marginally subversive

#analytics practitioners need personal integrity, too

I guess nobody's perfect: analytics professionals need to have a sense of integrity as they search for answers for their clients' businesses. after all, their results could lead to misspent dollars - or worse, lives and jobs of people.

Analytics professionals need to understand the responsibility that rests on their shoulders.

 

Created by: Clinical Psychology

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.

"like us on facebook": the value of fans (and likers)

Almost all of the print and outdoor ads that I notice these days have got this icon, exhorting - imploring - anybody and anyone to

"Like us"

I know what it means - but how it is crafted sounds, mmm, pathetic.

"Why should I like you?  Just because I saw your ad on an outdoor poster?  Or because I want some more information about you?  What if I just search about you - if I were interested, then I'll do that.  Or ask my friends about you - that is if I were interested."

Maybe it's just me.

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Plastering "like us" on every and any ad may be generating some "likes" on social networks - and some following.  I am not sure of the success of such ads, really since I have not conducted any study about it.

(I've conducted a study however, that shows that traditional advertising - i.e., on TV, radio, press, magazines, and outdoor - has a significant impact on category-related keywords and conversion-rates from visits to actual, revenue-generating action.)

But what does "liking" a brand really do?

I'll quote Jeremy Owyang on one of his entries on this:

Don’t focus solely on fans and followers as a primary key performance indicator, instead focus on the business goals the fans and followers yield for you.

- Jeremy Owyang

 

Yields - revenue and profit yields - are what you ought to be looking for.  Not fans, not likes, not followers, not impressions, not loves/likes and hugs.

At the end of the day, your brands ought to build business for your company.

And likes, fans, loves, and hugs won't matter to the CFO unless there is a dollar value attached to them.

 

 

 

 

how do you build business?

I had an interesting conversation with a former client.  We were talking about building businesses for his clients.  And one thing he told me stuck with me: "You don't have to change behaviors in order to build business.  Besides, advertising cannot change behaviors."

I went "What?"

But since he was not paying me for strategic advice but merely to create and design his much-loved slides and translate his English into something his clients would understand, I didn't argue. 

However, above is my free advice to him.

And if advertising is not supposed to change behaviors, then why are we here for?  Awards?  Kudos?  Pats on the back?

B******s.

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

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.)

it's the season for discounts... and time to buy?

a friend has this philosophy about the discount season:  "sale seasons are designed such that you spend in order to save.  hence, i don't wait for them.  i buy regardless if it's the discount season or not - as long as i need it and i have the cash to get what i want."

the same is true with marketing:  a lot of people are so concerned with ROI - and i mean the numeric ROI that comes after a thorough understanding of the factors that make up the formula (returns/costs).  it doesn't matter whether the costs are high or low - so long as they get a "decent", acceptable ROI.

well.

ROI - when misused - is like spending lots in order to get more.

ROI planning is not bad.  but it is only one facet of planning that needs to be taken into account.  there are more. 

(and as an aside, I should say, what really is ROI?  my first bosses impressed on me that ROI is not savings, discounts, nor simple calculations.  we went into discussions of NPVs, FVs, cashflows, future value of money, internal rate of return, and all that - things i never really mastered [i doubt if i understood them at all] in order to really calculate the ROI.  and yes, this was in the marketing department.)

i guess, to each his/her own with regard to measuring ROI.

and i guess that is also where the problems lie.

a focus on ROI can lead to misuse - and disastrous, contradictory - and perhaps, unintended - results.

 

 

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.

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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.

here we go again…

Media planners have always wanted to measure the “optimum level of reach and frequency of different combinations of media channels”.

The question is – to a certain extent – valid: What is the best way to manage finite financial resources across TV, radio, press, and other forms of communications to achieve the best impact?

It is not entirely a bad thing:  Once we know the answers – or the principles related  – to this question, we can then move towards optimization.

Given the fragmented media landscape and increasingly declining budgets, all the more that we should be answering this question and deciding based on those answers.

But should the internet and social media be included in these process?

 

 

TV, radio, presses and outdoor are still measured in terms of the ‘number of people who have seen (or who claim to have seen) the medium’.  That’s what GRPs – even PeopleMeter measures – are based on.

But the web – in all its myriad forms from banner ads, rich ads, search, action-seeking ads (either recommend, forward, click, save or download) – is an entirely different thing.

Whilst PeopleMeter and diary ratings are operationally defined as “people who click on a button in front of the TV to identify themselves” or “people who record their media exposures on a predefined diary”, the web is entirely different. 

 

Let’s not even talk about mobile phones – which encompass SMS, location-based and on-demand apps, bluecasting, mobile search, location-based push-SMS/messages…

 

The point is: Reach and frequency – and other OTS (opportunity-to-see/hear) metrics – on these “new” media.  It is not actionable nor relevant to say that “TV generates 70% of unduplicated reach, whilst the internet delivers an additional 15% reach”.

 

Nor can we say: “Site A delivers 35% reach, and with the additional Site B buys, we will have an additional 12% reach – resulting to 47% unduplicated reach of the internet campaign".

 

 

I understand the need for a single currency – because how can we optimize if we don’t have a single currency that would define goals and constraints?

But the call for a single currency shouldn’t disrespect the medium and its innate characteristics, strength, and roles in the lives of consumers.

 

There must be another way of creating a single, standard currency that applies to all.

In my opinion, that’s consumer response to the ads carried in different media.

on numbers and maths...

i stumbled upon this video from IBM's Smarter Planet channel on YouTube.

and i have some problems with it.

it might sound a little weird since i use numbers and maths and spreadsheets a lot in my line of work.

having worked with numbers and equations and spreadsheets and statistics for quite a number of years, i know that they can do good.  it quantifies what is presumably unquantifiable - and by making "it" measurable, it becomes observable and manageable.  it can then lead to efficiencies - as in the case of generating more savings - or effectiveness - like in making sure that each buck invested in an ad campaign generates more bang than it should by putting it in the right place (identified through a number) at the right time (identified - again - by a number).

but i also know that numbers have limitations.

and numbers can lie.

and numbers can create havoc.

just think of the financial crisis - it was all about equations and probabilities and risks.  risk theoreticians and academicians thought they could quantify ALL risks involves in investments, and therefore put a number - a price-tag - to it so they can trade it and create more money out of it.

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[ numbers make us confident - but they can also make us too confident to realize that not everything can be quantified and reduced into a mere set of equations, statistics, and models.]