marginally subversive

the problem with “traditional reach” in media planning…

… is that the metric does not capture the new ways that people get information.

Traditional media – such as TV, radio, presses, and outdoor media – have been traded on reach, GRPs, and frequency.  The most basic of dictums in media planning is “reach X% of people at least Y number of times in order to effect a change in their behavior, as cost-efficiently as possible”.

This statement is in essence based on a “trade-off” between the number of people one reaches and the information complexity that a medium can carry and deliver.  It is the foundation of media economics – and why TV ad lengths rarely go beyond 30s or 45s, why presses are limited to full-page/full-color, why radio ads are usually 30s or 60s, and why outdoor ads – which are probably the most wide-reaching and high-frequency media [and cost-efficient too!] – can’t communicate information details.

The ‘trade-off’ between complexity and reach is also the reason why media planners think of audiences in need of as constant “learning” through “effective-frequency”-based planning.

This is no longer true.

The tradeoff is based on several assumptions that have been taken to be reflective of reality in the marketing world:

1. People do not interact
2. Information flows in a linear manner from one person to another
3. The medium can be separated from the message or the information it carries
4. Nobody can “touch” the message – there is no ‘message decay’ or ‘information depreciation’
5. There is a constant “noise-to-signal” ratio from one person to another, from one time to another, from one place to another

What do we do?

It’s time to create a new currency for media planning.  It should be one that takes into account not just the number of people reached, but the information’s complexity and the ‘quality’ of the reception amongst its intended targets.  It must also be one that takes into account the possibility of “non-linear jumps” in the information transmission.

Could epidemiology point us to the new currency?

Potentially.

I am not an expert in epidemiology nor the models that epidemiologists employ in explaining and predicting how diseases, such as measles, the flu, and colds, evolve across time. It appears – from remnants of my university days that the models in epidemiology could potentially help in the creation of a new currency. 

One of the most common ones is the Susceptible-Exposed–Infected-Recovered [SEIR] model, which can also be extended as follows:

The equations are far more complicated than the usual Reach = GRPs x Average Frequency that we are used to.

And one could argue that “wait, that is a process – not a currency”.

And that is the point.

A currency for media planning needs to be based on how information flows – or at least how specific media or channels contribute to the process.

New wine in old wineskins…

Technology in the hands of consumers and of audiences have changed the way media are used and consumed.  It has also changed the ways information is transmitted, digested and internalized, and retransmitted.

We can’t forever be stuck with the old “GRP” and “reach” framework given the new realities that we are now facing in communications planning.

Reach is simple – but given the complexities we now face, it has become simplistic.  And incomplete.

It is time to shift.

what is "REAL" econometrics-based analytics?

A number of my friends have told me that the sexiest job in the world soon would be that of a statistician. 

I am not a statistician - I am a researcher who happened to believe that statistics is a tool that could be used to extract information and uncover insights from phenomena in the world.  I also happen to be interested in all things "numeratic": linear algebra, calculus, ordinary differential equations, operations research, queuing theory....

I have used statistics since day1 in my first job as a media strategist at Basic Ad, working on accounts such as McDonald's which demanded that their media plans be as accurate as possible (i.e., gotta use linear regression to predict with certainty the ratings) and which encouraged us to assess the relationship between their sales and advertising promotions.

Since then, I have not looked back.

A few years ago, I got introduced to the theory of econometrics - how statistical principles are used to explain economic phenomena, either in the macro- or microeconomic setting.  Then there was the growing interest in the marketing field towards using econometrics to justify media budgets and laydowns, as well as channel selections and all that.

It took not that long enough to realize that well, anybody who has the software and the patience to go through some wonderful things with numbers using matrices and spreadsheets, can do any model.  Heck, I even fell into the same trap - creating models that didn't make any sense.

That is when I realized that econometrics is all about - and perhaps, what statistical modeling on which econometrics is based: 

Creating, formulating, and coming up with equations relating two or three or more variables can be done by almost anyone who is interested.

It is like anybody who has the capacity to understand the saying (questionable, it may be) that "the past could be a good indicator of the future" and has the mathematical interest and bandwidth to create models can.

