Redefining a market

Do consumers really see all video, online and offline, as one as we say in the industry? I suspect not. On the contrary, I think they draw a clear distinction - For e.g., " I don't watch TV anymore, I have Netflix!" and so on...

However,the convergence from the business point of view is very clear. In a nutshell, TV is moving towards the data-driven targeting and automation aspects of digital and digital video is trying to be, well, TV-like on several things from content type to positioning in the advertisers' / ad buyers' mind.

Trying to be 'TV-Like' seems to be the more dominant pattern of the two right now.

Consider some of the recent news from the US : Google's OTT play on Cable & Satellite TV with You Tube TV (and thereby on to the Addressable TV space through distributor ad slots) and re-entry into Programmatic TV for linear inventory via DBM; Plans from Facebook, Twitter,Snap, Amazon et al to get into TV-type shows and the focus on premium content in general ; the related conversation about the living room TV set as a screen for digital video; the very existence of something like the NewFronts that, unlike the TV Upfronts ,is a marketing rather than a transactional exercise. Etc.

Why should this be when the market is clearly going in favour of digital ? For example, digital ad spend is expected to equal and, according to some sources, even exceed TV this year in the US.

And therein lies the flaw (in this video-value context ) in comparing those two.

Unlike TV, 'digital' isn't one entity. Specifically, Search is a separate format for a separate objective and a different market that exists pretty much independently.It has very little significant influence on most (read non-Google) of the digital market. Stripping it out reflects the real battleground on which all of those players find themselves, i.e. the market is smaller and TV's share larger. (Ref. chart below)

Arguably there are only two ad markets : branding and search/performance. TV dominates the first , Google the second. Media and channels may and do have elements of both - e.g. Social - but the fundamental drivers remain those two. This is a redefining of the market that the players themselves , either consciously or otherwise, have already worked out but something that the rest of us tend to miss out on from the headlines.

Now add to the above the TV subscription market and you have a big value bucket to draw from (as the likes of You Tube TV, Sling, Hulu, Netflix and the like are already) - but that is a separate story on which more later.

Thanks for reading. Cheers

" ... Search, Banner, Video, 'MOBILE',.." - Er, Why ?

The latest IAB's US digital ad revenue report for 2016 has just been released. Although US-specific,it good bellwether stuff. Key takeouts (not many surprises albeit) :

1. Total digital ad = US$ 72.5 Bn., FY15 +22 %, 5Yr CAGR + 16 % p.a. 2. Growth driven by video FY + 53% overall + 146% on mobile, + 15% desktop, although overall share of total is low at 12% 3. Search @ 48% share, moving to Mobile (FY + 91% vs -13% on desktop) 4. Mobile now leads at 50.52 % of total, F15 + 77 %, 5yC + 87 % p.a. 5. Desktop fell FY -8 %, | 5yC +4 % p.a., video the only growth format 6. Top 10 players stable at 73% share (Ten year : low = 69%, high = 75%) 7. Sectoral contributions stable, Led by Retail (21%), Finance (13%), Auto (13%), Telecom (9%) and Travel (8%), No change in CPGs at 6% of total

You can read and download the report here.

Oh, and why do we overlap format and channel and say " Search, Display,Video, Mobile, Social,.." ? Context-specific is easy to understand - for e.g., to parse out Mobile-only buys, etc - and IMHO is how it should be too ; but as generic classification ? I haven't figured it out yet. Any thoughts ?

Thanks for reading. Cheers :)

Rock.Hard place.Music industry.

My friend’s little home studio where I recently recorded some music of my own. 
What a time to be alive ! 

The much embattled music industry complains about You Tube not paying them a fair share of the ad revenue that it makes from music videos. (Read here). As a lifelong music fanatic and a big You Tube lover - among other things, You Tube has put several lifetimes worth of music within easy access, after all ! - this puts me in a quandary. It's a tough one.

On the one hand, according to industry body the IFPI, the revenue per user going to the music industry from You Tube and other free services is a staggering 20-30x lower than from paid streaming services ($0.6 vs $18 in the chart below). On the other, You Tube et al deliver 5x the audience. Wouldn't the biggest distributor hold and exercise similar bargaining power anywhere ? And from the POV of music itself, You Tube are one of those at the frontlines of democratizing and leveling the field for whoever wants to give it a shot - an often unacknowledged cultural-good role that it plays. Could that be a worthwhile 'externality' (in economics-speak) ?

