marginally subversive

The principles of Behavioural Economics can make communications work harder says Janet Hull, consultant head of marketing at the IPA..

via mediatel.co.uk

I am quite surprised that it is only now that marketing practitioners are actually asking the question whether behavioral economics, communications, and marketing are linked.

Hmmm. Isn't that obvious?

OK. Let's say for the sake of argument that a marketing practitioner is not familiar with "behavioral economics" and what it is.

But surely, one has studied in university basic economics - and what is economics? The study of the allocation of finite resources to optimize utility - however "utility" is measured (e.g., returns, production, revenues, production, happiness... ok, perhaps not the last one...)

Isn't it obvious then that economics has a place in marketing and in communications since everyday, we deal with decisions on how to optimize finite budgets to get the best returns on marketing moneys?

Now put in behavioral: That suggests... people. Individuals. Consumers.

Put the two together: study of people and individuals as consumers or audiences who are faced with options and are motivated to maximize their finite resources - money, time, attention, emotions - to generate the best returns.

I thought discrete choice theory, conjoint analysis, and similar techniques - and Bayesian statistics that allow for hierarchical and individual level analytics - are beginning to be a part of the world of marketing and communications.

Red herring or red hot? C'mon.

================================
What is behavioral economics?

From Wikipedia:

Behavioral economics is that branch of one,which deals with the study and application of analysis with scientific approach on social,emotional factors for understanding the consumers,investors and the market,and the resources.

The field is primarily concerned with the bounds of rationality (selfishness, self-control) of economic agents. Behavioral models typically integrate insights from psychology with neo-classical economic theory. Behavioral Finance has become the theoretical basis for technical analysis.

storytelling in a chart of circles and axes

via charts.jorgecamoes.com

(OK. For the sake of argument - but you'd see it won't really matter in answering the question, let's say the vertical axis is Market Penetration; the horizontal axis is Competitive Penetration; and the size of the bubble is the company's revenues. The one on the left has colors - let colors be the different geographic markets you're in.)

Which chart is better?

My answer: Depends.

Chart 1 gives me the entire story - although presenting it probably will be hellish, it would probably do good in a written report.

Chart 2 might be simplistic to some - but it highlights one and only one city. Most likely this would be the chart I'd use in a presentation to tell a story.

One thing I am learning - tell a story in bits and pieces without losing the plot. And never, ever be the slave of slides.

p

holistic enterprise-wide measurements and business performance

Measuring and improving employee retention is an increasingly vital challenge which is essential to the smooth function of “multi-location” operations that involve far flung managers and staff personnel. The power of key-driver analyses not only identifies what is important to improving employee performance; it provides a measurable basis for each business unit to target the specific areas that needed to improve. Not all management teams are equally good at communication, training, etc. The employee survey system that was developed lets managers know their real effectiveness on the important issues that impact employee performance, and how to improve the company-employee relationship.

via marketresearchbulletin.com

From www.marketresearchbulletin.com

The last couple of weeks, I have reading up on Enterprise Risk Management - ERM - in prep for my attempt at getting a certification in risk management from GARP and PRMIA next year. The goal of ERM is simple: to create a culture that acknowledges the fact that everyone in the company is a risk-manager.

This entry from MarketResearchBulettin.Com made me think: in addition to ERM - and perhaps, even prior to establishing an ERM - there must be an integrated, enterprise-wide measurement system that (well) integrates performance metrics, socialization metrics, "shared culture and values" metrics, financial metrics, project management and efficiency metrics, and others.

True: this will make life for CFOs and CROs harder.

But that may be the key to a sustainably profitable, positive-cashflow company.

On payment-by-results in the marketing world...

A couple of months ago, I presented to the leaders of the organization I am connected with.  The topic was simple: accountability in the marketing world and how we can be prepared for the emergence of payment-by-results.

Seth Godin now writes about "everyone gets paid on commission" - which at first glance, made me wince because the "C" word was sort of like anathema to me.

But after reading what he had to say about commissions, it was not entirely about "commissions" as we know it in the ad and marketing world:  it's about delivering results - and getting paid for it.

Ad agencies started out as "media space brokers" - and that's the reason, I believe, why they were called "agencies".  In the same manner that travel agents get a commission from the vendor (the airline, the hotel, the vacation packager) for every trip they book, ad agencies were also paid based on the amount of media space they book - it used to be 15% of the gross amount or 17.65% of the additional amount.