Take this case for example:

I am fascinated by the movements of the stock markets.  (And I am not an expert in finance nor in equity trading.)  Particularly, I was interested in the movement of the stock market.

I hypothesized - urged on by the current global economic crisis - that stock markets must be related one way or another:  That the Straits Times Index (STI) of Singapore should be affected by movements in the NASDAQ, the S&500, FTSE, VIX, and others. 

My hypothesis was that investors in the STI do take an interest in the overnight performance of these indicators: NASDAQ and the S&P500 were indicators for the US, FTSE is for the Euro markets, and VIX is an indicator that somehow reflects the "risk-attitudes/risk-expectations" of investors.

I therefore hypothesized that when the NASDAQ goes up and the S&P500 go up, and so do the FTSE - the STI would also go up.  But since investors are risk-averse, I also thought that the higher the VIX (and therefore the higher the expectations of risks and volatility in the future), it should impact the STI negatively. 

With this very little amount of information and hypothesis, I went on and crafted my models.

(Hold you criticisms of multicollinearity and autocorrelations...  For now, I am loosening the assumptions for my simple experiment.)

So... after several attempts, I got the following first model:

The model used the abovementioned weekly variables - FTSE, NASDAQ, S&P500, VIX - as predictors and ST as the variable.  (I also created an index - a NASDAQ/S&P500 index - since NASDAQ was more tech-orientated and SIngapore is more closely related to the tech-orientated indutries.)

Using data from 2000, the model (in red) seemed to track the data fairly well.  It was had several underpredictions at first, which attributed to the post-tech-bubble and 2001-2002 uncertainties.  The goodness-of-fit indicators were acceptable.  Adjusted R-squared is 0.721 - not bad!  The residuals were also fairly good.

(Again, hold off discussions of autocorrelations and multicollinearity.)

I figured, "Hmm, there must be a better way - could there be a better way?"  Can I increase my R-squared and other GIFs?

So after several (perhaps 25-30) iterations, I achieved the following:

 

Not too shabby, I said to myself - indeed, the GIF's increased.  Amongst them, an R-squared of 0.976 - for the movement of the STI.

(To the technicaly savvy: This was arrived using the same variables, but this time using weighted least squares.)

Obviously, I was giddy - I may just have modeled the movements of the STI in the past 9 years with simple mathematics and good old Microsoft Excel (and an add-in called XLSTAT).  I have derived the respective weights and parameters of each of the variables - and established their significance at an alpha-level of 0.01! (In some cases, some variables are actually less than 0.01!)

Then the realization crept in:

But modeling - econometrics, statistical modeling - is beyond creating models and equations.  It is creating explanations of why things have moved and acted that way in the past in order to clearly see the future.

And that was when I got into trouble.

 

So... the above are the most significant positive predictors.  The "FTSE x SP500" taken together explains a significant amount of volatility - not my hypothesis realy, but close enough.  But surprisingly, VIX also had a significant positive effect.  Not only that: If we multipled NASDAQ with VIX, there is an almost equally strong effect on the STI.  Squaring the NASDAQ and dividing it by the S&P500 also indicated that, well, it explained the changes in the STI - at an alpha level of less than 0.0001!

It became interesting when I started looking at what I called the "STI dampeners":

S&P500 and FYSE on their own were dampeners - and could shave off values in the STI.  Multipliying VIX with S&P500 also indicated a significant decline.  The most perplexing one was the last variable: VIX (risk-perceptions/expectations) x NASDAQ cumulatively divided by the S&P500 was the most significant dampener!

And again, modeling - econometrics, statistical modeling - is beyond creating models and equations.  It is creating explanations of why things have moved and acted that way in the past in order to clearly see the future.

And that was when I got into trouble: why?

Why would these relationships exist?  Why would the FTSE and the S&P500 be dampening the STI?  Don't we get cues from these two markets?  NASDAQ - on its own - was not a significant predictor, with t-statistic that is way below the acceptable range.

