marginally subversive

Planning for Marketing? Think Finance - Think Lifetime Value

via www.youtube.com

Marketing has borrowed quite extensively from Finance in the last 10 years.

As marketeers, we've started talking about returns on investments - finding the "biggest bang for our buck".

Some (like my former team in a previous company) have started talking about risk management procedures - not just in managing contracts (and employing "options"-like buying techniques), but also in delivering gross impressions and shares.

All these are well and good.

Perhaps this is one way of the marketing department proving themselves to the finance department - an attempt to say that 'we are more than just an expense line on the balance sheet'.

But as marketeers and suport-teams of the marketing department, we have got to do more.

Employing evidence-based marketing, metrics-based objective-setting and results evaluation, real profitability analyses that take into account present and future value of marketing investments --- all these need to be more apparent in the marketing department's language and in the language of its partners in the advertising, media, promotions, PR, research, and digital space.

on business objectives and returns on investments: QUANTIFY IT!

If business objectives are not quantified - it's nothing but a set of big, hairy audacious goals.

If RoI is not quantified - it's nothing but useless chest-thumping, "I-saved-the-company/world-all-by-myself" denial of what's real.

And oh - you can't set business objectives unless you know where you currently are - if your business sucks now, it doesn't help if you deny that it sucks or 'beautify' it with "yes, we suck but...".  No "but's" about sucking, dude.  

If you suck, you suck.  Period.  Admit it.  Move on.  Set new directions - real, realistic directions.

 

'cause we are living in an "algorithmic" world...

 

What is an ALGORITHM?  From Wikipedia: 

In mathematics and computer science, an algorithm (ⁱ/ˈælɡərɪðəm/) is an effective method expressed as a finite list^[1] of well-defined instructions^[2] for calculating a function.^[3] Algorithms are used for calculation, data processing, and automated reasoning. In simple words an algorithm is a step-by-step procedure for calculations.

 When will marketing planning and decision-making be replaced by algorithms?

With the explosion of data and the continued emergence of analytics as part of marketing planning, algorithms are not too far off in the future of marketing.

Will people be replaced by algorithms?  I don't believe so.

Algorithms and any decision-making process (which is what algorithms are geared towards) will not be accountable for results - people are the only entities who can be accountable for choices that they make for themselves and for others.

 

new consumer-media-brand landscape demands new marketing metrics

"New" is used loosely above.  Social media have always been around - technology just amplified it by empowering people to connect and communicate with each other more effectively.

With this "new" trend came problems for marketeers: "How do we use them?"

Some have jumped on the bandwagon by creating Twitter accounts and Facebook pages and Groupon promos, measuring retweets, reblogs, and "likes".

But these metrics are just skimming off the surface, really.

There is more to The Social than these metrics.

Jeremy Owyang of Altimeter and John Lovett of WebAnalyticsDemystified shared this presentation which I thought was an excellent start towards creating an integrated approach to measuring the effects of The Social on brands' and their businesses. 

Oh - and just for the fun of it, let me state that "digital" doesn't mean "search-strategies" only anymore. ":D

 

 

Social Marketing Analytics

View more presentations from Jeremiah Owyang

of chickens and eggs: is there a definitive answer?

I had a very interesting conversation with a good friend a couple of weeks ago about causation and inferring causal relationships haphazardly.

One of the questions that stuck in my head was this:

Are big companies big because they advertise a lot?  
Or do they advertise a lot because they are big?

Sounds like a chicken or the egg situation: which came first, the chicken or the egg?

(My friend thinks it's neither - it's the archaeopteryx.  To which I argued, "That was a bird." To which she said, "Nope, that's the ancestors of birds." ... I have weird friends, I know.)

But it is something worth pondering - because at the root of this question is the question of causality.

Causality is supposed to be simple:  

If A happens, B happens.  
Without A, B does not happen.
Therefore, A causes B.

Or is it?

What if there is a lurking variable C that is somehow related to both A and B, such that:

If A happens, C happens.
If C happens, B happens.
If A does not happen, C does not happen.
If C does not happen, then B does not happen. 

Can we still consider arrive the same conclusion that "A causes B"?

 

Let's make it even more complicated:

If A and C happen, B happens.
If A happens, B does not happen.
If C happens, B does not happen.
If A and C don't happen, B does not happen.
Therefore A and C happening cause B happening.