Commercially, one way of looking at it is to ask the opportunity cost of not being on You Tube. Who can tell for sure but one suspects it would be significant - going by what we see all around us across music or video or gaming , surely most of those being monetized by YT would not otherwise opt for a paid service ?!

All of which is not to say that artistes and/or creators should not get paid their due. On the contrary ! Certainly the reported 30x 'Value Gap' (read and download the IFPI Global Music Report 2017 here) seems excessive and should ideally be reduced. The point,though is that it's, well, complicated. The ground under 'due' itself has shifted dramatically thanks (or no thanks, if you prefer) to the massive tech-disruption that this particular market has been confronted with. 

There are just no easy answers.

It's heartening to read that the music industry has turned a bit of a corner recently and reversed years of decline to post a + 6% revenue growth driven by streaming (+60%). One hopes this continues and that all parties come together to find a mutually beneficial solution. Like most human endeavors, music (and art in general) relies on commerce to thrive ; but like only those few special endeavors, I believe it goes beyond commerce !

I for one will be watching this space. Or 'listening' to it ! :) Thanks for reading. Cheers !

Removing the Measurement Cap !

Measurement hasn't perhaps got the attention across the wider marketing ecosystem that it deserves. While the challenges of digital measurement - Person vs Cookie, Third Party vs Walled Garden, Cross-device/platform measurement and attribution, etc.- are out there in the conversation , it tends to be mostly among specialists.

The cost from the lack of measurability should be assessed more rigorously and appreciated for what it is - a cost. And this is where a ground-level micro perspective may play a part.

For example, in the simple - though not simplistic! - case of skippable online video (OLV) advertising when average viewing frequency per cookie is known , the duplication rate makes a significant difference to the actual 'person'-reach and therefore frequency and cost.

Imagine a hypothetical OLV campaign of 500,000 paid views with a specific creative copy with an average VTR of 20% targeted at a demographic base of 1 million individuals with an average cookie view frequency of 1.5. As the chart at the bottom shows, depending on what the duplication is in reality, reach can be anything from 33% all the way to 5% and below. Cost per reach obviously increases accordingly.

But it goes beyond just  cost per reach.

Assume that the actual 'person'-reach is close to unique cookie viewers, i.e. an actual 'person'-frequency of around 1.5-2.0 ish (meaning the user wouldn't watch the same spot more than a couple of times or three - a reasonable enough assumption on anecdotal evidence in the absence of anything else ), what happens when another 500K views are bought , say, in the following month ?

Now, at 20% VTR, around 2.5 million impressions would have been already served first time around to generate those 500K views. These impressions would have covered most if not all of the addressable 1.0 M TA base already. So in terms of incremental audience in Month 2 , what are the chances that users opting to skip or drop out the previous month would choose to view the same spot now ? Or that those who viewed it last month would view it again this month ? Neither case is impossible or even improbable but ,well,it doesn't sound very probable either ! The math just does not stack up. Now this becomes not only a question of X% additional cost per reach but also the very tenability of the campaign, i.e. the possibility of a 100% additional cost

This example is obviously illustrative - and,yes, extreme ! In reality, a buyer would take audience size and related info into account before deciding on the buying volume. Equally important, copy would be refreshed regularly. And this is only a case of purely demographic-targeted buys which in reality is a relatively small number of buys.

(Measuring outcomes differently - say, through engagements, etc - doesn't affect this argument. Firstly, they are not mutually exclusive - one doesn't preclude the other. Second, this goes for those measures too, e.g., substitute 'click' for 'view' and the same Person vs Cookie discrepancy holds. Third, 'engagements',for example, tend to be low and stable in this format and , moreover, still a function of scale)

The point here though was to illustrate costs that may fall in the cracks of micro campaign management away from headlights and headlines.Should they - and numerous other more complex cases across channels and formats - be thought about, quantified and aggregated, it could provide the urgency and push which would expedite the move towards better measureability sooner.