The name got stuck - in spite of the fact that ad agencies started to move towards "hour-/time-based" fees - which supposedly would allow agencies to recommend to "not advertise when the time is not right",  something that would have been unheard of in the world of commissions.

It is supposed to be something that should benefit both clients and their agency partners.

Now, we are moving towards payment by results:  Agencies get paid based on the results that they generate for the clients' business.

This - I believe - is what Mr. Godin is writing about: being paid for the results that you generate.

Photo from Corrie at Flickr

However, implementing this in the world of marketing and communications is that easy.  Here are some of the barriers that I can think of (and feel free to add more):

1. Agencies think that they have no control over other factors that affect sales (or profits or any other business metric/KPI).  A valid reasoning - if agencies are supposed to be measure on sales or profits, it follows that they should have influence over how the business' marketing communications and marketing operations programs should be created.  And not all clients are ready to give agencies that seat on the decision-making table.

Clients have yet to truly realize the value (yes, in spite of the history of advertising) that agencies can bring to their business - and most clients still see agencies as "go-for-it/do-it" companies.

The onus is on both agencies and clients:  Agencies have to demonstrate that they are willing to be measured against sales and real business metrics (and circumvent this thinking and #2 below) - and clients will have to give agencies far more than just a "go-for" status and be real business partners with them, in the same way that they treat consultants from McKinsey, Booz, and Accenture.

2. Agencies think that their business is not about driving sales - but influencing people to buy by reminding, encouraging, and inspiring consumers to buy.  Which I think is not entirely valid. 

Sure, some campaign briefs talk about creating awareness and driving engagement.  But no marketeer in her right mind - and definitely no CEO in her right mind - would want to spend money just to create awareness for her brand and drive 'differentiating brand experiences' that do not get translated to sales and profits.

Agencies need to rewire their brains - and accept the fact that all the things that we do from 9 to 5 (or beyond) point to one thing: driving sales, revenues, and profits for our clients' businesses.

Sure.  Getting there entails creating awareness and driving engagement and loyalty to a brand.  But what use is awareness and engagement and loyalty if it does not translate to sales and/or profits?

Marketing investments are (1) expense lines on the clients' balance sheet - that is a fact that we have to accept, and (2) investments - and therefore ought to deliver returns that would allow the client to further her business.

[There is some truth - of course - to this objection.  "If a client wants to decide on whether to pursue a geographic expansion or not, can an agency help?"  "If a client wants to do pricing promotions with key retail accounts, does the agency have the expertise to advise?"  "If a client wants to create a new product with R&D, can agencies advise?". 

My response: Agencies should be able to do so.  If the expertise doesn't exist, then agencies ought to resign the business - after all, agencies ought to know the business they are managing inside and out.]

3. Clients are not sure what metrics they should be measuring.  Should clients be measuring revenues?  Number of transactions?  Value per transaction?  Profits?

I think part of the reason why we never got to really implementing payment by results is because clients themselves are not sure what to measure in order to judge their agencies.

My response: It depends on your business model and the short- and long-term goals of the business.

Dell - I would reckon - would be very much interested in immediate response per campaign in the form of number of transactions and value per transaction, because their business model is built on JIT deliveries to consumer and efficient corp-to-consumer relationships.  Microsoft's could be very different.

P&G, Unilever, Coke and PepsiCo brands could very well be focused on immediate short-term sales - whilst at the same time ensuring long-term business goals are reached. 

Immediate response to and sales relationships with promotions (price-offs, new packaging, new innovations, brand extensions) amongst consumers AND amongst key retail accounts are probably the best metrics for short-term effects.

For some, it's about profitability - ensuring that they are profitable, since sometimes, revenues and profits are not entirely linear.

Whatever it is, clients - and their marketing and finance teams - ought to start thinking what metric they should be monitoring - and judging their agencies on.

I believe marketing needs to change - and the current economic scenario has hastened that need for change.  The area of marketing accountability needs to be fully entrenched in each and every agency and client organization. 

That means embracing what Seth Godin is suggesting:  Everyone gets paid on commission - on results.

Adquants and Creativity: Opposite sides?

(This post is inspired by an article on CNNMoney.Com on adquants.)