 But what if A and C are themselves related somehow?  Worse, what if A causes C?  Or what if there was another variable D that we have not considered that is making the presence-absence of A and of C?  Can we still arrive at the same conclusion?

I am no logician.  And I am pretty sure that there is an answer to these.  

I can already hear my psych professor going "Do a proper experiment in a laboratory setting eliminating all potential confounding variables, and measure properly - and do it several times - and get others to do it several times too".  But sometimes, experiments are not easy to do.

Even the most stringent of experiments still tend to have some flaw in it.

So what now?

All I can say, for now, is - 

And for now, I have no answer (because my brains are swimming in some melatonergic medicine).

 

 

 

 

customer lifetime value and media investment lifetime value

From good, old Wikipedia:

In marketing, customer lifetime value (CLV), lifetime customer value (LCV), or lifetime value (LTV) is the net present value of the cash flows attributed to the relationship with a customer.

The use of customer lifetime value as a marketing metric tends to place greater emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term sales.

The formula for calculating CLV is:

... where GC is the yearly gross contribution per customer, M are the associated costs in retaining customers, n is the number of years, r is the yearly retention rate, and d is the yearly discount rate.

 

Two questions:

1. In this social age, where connections between customers are far stronger because of social-media enablers/technology, is this formula still valid?

Certainly, the "gross contribution" of a customer should take into account not just her own purchases - but also the impact that her purchases have on her experience and her propensity to recommend the same brand (or not) to others.  

Additionally, the M - the cost associated of retaining a customer - should take into account the cost of encouraging her to talk about your brand and recommend it to others.  From this vantage point then, M is more than just the amount that one spends on her directly (i.e., 1to1 channels), but also things like advertising (spillover/tangential or directed to her) and effects of other people on her purchase decisions.  Amongst other things.  

How do we account for that in GC and in M?

 

2. Why is it that we don't have a similar measure for media investments?

 In media planning, we talk of GRPs, shares of voice, reach, frequency, impressions... and the cost of each (e.g., Cost per GRP, Cost per Reach Point, Cost per Impression).  Some more sophisticated companies will measure returns on investments per medium or per level of investment in a certain medium.

But these metrics - cost-efficiencies and ROI - are all essentially a measurement in a point in time - NOW.  And much as we should be planning for the issues NOW - the impact of what we do NOW will last way into the FUTURE.

So should we not also be talking about the NPV and FV of media investments whilst we are allocating moneys across different media, different levels, different markets?

 

One last question...

Why are media companies not talking about these things when they are supposed to be the investment experts?

 

 

(*** Written whilst multitasking and in a hurry.)

Fitting equations...

I am fairly sure that if one tries hard enough, one can describe a set of seemingly random data points using an equation or a set of equations.

What I am not sure of is if one can explain WHY such an equation or systems of equations hold or should be true.

of statistical and econometric models and reality

A former colleague of mine who was one of those few econometricians who could make a non-econometrician understand what they are trying to say by focusing on the "spirit of the equations" (his words) had a very great way of putting how to approach stat/econometric models.

This is how he said it:

A model helps you grasp reality easily.  It doesn't mean it is real.  If it were real, then life would be a lot simpler that it really is.  It also doesn't mean it is NOT real: it is an abstraction of what is real - i.e., lifted off what is really happening.  The end goal of the abstraction is for us to be able to understand reality.

Think of it as the map of London Undeground:

This helps you go around the City.  But it doesn't mean this is exactly how it is in reality.

If you really want to look at how the tubes are laid down in London, here is how it looks like:

The tubes are laid down complicated - but the schematic diagram of the tubes' stations makes it a lot easier to go around the City.

That's what equations and models are for: Guideposts.  Schematic diagrams of complex phenomena.  That one can then use with confidence to get from one point to another.

-----------

In the years that I have been working with statistical/econometric models, I have observed that there are three main categories of people - from clients to agencies to finance people - based on their responses to the models.

There are those who would look at the innards of a model, turn it upside down, question every probability value and statistic, and attempt to backtest/stress-test the model - until they are satisfied it works.  After such satisfaction, the equation is the end-all/be-all of decisions and choices.

Then there are those who would look at the model - and turn up their noses towards it, with the belief that the equaiton cannot capture reality - that it is just a set of equations and numbers.  That after all is said and done, it is the "human mind, wisdom, and experience that really count in making decisions".  There's always the belief that "not everything can be captured by an equation - situations are far too complex to be simplified into one equation".