Almost the entire illustration here is conjecture built on assumptions. Only the facts and figures could really tell. And for that to happen requires an understanding from the ground-up and cooperation among both marketers / buyers and platforms / sellers. Most questions are not easily answerable and require advanced measurement , including (especially ?) Third Party but some 'clues' could also be provided by platforms - for example, viewing distribution even if at cookie level. The buyer needs to think about that and ask , the seller needs to appreciate the market growing potential of such moves and provide ! The onus is on both because the benefits go to both.

As a post script : talking about frequency and such leads me to a sign off on Frequency Capping. It's a no brainer that Frequency Capping is a huge boon in today's digital era.

But how meaningful is it in the context of served impressions for skippable videoads with completed (or at least paid) views as KPI ? In the above hypothetical example of 20% VTR and an average View frequency of 1.5-2.0, how meaningful is a Frequency Cap of , say, 5 (or 4 or 6 or 10) here ?

When it is highly unlikely that a person will watch your ad three or four times , the cap becomes redundant at best. And at worst, you are limiting the chances of future exposures by not serving it again.

Also,as an aside, when a viewer has actively opted to watch an ad multiple times, is that still a waste ? One to ponder

Two Elephants in the Ad Measurement Room

The first elephant is the difference between digital consumption and digital advertisingconsumption. All the usual issues of viewability, bot fraud and, well, plain unnoticeabilty - and I'm looking squarely at you here, little banner ads ! - is why the percentage of ad exposures will remain relatively stable even as online consumption grows exponentially.(Though,obviously, the absolutes of ad exposures will grow with it)

The good thing with online video advertising where the difference is clearly quantified as the difference between ad impressions and ad views is that the elephant is easily sighted, understood and can be responded to.

The second elephant in the room is the less obvious one : TV ad avoidance.

There is simply no way of knowing whether people really watched your ads on TV. All that commercial ad break ratings tell us for certain - and this is keeping aside markets such as ours in MENA where these are not even available in the first place ! - is that people didn't switch off the TV and didn't zap channels.

Whether they walked out of the room or switched their mind off or buried their noses in their phones or , indeed, watched the TV commercials with love and adoration during those three or five or ten long minutes we do not know. And have no way of knowing.

So in effect we are penalizing online video for being transparent while not holding up TV to the same level of scrutiny and accountability. So TV ad exposures* all over the world are likely to be overstated simply because of a quantification gap (see figure)

* This post is only from the limited point of view of ad exposure. Engagement, impact, sales outcome et al are a different- though surely correlated !- matter.

While realistically speaking this gap can't be eliminated, can it be reduced ? Perhaps through syndicated sample survey-based research or,say, through more pervasive individual advertiser-level A/B experiments ? Hard to tell - but as viewer consumption boundaries blur and the market battles intensify, more attention will probably need to be paid to this TV elephant to size it up to some reasonable approximation.

(click to enlarge picture)

Data and the problem of plenty

"Won't be nothing, nothing you can measure anymore" - Leonard Cohen : The Future

As I listen in remembrance to one of the greatest pop poet-musicians of all time in the wake of his passing, I am also thinking of the problem of plenty in marketing data today. Namely : the more data there is the less data there is.

That paradox sounds (almost) Cohenesque but it's certainly a lot less than poetic for us practitioners out there !

Marketing data today has immense depth but very little breadth. There is a treasure trove of KPI metrics for each digital 'walled garden'* in your marketing mix but little or nothing by way of the same metrics aggregated for the entire digital mix (let alone the entire overall mix as a combination of online and offline)

(* I borrow the term in this context from an excellent piece by senior and very respected industry leader Gowthaman Ragothaman which you can read here)

Later if not sooner this is likely to place at least some constraint to business growth for these advertising dependent gardens - and which is why I am sure a solution will arrive sooner rather than later ! The recent announcement from Facebook (read here) about the launch of a 'Measurement Council' along with Third Party verification measures is a step in that direction. More will no doubt follow from more players. After all, it is some very important value delivered to their customers (viz. advertisers and agencies) - and as we all know to superior customer value goes the spoils !