One could argue that it is the complexity thinking of investors - but still, why would VIX not be a dampener?  And it is only when one multiplied VIX with the S&P500 that I could accept that VIX is a dampener for the STI!

Econometrics is about why's - and not just the numbers and formulae.

I am no finance expert - I have a very basic understanding of the equity markets.  I have some knowledge of finance theory.  But I just cannot explain these numbers - and the formulae that I have derived.

If you ask me what the numbers and the formulas meant, I wouldn't be able to explain.  I could only say for certain that the suggested model from the equations tracked the real data well.  I could recite - in a non-monotonous voice (because I try to be not too boring when I present data) - the effects of each in terms of numbers.  And perhaps, even come up with an interesting visualization of the impacts of each of the factors and their interactions.

But ask me why VIX is positive on its own but now when it is multiplied with another variable - and I'd be lost.

Because econometrics - and statistical - modeling is all about uncovering the stories behind the numbers and explaining them.

Sure, because I now have the formulae for predicting the future of the STI given varying levels of the FTSE, the NASDAQ, the S&P500, and the VIX indices (predicting them altogether is another story), a software can be created, risk-tables associated with varying levels of the predictor indices can be programmed, and decisions can be made.  One can even make 'self-correcting', dynamically-adapting models from these models.

But as to why these formulae hold, I have no clue.

And that is where I would fail against the tenets of econometrics and statistical modeling:  The explanatory power, the stories, the why's and the how's and the how-come's.

That is where people's brains - and dare I say, people like me - come in.  Because after all the formulas have been established and tested, after all the parameters have been derived, and their individual and aggregate/combined effects on the predicted variable have been uncovered, the question then becomes "Why?"

The bottom-line:

• Whether we like it or not, theory and hypotheses before even the modeling starts is critical.
  
• Whether we like it or not, computers - for now - cannot take the place of creating explanations and stories.  Maybe, there will be a time when stories can be crafted by computers.  But for now, brains still matter.
  
• We need both people with technical and theoretical statistical and econometric background - but in the field of finance, of consumer behavior, of marketing... we also need experts who can ground these formulae into the real business world.  Into answers for real business questions.

To paraphrase Joyce Kilmer:

Models are made by fools like you and me
But only real integrative minds can make it gounded on reality.

---------------------------------------------------------------

PS. I got the data from Yahoo! Finance (http://finance.yahoo.com) which I think is one of the more friendler consumer-orientated finance sites on the web for all the indices from January 2000 till August 2009. 

In the first model, I used OLS, for the second model I used Weighted Least Squares with the last observations weighted 10x more than the older data. 

The residuals, by the way, for the first model are slightly heteroscedastic.  The second one fared better.  Both models, however, fail the Durbin-Watson Tests, which means they are autocorrelated at order 1.  The models also fail the more general Breusch-Godfrey Test, which means there are higher AC-orders.

My suggestion: use time-series if you are really interested.  ARIMA would do you good - and GARCH modeling.

Uncovering the Drivers of the Singapore Media Industry's Growth

Not a lot seems to have been written about the effect of the slowdown of the economy of Singapore on the media industry and its allied professions.  (Either that or I have been quite late with the news.)

In any case, I have decided to take matters into my own hands and looked at the trends in the media marketplace.  What I did is looked at the past four years in terms of advertising spends for those which have been monitored by Nielsen Media Research.

Here is what I saw:

Looking at the above trends, we a generally upward, linear trend in the way advertisers are investing in the media marketplace - though several things caught my attention:

1. There appears to be some volatility in the data since 2005.  There is some seasonality - particularly in the middle of the year during the summer holidays and towards the last quarter, we see a general uptrend. 
   
   On the opposite trend, we see that post-Chinese New Year, we see that investments in the media marketplace declined dramatically.  This post-New Year could very well a "recovery phase" after having concentrated moneys in the fourth-quarter and the weeks leading to the festive season.
2. As soon as we entered 2009, we began to see a deviation from this 'average' upward trendline. 
   
   If you look at the last data point, you will see that there is a significant deviation from the linear expectation for January and February 2009.

