My word of the day: Incubation – a learning experience from a WebMob Thailand’s meetup

It was my first experience to join a meetup group called WebMob Thailand, an open community group that has similar interest in web and mobile technology. The attendee was surprisingly large. I think it was well over 150 people and we were gathered in a medium-sized bar located just 100 meters from my office in Sukhumvit area.

There were 3 presenter during the gathering: Alpha Founders, a venture capitalist company that were looking for talents, new startup opportunity; Crowdonomic, a Singapore’s based crowdfunding company which shared their success story and also looking for talents, investors and startup opportunity; and the last was from, the biggest portal company in Thailand. They were sharing their success stories how they’ve increased the traffic site by 300% and they are also building mobile messaging application, WeChat, that uses geo-location base feature that let brands do marketing program and pinpoint sales promotions to a selected target of customers.

The attendees were not less interesting people than the speakers. Mostly are startup founders, creative designers, angel investors, mobile technologist and social media consultants.

Interestingly, there are similar topics from the presenters. The VCs, the founders and the senior technologist were talking about what is important in their organization; how to select the best talent; how to create a positive working culture; and how to create a product that is useful to the users.

There was 1 thing that I really learn from the gathering. One single word, which was my word of the day: incubation. The word was repeatedly mentioned by the speakers and somehow it struck me.

Incubation is a very common word in startup world. Incubation is a program designed to help startup to go to the next phase, from a small business to medium to a big company. It includes mentoring programs, funding assistance, business services, provide insight and experience and sharing their social network to other experts. According to Wikipedia, once a startup is done with the incubation period, they will be called incubator graduate, a successful completion of business incubation program to become a steady business for longer term.

In summary, my take for the word incubation is: mentoring, assistance and sharing.


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Predicting the reality show winner using big data: X-Factor/Britain Got Talent/American Idols

I wonder, using big data and predictive analytic, can we predict the winner of x-factor or American Idols from the start of their audition performance? I think we might have a good chance to predict the winner right away.

What if we could only have the information from their first performance, what should be the variables to be used in the predictive model? Here’s from what I could think of:

  • The voice: quantified timbre, energy and rhythm
  • Song selection: how popular the song was, type of music (pop/jazz/country)
  • The singer appearance: body mass, hair color, skin color, clothing, type of shoes, color contrast, etc (some of these variables might not be legal to use)
  • The early response from panel of judges: number of yes/no
  • Wisdom of the crowds: mentions at twitter, number goods vs bad sentiments, number of videos uploaded to YouTube, number of download from iTunes, etc.
  • Audience claps: number of decibels from audience claps

Once we have all of these variables, we might be able to predict the winner this coming season X-factor/American Idols/The voice.

However, if the model is able to predict, who should be benefited from this algorithm? The producer of the show could be one. Once he/she knows who should be the winner, he/she can play with the TV viewers’ emotions by altering some of the significant variables. The audience could be more attached if their favorite singer is about to lose and need more support. With more viewer’s getting more attached, the TB can have higher rating and higher advertising income.

Hear the KA-CHING?


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Predictive Modeling is Useless!

If you’re a modeler, you might say, “who the heck is this guy telling me that my precious thing is useless?” Wait a minute. I will explain later. If you’re new to this, let me tell you what predictive modeling is: It is the power to predict the future. Like a prophecy, except its using data, lots of them. Sounds cool? Yes, but it’s useless. Sad? Me too. I’m also a modeler.

Let me tell you a story. Once upon a time, I was preaching in front of senior management on how we could get more money. Using predictive model, it was proven that business could get cleaner leads, the right customer with higher likelihood to take our product. On paper, we could increase the revenue. It turns out, the customer response went double and revenue went sky rocket. However, 3 months after the implementation, it went back to pre-model performance, even worse. What has happened, Is predictive modeling become useless?

We dig a little deeper to find out what went wrong. What really happened during the implementation? It turned out something we did not realize happened during the leads distribution. Somehow, the sales team leader were all gone.

The sales team has hundreds of sales troops which were led by team leaders/supervisors. These team leaders have the expertise and experience to distribute the leads to the right sales person.Should the “good” leads be given to a more senior sales person or junior/trainees?This methods were proven to provide good result most of the time. These team leaders have vast sales experience that gives them instinct on how to utilize the leads to the max. It worked beautifully until the perfect storm hit the sales room floor. These team leaders were suddenly away at the same time due to various reason for the whole month.

Suddenly, out of nowhere most of the team leaders were gone. The leads were distributed randomly to all the sales troops. Suddenly, the predictive model is useless.

This is what we call sales bias. If the model was implemented through different channel: direct mail, SMS or email campaign, there will be no bias and the predictive model will work perfectly. In our case, the model still has dependency on outside component: the sales person. The customer response will depend on how good the sales in pitching the product. What should we do? Can we replace “team leader experience” and distribute the leads “independently”?

Yes there is a way. We could actually provide logic to give “score” to each sales team member. The score will depend on sales’ past performance: how good they are in converting the sales. This score will then be matched to the leads’ score. The question now, should the high-score-leads matched to the high-score-salesperson? The best answer is to test it and make the mix and matched to give better result.

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