Even leaving the whole third party thing to one side , improvement in existing reporting within each single platform is where it can and needs to begin. An obvious if simple example is unduplicated individual person-level reach. Sure, it is complex but something that can be solved given the right commitment and resources. Far more complex problems have been solved after all ! The question is when it will be deemed a necessity rather than an also-good-to-have. I suspect it will be sooner rather than later. One step at a time and the rest follows. This diagram illustrates very roughly how that can unfold. We await that first step !

L'affaire Facebook and the 1-0 Bunnies

The weekend was abuzz with the story of how the reported average time spent on Facebook videos had been inflated for the last two years. The error came from excluding views of less than three seconds from average time spent calculations thereby inflating the average.

Important as the news is, especially to content publishers, it's less so to practitioners from the marketer and agency side. On day to day operations built mostly on raw campaign data on views, pricing,etc, the metric is almost inconsequential. But even on the larger plane of,say, budget allocations on which it has the most bearing, this should matter less than may be presumed.

First, such a metric is only one among other factors that decides allocations. Second , the clear line between platform/channel consumption versus advertising consumption is as evident from daily-life observation as it is from analytics data. Something like a share of time spent doesn't necessarily equate to share of budget. Campaign analytics data provide a good enough input into what that share should be.

So to me the misreporting itself (inadvertent or otherwise) is secondary. Facebook with its 1.7 bn. monthly active user base - and psychographics revealed (almost literally !) for all the world to see is - is obviously one of the largest and richest ever one-stop Target shop in mass marketing history. Any improvement on channel or product - video, in this case- is at worst a tweak or two away. Where's the case against ?

The beef I take out from the story, instead, is the roles both of third party validation AND second party vigilance in using first party data.

Third party validation is the obvious one and doesn't need elaboration. The entire ecosystem , digital giants included, should only welcome something that'd grow the pie by removing doubt and uncertainty.

The Second Party stuff though ? Much overlooked. And very important !

This senior, very experienced and unabashedly old school data specialist who I have learned a lot from once used a phrase to describe a bunch of enthusiasts who were so impressed with a website's new metric that they not only didn't notice its obvious flaws but also immediately began advocating its use. Digital Bunnies, he called them.I thought that both funny and pretty much on the money.

As click-of-the-mouse analytics packages and the like proliferate  with their masses of first party data , it's tempting to leave everything to them. In fairness , when there's so much data placed out there so transparently (give or take !) that's reasonable enough.

Up to a point.

What still remains are the interpretation of those numbers in context - e.g. how big are those views ? How good is that VTR ? - and thinking about the questions which are outside the scope of the data - e.g., how likely is a claim based on it. That's where second party vigilance comes in with its evaluation of the numbers and assessment of the pros and the cons. And that's where we probably could do more.

The good old Think For Yourself is part of the toolkit too - even if the 'too much too fast' digital marketplace can overwhelm one into forgetting that !

That funny bunny could do with some stick !

Of sucky creatives and not-so-sucky results

First things first. Good ad copy is more likely to sell products than bad copy.  Duh number one. 

For all its intuitive logic, though, there's isn't too much data on it when you go looking. So this piece on HBR is a welcome find. (Read here). I especially like the analysis of the 'stylistic' elements. (From the 'creativity can't be measured' school ? Feel free to call it anything else you like then)

That the connection isn't a straight one, however, is my duh number two.

There's plenty of good copy that don't necessarily produce sales results. Back in India in the nineties, for example, there was the One Black Coffee' spot from Ericsson mobile phones that did everything else - entertained, got talked about, won awards - but didn't move product, apparently because people couldn't recall which brand it was for ! 

More interesting (because unusual) , though, is the converse possibility  of indifferent copy producing decent results. 

Around ten years ago , this Indian grocery store - call it X -  in Dubai was airing their TV spot a million times a day during cricket matches.  The message was simple : X had the best quality Indian spices specially imported from India. The production ? Horrible is putting it politely. (Rumor had it that it was shot on home camera  and featured the owner's daughter) It was an assault on sense and sensibility. But - and here's the thing - it was an assault at scale (relative to their business) and that seems to have paid off despite everything. 

That was the first most of us had heard of X. Today many of us shop there for Indian spices (and other grocery items) that one doesn't easily find in other stores. In the intervening period, X has expanded to several new locations .By all accounts, X - and by extension, that horrible copy - has been a success. 

