some questions like:

What does it actually mean that we own our data?

What legal avenues are available to enforce these rights?

Is there a way we can extract our data and move or save it elsewhere?

Policies such as this are helpful and important but often vague, and fail to give meaningful examples to users of what the actual consequences of their participation could be. Facebook has certainly faced criticised for this before.

For those that are concerned about the invasive accuracy of data mining and predictive analytics, one has to wonder whether it is that different from any sort of audience research. Television ratings and research have been used to sell audiences to advertisers for years. Classic methods of measurement include written viewing diaries and the people-meter (introduced in 1987), to track television consumption in the home (Sullivan, 2013). Both these methods rely on volunteer provision of information by participants. In many ways, data mining is not that different, except that it has extremely widespread participation and as a result, is conducted on a much grander scale. This can result in targeted, efficient advertising and information about products that consumers might actually enjoy or value.  

Whilst data mining is considered by many as a form of surveillance, it is important that participants educate themselves about how their data could be used, however companies such as Facebook must also ensure honest and transparent policy disclosure.

References:

Dara Kerr. 2013. "Facebook faces criticism over its privacy policy." CNET. September 5. Accessed May 12, 2014. http://www.cnet.com/au/news/facebook-faces-criticism-over-its-privacy-policy/ 

David Cohen. 2014. "Facebook Audience Insights gives marketers an overview of their potential audiences." Inside Facebook. May 8. Accessed May 12, 2014. http://www.insidefacebook.com/2014/05/08/facebook-audience-insights-gives-marketers-a-better-overview-of-their-potential-audiences/ 

Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.

Sullivan, John L. (2013), “Chapter 4: Media ratings and target marketing”, Media Audiences: Effects, Users, Institutions and Power, Los Angeles: Sage

Woodford, Darryl, April 24, 2014. “Week 9 – Big Data and Telemetrics.” Accessed May 13, 2014. http://blackboard.qut.edu.au/