This led me to the question:  So what is causing these deviations?

The deviations for the first two months of 2009 - and perhaps, even the second half of 2008 - could very well be explained by the onset of the financial crisis and the recession.  But then again, the inquisitive - and quantitative-orientated - mind asked: "Can that be quantified?  Can the effects of the global and local slowdown be quantified?"

Together with some members of the UM Singapore team, we looked at possible explanations.  Using good old Microsoft(R) Excel, we came up with several models incorporating different variables that could explain the variations of the marketplace in the market.

After reviewing more than 10'000 combinations of economic indicators - such as GDP growth, seasonality, consumer price index (which we assumed to be an indication of demand and supply of basic commodities), retail sales and catering indices, and others from SingStat, arrived at a model that was 'good enough' and has explanatory power.

The model above compares the actual (in blue) and the modelled values.  The adjusted R-squared - an indication of how much variance could our model explain - amounted to almost 90% (0.889, to be exact).  In addition to this high adjusted R-squared, other goodness-of-fit indicators were also significant. 

(Visually, it was able to explain the significant decline in January and February 2009 - which is the most problematic data point.  There was one outlier that we had to sacrifice: June 2007.  It appears that June 2007 saw a significant increase that our model couldn't explain.)

As with all models, we wanted to go beyond pure fits.  We wanted explanations.  And we broke down the effects of each of the different components of the model.

I cannot divulge the entire model and the equation, but here is a snapshot of the model's breakdowns:

Several things to note from the analysis of the past four-years' advertising media investments in Singapore:

1. There is a general uptrend - as I have noted above.  We call this the innate momentum of the marketplace.  This innate momentum is significantly strong to drive the media industry upwards in the future.  Our tests indicate that even in the rest of 2009, this innate momentum could potentially be the base on which the market will build on.  Whilst it is on an uptrend, it explains only about 40% of the total movements in the marketplace's investments in traditional meda.

2. There is seasonality in the marketplace with each month contributing significantly.  In fact, if you looked at the last two months in the data set - January-February 2009 - it was the seasonality factor that held the fort together with the innate momentum that we have uncovered.

2a.Special events (which would not be a secret to those with keen eyes) would drive some uplifts - in addition to seasonality. 

3. There are two economic factors that we have uncovered that affect the media landscape's investments - one (the green one) accounted for almost 40% as well of the movements in the investments in the past five years.  The green economic factor was quite strong that it had 'spillover' effects up to two months.

The other one - in purple - had very limited effects on thye changes in the media industry.  If you look closely however, the purple factor could hold the industry on its own.  See, for example, August-October 2007: the green economic factor was on the decline, but the purple economic factor was contributing a positive impact on the total media investments.

The next question...:  What's going to happen next?

A very good question, really.

The next step is to identify what's going to happen in the future.  But remember: our models were based on economic indicators and at least one economic indicator accounted for about 40% of the data's movements.  That makes predicting the future of the media industry's size in 2009 and 2010 even more difficult.

But my team and I may just have fuond a way.

That will be for another day.

The Usual Caveats

These are based on my personal estimates and are not necessarily reflective of the company and brands that I work for.  The models - whilst acceptable from a statistical and econometric POV - are just models: they cannot explain everything.  And as we learned from the financial crisis that is now gripping the world, the past is not necessarily a good predictor of the future and hindsight is always 20/20.

These are what I personally call "descriptive models" - they describe what have happened in the past with the hope that we learn something.

I used Nielsen Media Research data on this - thank you very much.  In addition, the econometric modeling was done using a combination of pure manipulation of numbers on Microsoft Excel and a proprietary tool of UM/Initiative.

 

of oranges, correlations, copulas, and the credit crisis

(Image is from Wired.Com)

This is apparently the formula that got us into the credit mess in the US and now is threatening the entire world’s financial system. Developed by David Li, a Chinese-born New York banker, the formula came out of a “broken heart”. The hypothesis that David Li started with was this: People tend to die faster after the death of a beloved spouse.

With this simple formula - called copulas - Li was able to relate different variables and measure the intensity of each variables’ effect on one another.