A lot of people think that people are either predominantly right- or left-brained.

If one is predominantly right-brained, he or she is more likely to be creative, more "feeling"-orientated, more open to new things, and more open to the possibility of the absence of structures.  Organized chaos, according to common wisdom, is their mantra.

The left-brain orientated ones are far more structure-driven, more about equations and proofs and evidences, more about organization - less about chaos.  For left-brain orientated individuals, they seek evidence and proof more than hunches and gut-feeling.This belief I think is at the root of the "adquants versus creatives" debate in advertising.

Creativity demands an openness to possibilities and connections that no one ever thought existed.  For that to happen, it is believed that one has to be as "right-brain"-oriented as possible.

But these challenging economic times demand that marketeers be prudent and judicious in their investments of marketing dollars.  The "left-brains" then come in - because they are the ones who have the numbers, the scenarios, the 'evidence' whether certain decisions are are logical and thoroughly-researched, and would increase the probability of success.

Is there - however - a middle way?  Can adquants and the creative types co-exist?

My answer is yes.

And it goes beyond more than just making the two sides be in one room, in one department, or in one meeting.

Both will have to learn from each other in their quest for an effective marketing campaign.

• Adquants have to look at the ability of the creative types to create stories out of numbers - to weave stories based on numbers, and understand - beyond t- and F-tests, beyond eigenvalues and eigenfactors, beyond regression lines and probability - the impact of their formulas and equations.
  
  Adquants need to adopt the ‘right-brain-ness’ of the creative types to go beyond stat tables, the software results, spreadsheets and simulations, and all the crunched numbers to answer questions that creatives have also been tasked to answer.
  
• Creative types have to look at the ability of adquants to dissect problems and to work on a "question-hypothesis-test-course-correct" basis, and be open to possibilities that may seem to be "uncreative".  They will have to learn the structures, limitations, and findings that adquants create out of their inquiry of the world (from a numeric perspective) – and move within it.
  
  When creative types create solutions within the parameters that adquants may have uncovered – and know the parameters deeply so they can be incorporated into a campaign (and broken, if necessary), that’s when they will have worked together effectively.

The greatest challenge in the adworld today is not merely to marry the two disciplines - it is to marry the two mindsets that define those disciplines.

This reminded me of a conversation I had with a friend of mine from college on phenomenology, something that I think both adquants and creatives are very much involved in:

In phenomenology, you are essentially trying to explain something possibly unexplainable as objectively as you can whilst admitting that your mind cannot be perfectly objective.  To do this, you break down the phenomenon into its pieces – you create abstractions – and you understand each piece individually and how each relates with other pieces.  Then after you’re done, you bring it all together again – knowing that even if you have done so to the utmost of your ability, there is still more of that phenomenon that you cannot – and perhaps, never will – understand.

That is what I believe is necessary for adquants and creatives to coexist, work together, and be better thinkers and solutions-providers: the ability to think within – and outside – the parameters their expertise demand.

Photo from Quasimondo on Flickr.Com

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.

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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.

of risks and rewards in marketing and advertising

The more you risk, the higher the reward:
Realistic or not?

We've heard that adage so many times.  I myself have used that in trying to convince clients to take on a new idea that I deem to be groundbreaking, creative, and would be a "first of its kind" execution.

But my viewpoint has changed.

After having learned more about risks and its management and mitigation (albeit from a financial perspective), I changed my mind.

It is not true that the more you risk, the higher the reward.

I am not sure who coined this - but I am guessing finance people. 

What is interesting is that it's also being used by creative executives (and media planners, like myself) to sell concepts that are rather risque and controversial - all under the guise that "you can't bore your consumers into buying your product - you can only excite them".  (I believe that's a paraphrase the great David Ogilvy.)

But the model - as depicted above - has some problems: 

1. It assumes that for every unit of risk that a client takes, it generates a commensurate amount of return to the client.

It may not be a one-to-one relationship - but it is linear nonetheless:  for every 1 unit of risk you take, you get some unit of reward.

I don't think that's realistic.

There must be a point beyond which taking on more risks won't generate as much returns or potentials for rewards.

2. It also assumes that the more risks you take, the expected the returns ought to keep on coming - so one should keep on taking risks. 

So if the amounnt of risks that a client takes approaches infinity, the rewards also should approach infinity.

Again, I don't think that's realistic. 