In hindsight it makes sense : have good product, make it known, sell. On the cheap.

This goes back to the age old message-vs-media question. Do you spend more on creative or airing ? Unlike big established advertisers for whom production costs are so much lower than media costs,  X would have had to take the call to invest their little pot on airtime rather than on pleasing copy. That it worked ticks off the established norms about how effectiveness depends on the product category,market and target audience. Such shoddy copy couldn't have worked with hipper , fancier, bigger brands.

Or, dare I ask it, could it ?

Because it also opens up questions. First is about how exactly advertising works. How do people process ads ,especially subconsciously ? While great copy will be remembered and bad copy disliked , do sales reflect that ? What's the role of brand-name versus reason-to-buy ? Taking one side to its provocative extreme : can bad copy work by being so terrible that it sticks ? It's obviously no one's case to go out and produce bad copy - duh number three - but the point is that the creative strength (or weakness) of any copy cannot be taken for granted when planning.

Second are the new questions in today's ad-avoiding digital landscape. What holds a person's attention well enough to keep that Skip Ad button at bay , what copy lengths are best (or, 'what is the shortest you can go' ?) , how does it fit into a phone screen ...etc. Also some less obvious ones : like , say, about how you should evaluate a static banner ad. Sure, it's almost conventional by now to downweight banners for their low CTRs but could they be contributing as pure, non-response display communication ? That can't be answered by web analytics. Nor is it easy to run sales effectiveness analyses. In practice, many questions are simply unanswerable. You just need to take the judgement call. `

Another aspect of this is the democratization of business today. Entry costs of marketing have never been lower as small businesses get on to digital. While search and static display remain the predominant channels,  the share of video will grow fast and the X sort of examples will be heartening. The implications also extend to the agency ecosystem : competition could well go beyond rival agencies to under-the-radar freelancers and the like ... but that's another story !  

But to the owner of X goes the final word in my piece. One can picture him / her being dismissed , even laughed at outright, by agencies - but I guess you'd know better than to underestimate the guy who's put his own money on the line !

Zen and the Art of Killing the Buddha

When you see the Buddha , kill him

That's a Zen proverb to shock-illustrate Zen Buddhism's core tenet that life should be lived in the moment and separate from words and concepts about reality. 'Buddha' the word is not the same as the Buddha himself - and even the Buddha himself is irrelevant to your own life's realization or nirvana.

Jargon is jargon only when it is used as jargon. Nine times out of ten, the language itself usefully conveys the meaning. It's only how we then abuse it that makes it jargon - that killer of clarity and the grand wig on the bald head of non-clarity.

Man, there ought to be a law !

Or at least a university course or an office training module or something like that. Titles anyone ? 'Basic Jargon Buster' , 'Advanced Jargon Buster', 'JarGONE : Jargon-Free in Five Days', etc etc ?

(You may have guessed where this post is coming from. Yep, I was subjected to some recently . In buckets. Ugh. ;) )

And fittingly concluding this post with a link to its Zen referencing opening, here's a very interesting piece about how origami is driving cutting edge technology (read here)


(The header image is my own photograph of rural Assam,the Indian state I am from) 

The consumer is not a moron, she is 'WhoKnows?'

She is your wife , said the great David Ogilvy.

We are often blind sighted enough by the heat and light of our own work to miss that fundamental truth. The consumer is a flesh and blood person.

Who in our agency world has not had the experience of having the excitement of running a special campaign watered down by an indifferent outside world - including, yes, the spouse ! - that's either not seen it or is considerably less enthused about it ?!

The consumer is your wife. She hasn't seen that great special execution you spent all of the budget on within a single week.Could some of that money have been used for a less costly if less sexy option to reach over over the following weeks ?

Who knows ?

It is , however , so important not to lose sight of the obvious : the consumer in us and the consumers that are our families and (non-industry) friends

BUT , and here's the thing , only to an extent. Because this game also runs the other way !

While the consumer is not a moron , she is also NOT your wife ! That is the corollary David - a big and early proponent of research as a fuel for creativity - would have agreed with. The fact is that we as practitioners can often also end up extrapolating our own lives onto the consumer universe. The Madison Avenue-isation syndrome , if you will , or closer home , the Dubai Media City syndrome !