A good example would be this: Let’s say you’re going to the supermarket. You look at an orange. In the normal world, you would see the price of the orange - let’s say 10USD (some expensive oranges from Japan - flown overnight, first-class, and straight from the orchards on the slopes of Mount Fuji… I am exaggerating).

That’s a pretty big sum of money for the simple pleasure of eating an orange.

There are risks involved in your purchasing that orange at 10USD: what if

(1) when you get home, it’s not as great as you expected it to be? I’d call this the “satisfaction risk”;

(2) when you get home, the orange tastes like a normal apple that you could have purchased at a lower price? I’d call this the “relativity risk”

(3) in spite of it being orangey and brightly-orange, it actually is rotten inside? “Real-freshness risk”

(4) you don’t get to eat it - and you had to postpone eating it… and this 10USD-orange will only last for only 12-24 hours after having been purchased? “Time-Effects Risks”.

In the world of copulas and correlations, this apple would have taken into account the different risk aspects. So in this world, you will see not only the price - 10USD per orange - but also descriptions of the risks. The 10USD pricing takes into account not just the price of produce but also the risks involved in your purchasing the orange.

Or better yet - and this is what happened with the CDOs, SIVs, and what-nots - you have different bunches of oranges priced differently to take into account their risk-profiles:

1. In this risk-world, you’ll have 10USD oranges - high-grade, minimal risks across all four risk-dimensions.

2. You’ll also have 8USD oranges - still high-grade, but perhaps there is a higher likelihood of it being not fulfilling the Real-freshness/Relativity Risks.

3. You’d also have 5USD oranges - which can guarantee risks on satisfaction, relativity, and real-freshness - but not necessarily the time-effects risks.

4. You’d also have 2USD oranges - which are “junk oranges” - high-risk, caveat-emptor, ‘junk’ oranges.

The idea behind it is that, the risks have been ‘dimensionalized’ and have been ‘measured and priced’ accordingly. (This is what ratings agencies - such as S&P, Moody’s, and Fitch - do.)

And another core tenet is that there is a market for these tranches of oranges - regardless of whether they are high-grade or junk. Why would anyone buy the junk ones? Well, remember the tenet “Without risks, there are no rewards” and that “The higher the risks, the higher the reward”. So even the junk ones can potentially reward a buyer with high returns.

And another thing: Because you have the copulas, you can even segment the 2USD oranges when you have bought them into different segments: If I bought 100 of the junk oranges, I’d be able to sub-divide them even further - say into 5 more sub-division - based on their risk profiles and resell them for a profit.

I could probably have 2USD oranges, 1.50USD oranges, 50cents, and 25cents.

(Remember: there’s always a market for everything in the market!)

This is what copulas have allowed financial engineers to do: peek into the risk profiles of each of the different oranges, put a dollar value into them, and sell (and resell) them.

The formulas essentially created for us a way to put a number to the correlations and co-relations between different variables, which at first are seemingly unrelated, put a dollar value to that risk, and package them and sell it to others.

I’d say this is pretty neat.

But what’s happened in the credit-crunch is different: You see, you were not just buying oranges - you were buying people’s debts. And the assumption was that people are going to be rational when it comes to paying their debts - particularly their home mortgages.

What was forgotten in the credit-crunch is that, people are not rational: we tend to move in herds - and we tend to move in response (logical or illogical) to news (real or unreal) that are broadcast in the media (which tend to be gloom and doom).

Essentially, people are not oranges. And that’s where the financial engineers failed.

(Disclosure and disclaimer: Much of this came from reading Wired.Com cover story. I am not an expert on risk management - I am currently studying risk management but I am not an expert. These are my interpretations - and my attempt to understand them. Copulas, having tried them in an exercise in a class, are I think a great tool. There is more to copulas than these, I am sure. Copulas are not - in themselves - good nor bad. They just are tools - and it is the people who use them and abuse them that make the difference - or not. Like the Black-Scholes Options-Pricing Model, it’s neither good nor bad. It is what it is - a set of formulas that can be used for good. Or for bad.)

