OK our latest installment to Sony Football Asia – introducing the KICKATHON. Can you kick your way to South Africa? Well if you can there are some 2010 World Cup tickets in it for you – check it out here.

Wouldn’t it be great if we can one day predict the brand of car our customers will buy when they are at their peak, what brand of cornflakes they would eat every Sunday morning, when they will watch another 2012/Independence Day-type movie, right down to what gender of baby they are likely to have when they marry? “Predicting the unpredictable, quantifying the unquantifiable”, says Dogbert the guru*.  But before we are able to fully achieve that in the twilight years, lets see how we can predict the future with the pre-historical method of predictive analytics, summarized in eight easy steps (inspired by Dogbert).

1.) Use a previous campaign or a test as your base.

Say for example you need a conversion of 1000 respondents, and the industry/benchmark average is 2%, then we probably need a base of 50,000 to achieve your target or do an adequate test.

2.) Add geographic, demographic, psychographic & behavioural data to your base

Both the responders and the non-responders. Who wants to be known as Segment A when we can be described more adequately and be given a nice personality.

3.) Know what are all the possible ways (and the most effective way) of reaching your base

We do have mobile phones and a life in the social space.

4.) Test and Control

Divide your data into at least 2 groups everytime, equally. Test group carries the hypothesis, control group validates.

5.) Trash the anomalies

Don’t you hate it when you thought one of your content pillar is doing extremely well in terms of average time spent and when you deep dive into the data you realise that it’s some idiot who left the computer on with your website in the browser running throughout the lonely night? Discard the outliers, be it purchase patterns or web patterns.

6.) Design your modeling framework

We will usually start out with a typical multiple regression model before we move on the the more sophisticated models like CHAID etc. “A regression is an equation that describes the relationship between a dependent variable and more than one independant variable”.  Statistical definition of dependant variable connotes the action/consequence that will be influenced by how you set up the test environment, and indenpendant variable as the components that will not be influenced (e.g. demographic, geographic, behavioural data).

7.) Grade and weigh each variable and develop an algorithm

This step will help you in shortlisting the most important variables that will influence your results (e.g. income, age, family size, etc). One will probably need a PHD in statistics and a SAS or SPSS software to run a regression model and weigh the different variables, and to finally develop an algorithm ranking the deciles.

8.) Score the validation group

If the predicted results derived from the algorithm is a close match to your previous campaign/test, then  the algorithm developed will be useful to predict future campaigns. It should also help you identify the target segment more likely to respond to your campaign through the scoring exercise.

If all else fails, hire a consultant to do the work while you make yourself another cuppa! (*highly recommended)

Last but not least, meet Dogbert, the data guru.

An article on PC World: “3D TV, HTML5, video over Wi-Fi, superfast USB, and mobile “augmented reality” will emerge as breakthrough technologies in the next few years. Here’s a preview of what they do and how they work.” Read on.

A great campaign for VW in Europe. It really demonstrates the power of a rewarding interaction – something so powerful such as giving something first (in this case happiness), instead of a marketing message will often result in a change of behavior far more greater then expected.

From NY TIMES: Kristen Nagy, an 18-year-old from Sparta, N.J., sends and receives 500 text messages a day. But she never uses Twitter, even though it publishes similar snippets of conversations and observations.

“I just think it’s weird and I don’t feel like everyone needs to know what I’m doing every second of my life,” she said. Read more.

Congratulations to all the HP Touch team! We just pulled in two awards at the New York Festivals, a respectable Bronze and Silver. I will save you all our acceptance speech : ) Well done team, client and partners.

View the work: http://www.agency-showcase.com/hp_emo/hp_microsite.html

• Do you like looking at numbers?
• Do you think that you understand human behaviour?
• Do you keep up with the latest technological and marketing trends?

If your answer to the above three questions is a resounding “YES!”, then we have a job for you! XM Asia Pacific is hiring a marketing analyst for the Planning and Analytics department. We are looking for someone who can derive meaningful understanding of how people interacted and behaved with our digital campaigns.
Read the rest of this entry »

Infocomm Development Authority (IDA) of Singapore continuous effort to help small and medium enterprises (SME) to move up the value chain through technology adoption and advancing in the digital world. The following PDF provide details on how SME can benefit from the various affordable infocomm packages.

If you runs a SME, clieck here to find out more.

Since its inception, XM Asia Pacific has always preached about making use of data to improve on the final product that we deliver to our clients. So when we recently revamped our corporate website, we also made sure that we were collecting data about the use of its content. By doing so, the team can accomplish a number of goals, but the main one is that we have a better idea of the information that you, our readers, want from us.

To keep things simple, we’ve implemented Google Analytics on our corporate site and this blog. But beyond adding in the few lines of code, we’ve also customised our setup so that we can derive greater insights from our audiences. After all, a good carpenter is judged not by the tools that he uses, but how he makes use of those tools.

In future entries, I plan to demonstrate how even a free and easy-to-use tool like Google Analytics (or “gee eh”, as we like to call it internally) can be used to answer some challenging questions. And I hope you’ll join me in this journey of discovery of Web Analytics.

This entry was written by Balasingam-Chow Yu Hui. He has worked as a Marketing Analyst at XM Asia Pacific since 2006.

You may remember him as author of “What is Web Analytics?” or from within the Singaporean social media circles. Or you may have seen his musings at his blog, Yuhui’s Blogger, Twitter, or at Facebook. When he’s not working on Web Analytics, he can be found analysing how to improve his performance at Mario Kart Wii.