Yes, the young Saudi woman is on the Latest Social Platform all the time these days. But has she seen your ad there ? And does she remember it ?

In the end , it's all about striking the right balance between having a pulse of the 'real world' - the consumer as your wife - and understanding the limits of that pulse and the need to understand more through objective outside sources - the consumer as your not-wife.

Easier said than done, yes, but not so difficult either !

The common underlying strand behind both poles is that old conceptual framework of advertising as a weak (or, indeed, strong) force. Anybody remember that one ? Here's an old piece by John Philip Jones on the topic (read here). While these things have been all but shoved to the back of the warehouse, I think it is more relevant today than ever before as technology provides people with the means to do what they have always wanted to do : avoid ads.

IMHO , the starting point to any planning should be the presumption that you are up against the challenge of everybody wanting to avoid your ad and so - what are you going to do about it ? Prepare for the worst to work for the best, Understand the limits inside out in order to breach them.

Agency Vs procurement : The quantification gap

Prior to an annual media house negotiation meeting a few years ago , a marketing client shared some sales data with me to make the point that it was more important for her brand to be on-air than which specific TV stations it was on While that was just her being the excellent hard-nosed negotiator that she is and using a somewhat incomplete picture as a negotiating chip , it was nonetheless a perspective.  

I'm reminded of it today when there's unprecedented pressure on agencies' remuneration. The  focus on the advertisers' side seems to be on procurement while agencies point towards the value they bring to the table both from 'hard' strategy and from 'soft' passion.

It's increasingly becoming less of a contest  - procurement is the order of the day. The rule-proving exceptions are becoming rarer.

There was interesting news last week (read here) about Pepsico eliminating its procurement department but to me , the nub lay in the last para of the piece : removing a department is not the same as removing its function , or at least the approach. 

The whole difficulty arises from what is fundamentally a quantification gap :  the difference in perceived value between the agency seller and the procurement buyer. (Ref. diagram below).  Both sides basically agree on the value of advertising, the very tangible difference between 'on' and 'off' (it's why they're sitting across the table in the first place !) The disagreement is about the various scenarios of 'on'. From a stated negotiation position at least, the advertiser sees diminishing marginal value between , say, a 'low strategy' media plan and a 'high strategy' one. 

Unfortunately, it's a gap which is exceedingly difficult for the agency to quantifiably equate with a fair price even as it knows better. (The unwillingness of procurement to be persuaded is a  non-factor here : it's their job not to be !)  It has to in effect  manage a particularly nasty strain of the  'which-half-works' virus   even as that virus has already been declared invincible ! As tough ones go , this one is a whopper. But it's imperative to keep trying.  Perhaps a good starting point would be to size up the challenge for what it is and work forward from there.   

Even then, the quantification gap problem is unlikely to be solved soon , if at all ! More likely is  an evolutionary change of the entire ecosystem - even perhaps less gradual than supposed.  Given the economics of the business, something has to give. 

One hopes both parties in their evolved form - agency and advertiser including , yes, procurement ! - are at a better and fairer place of  value when that happens.

The Medium Data world of audience research

Mayer-Schonberger and Cuckier's Big Data ...  book (my review here) sparked a few thoughts  on how its N=All argument -redundancy of sample extrapolation in the availability of all of the datapoints- relates to media audience research. 

Audience measurement today uniquely straddles both the big data world of digital media where the entire audience  is captured in real time - indeed, in the N=All sense, it is one of the purest play big data situations there is - and the 'small data' world of offline media with sample-extrapolated audience. 

I say 'uniquely' not because other domains are exclusively one or the other but because of two unique characteristics. First is the asymmetry between data availability and the share of pie of each part.  Half or more of the ad spend is based on “small data” measurements. Too much is riding on sample extrapolation for it to go away anytime soon.

The second unique characteristic  is the less obvious reason why  : the interdependency between the two types of data. Sample-based ‘small’ data is a critical requirement for unlocking the full value of the available big data itself.

At the heart of this is the need for machine (server / IP / browser) –level data to be translated into human target audience- level data. Sample surveys are the crucial bridge. The interdependency flows in both directions. Big data is being used to improve sample-based systems.