Re-blogged from Experimentalist.

Singaporeans Bunker Down for the Global Financial Crisis

56% expect local economy to perform better than our neighbors

Singapore, November 6, 2008: The global financial crisis has become an omnipresent spectre for the people of Singapore. No one can escape the overwhelming media coverage of the billions of dollars being poured into the world’s financial systems by governments desperate to avoid a meltdown of their most fundamental institutions.

What does this all mean for Singaporeans? How do we feel about the crisis and what does it mean to us? What are we doing about it? What do we think will happen?

Universal McCann, a media communications agency, surveyed 400 people aged 20 to 50 years to find out some answers as we move towards the end of 2008. The survey was conducted between October 20 and 25^th.

Key results:

· 72% of the Singaporean residents expect the global economy to turn for the worse in the next six months. Only 12% were upbeat in their expectations of the global economy, believing that it will be better, and only 16% believed that it will remain the same.

· Their perspective on the local economy is pretty much correlated with this belief: 67% of the 400 Singaporeans aged 20-50 that were polled are of the belief that the local economy will also turn for the worse in the next 6 months, with only 19% believing that it will remain the same and the rest (14%) believing that it will be better.

· These negative perspectives are in line with people’s expectations of the local stock market’s performance: 68% believe that the local stock market is yet to hit rock-bottom, turning for the worse in the next six months, with only one in three (32%) believing that it will remain at least the same or grow.

· 71% say that job opportunities are going to be tighter in the next six months, and close to 60% believe prices of household and food items will be a challenge in the next 6 months.

· In the real-estate sector, 69% of people expect a decline in value in the next 6 months as the economic downturn takes its toll in Singapore.

 

Rays of Hope

While there is some negativity in the market, there are signs of confidence and hope among Singaporeans: About 64% expect their personal financial situation in the next six months to be the same or even better. Close to 70% are secure in their household financial situation, with opportunities of getting slightly better in the next six months.

In fact, Singaporeans believe that the local economy is better poised than our neighboring countries to weather the economic storm. More than half (56%) of Singaporeans surveyed believe the economy is going to fare better than its neighboring countries.

Against major economies such as the US and the UK, the epicenters of the global economic slowdown, only 21% believe that Singapore is going to perform worse than these economies. Twice this amount – 40% – believe that Singapore is going to fare better than these major economies.

 

Technorati Tags: Singapore,Economy,Economics,Global Crisis,Recession,Singaporeans,Universal McCann,Consulting

Against All Odds - Singaporeans, Risks, and the Future

 

Draft Version  

"As civilization has pushed forward, nature's vagaries (and randomness)
have mattered less and the decisions of people have mattered more"
– Peter L. Bernstein, Against the Gods: The Remarkable Story of Risk

“Life is full of risks anyway, why not take them?”
– Lindsay Lohan, actress

 

Against or for Risks: Where do we stand?

A number of hypotheses have been put forward – and fingers have been pointed and wagged – in the past few months on the cause of the financial slowdown that is now enveloping the world. A number of financial experts have zeroed in on previous Fed Chairman’s leadership; some have pointed fingers at the Democrats in the US for pushing for “cheap housing for every American”. Others – perhaps stung by the market – have even put forward the idea that “perhaps, this globalization-thing is not too good after all”. Still others are blaming corporate and individual greed and dishonesty.

But the reality remains: The slowdown is imminent – and at least for now, there seems to be no end in sight.

Peter L. Bernstein, author of Against the Gods, puts forward the thought that it is an amalgam of different factors, but if distilled, it all boils down to the availability of information – and humanity’s desire manage, control, and make sense of these information that are now made available. It is these two factors that have contributed significantly to the development of the discipline of ‘risk management’ – which in turn resulted to us believing that ‘we can profit from manipulating, spreading, and trading on risks’.

One interesting thing that Bernstein suggests is that, the very idea of being able to manage risk is perhaps – after all – a sign of our humanity. It is innate amongst us to ‘defeat the odds, manage the nature of things, and conquer that which cannot be explained’. There is perhaps some value to what Lindsay Lohan then said: “Life is full of risks, anyway; why not take them?”