Then there are those who would look at the model, take an interest in its explanatory power, question its validity, backtest it, stress test it - and then look at the implications of the various parts of the model and how it could affect reality and decisions that they have to make into the future.

Thanks to Kottke.Org for the photos.

marketing analytics: some more thoughts...

it has almost been a year since I wrote about marketing analytics and my attempt at capturing the "principles" that could drive at making marketing decision-making far more evidence- and fact-based.

i am republishing these thoughts again, but with a stronger emphasis on what's really marketing analytics is all about: interpretation and transformative insights.

marketing analytics is far more than equations and cross-tabulations and significance-testing - although these techniques form part of that.  it is the interpretation that really matters - the "so-what?" and "what-now?"

i realized this after almost a year of not dealing with equations and wonderful world of econometrics, matrices, error-testing, and all that.  [well, *almost* not dealing with equations - i still had to fit curves against numbers and trends and all that beyond OLS and all that...)

equations, econometircs, statistical modelling, regression, arima, arma, time-series, curve-fitting, stat-tests, and cross-tabs are all means towards bigger end: creating an interpretation that makes a difference in the decisions that we will make in an uncertain world.

here are - once again - my thoughts on "marketing analytics principles" with some additional thoughts or comments in brackets

[i will keep the quotes until - well - I retire and/or leave the world of marketing analytics as i don't think i am qualified at this stage to set the principles as real principles.]

 

Marketing Communications Econometrics ‘Principles’
Draft by PTiongson • Wednesday, September 30, 2009

[comment: indeed, this was written on 30 September 2009; how time flies...]

1. Every investment in marketing has an effect on the company’s sales/revenues business.

The goal of econometrics in marketing communications is to quantify these effects and be able to compare-contrast the effects of investments in marketing channels in order to make decisions.

[we are not in the business of driving awareness.  we are not in the business of "being present on-air".  we are not in the business of being on-air where our buddies can see our products - thereby giving us the chance to gloat.  

we are in the business of business:  driving revenues, sales, profits.  

i will even go further and say that we are not in the business of driving impressions, ratings, share of 'voice', 'share of consumer experiences'... if we are not aligned on that, there is no point in investing in advertising.  and yes, investing - not spending.]

2. Revenues and marketing investments have don’t have a 1to1 relationship.

There is a point where additional investments in marketing will not have any significant effect on revenues or other marketing KPIs; beyond which, any additional investments are considered wasteful.

On the other hand, there is also a minimum threshold that needs to be met in marketing investments in order to ‘break through’, be competitive, and generate returns on marketing investments.

This is the economics behind marketing investments – economics is all about the optimal allocation of finite resources, and marketing econometrics is aimed at understanding the brand’s opportunities to optimally allocate finite resources that are not linearly related.

This is where the concept of S-curved and diminishing-return response rates comes in.

[i believe it is human for us to believe that if something is good, then more of that same thing should be better.  case in point?  money.  that same belief seems to hold in decisions that we make for marketing: if 200Mln USD of investments in advertising worked in the past - then we need 600Mln USD will triple our profits or revenues.  Not.

even in my non-regression/non-econometric work in the past have shown that more is not always better - and that when it comes to investing in marketing, bigger is not always better.  i would go on to say that the higher your investments in marketing, the higher the risks - but not necessarily the profits and the returns.] 

3. Investments in marketing communications have an immediate effect on a brand’s revenue.

Marketing communications spent in time period “t” has a measurable impact within the same time period “t”. Marketing econometrics is aimed at understanding the immediate effects of investments so decision-makers can course-correct accordingly.

[we are not in the business of driving awareness.  we are in the business of driving revenues, sales, profits.  if we are not aligned on that, there is no point in investing in advertising.  and yes, investing - not spending.] 

4. Investments in marketing communications have a ‘lingering’ effect on a brand’s revenue.

Marketing communications spent in time period “t” has a measurable impact after time period “t” – though such an impact may well be diminished and not so strong as the initial impact.

Marketing econometrics is aimed at quantifying the persistent effect of a campaign on a brand’s revenue through the use of concepts such as decay rates and ad-stock.

5. Marketing accountability – and therefore, econometrics – needs to take into consideration differences in execution and messages.

While econometrics is a quantitative technique and a science aimed at quantifying relationships between two or more variables, it should take into account the messages that are being conveyed by the brand.