These dynamics, together with concerns about whether current systems are adequately measuring the complex media consumption of today are driving ‘hybrid’ research -  the fusion of ‘census’ (N=All) big data and ‘survey’ or ‘panel’ sample data to estimate audiences that each could not do adequately alone. 

Three key applications here would be (a) Target Audience Reach/Frequency metrics for online campaigns - wherein clickstream data (impressions, uniques, etc.) are matched against panel demographics through website tags / cookies  (b) Supplementing existing TV audience measurement with Return Path Data (RPD) from set top boxes - thereby augmenting the sample size especially for vehicles like Pay TV which may be underrepresented in the normal panel. It'll also be a   crucial part of ‘Addressable TV’ advertising (the digital insertion of one-on-one adverts on the TV set ) and (c) 'Total Video' measurment - combined TV + online video viewership at both levels - the total audience of TV stations across standard and online channels AND the total video exposure of ad campaigns across various online and offline channels. 

Given the size of TV and the growth of multiscreen online video, ‘Total Video’ as a combination of both is arguably the Holy Grail of audience research today. This is where most of the industry focus is currently on and where much is indeed underway. Many projects are being directed by industry Joint Industry Committees (JIC) and  at the forefront of most are the current TV measurement agencies - Nielsen and Rentrak in the US, BARB in the UK, AGF/AGOF in Germany, Mediametrie in France, etc -. Among the notable others are  comScore and (reflecting the broadening stakeholder base of this field ) and Google which has projects with measurement agenies like Mediametrie and Kantar. Closer home in the MENA region, Ipsos Connect have a Fusion project in its initial stages. These are only a handful of examples mostly from the US and Western Europe but other similar projects are in progress there and elsewhere.

All   of which is not to say that there is a magic button in place now or even around the corner. These projects are very much work in progress. Adding to the methodological complexities and logistical difficulties (increasingly including issues of viewability and ‘Bot’ fraud) are the operational challenges of working with multiple bodies, stakeholders and partners with their various expectations and interests. Costs are a major challenge - especially when there's often not enough clarity in the first place about the demand. 

Given these challenges and complexities, turnaround into usable planning software is bound to lag the rapid evolution of media consumption. That should not, however, prevent planners from applying the understanding of these measurement issues to creatively think out of the box vis-à-vis the available data and develop their own back-of-the-envelope planning guidelines.

In the MENA region,for example, one needs to go beyond E-GRPs, which is relatively simple, and into calibration against TV GRPs, wherein suboptimal measurement differences add significant complexity. As it stands, it's an apples-to- oranges  comparison  : 'actual’ online video ad views captured in real time versus sample-extrapolated, next-day telephone interviews (CATI)-based views on TV.  The latter relies on the respondent’s memory recall down to 15 minutes on the previous day and does not capture commercial break ad views.

An apples-to-apples calibration could work  via estimating the ‘accuracy losses’ involved between the two systems via comparable differences versus electronically measured TV systems such as Peoplemeters which capture ad viewership. While such a system is only indicative at best, it can nonetheless provide useful guidelines for video budget allocation between online and offline channels rather than have this decided more arbitrarily.

Returning to the starting point of this piece in conclusion,  big data in media audience research in the strictest N=All sense is limited mainly to online campaign analytics data wherein all of the exposures are captured. The rest  needs to be qualified to N=Nearly All.  Either because most census big data is actually proprietary (e.g. server data of websites) or is difficult to extract (e.g., all STB data across all operators in a specific market) or is non representative of the total market (e.g. IPTV STB data in the MENA market) or any combination of these, all of the data points are simply not available. What census big data does is to add on to panel sample data to give it more depth and breadth.  

 In other words, be it with  the largest formal systems being developed at an industry level or the smallest in-house calibration projects, the alleged death of sampling in the big data era simply does not hold up to the reality of media audience measurement. Sampling quality affects not only the offline media it directly measures but also the comparative relative value - and therefore budget share- of the online media it shares   the pie with. Hence there's a real need to ensure that the old fashioned checks are in place, that samples are robust, random and representative and that the analyses are rigorous.  This is particularly true for markets where those checks were much less in focus to begin with. The big data era, far from making it redundant, accords an arguably greater importance than ever before to ‘small data’.