Is this true amongst Singaporeans? Are Singaporeans intrinsically driven to take the risk, plunge, and then, live with the consequences? How have the recent events shaped their view of risks and the world? Have we become risk-averse – or are we still in the throes of our basic humanity, that which seeks to conquer the unconquerable?

 

Contradictory Beliefs on Risks?

In a recent study amongst 400 Singaporeans, aged 20 to 50, UM and UM|Consulting found that 91% believed that “success is being at the right place and at the right time” – indicating that a large majority of Singaporeans has a tendency to believe that success comes randomly. To be successful, one has just merely got to be at the right place at the right time. However, a similar percentage of Singaporeans also believed that “success is a matter of being hardworking and persistent in one’s goals” – which indicates that success has to be worked hard for and that it is within one’s control whether she becomes succeeds or not.

A contradiction began to emerge.

It didn’t help that 76% of the respondents believe that “there is such a thing called destiny”, whilst another 81% believing that “I am in control of my life”. Close to 80% believe that soulmates exist and that we are destined to find them – and yet, 79% also believe that “my close friends would call me rational”.

These seeming contradictions encouraged us to dig deeper into the attitudes and beliefs of Singaporeans about risks, destiny, and the future. We believed that there is more to these contradictions than what meets the eye – and that these are glimpses of what drives us as human beings specially in these risky times.

 

Against the Odds: Dissecting the Attitudes of Singaporeans
towards the Future and Risk

Four dimensions of attitudes towards risk-taking and the future were uncovered as UM and UM|Consulting dug deeper into the data.

The first dimension is Manipulative Rationalism. It characterized by cautious rationalism and pragmatism towards the future. It is not necessarily pessimistic – it is merely ‘anticipatory’. This dimension includes one’s level of involvement with how the future could evolve and being prepared for it by planning for it in a very rational, structured manner. Singaporeans who score high in this attitudinal dimension are big-picture thinkers. When their investments get hit, they will tend to relook at their financial goals and objectives, engage their financial planners, consider all the scenarios, and decide. They are cautious – and risks are taken in a very calculated manner. Individuals who have high Manipulative Rationalism scores attend to see the world and phenomena around them as “within my control”.

The second dimension is “Super”-Avoidance. This is characterized by a strong belief that whilst superstitions, luck, horoscopes, and randomness could wreak havoc in their lives and have an effect on their futures, all these could also be controlled, managed, and avoided. People who exhibit a high level of “Super”-Avoidance read their horoscopes regularly, and perhaps even consider auspicious dates and signs when investing. These behaviors could at first be seen as a lack of control over their environment. However, these ‘indicators’ of their possible futures are consulted so they can avoid potential hazards. Singaporeans with high “Super”-Avoidance scores balance their beliefs in superstitious and luck with control and planning.

A contrasting dimension, Deterministic Helplessness, is characterized by a strong belief that “everything is borne out of fate and is predetermined – and nothing can be done about it”. People with high levels of Deterministic Helplessness will tend to see the future as beyond their control because it has been predetermined and inevitable. When looking at the past, people with high scores on this dimension will believe that “things happened for a reason – and there was nothing that we could have done to avoid it”. There may be a sense of apathy towards the future and the past amongst people with high levels of Deterministic Helplessness.

Of the four dimensions uncovered, it is the Inherent Exuberance that seemed to characterize a high level of risk-acceptance and risk-taking, a strong desire for excitement, and a generally-optimistic disposition towards the life and the future. People with high scores in this dimension believe themselves to be destined for greater things – “if only I take the risks now”. They will also tend to consider themselves leaders rather followers – and will most likely take the proverbial bull by the horns. Rather than dwell on the past, people with high scores in this dimension understand the investment tenet that ‘with high returns come high risks’. And most likely, they will take on the risks – and positively expect the best.