There's only so much "reward" that one can get - unless one believes in The Secret, which states that "there is an infinity of abundance out there".

A more realistic proposition:
Risk and Potential Rewards are 'hyperbolic'

OK.  Before you tune out, let me visually show you what I mean:

 

I believe that the above more accurately represents the relationship between taking risks and the possibility of a reward or returns on investments.

Let's break down the possibilities in this visual representation:

1. If you don't risk, you don't get anything.

The curve starts at zero - which means, if you don't take a risk, you won't get anything.  I think we can all agree with that one.

2. The relationship between risks and rewards/returns is not linear but is a curve that is similar to the "diminishing returns curve".

Focus on the top half of the figure: There must be a point beyond which taking on more risks won't generate any additional potential for return.  There is a 'decreasing efficiency' or 'decreasing risk-to-reward conversion'.

Let me illustrate:

If I took on some "b" risks ("b" being a number), the returns I get would be much higher than if I had taken some "a" risks.  However, if I took on "d" risks, the returns would not that different had I taken only "b" risks.

That's the law of diminishing returns - nothing lasts and goes on forever, basically

3. For every risk one takes - regardless of the amount - there is an equivalent possibility of failure.

The first, linear model assumes that if you take risks - there is no possibility of loss, no possibility of a backlash.  It's all good - and it lasts forever!

This model, however, acknowledges the real meaning of risk:  to take risks - any amount of risk - means being open to both possibilities of failure and of success.

If I took on "a" risks, the possibility of success is there - but so is the possibility of failure.  If I took on "d" risks, the possibility of success is also there - but so is the possibility of failure. 

The possibilities between success and failure may be imbalanced - but that is not the point:  The point is, for every risk, there is a possibility of success and the possibility of failure - simultaneously.  Otherwise, it is not risk.

(In this visual diagram, I have made the possibility of success a mirror image of the possibility of failure.  But it may not necessarily be so.)

So what do these have got to do
with marketing and advertising?

Let's stop the idea that "the more risks you take, the more rewards you get so let's get creative..."  I think it is irresponsible of ad and marketing professionals to recommend this approach: There are no guarantees that if you risked a lot, you'll get as much as what you risked.

Let's also start thinking in terms of risks in advertising and marketing:  If we are to elevate marketing discussions to be more than just "advertising and promotions", we need to accept, take on, and include in our thinking and philosophies the concepts of risks.

When we talk of "ROI" or "accountability" or "ROMI", we need to talk about not just "thresholds, creativity, optimal budgets" - but also risks.

So what's next?

The concept of risk may have originated from finance.  In fact, the charts above are from finance's portfolio management theories.

It's time that we treated marketing investments as investments.

It's time for clients to start asking their agencies and 'media brokers' to think like real brokers and financial analysts and consultants.  It's time that agencies to start thinking in terms of investments - in the real sense of the word invesment.

Word of Mouth, Social Media, and Marketing Planning

We are inundated by the supposed strengths of social networks and the emergence of web2.0 technologies that facilitate participatory and 2way conversations.  Facebook, Twitter, LinkedIn, blogservices, and the like have all emerged as leaders in the area of social media.  Windows Live is not too far off – and Google’s services (e.g., Youtube) in themselves have social-media characteristics.

But there is one thing that we are missing in all these discussions:  Social networks have always been around.  It is just that digital technology has emphasized its importance and has forced marketers and planners such as ourselves to acknowledge its significance.

We’ve also tried to measure “Word of Mouth” as if it were something similar to TV, radio, press, digital, and advertising in general.  But there is one key difference:  WOM is the voice of the consumer.  It is a response that is elicited from their holistic experiences of the brand – from a brand’s advertising messages, marketing communications, and up to its delivery of its supposed promise.

True, it is in itself a medium: If I tell you that Coke is the best soft-drink in the world and that I cannot live without a Coke (aside: I can J), I have turned myself into a medium.

But Coke can’t ‘control’ me – as it can control its advertising on TV, presses, and even digital media.  The decision to speak of Coke – positively or negatively – resides with me, the consumer.

My 2cents’: Social media facilitates word-of-mouth.  And so do other media that in aggregate, create a differentiated brand experience.  You can – perhaps – plan, control, manage, measure and deploy social media plans.  But WOM is something else. 