Welcome to the Medium Data world of audience research! 

On Big Data : A Revolution That Will Transform How We Live,Work and Think

Big Data : A Revolution That Will Transform How We Live,Work and Think  , the 2013 book by Victor Mayer-Schonberger and Kenneth Cuckier which I read recently  can be divided into three broad parts (division mine).

Part 1 : "By changing the amount, we change the essence"
The authors begin by arguing that the sheer volume of data today marks three fundamental transformations which they cover in three succinctly titled chapters , namely :  (a) "More": The ability to collect all or nearly all of the datapoints makes sampling-based extrapolation redundant. Today , with increasingly more  data available, the sample size is the universe , or , N= All. (b) "Messy" : With this, the need for exactitude recedes as error margins become relatively more insignificant. You can live with  messy datasets as long as they are big enough to deliver insights and results that smaller ones can't. (c) "Correlation" : What matters are associations ,not their explanations. Predictive quality is all and causality nothing. It is good enough to know that there's a correlation between variables even if it may not be clear why.

Part 2 : Data as the 'oil of the information economy'
The second part covers the increasing 'datafication' of our world in general , the value created by the datafication of business specifically and the implications of that on the ecosystem. The book draws an interesting distinction between 'datafication' as the process of quantifying the world in analyzable formats and 'digitization' as the means that "turbocharges" that process. The value of data is likened to an iceberg , most of it is below the surface.Value  accrues both from the primary use for which it was collected and  from its resuse and extension beyond that purpose. This is the "option value of data" and is a key driver of the ecosystem today.   'Data exhaust' - information from users' usage and interactions online - is being used to "train" the system to drive improvements in areas like speech recognition, translations , etc. The book identifies three types of players in the data value chain : those who own the data (often only incidentally) , the analytics experts who apply their skills on others' data and those with the 'mindset' - the entrepreneurs who see the opportunity and build businesses around data. All are trying to position themselves at the centre of maximum leverage and data owners are unlocking value by processing and selling information to outside parties. The authors argue that over time as data skills and mindsets become more common , it's the data itself and data owners who will be the winners in the chain. The authors also discuss the end of domain specialists with data scientists 'letting the data speak' and making the decisions.

Part 3 : The dark side of big data 
The third section covers privacy, data protection and related legal / regulatory issues. It talks about the risks of a big data world to individual liberty. On the controls side, as it can not be known in advance how exactly individuals' data is going to be used, the authors argue for a move from "privacy by consent" to "privacy by accountability" whereby data users take on the responsibility of ensuring that it is not misused. Interesting but iffy ! This last section didn't grip as much as the first two.

The book is laced throughout with interesting examples which fuelled the arguments and concepts very well. Some are the familar generic types - predictive analysis based recommendations on E-Commerce sites like Amazon or Netflix , sentiment analysis from social media data , etc. Then there are specific well-known examples - Google and flu trends,  Walmart stocking Pop Tarts before hurricanes, Target and the pregnant teenager . And there are some lesser known but equally fascinating examples. Consider how the pressure applied by a person on the car seat can be mapped through sensors to assign a unique digital id and used as an anti-theft feature (and for other purposes like safety , to boot). Or how in 2008 the Billion Prices Project by MIT in the US used web crawling software to track five times as many more  product prices in a day  than the official CPI system did in a month and predicted the post-Lehman deflationary swing  a couple of months in advance. A standout example was  the automaker who having discovered a faulty part through data collected from their cars goes on to sell the patent of the fix to the supplier !

On the flip side, the  book tends to be a bit repetitive.  I can also see how it could perhaps be too basic for hard core practitioners. Finally, the book had a bit of an All or Nothing ring to it, especially with some of the bolder arguments like the deprioritization of causality or the end of specialists. That they are valid only within a context is not made clear and the exceptions are perhaps glossed over. However, it presents those contexts as they are and doesn't tip over into exaggeration as a book like this easily could have.A paradigm shift is undeniably on the way and that this is the starting point is very well brought to light by the authors. 

Overall , I  found the book to be an excellent layperson introduction to this important and very current topic-  and a very enjoyable read at that !