This may be achieved by incorporating dummy variables that code different campaigns of the brand, the use of granular (by-version) data, qualitatively reviewing and comparing-contrasting messages and executions, and other similar techniques.

[the above paragraph is a bit too focused on 'coding' dummy variables - the only way one can have a true understanding of the dynamics of a market is to have a synergistic approach to marketing analytics: one that encompases quantitative, pseudo-quantitative, and qualitative techniques.  sometimes, equations cannot answer the question "why?" - and therein may lie the need for further analyses using non-quant or hybrid-quant analytic strategies.]

6. Marketing econometrics analytics should result to action points.

Econometrics should result to potential action and/or decision points for the clients. These may include (1) making decisions on how much to spend on specific brands, (2) optimizing revenues and other campaign-effects against constrained, finite budgets, (3) optimizing investment lay-down to optimize revenues and returns, (4) creating different scenarios with varying effects, and (5) deciding the optimal course of action given current knowledge.

[if it doesn't lead to action, then what was the whole project for?]

7. Decisions about the future carry risks; these should be taken into consideration.

Econometrics is an application of statistics and probability to test hypothesis on the economics of marketing. As such, there are risks involved. The most basic of econometric models –

Y_t = β₀ + β_iX_i + ε_i

suggest that there are ‘errors’ that cannot fully be accounted for. These ‘errors’ – or innovations – are the source of risks that no matter how robust a certain model is, there will always be unaccounted for variation – and there will always be errors and risks.

Risks should therefore be considered in making decisions based on marketing econometrics – and any other technique.

[nobody has seen the future.  even the most prolific of quant-traders from the field of finance failed to project the breadth and depth of the financial crisis.  and with all the changing variables and scenarios that are all around us, it is foolish to say that "because the equation has a probability of error of less than 1%, therefore it is correct..."  (which is in itself a contradiction).

risks abound - and therefore must be accounted for, and not necessarily measured or quantified - but prepared for.]

8. Risk management principles need to be integrated into marketing analytics econometrics.

Risk management aims at quantifying and mitigating the impact of random, stochastic variation (and shocks) in the future. Marketing econometrics provides an opportunity for marketing executives to ‘look into possible futures’ – and a basis to mitigate the effects of negative events.

In the practice of marketing econometrics, this entails not just a review of the distribution of “errors”/“residuals” and their corresponding impact on the brands’ business. This also entails the understanding of each variable/factor’s contributions and effects on the company’s revenues, and the risks they pose on the company’s revenues.

[equations are usually back-tested (tested on previous data) to ensure their accuracy.  but that is not enough.  the past is not always a good predictor of the future - and errors, innovations, shifts happen.  and even with the most robust of models cannot capture all these possibilities.  risk management principles - "management" used very loosely - need to be put in place.  risk cognizance, risk awareness, and preparedness need to be put in place.

and that will require far more than just models, statistical tests, inferential statistics, back-testing, and everything numeric.]

Marginally Subversive Thought of the Day: Success is the enemy...

Our marginally subversive thought of the day comes from Charles Hampden-Turner and Linda Arc, as quoted by Jamshid Gharajedaghi in the book "Managing Chaos and Complexity":

The most stubborn habits which resist change with the greatest tenacity are those which worked well for a space of time and led to the practitioner being rewarded for those behaviors. If you suddenly tell such persons that their recipe for success is no longer viable, their personal experience belies your diagnosis. The road to convincing them is hard. It us the stuff of classic tragedy.

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

That we live in fast-changing times is an open secret. And yet we remain to be stuck with our age-old paradigms and schemas of the world. We invest and we gain - that was what it used to be in finance, in marketing, and in business. It was simple.

But we no longer live in simple times.

We are faced with competitors who are as smart as we are. With suppliers who are as protective of their businesses as we are with ours. With consumers who are armed with information gathered from a vast, constantly growing library called the Social Web. With staff and employees who are fast realizing that life in the Firm is not the only life worth living. With leaders who are far more concerned with their parachutes and exit strategies and with pleasing quarterly-results-focused investors than with long-term brand- and business-building.

And yet our response mechanisms remain to be rooted in the same old assumptions.

It is time for businesses and marketeers to wake up and smell the coffee - perchance its smell will reinvigorate them - and us - to think radically different from how we thought in the past.

Things have changed. Things are changing.

So why aren't we shifting? Why aren't we challenging our old assumptions?

Just because it worked in the past doesn't mean it will work again. And again. And again.

The world has changed.
It's your turn.