As one of my colleagues once mentioned: “WOM is a result…”

I would add that it is an interesting, important, and critical medium/channel – but we still have a lot to learn about it.  But the most critical thing to keep in mind is: It is the consumers’ voice, not ours.  And hence, we do not own it.

"Is media planning art or science?"

When I started working in advertising, one of the questions that my boss - Hermie - used to ask the team was "Is media planning an art or a science?"

The question is - in spite of its simplicity - profound because it affected the way we think and the way we approach media planning:

If it were pure science, then anything and everything that the computer, the surveys, the algorithms spewed out - whether they were ratings, optimizers, or indices - were to be followed.  All arguments would be centered on which one was cheaper and more effective - and which was delivered the better measurable value.

Even when it came to targeting, it was all about identifying the profitable target demographics - those who were most likely to buy the product - based on research that was conducted by specialists in the field of data-gathering.

But where does that leave creativity?

That "umph" that makes a media plan for a certain brand its own unique property.  (After all, media plans - and communication plans - are desigend to address a specific brand problem.)

Where does that come in?

On the other hand...

If it were pure art, then what's the point of subscribing to countless research and validating/cross-validating research on viewing and reading habits of audiences?  What was the point of identifying the "zapping" threshold (a big issue back then - otherwise known as "channel surfing") for consumers?  What was the point of quantifying "involvement" and putting it as a criterion for selection of TV programs?

If it were pure art, then what is the point of understanding the response of consumers to advertising - either in the form of recalling the ad the day after it was first aired and a few weeks after or of actually trekking down to the stores and picking one up off the shelves?

If it were art, then everything becomes subjective...

That's when I started thinking that media planning and communications planning were not pure art - neither was planning pure science.

It was a careful balance between art and science.

Science provides the structure and the discipline - the starting point.  Art provides the weaving of a story out of the numbers.

Science provides the "rules" - that once understood can be bent and broken as and when necessary through the principles of art.

Art provides the inspiration and the hypotheses - science provides the currency to measure whether a certain hypothesis holds water or not.

I used to say in trainings that I conduct with teams that a media planner is an amalgam of so many professions and disciplines:  They are psychologists, sociologists, social observers, experimentalists, group therapists, psychoanalysts, storytellers, and design-/experience-creators.  At the same time, they ought to be economists, auditors, accountants, statisticians, researchers, and eyeball- and transaction-counters.

The golden mean.

The yin and the yang.

The balance.

That's where great campaigns - I think - come from.

Thanks to Woooody on Flickr for the photo

Remaking (and remarketing) marketing...

Seth Godin wrote a very strong blog-entry on marketeers, aptly titled "Poisoning the Well".  Here's what he has got to say about marketeers:

Marketers have spammed, lied, deceived, cluttered and ripped us off for so long, we're sick of it.

Which means that even if you have a really good reason, no, you can't call me on the phone. Which means that even if it's really important, no, I'm not going to read the instructions. Which means that god forbid you try to email me something I didn't ask for... you're trashed. It's so fashionable to be skeptical now that no one believes you if you attempt to do something for the right reasons.

Selfish short-sighted marketers ruined it for all of us. The only way out, I think, is for a few marketers to so overwhelm the market with long-term, generous marketing that we have no choice but to start paying attention again.

I would say amen to that.

This morning, I opened my mailbox - and sellers of condominium units and properties in Singapore have decided to rename me as "The Resident".  Sorry.  I didn't even bother opening those up.  If you really want me to consider your offering, at least address me properly.  I am not "The Resident" - I am not "Mr. Resident".

A couple of days ago, I had a phone call from a card company - asking me if I wanted to get a new card with them.  The problem was, I already had a card with them.  And in case they haven't noticed, I am quite OK with their services since I use their card several times in a month.  The funng thing? As soon as the telemarketer heard that I already had a card, she just went "Ah, OK.  Thanks.  Goodbye".

Hello?

And spam: The trend these days is "text/SMS spamming".  From my bank.  From my phone service provider.  From my cable TV provider.  From the bookstore.  From the skincare brand that I tried for the sake of trying.  From some company whose services I don't even know.

OK.

These are indeed tough times.

But these marketeers - and their brand managers - need to understand that in these tough times, it is also a lot easier to piss your current clients off.

Image From Feureau on Flickr.Com - Thanks!