We correlated the scores of Singaporeans polled on the four attitudinal dimensions towards risks with their respective ratings and expectations of 2008 and 2009. Those who scored high in Inherent Exuberance have higher expectations in 2009 and tended to see the current year with relatively more positive light. Those who have are far more cautious – with high scores in Manipulative Rationalism and “Super”-Avoidance – tended to have a more tentative viewpoint of 2009 and a slightly more negative view of 2008.

In fact, if we used the attitudinal dimensions’ scores as indicators of expectations for 2009, we see those with high Inherent Exuberance scores to see that everything is set to get better soon.

 

What do these all mean for Marketing Directors and
Communication Campaign Strategists?

What do you think?

 

 

Technorati Tags: Advertising,Singapore,Exuberance,Economics,Psychology,Social Psychology,Marketing,Strategic Planning

So are we really ready for the downturn?

I just finished some 'runs' (i.e., data processing) on the data that we have gathered from a poll of about 400 Sg respondents about their views about the next 6 months.  I would say we got some interesting results.  It's - for lack of a better term - interesting.  One may look at it and say "conflicting viewpoints" - but further analysis actually indicate that it all makes sense.

Anyway, if you're interested about what I am talking about, just ping me or leave a comment and I'll get you the stuff as soon as they are out.  I am still writing and writing and writing - whilst at the same time churning stats and tables and conducting tests.

Reality checks though...  This is a presentation from Sequoia Cap which VentureBeat.Com called "RIP: Good Times".  I honestly don't know much about finance - I am still studying my basics after having realized that I can't those frigging numbers - and I don't know much about macroeconomics, having focused on micro an behavioral economics in my electives in college.  So I really can't agree or disagree in indeed, it is "RIP" to good times.

Have a read through the presentation:

Sequoia Capital on startups and the economic downturn

View SlideShare presentation or Upload your own. (tags: depression recession)

 

Here are my thoughts:

• As I have said, I don't know much about economics.  But all I can say is there seems to be a lot of evidence cropping to suggest that we're in bad shape - regardless of where we are.
  
• The supposed benefits of globalization and global economies are still there - BUT so are the risks and the threats of globalization.  Friedman was right - the world is flat - and the thing is, with 'flatness' comes a faster 'negative contagion'.
  
• This is perhaps already apparent in all the things that have been said and done about the crisis: but we're talking about a system here.  It's not a "US problem" - it's not even "just a subprime" problem.  It's a systemic problem - that will require a solution that is integrated and isn't piecemeal.
  
  (Michael Porter, author of Competitive Strategy, has a great article on the latest issue of BusinessWeek.Com on economic strategies - and why the US needs one.  Worth a read for all CEOs and strategists.)
  
• The last slide is the real killer slide: GET REAL or GO HOME.  We need to acknolwedge that there is a mess.  The funny thing is, there are still camps of people who think that "this too shall pass".  Yes, in the long run, it will.  But Keynes said "In the long run, we'll all be dead!"
  
• In the mean-time, however, we need to placate the markets and the consumers.  The thing that really fuels all these is our humanity.  Our fears.  That instead of "things are going to be better tomorrow", it's "things are getting worse tomorrow..." 
  
  Now that's really being "Polyannaish" of me - but that's the only way to get out of the downward spiral: reinstill confidence in the market.  Reinstill confidence in the consumer.
  
  Easier said than done - considering that even banks don't trust each other - and the effectiveness of the bailout the Feds and the Treasury of the US have initiated is being questioned.
  
  But that's the only way to get out of this mess.

So is anybody immune?  How much longer will it take?  Will we still be in this rut next year?  And more importantly, will we still have our jobs?

Well, we'll see.

This is an interesting presentation picked up from SlideShare.Net about the subprime mess. A little rough - and definitely not meant for presentations using a projector. But hey, who cares? It's fun. And it's informative.

Subprime Primer

View SlideShare presentation or Upload your own. (tags: subprime mortgages)

Robert Shiller, economist and author, on the US Economy

Robert Shiller talks about the US economic trends and financial trends in the marketplace.  What interests me really is his point of view that these problems are largely because of "fundamental" inadequacies of the financial system when it comes to risk management impact not just the immediate financial and business world.

Oh well.